Resource Management and Energy Cooperation in Wireless ...

Resource Management and Energy Cooperation in

Wireless Cellular Networks

Faran Ahmed

CIIT/SP14-PEE-003/WAH

PhD Thesis

Electrical Engineering

COMSATS University Islamabad Wah Campus, Pakistan

Spring, 2018

COMSATS University Islamabad, Wah Campus

A Thesis Presented to

COMSATS University Islamabad, Wah Campus

In partial fulfillment

of the requirement for the degree of

PhD (Electrical Engineering)

Faran Ahmed

Spring, 2018

A Post Graduate Thesis submitted to the Department of Electrical and

Computer Engineering as partial fulfillment of the requirement for the award of

PhD (Electrical Engineering).

Name Registration Number

Faran Ahmed CIIT/SP14-PEE-003/WAH

Supervisor

Dr. Muhammad Naeem

Associate Professor,

Department of Electrical and Computer Engineering

COMSATS University Islamabad

Wah Campus, Pakistan

September, 2018

Author’s Declaration

I, Faran Ahmed, CIIT/SP14-PEE-003/WAH, hereby state that my PhD thesis titled

“Resource Management and Energy Cooperation in Wireless Cellular Networks” is

my own work and has not been submitted previously by me for taking any degree

from this University i.e. COMSATS University, Islamabad, or anywhere else in the

country/world.

At any time if my statement is found to be incorrect even after I graduate, the

University has the right to withdraw my PhD degree.

Date: September 13, 2018

Faran Ahmed

Plagiarism Undertaking

I solemnly declare that research work presented in the thesis titled “Resource

Management and Energy Cooperation in Wireless Cellular Networks” is solely my

research work with no significant contribution from any other person. Small

contribution/help wherever taken has been duly acknowledged and that complete

thesis written by me.

I understand the zero tolerance policy of HEC and COMSATS University Islamabad

toward plagiarism. Therefore, I as an author of the above titled thesis declares that no

portion of my thesis has been plagiarized and any material used as reference is

properly referred/cited.

I undertake if I am found guilty of any formal plagiarism in the above titled thesis

even after award of PhD Degree, the University reserves the right to withdraw/revoke

my PhD degree and that HEC and the university has the right to publish my name on

the HEC/university website on which names of students are placed who submitted

plagiarized thesis.

Date: September 13, 2018

Faran Ahmed

Certificate

It is certified that Faran Ahmed (Reg. No. CIIT/SP14-PEE-003/WAH) has carried out

all the work related to this thesis under my supervision at the Department of Electrical

and Computer Engineering, COMSATS University Islamabad, Wah Campus and the

work fulfils the requirement for award of PhD degree.

Date: September 13, 2018 Supervisor:

Dr. Muhammad Naeem

Department of Electrical and Computer Engineering

Head of Department:

Dr. Nadia Nawaz Department of Electrical and Computer Engineering

DEDICATION

Dedicated to my beloved family

ACKNOWLEDGEMENTS

I bow my head in gratitude before Almighty Allah, who bestowed upon me this

opportunity and guided me to successfully complete my PhD studies. I also feel

indebted to my family, friends and colleagues who in one way or the other helped me

in achieving this milestone. In this pursuit of knowledge, the prayers and well wishes

of my parents and my kith and kin, especially my better half and my children, were

the anchor for my ship.

One would say that the journey in pursuit of knowledge culminates with a Ph.D

degree. However, one is forced to believe that the learning has just begun and that this

is just another step in the vast field of engineering knowledge. In this regard, I am

highly grateful to my supervisor, Dr. Muhammad Naeem. for his dedication and

support throughout the programme. I am very fortunate to have a person like him

supervise my research, who is most kind, judicious and a learned professor. He

always encouraged me to overcome all hurdles and achieve results. Equally

supportive and helpful was Dr. Muhammad Iqbal, my co-supervisor, as were my

colleagues at CAE, NUST.

I am also thankful to the faculty of Electrical and Computer department of

COMSATS University Islamabad, Wah Campus, especially HoD, for supporting my

effort and making it convenient for me to pursue the programme while commuting

from Risalpur. The journeys were often made in the silence of night which gave me

an opportunity to reflect and contemplate.

Last but not the least, I am thankful to my wife, for her constant support and care, to

my parents and siblings for their prayers and well wishes, and to my children for

bringing me joy and happiness, all of which enabled me to complete this arduous task.

Faran Ahmed

ABSTRACT

Resource Management and Energy Cooperation in Wireless

Cellular Networks

Wireless communication has seen exponential growth in the past few decades due to

advancements in digital communication technologies resulting in emerging wireless

technologies such as LTE-A and WiMAX. Resultantly, wireless communication is

becoming the main choice for voice as well as data communication. However, the

increasing voice, data and internet services are costing heavy on resources. The

consequent resource constraint is driving the technology developers to look for

resource optimization solutions in all domains, particularly energy.

The future radio access networks (RAN) like 5G will comprise denser and diverse

heterogeneous networks (HetNets) of macro, micro, pico and femto BSs. Energy

resource management of such networks is of prime concern besides improving

throughput, latency and quality of service. This involves improving energy efficiency

of all elements such as back haul network, data centers, base stations and mobile

terminals. Amongst these, the base station is the most energy hungry entity,

consuming as much as 60% of the networks energy. Research is, therefore, focusing

component, system and network level energy efficiency improvements by employing

schemes such as 'energy cooperation' between base stations.

The number of BS sites, worldwide, are expected to increase to more than 11 million,

consuming 98 TWh annually, by year 2020. Consequently, it is resulting in increased

GHG emissions since most of the power comes from the fossil fuel based energy

sources. Thus, BSs have become a strong candidate for different energy efficient

techniques as well as incorporation of renewable energy sources (RES) such as solar

panels and wind turbines. Base stations are ideally suited to have renewable sources

installed because all four elements of energy generation, transmission, storage and

consumption are located at one place. RES are not only feasible for stand-alone or

off-grid BS, but also for on-grid BS, especially smart-grid tied.

Equipping base stations with renewable energy sources of solar and wind is feasible

for areas having good sunshine and windy conditions. By considering the fluctuations

in the base station load because temporal and spatial variations in traffic, it is possible

to have energy cooperation between nodes. A base station having deficient green

(harvested) energy is encouraged to borrow it from a neighbor rather than acquire it

from GHG emitting sources such as diesel generator. A novel extension of this

scheme is designed to combine it with sleep mechanism in networks where lean base

stations are put to sleep and their energy and load are distributed in the network. The

strategies of energy resource optimization thus incorporated yield positive results in

energy cost savings for the network.

In this research, initially, a PV array of 7.8 kW and a wind turbine of 7.5 kW peak

power has been modeled for Islamabad region, for a BS consuming 2.35 kWh peak

energy. It is shown that base stations harvesting renewable energy may have surplus

energy that can be shared with other base stations or even sold back to the grid

through net metering. Since the energy consumption of a BS is not fixed and

fluctuates with the traffic load, the energy produced from renewable energy sources

may be more than the energy consumed, especially during off peak hours, opening the

venues for energy cooperation between nodes.

We consider a cellular network of N macro BSs equipped with energy harvesting

systems (solar, wind or both) modeled for site whose weather parameters are known.

The network is powered by the conventional grid (Utility), with a diesel generator

providing backup power at each BS. We consider a finite horizon time slotted system

where the decision to share energy is made for a definite time t (1 ≤ t ≤ T). The key

elements of our system model are; solar/wind energy harvesting base stations, a

battery bank for energy storage at the base station, inter-connectivity between the base

station through grid, smart grid or central controller, for energy transfer, and an

energy management unit at the base station running the algorithms.

We propose a frame work for traffic aware sustainable and environmental friendly

base station operation through energy cooperation (TASEEC) in grid connected green

cellular network, where each base station is encouraged to acquire energy from

renewable source and all base stations are also connected to the utility grid. The

mathematically modeled framework jointly takes care of static and traffic aware load

on the BS. In TASEEC, the optimizer always selects economical power source for

buying purposes. The frame-work is based on the fact that the base station operators

have an agreement on energy cooperation and on cooperation tariff. The main aim is

to jointly minimize the operational cost and greenhouse gas emissions. The cost

includes self-generation cost, cost of energy purchased from other BSs and cost of

energy procured from grid. The non-linear problem is linearized by applying

McCormick approximation and solved through interior point method. The framework

is further extended to a heterogeneous umbrella network with base station on/off

switching incorporated in addition to energy cooperation scheme discussed above.

The results are shown for individual base stations and the energy cost savings -as a

result of proposed energy cooperation strategy - are depicted as a percentage

reduction in network’s energy consumption cost.

Table of Contents

1 Background and Motivation 1

1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.2.1 Rising Energy Needs and GHG Emissions . . . . . . . . . . . 5

1.2.2 Renewable Energy Sources for Cellular BSs . . . . . . . . . . . 7

1.2.3 Energy Cooperation in RES Enabled Cellular Networks . . . . 8

1.3 Research Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

1.4 Research Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

1.5 Thesis Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

1.5.1 Chapter 2 : Energy Optimization in RES Enabled Cellular Net-

works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

1.5.2 Chapter 3 : Modeling Renewable Energy Sources for Base Station 17

1.5.3 Chapter 4 : Sustainable Energy Cooperation in Base Stations 17

1.5.4 Chapter 5 : Energy Cooperation with BS Sleep Mechanism . . 17

1.5.5 Chapter 6 : Conclusion and Future Work . . . . . . . . . . . . 18

1.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

1.7 Publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

2 Energy Optimization in RES Enabled Cellular Networks 23

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

2.2 Evolution in Green Communication . . . . . . . . . . . . . . . . . . . 24

2.3 Literature Review: Resource Optimization in RES Enabled Cellular

Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

2.3.1 Optimization of Energy Resources . . . . . . . . . . . . . . . . 30

2.3.2 Optimization of Radio Resources . . . . . . . . . . . . . . . . 32

2.3.3 Battery/ Storage Optimization . . . . . . . . . . . . . . . . . 34

2.3.4 Multi-cell Cooperation . . . . . . . . . . . . . . . . . . . . . . 35

2.3.5 Energy Cooperation . . . . . . . . . . . . . . . . . . . . . . . 38

2.3.6 BS Sleep (On/Off) Mechanism . . . . . . . . . . . . . . . . . . 40

2.3.7 Resource Optimization of Cellular Networks in Smart Grid . . 43

2.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

3 Modelling RES for Energy Sharing Microgrid 48

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

3.2 Energy Consumption in Cellular Base Stations . . . . . . . . . . . . . 51

3.2.1 Energy Consumption Model of a Base Station . . . . . . . . . 53

3.2.2 Spatial and Temporal Load Variations . . . . . . . . . . . . . 55

3.2.3 Traffic Pattern Based Energy Consumption . . . . . . . . . . . 56

3.3 Design and Optimization of Hybrid RES . . . . . . . . . . . . . . . . 58

3.3.1 Subsystems of Hybrid Solar/Wind Enabled BS . . . . . . . . . 59

3.3.2 Modelling Energy Sources for Powering a BS . . . . . . . . . . 62

3.4 Green Energy Sharing in Microgrid Without Cooperation . . . . . . . 63

3.4.1 Mathematical Modelling . . . . . . . . . . . . . . . . . . . . . 65

3.5 Solution and Results for Microgrid of Green BSs . . . . . . . . . . . . 66

3.5.1 Power Generation vs Power Consumption . . . . . . . . . . . 67

3.5.2 Power Sharing with Community in Microgrid . . . . . . . . . 71

3.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

Appendices 75

3.A Solar Energy System Model . . . . . . . . . . . . . . . . . . . . . . . 75

3.B Wind Energy System Model . . . . . . . . . . . . . . . . . . . . . . . 77

3.C Battery Bank Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . 80

4 Sustainable Energy Cooperation in Cellular Networks 83

4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

4.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

4.2.1 Energy Utilization and Generation at BSs . . . . . . . . . . . 88

4.2.2 Energy Cost Model of RES Enabled Network . . . . . . . . . 89

4.2.3 Energy Exchange and Energy States . . . . . . . . . . . . . . 90

4.2.4 Tariff . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

4.3 Energy Cooperation in Green Cellular Network . . . . . . . . . . . . 92

4.3.1 Mathematical Formulation and Solution Approach . . . . . . . 93

4.3.2 Energy Cooperation Results . . . . . . . . . . . . . . . . . . . 101

4.4 Energy Cooperation with GHG Penalty . . . . . . . . . . . . . . . . . 105

4.4.1 Mathematical Formulation and Solution . . . . . . . . . . . . 105

4.4.2 Solution and Results . . . . . . . . . . . . . . . . . . . . . . . 108

4.4.3 Energy Cooperation with GHG Penalty Results . . . . . . . . 109

4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117

5 Traffic Aware Energy Cooperation with BS Sleep Mechanism 118

5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119

5.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122

5.2.1 Energy Saving in Sleep Mode . . . . . . . . . . . . . . . . . . 122

5.2.2 Traffic Load Sharing in Sleep Mode . . . . . . . . . . . . . . . 124

5.2.3 Network Energy Model . . . . . . . . . . . . . . . . . . . . . . 125

5.3 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . 127

5.4 Simulation and Results . . . . . . . . . . . . . . . . . . . . . . . . . . 129

5.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132

6 Conclusion and Future Work 138

6.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139

6.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141

6.2.1 Cooperative Relays . . . . . . . . . . . . . . . . . . . . . . . . 142

6.2.2 Energy Cooperation in C-RAN . . . . . . . . . . . . . . . . . 142

Bibliography 144

List of Tables

1.1 Symbols and Notations used in the this thesis. . . . . . . . . . . . . . 22

2.1 Energy cooperation scenarios for cellular BSs enabled with renewable

energy sources. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

2.2 Dynamic and static power components for different BS types, including

the sleep mode [1]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

3.1 Power consumption breakdown for different types of base stations . . 53

4.1 Energy Cooperation in cellular base stations found in literature . . . 86

4.2 Percentage decrease in cost due to cooperation in base stations . . . . 104

4.3 Energy classification into Green and GHG components. . . . . . . . . 105

4.4 Statistical analysis of proposed framework: Case study for Islamabad,

Pakistan. Base station dynamic load is poisson distributed. . . . . . . 116

List of Figures

1.1 Projected growth in energy consumption of BS as number of sites and

data throughput grows. . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.2 Power consumption of cellular elements of mobile communication, with

BS consuming upto 60% of network energy. . . . . . . . . . . . . . . . 5

1.3 Base stations harvesting green energy may have surplus energy that

can be shared with other base stations through ’Energy Cooperation’. 8

1.4 A smart grid facilitates bi-directional flow of energy and distributed

generation of green energy. . . . . . . . . . . . . . . . . . . . . . . . . 10

1.5 Overview of Thesis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.1 Distribution of Green House Gasses (GHGs) amongst different ele-

ments of ICT sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

2.2 Classification of optimization schemes in renewable energy enabled

base stations and networks . . . . . . . . . . . . . . . . . . . . . . . . 31

2.3 Energy cooperation in green energy harvesting networks. . . . . . . . 37

2.4 A smart grid incorporates distributed generation of electricity with

demand side management, facilitated by ICTs. . . . . . . . . . . . . . 45

3.1 Typical layout of power consuming modules of a cellular BS. . . . . . 52

3.2 Traffic load fluctuations throughout the day in different areas of a

cellular network result in fluctuating energy consumption. . . . . . . . 56

3.3 Basic layout of a typical BS powered by main grid, a back-up generator,

and renewable energy sources of solar and wind. . . . . . . . . . . . . 60

3.4 Combined average daily yield of the 7.8 kW PV panels and 7.5 kW

wind turbine at BS site for Islamabad. . . . . . . . . . . . . . . . . . 63

3.5 Energy cooperation between two RES enabled BSs and Community

through a microgrid in a rural area . . . . . . . . . . . . . . . . . . . 65

3.6 Traffic-load generation for four BS, showing spatial and temporal di-

versity between sites. . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

3.7 Example of grid power outage simulated for a day. . . . . . . . . . . . 68

3.8 Energy harvested and the power demand of four RES enabled base

stations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

3.9 Results of energy balancing at each BS, showing surplus harvested

energy at each site for the consumption of community. . . . . . . . . 72

3.10 Solar energy data for Islamabad area . . . . . . . . . . . . . . . . . . 76

3.11 Wind energy data for Islamabad region (source: Islamabad meteoro-

logical department and) . . . . . . . . . . . . . . . . . . . . . . . . . 79

4.1 Energy cooperation amongst renewable energy harvesting base stations

entail flow of energy from one to another. . . . . . . . . . . . . . . . . 88

4.2 Traffic aware dynamic power (load) requirement of BSs. Total load of

each BS will be sum of static load and dynamic load. . . . . . . . . . 98

4.3 Each Base station’s energy demand vs the energy harvested from solar

and wind sources for a day. . . . . . . . . . . . . . . . . . . . . . . . . 99

4.4 Result of energy cooperation algorithm showing buying/selling for each

BS. A BS buys energy from utility or another BS. Where none is avail-

able from either source, the diesel generator is turned on. . . . . . . . 100

4.5 Energy demand based on traffic-load and the RES generation profile

is shown for three different BS. . . . . . . . . . . . . . . . . . . . . . 111

4.6 Results of energy sale/purchase are depicted both for green (top row)

and that of GHG type (bottom row). . . . . . . . . . . . . . . . . . . 112

4.7 Energy cooperation results in the realm of GHG penalty showing pref-

erence for energy procurement from Green sources. . . . . . . . . . . 114

5.1 A heterogeneous umbrella network of N micro BS, enabled with RES. 123

5.2 The BS load can be bifurcated into static and dynamic load, whereas

the base load is the minimum load when BS is put to sleep. When

micro BS 1 and 2 are switched off their corresponding dynamic load

gets added to the dynamic load of the central macro BS. . . . . . . . 126

5.3 Flow chart for solving the mixed integer linear problem (MILP) pro-

gramming algorithm. . . . . . . . . . . . . . . . . . . . . . . . . . . . 130

5.4 Results of BS sleep algorithm showing number of BS awake (vertical

bars) over a 24 hr period for different sized HetNets. . . . . . . . . . 133

5.5 Results of BS sleep algorithm showing number of BS awake (vertical

bars) over a 24 hr period for different sized HetNets. . . . . . . . . . 134

5.6 Monthly energy costs for network comprising 4 sites. . . . . . . . . . 135

5.7 Results of net energy savings for different network sizes for each month.

The negative bars show revenue and the positive bars indicated expen-

diture. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136

5.8 Results of net energy savings for different network sizes for each month.

The negative bars show revenue and the positive bars indicated expen-

diture. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137

Chapter 1

Background and Motivation

1.1 Introduction

Wireless communication has seen exponential growth in the past few decades due to

advancements in digital communication technologies resulting in emerging wireless

technologies such as LTE-A and WiMAX. Resultantly, wireless communication is be-

coming the main choice of consumers for voice as well data communication. However,

the increasing voice, data and internet services are costing heavy on resources. The

consequent resource constraint is, therefore, driving the technology developers to look

for resource optimization solutions in all domains, particularly energy.

Over the past decade concepts such as energy conservation and energy efficiency

have found their way into all technology sectors including information and commu-

nication technology (ICT). The reason is twofold; firstly, the rising operating cost of

powering the energy intensive systems is being felt all over as technology encompasses

every facet of our lives. Secondly, the ICT industry, being the fastest growing sector,

realizes its obligation in reducing harmful CO2 emissions attributed towards it [2].

Amongst the sub-sectors of ICT, the telecomm sector in general and cellular net-

works in particular have shown huge potential for improvements in energy efficiency

and converting systems on clean (renewable) sources of energy. As a result, communi-

cation technologies are not only focused on spectrum management and throughput or

quality of service (QoS) anymore. Rather, a new paradigm has come in, i.e., energy

efficiency with reduced carbon footprint, also called green communication [3].

Green communication has thus become a realistic goal for which new ways and

means are being explored by the industry. [4–8]. Thus, a lot of research has been

undertaken in this regard in recent years as over viewed in literature. Recent research

shows that powering BSs with renewable energy is technically feasible. Although

installation cost of energy from non-renewable energy sources is still lower than RES,

optimized use of the two can yield the best results. Renewable energy sources are

Figure 1.1: Projected growth in energy consumption of BS as number of sites anddata throughput grows.

shown to be not only feasible for stand-alone or off-grid BSs, but also feasible for

on-grid BSs. Energy management strategies incorporated in the realm of smart grids

and microgrids increase the possibilities for energy efficiency further by employing

schemes such as ’energy corporation’ between base stations.

1.2 Background

Cellular communication is the fastest growing component of telecom sector in par-

ticular and ICT in general [9]. The main contributors of energy consumption in ICT

sector are ’data centers’ and ’cellular networks’. In cellular networks the BS is the

main consumer of energy, which is traditionally powered by the utility and a diesel

generator. This energy comes at a significant operating cost as well as environmental

cost in terms of harmful greenhouse gas (GHG) emissions. It is reported in [10], that

the number of global BS sites is estimated to increase from 3.3 million in 2007 to more

than 11 million by 2020 (Fig. 1.1). In consequence, the global BS power consumption

is envisaged to grow from 49 TWh in 2007 to 98 TWh in 2020 [11]. Thus improvement

in the energy efficiency of cellular networks is the need of the hour. Improving energy

efficiency in cellular networks involves energy reduction of all network elements, such

as mobile core network, mobile switching centers, base stations (BS), mobile back

haul networks, and mobile terminals [12,13].

Amongst the mentioned elements, a BS is the most energy hungry component,

consuming more than 50% of the total energy required by the network [14], as depicted

in Fig. 1.2. For the BS of a 4G and LTE network this ratio increases to 75-80%

[4]. Thus, BSs have become the prime focus of research for energy efficiency as

far as cellular communication is concerned. A BS is designed to operate at full

capacity whether it is operating at peak traffic hours or off-peak hours. Thus BSs are

not optimized for energy consumption in accordance to the varying traffic conditions

and operate constantly at one energy level. This introduces the notion of energy

conservation through schemes such as energy cooperation and BS sleep mode. These

energy management schemes gain more significance for BS equipped with renewable

energy sources such as solar panels and wind turbines.

Green wireless communication can be described as a set of concepts and frame-

works put together to improve the energy efficiency of communication systems. The

research on RES enabled cellular networks is relatively new and there are many is-

sues/challenges that are under investigation. Efficient management of the energy

resources - grid, generator, renewable - is the fundamental objective. In modeling a

cellular network supported by RES, the objectives are to determine the most advan-

tageous network characteristics, in terms of density and topology of BSs and RESs.

The energy management strategies such as sleep algorithms, BS energy cooperation,

multi-cell cooperation, on/off switching of BSs etc, have been employed so as to si-

multaneously provide the desired QoS to end users, power the BSs with RES systems,

and minimize the overall network energy cost [15]. For example the scholars in [16],

Retail

Data Centre

Core Transmission

Mobile Switching

Base Station

Figure 1.2: Power consumption of cellular elements of mobile communication, withBS consuming upto 60% of network energy.

have demonstrated that it is possible to save energy by optimizing the sleep cycles

of a BS. We take a closer look at some of the pertinent optimization strategies in

chapter 2, including scenarios where BSs cooperate with each other and share their

resources.

1.2.1 Rising Energy Needs and GHG Emissions

We have experienced exponential growth in technology over the last century, resulting

in an equal increase in demand for energy, especially electrical energy. Although

the overall growth in electrical power generation remains sensitive to factors such as

country’s income levels, its regional resources, urbanization rates and supply risks [17],

the global energy requirement is estimated to increase by 37% from 2013 to 2035,

which is equal to an average increase of 1.4% per year [18]. This demand is being

met from fossil fuels i.e., coal, oil and natural gas that meet 80% of the worlds

requirement. The other 20% comes from a mix of renewable, hydro and nuclear

sources [19]. Coal remains the biggest contributor of electricity generation (40%),

whereas renewables amount to mere 5% (this ratio is fast improving though). As

noted in [20], electricity consumption in 2005 globally stood at 2 TW, which required

an energy consumption worth 5 TW because of the poor efficiency of power plants,

which is about 38% (The new generation of gas-fired plants reaches a substantially

higher efficiency of 55%). in a detailed review of the global energy outlook, the EIA

(Energy Information Administration) of US, IEA (International Energy Agency) and

BP (British Petroleum), have all reported in their reports that the global consumption

of oil and its equivalents is likely to double from its 2009 figure of 9 billion toe (tons

of oil equivalents) to almost 18 billion toe, by the year 2035 [21].

Mankind is paying a heavy price for the manifold increase of energy consumption

in the form of the harmful GHG emissions, resulting in adverse effects on climate [22].

Use of fossil fuels have caused record high levels of CO2 emissions in the atmosphere,

reaching in excess of 410 PPM (parts per million) in pollution. This has resulted in

world wide temperature increase by an average of 2.3oF as compared to the temper-

atures in year 1900, and the trend remains upward [23]. The International panel on

climate change, as well as other world bodies have clearly stated that these adverse

effects can only be mitigated by cutting down on GHG emissions in every sector.

The unprecedented growth of technology in all sectors is causing this rise and ICT

sector is no exception. Although ICT is not contributing in CO2e emissions as much

as some other sectors such as transportation and construction, it is the fastest grow-

ing of all. ICTs are also the key to reducing harmful emissions as advancements

are made in these technologies and has the potential to help all other sectors since

ICTs are embedded in most other technologies. The main contributors of GHG emis-

sions in the information and communication technology sector are telecommunication

infrastructure, PCs & peripherals, and the data centres [9].

1.2.2 Renewable Energy Sources for Cellular BSs

The use of RES is gaining widespread coverage in all sectors due to the improve-

ments in technologies related to photo-voltaic (PV) and wind-turbine (WT) systems,

deep-cycle rechargeable batteries, power converters etc. as well as simulation and

maintenance softwares [24]. The advances made in simulation software as well as

hardware such as bi-directional inverters, automatic controllers, and sensors have led

to the development of proven and cost effective hybrid systems [25]. The hybrid sys-

tems comprising conventional and RES have been shown to significantly decrease the

overall cost of the isolated power systems over their total life cycle [26]. Also, BSs

have all four elements of energy generation, transmission, storage and consumption

located at one place, which makes them ideal for equipping with RES. The ability of

a network based on cognitive radios to intelligently modify its parameters or recon-

figure itself, allows the NW to manage the resources optimally and dynamically, and

that means reduction in energy consumption and thus power savings [27–29].

A BS site is ideally suited to have renewable sources such as wind turbine and solar

panels installed because a BS site is generally located on a relatively high ground with

good all around visibility. In cellular applications, the main attraction is to provide

power to the remote BSs, which are off the grid, thereby saving the cost of fuel as well

as its transportation cost. In fact, research shows that green BSs are equally beneficial

in energy cost savings and maximization of energy efficiency in networks that are

connected to the grid or off the grid [30]. Renewable energy provides an opportunity

to bridge the energy gap and power systems such as cellular BSs in off-grid areas

for both developing and under developed countries [31], [32]. The incorporation of

renewable energy sources into cellular base stations not only cuts down on energy

cost in the long run, but also helps in reducing the harmful GHG emissions, which

can be substantial given the huge number of BS deployed world wide [33]. We find

CONTROL

UTILITY

Figure 1.3: Base stations harvesting green energy may have surplus energy that canbe shared with other base stations through ’Energy Cooperation’.

many studies in literature related to feasibilities on equipping a BS with wind turbines

(WT) and PV panels. The sizing and capacity modeling is carried out using factual

site data as well as simulations carried out using softwares such as HOMER [34]. For

example, the sizing and capacity of a WT or PV array may be carried out using the

availability of natural resources as per the ’most unfavourable month’1 method.

1.2.3 Energy Cooperation in RES Enabled Cellular Networks

Green cellular networks can maximize the use of clean energy harvested from nature

by employing different techniques such as cell zooming, BS on/off switching and en-

ergy cooperation between BSs etc. The basic approach in multi cell cooperation is

to adjust the cells by switching off BSs while associating users with neighbor cells

1The data for the month having least favourable wind/solar conditions is used as basis forcalculations.

by extending their coverage area, which requires centralized channel state informa-

tion and traffic load information of every cell. Another is expanding coverage of BSs

powered by RES while constricting the coverage area of BS powered by grid. Co-

ordinated multi point (CoMP) transmission by BSs is another technique in which

BSs cooperatively transmit data to cell edge users, which requires joint processing

and coordinated scheduling strategies by the BSs interconnected on high speed data

links. There are two fundamental aspects associated with on/off switching of BSs,

how to associate users from one BS to another BS without compromising users’ QoS

[35], and, how to perform the on/off mechanism on a BS; centrally or locally?

The base stations harvesting green energy from nature may have surplus energy

that can be shared with other base stations or even be sold back to the grid. Since

energy consumption of a BS fluctuates with the traffic load, the energy produced

from renewable energy sources may be more than the energy consumed, especially

during off peak hours. This is a practical scenario and opens many avenues of energy

conservation through ’energy cooperation’ between green BSs. The main objective

is sharing of renewable energy to meet BS demand so as to reduce the network’s

utility cost. Energy cooperation is particularly viable for green BSs powered by the

smart grid. The inherent features of a SG make it possible to have the necessary

coordination needed between BSs to allow energy exchange. A localized microgrid is

also a prospect whereby the energy harvesting BSs act as distributed generation nodes

and the flow of information and energy takes place through the local grid station (Fig.

Authors in [36], have proposed an online algorithm for energy cooperation between

the base stations under centralized control, which has been shown to significantly re-

duce energy borrowing from the utility. In this regards a smart grid (SG) offers

substantial advantages in terms of two way flow of energy as well as information be-

tween the nodes and grid. Similarly, in [37], energy cooperation amongst two cellular

Figure 1.4: A smart grid facilitates bi-directional flow of energy and distributedgeneration of green energy.

base stations is shown to enhance overall energy efficiency in both, for the energy

harvesting BSs as well as for conventional BSs [38]. Another on line line algorithm,

called EDA (energy and data aware), is shown to decide on allocation of energy and

distribution of traffic between the nodes based on data admission control [39]. Opti-

mization of energy resources has been carried out by researchers in [37], by utilizing

the energy state information in an online and off-line, as well as a hybrid, solution.

Their model considers bi-directional energy flow between BSs and unidirectional flow

from BS to SG, and is confined to a pair of base stations only. Another two cell model

is considered in [40], where the aim is to maximize the sum rate of all active users

by determining the required quantum and direction of energy to transfer between the

two. Thus different energy cooperation strategies have been considered in literature

in an effort to optimize the use of renewable energy in the cooperating base stations.

1.3 Research Motivation

Not only does wireless communication provide ubiquitous coverage but the ever in-

creasing data rates and demand for services mean an ever increasing requirement of

energy to sustain these networks. Cellular base stations consume about sixty per-

cent of a cellular network’s energy, because of the very large number of base stations

deployed to provide the quality of communication services demanded and ubiquitous

coverage. As a consequence of these demands, cellular BSs with LTE and LTE-A

technologies have been deployed to overcome the resource constraints, which are ex-

pected to be even more densely deployed. Thus there is a constant need to look for

energy efficient techniques in cellular networks, particularly the base stations.

One of the most promising avenues for energy efficiency lies in the exploitation of

temporal and spatial fluctuations in the network’s traffic. Another fact worth noting

is that cellular networks seldom operate at the designed capacity. Such findings

have motivated researchers to comme up with energy-efficient network management

techniques such as on/off switching of BSs, energy cooperation between the BSs,

transmitter power adaptation etc [41], and other resource optimization schemes.

Clean and sustainable technologies are mandatory for the reduction of carbon foot-

print in future cellular networks. Aided with technological advancements, renewable

energy is making inroads into all sectors including information and communication

technologies [42]. The incorporation of renewable energy sources in cellular base sta-

tions has emerged as a viable option to conserve energy of the cellular network in

the long run as well as reduce the harmful effects of GHG emissions. RES, especially

solar and wind, are emerging as a viable alternate to fossil fuel based energy, and are

particulary suited for deployment in developing countries.

The aforesaid, therefore, has made the basis for this research, which primarily

focuses on energy resource optimization in cellular networks. The research builds

on actual modeling of a BS’s traffic based energy consumption and the power ob-

tained from hybrid renewable energy sources. The viability of powering BSs with

solar/wind energy systems is done for specific area. Based on these energy genera-

tion/consumption values, the viability of energy cooperation can be evaluated under a

simple energy sharing mechanism in microgrid to a complex mechanism incorporating

BS on/off switching. A business case for implementing the RES in cellular BSs is a

promising domain to prove the efficacy of these systems. It is, however, an empirical

analysis that can be taken up for further research in this area. There are studies,

also referred in the thesis, that do cost analysis of RES in BSs at various sites, that

prove its financial viability in the long run. This dissertation focuses on the novelty of

resource optimization through energy cooperation and sleep mechanism. A business

case study employing HOMER software will be considered for future research.

1.4 Research Objective

In order to reduce the growing cost of energy consumption in cellular networks as

well increase the use of clean sustainable energy, equipping BSs with RES offers a

promising solution. For this we should be able to find a feasible size/capacity of RES

such as solar arrays and wind turbines. Both these systems require weather data

of the site as well as average daily energy consumption of the BS. Once we size the

PV-array and wind-turbine for a site we are in a position to determine the surplus

energy vis-a-vis the base station load.

Our objective is to analyse the cellular network from the varying energy consump-

tion point of view and based on this analysis, come up with an energy cost saving

scenario by mathematically modeling an energy corporation scheme. Our aim is to

minimize the overall energy consumption of the cellular network using green energy,

and sharing the surplus energy between the base stations enabled with renewable

energy sources. We also device a policy for the optimal use of renewable energy by

applying a penalty tariff on the GHG emitting (fossil fuel based) energy sources such

as diesel generator and gas/coal based power plants. By taking into consideration

the traffic pattern in a particular area of the network, we evaluate the energy profile

of each active BS. We aim to develop a scheme in which the energy from RES at one

BS can be provided to a neighbour BS that is in want of energy.

Furthermore, we explore the energy cooperation under the base station sleep mech-

anism where a BS is switched off and its traffic is handed over to a neighbour BS and

the harvested energy is directed towards other BSs. It is a very lucrative and viable

scenario as it has been shown that a BS consumes minimal energy if it is put in sleep

mode. In such case the traffic of the sleeping BS is shifted to other co-located BS and

its harvested energy can be provided to these BS, resulting insignificant decrease in

use of conventional energy.

The cellular BS consumes most of the energy in a cellular network (about 60%).

Also, the energy is coming mainly from diesel generator that is a source of harm-

ful GHGs. In order to reduce the growing energy cost of the cellular networks as

well as to increase the use of clean sustainable energy, we need to equip BSs with

renewable energy sources. For this, we have to find a feasible solution in terms of

right capacity/size of solar panels and wind turbine to be installed. Both these sys-

tems design requires weather data of the site as well as energy consumption data of

the BS. Once the size of the PV-array and the wind turbine is known, we are in a

position to determine the surplus harvested energy vis-a-vis the load. In order to

determine the surplus energy at any instant we also need to know the instantaneous

energy consumption of a BS, which comprises a static and a dynamic component.

For this purpose, the modeling of a BS’s energy consumption as a function of traf-

fic load is required to be carried out. The varying traffic load contributes dynamic

part of BS energy consumption, whereas, the static part depends on system hardware

configuration. Therefore, by modeling the size of PV-array/wind-turbine, and the

instantaneous traffic load we can determine the surplus energy at each time slot.

The network level improvement can be achieved by employing different energy

management schemes (discussed in literature review in chapter 2) such as energy

cooperation between its nodes; especially for RES enabled cellular networks. Due

to the temporal fluctuations in traffic, the instantaneous power consumption of a BS

may become less than the energy being harvested from nature. This leads to scenarios

where RES enabled BS can offer their surplus energy to the co-located BS. This is

called energy cooperation in literature and has practical implications for network

operators in terms of long term energy savings as well provisioning of sustainable and

clean energy for its cellular network. Furthermore, the stated cooperation scenario

can be made more effective if some BS, having lean traffic, are put to ’sleep’ and their

energy is directed towards their neighbouring BS or sold back to utility. For this,

the sleep mechanism and the associated load management needs to be defined and

formulated as well.

In this work, an energy cost minimization framework is proposed for a green cellu-

lar network, by formulating a novel energy cooperation scheme that ensures optimal

energy cooperation between green cellular base stations (BSs). In the proposed eco-

nomical and environment friendly frame work, the energy is saved by cutting down

on the grid energy and sharing surplus green energy among the base stations. The

intended scenario requires knowledge of harvested energy as well as traffic pattern

by the network to workout energy surplus/demand of each BS. A realistic objective

is developed under specific conditions, which entail modeling the PV-array and wind

turbine for an LTE BS and estimating the amount of surplus/deficient energy at each

site. Base stations having surplus energy are allowed to sell their energy to those

having deficient harvested energy, thereby reducing the use of grid and diesel genera-

tor, which in turn reduce the GHG emissions. The proposed constraint optimization

framework for energy cooperation has bilinear non-convex structure. In order to

convexify the optimization problem we use McCormick envelopes and transform the

bilinear non-convex optimization framework into linear optimization framework. The

numerical results verify the effectiveness of the proposed traffic aware sustainable and

environmental friendly base station operation through energy cooperation (TASEEC).

Keeping in view the futuristic HetNets in evolving 5G network topologies, the energy

cooperation mathematical framework is expanded to an umbrella network of micro

base stations and a central macro BS. The micro BS are put to sleep and their traffic

is directed to the macro BS, whereas the surplus renewable energy is sold to utility.

The main task of the modified algorithm is to determine the quantum of energy and

traffic diverted.

1.5 Thesis Overview

The thesis is divided into six different chapters, each chapter dealing with a distinct

topic of research undertaken. An overview of different aspects of the work is pre-

sented in this chapter i.e., background, research motivation and problem description,

after which more elaborate detail is presented in subsequent chapters, as enumerated

below:-

1.5.1 Chapter 2 : Energy Optimization in RES Enabled Cel-

lular Networks

In chapter 2 a detailed description of the ongoing energy resource management

schemes under research is presented. These include energy management strategies

for both stand-alone and grid connected base stations/cellular networks enabled with

renewable energy sources. These strategies include energy cooperation and BS sleep

strategy, amongst other network energy saving schemes.

Figure 1.5: Overview of Thesis.

1.5.2 Chapter 3 : Modeling Renewable Energy Sources for

Base Station

In this chapter we understand the dynamics of power consumption in a base station

serving the traffic and model the renewable sources of solar and wind to sustain the

load of a BS. We find out the energy consumed by a cellular LTE base station and

observe that some surplus harvested energy is available to us for other use, such as

serving local community through net metering.

1.5.3 Chapter 4 : Sustainable Energy Cooperation in Base

Stations

Based on the work derived in chapter 3 we formulate an energy cooperation scenario

in which the BSs are asses the energy generated and energy being consumed, based on

which they offer the surplus energy to neighboring BSs. The two way flow of energy

takes place under certain constraints that ensure a practical framework for the said

cooperation between the cellular BSs.

1.5.4 Chapter 5 : Energy Cooperation with BS Sleep Mech-

It has been established that BS operate well below their maximum capacity most of

the time. This has led researchers to discuss the notion of sleep mechanism for the

BS in order to conserve resources. The on/off switching technique and algorithm is

developed that address this scenario in particular over and above the energy coopera-

tion scheme developed in the previous chapter. The merger of the two further boosts

the utilization of renewable energy sources incorporated into cellular BSs.

1.5.5 Chapter 6 : Conclusion and Future Work

In chapter six, the last chapter, the research work is concluded and consolidated. The

futuristic scenario of LTE/LTE-A cellular BSs, renewable energy sources and smart

grid are analysed, which is the way forward. Other emerging systems such as C-RAN,

cooperative relays and cognitive networks are also discussed in relation to research

carried out here.

1.6 Summary

As mobile communication evolves, focus remained on the performance metrics such

as QoS, throughput and reliability/coverage, with hardly any emphasis on the energy

consumption of the network. However, as the networks became diverse and dense, and

realization about harmful GHG emissions increased, energy efficiency/conservation

became an area of concern. Thus, a new paradigm called ’green communication’

emerged, that aimed to explore the ways and means to reduce energy consumption

through green techniques such as energy cooperation and incorporate the RES to

mitigate the harmful CO2 emissions by harvesting green energy.

A host of component, system and network level research areas have emerged that

explore green communication and related fields. In this dissertation a network level

resource optimization scheme has been developed that not only focuses on energy

efficiency but also on incorporation of renewable energy systems. A realistic scenario

has been considered in this work to implement the aforesaid paradigm. A solar and

wind energy based hybrid system is developed by catering the weather conditions of

Islamabad region and compared with the daily energy consumption of a macro BS. It

is shown that surplus energy is available from such a hybrid system during off peak

hours that can not only be provided to community through a microgrid but can also be

made available to other BS through a novel energy cooperation scheme. Furthermore,

the self organizing network (SON) feature being gradually incorporated as a 3GPP

standard, has been exploited in chapter 5 to make use of the sleep mechanism (a

very promising research area) of BS in HetNet of micro and macro BS. Thus, the

green paradigm developed in the study highlights the efficacy of incorporating RES

on cellular BS under different energy cooperation strategies.

1.7 Publications

The following contributions have been made to the research community by the author

of this dissertation, thus far.

� Faran Ahmed, Muhammad Naeem, Muhammad Iqbal, Alagan Anpalagan, ”ICT

and renewable energy : a way forward towards the next generation telecom base

station”, Telecommunication Systems, Springer, Volume: 64 Issue: 1 Pages:

4356, 2017

� Faran Ahmed, Muhammad Naeem, Muhammad Iqbal, Waleed Ejaz, Alagan

Anpalagan, ”Resource management in cellular base stations powered by renew-

able energy sources”, Journal of Networks and Computer Applications, Elsevier.

1084-8045, Vol 112 (2018).

� Faran Ahmed, Muhammad Naeem, Muhammad Iqbal, Waleed Ejaz, Alagan An-

palagan, ”Sustainable and Environment Friendly Energy Cooperation in Cel-

lular Networks”, Applied Energy, Elsevier. ID: APEN-D-17-07742. (Under

Review)

� Faran Ahmed, Muhammad Naeem, Muhammad Iqbal, Waleed Ejaz, Alagan

Anpalagan, ”Renewable Energy Assisted Traffic Aware Cellular Base Station

Energy Cooperation”, Energies, MDPI, Volume 11, Issue: 1.

� Faran Ahmed, Muhammad Naeem, Muhammad Iqbal, Alagan Anpalagan, ”Re-

newable Energy Assisted Base Station Collaboration as Microgrid”, IEEE Elec-

trical Power and Energy Conference (EPEC) proceedings, Ontario, Canada,

978-1-5090-1919-9/16, 2016.

� Faran Ahmed, Muhammad Naeem, Muhammad Iqbal, ”Optimal Green Policy

for Energy Sharing by Cellular Base Stations Powered by Renewable Energy

Sources”, International Conference on Vehicular, Mobile and Wearable Tech-

nology (ICVMWT), Istanbul, Turkey, Conference proceedings, pp. 16-21, 2017.

Table 1.1: Symbols and Notations used in the this thesis.

Symbol DefinitionPb Power consumption of BS ’b’PA Power Amplifier moduleRF Radio Freq moduleBB Base Band modulePPA Power consumption of PA modulePRF Power consumption of RF modulePBB Power consumption of BB moduleP tb Power consumption of BS b at time t

∆p Slope of load dependent power variableα channel path loss factorN Number of BSs consideredC Energy cost of sourcest single time slotT Total finite timeDG Diesel GeneratorSG Smart GridWT Wind TurbinePV Photo-voltaicT Finite time horizonLtn traffic load of BS n at time tLto traffic load of macro BS at time tLtstatic static power consumption (load) of BS n at time tLtdynamic dynamic power consumption load of BS n at time tLtsleep BS consumption (load) in sleep modeEtm,n Transfer of Green Energy from mth to nth BS

Etn,m Transfer of Green Energy from nth to mth BS

Et,gn Generated Green Energy from RES by nth BS

Et,un Energy intake from utility by the nth BS

Et,Gm,n Transfer of GHG energy from mth to nth BS

Et,Gn,m Transfer of GHG energy from nth to mth BS

Et,gn,G GHG type energy from diesel generator by nth BS

Et,un,G GHG type energy from the utility by nth BS

Chapter 2

Energy Optimization in RES Enabled Cellular

Networks

2.1 Introduction

The growing demand for wireless communication especially cellular communication

is putting tremendous burden on the whole infrastructure to redesign and innovate

new strategies for resource optimization. Since energy is one of the main concerns,

it is a prime focus of research as a resource. Also, the compulsion on industry to cut

back on harmful GHG emissions is encouraging sectors to employ renewable energy

sources. The cellular BS is considered as the most energy consuming entity in the

network and consumes up to sixty percent of the network’s energy [11]. Renewable

energy incorporation has been shown to be a viable option for the base station as well

as the whole cellular network [43]. RES can be employed for not only cutting down

on the harmful GHGs but also for decreasing the energy cost of traditional networks

it the long run. Renewable energy is not only feasible for stand alone BSs that are off

grid but equally feasible for BSs deployed in a network powered by the grid. It is also

seen that small cells like femto and micro-cells need lesser energy. Thus, heterogenous

networks comprising small cells are more feasible for the RES to power them. In this

chapter, we take a detailed look at the energy saving strategies being developed for

cellular communication employing the renewable sources, particularly solar and wind.

2.2 Evolution in Green Communication

The phenomenal growth of information and communication technologies, especially

cellular communication, has led to increased energy consumption and resultant GHG

emissions. It is estimated that the global contribution of ICT industry towards CO2

emissions is approximately five percent but expected to grow as global demand for

data and computers rises [9]. Cellular communication is the fastest growing compo-

nent of the ICT sector. Keeping the current trend, the global number of base stations

is expected to increase from present figure of 7 million to 11 million by year 2020 [10].

In consequence the annual power consumption of cellular base stations will also grow

from 49 TWh presently to 98 TWh in 2020, with business as usual estimates.

ICTs can help in optimization of systems and processes for energy efficiency and

also help in the innovation processes of industries/organizations by simulating changes

in systems, environments and company strategies [44]. The Information and Commu-

nication Technology sector, is generally sub divided into three sub-sectors, namely (i)

telecommunication and devices, (ii) PCs, printers and peripherals and (iii) the data

centers. Over the past few decades, all three sectors have seen a continuous increase

in usage and consequently in their energy consumption. The combined carbon foot-

print of these sub-sectors was estimated at 0.83 Gt of CO2 emissions in 2007, which

is estimated to increase to 1.43 Gt by 2020 [33]. Fig. 2.1 shows the percentage wise

share of emissions of the three sectors, which is 31% for telecom, 40% for PCs and

peripherals and some 23% by the data centers [9].

On one hand growing energy consumption is causing harmful effects on climate

& on the other, the unavailability of electrical energy in under developed countries is

preventing billions from accessing technological advantages. The cellular base stations

deployed in remote/rural areas resort to expensive diesel generators for meeting their

energy needs. An alternate to these energy sources, a prime source of GHG emissions,

are the renewable sources such as solar and wind energy, which are not only clean but

also sustainable [11,45]. Good BS sites have usually good wind resources because they

have high local elevation and good all around exposure. A number of RES enabled

BS feasibilities have been prepared using factual site data as well as simulated data to

configure the size and capacity of wind turbines and photovoltaic (PV) panels [46,47].

These reports greatly recommend the use of RES enabled BS due to economical

levelized cost and zero emission of greenhouse gases.

A BS is well suited for RES because the electrical power generation, batteries

and load are all located at one place. Especially for remote sites that enjoy good

sunshine and windy conditions, RES offers a good alternate to diesel generators, eco-

nomically and environmentally. The advancement in the technology of rechargeable

batteries, maximum power point tracker and inverters make it possible to design ro-

bust hybrid energy systems [25]. In [47], authors analyzed the feasibility of renewable

energy assisted stand alone hybrid GSM base station with diesel generator as backup.

They state that a proper design of an autonomous wind-solar hybrid systems in good

sunny and windy location pays off in two to four years considering the operating and

maintenance cost.

For a stand alone base station the hybrid solar wind hybrid system can provide

the optimal mix of renewable and non-renewable energy sources. The estimation of

hybrid energy systems that harvest energy from renewable sources depend on the

following main factors:-

� The amount of natural energy such as solar irradiation and wind speed available

throughout the year.

� The availability of conventional sources such as diesel generator and grid.

� The energy consumption of the system over a period of time.

� The energy storage capacity or the size of the battery bank.

The futuristic smart grid offers inherent advantage of enabling energy cooper-

ation and distributed generation of energy as demonstrated in [48]. A number of

on-line and off-line energy management schemes for base stations have been proposed

in [36–39,49,50]. In [49], the authors investigate a number of real time energy manage-

ment schemes. These schemes minimize the cost of system with optimal scheduling

of battery charging and optimal integration of renewable energy. In [36], authors in-

vestigate a centralized on-line algorithm for energy cooperation among different base

Figure 2.1: Distribution of Green House Gasses (GHGs) amongst different elementsof ICT sector

stations. The authors show that with intelligent cooperation the base stations can

significantly reduce energy consumptions from the utility.

Energy efficient base stations are required for the future green networks; made

possible by improvements at the component, module and system level through ICTs.

Technological improvements in component design and better algorithms in conjunc-

tion with efficient deployment and management strategies will bring about the desired

results. The energy efficiency of cellular BS is rather poor at off-peak hours, when

traffic load is minimal [51]. It must be improved by technological improvements at

the component, link and network level for the following reasons:

� Component level. At lower output power i.e., in off-peak hours, the efficiency

of power amplifier (PA) degrades substantially in present designs.

� Link level. Certain signaling such as synchronization and pilot signals need to

be regularly transmitted, forcing the BS to consume energy continuously.

� Network Level. The networks are deployed for full capacity handling and are

not dynamic in topology so as to adjust energy consumption in off-peak hours.

2.3 Literature Review: Resource Optimization in

RES Enabled Cellular Networks

The topic of energy efficiency in cellular networks is vast given the large number of

perspectives available for research. Not only academia but industry as well as govern-

ment and non-government organizations are exploring the realm of energy efficiency

in wireless communications [52]. In green cellular networks, the main objective is

to maximize the use of renewable energy, for which research has focused on energy

consumption strategies, resource management strategies and performance analysis

of demonstration systems [53]. In modeling a cellular network supported by RES,

the objective is to determine the most advantageous network characteristics such as

density of BSs, topology of BSs/RESs, sleep algorithms, BS interconnection, multi-

cell cooperation etc. Powering the BSs with RES systems of manageable size is a

challenging task especially when it aims to minimize the overall network energy cost

without compromising the user QoS [15]. The research on energy efficiency in cel-

lular communication has been carried out from different perspectives, which can be

broadly categorized into five categories:

� Energy efficiency metrics and consumption models: Green spectrum

management for mobile operators is an area that deals with the quantification

of energy consumption and formulation of energy models similar to real time

scenarios [54]. The quantification of energy is not only done at system level but

also over life span of technology to come up with accurate metrics for energy

assessment [55].

� Energy efficient hardware and technologies: Another area of interest is

the hardware that can be made more energy efficient by improving design and

technology, e.g., the power amplifier (PA) is a big candidate for improvement

in energy efficiency. It also includes software improvements such as cross-layer

and battery operation optimization.

� Energy efficient architectures: Energy efficiency in wireless networks can

also be achieved through different network architectures, such as cost effective

deployment strategies of heterogeneous networks (HetNets) [56], multi-cell co-

operation, cell zooming or using low-power micro base stations compared to

today’s high-power macro BS schemes etc. [57, 58]. Power consumption can be

reduced using multi-hop transmission in cellular networks [59] or self-organized

energy efficient cellular networks [60].

� Energy efficient resource management: Management of both radio and en-

ergy resources is vast topic of research from the point of energy efficiency [61].

Radio resource management involves efficient spectrum management and user

traffic management. For example, authors in [16] have demonstrated that it is

possible to save energy by optimizing the sleep cycle of a BS. Sum-rate max-

imization and cost minimization are similar objectives [30]. Energy resource

management involves schemes such as energy cooperation and optimization of

different energy sources [62]. Multi-radio access network technologies (Multi-

RAT) management and novel paradigms for delay tolerant services are also

some resource management techniques. Authors in [63] present a trade-off be-

tween energy and spectral efficiency in down-link orthogonal frequency-division

multiple access (OFDMA) networks.

� Incorporation of renewable energy sources (RESs): An upcoming paradigm

for energy efficiency is the incorporation of RESs such as solar and wind, par-

ticularly on the BSs. In [64], authors have proposed a scheme to optimize the

utilization of green (solar) energy during the peak traffic hours, i.e., day time,

when solar energy is available.

This reveals that there are many ways of achieving energy efficiency in a cellular

networks/base stations by improving efficiency of the hardware, improving network

protocols, improving system architecture and network deployment tailored to traffic

requirements, and using low-power micro BSs [57]. However, the focus of research in

this dissertation has been energy resource management for renewable energy based

designs and strategies because of the increased interest in simultaneous energy effi-

ciency and reduction in CO2 emissions. Energy resource optimization for a single BS

as well as for the network is classified from different perspectives in Fig.2.2. These

perspectives are discussed in the proceeding sections with some examples, to get a

holistic view of the ongoing research towards green communication, especially for

cellular applications.

2.3.1 Optimization of Energy Resources

A BS having RESs, such as solar panels, in addition to utility and DG (diesel gen-

erator), is optimized in a way such that the energy sources are optimally used and

overall energy cost is minimized. Different energy resource management strategies

for RES enabled BSs have been proposed by researchers. For example, in [65], en-

ergy management strategy for a battery-diesel stand-alone system with distributed

SMART GRIDCONNECTED

BS ON/OFF SWITCHING

ENERGY COOPERATION

OFF-GRIDBASE STATION

ON-GRIDBASE STATION

CELLULAR CONFIGURATION

CONVENTIONAL GRID

CONNECTED

GREENBASE STATION

MULTICELL COOPERATION

GRIDOPTIMIZATION

PV/WIND SYSTEM

OPTIMIZATION

RADIO RESOURCE

OPTIMIZATION

ENERGY RESOURCE

OPTIMIZATION

BATTERY / STORAGE

OPTIMIZATION

SINGULAR CONFIGURATION

Figure 2.2: Classification of optimization schemes in renewable energy enabled basestations and networks

PV generation based on grid frequency modulation has been developed. The battery

inverter increases the grid frequency to reduce power. The proposed strategy also

optimizes the life time and efficiency of DG.

Optimization of energy consumption in different systems of a cellular BSs can also

lead to significant energy conservation. In [66], authors have carried out optimization

of energy consumption in a BS shelter by optimized control of the ventilation resulting

in reduced usage of air conditioning. The electronic control unit regulates operations

of cooling devices by sensing temperature through the sensors. Thorough dynamic

regulation of the air conditioner and fans through an heuristic algorithm running on

the control unit, they claim 70% reduction in the overall energy consumption of the

RES enabled BS powered by a smart grid has an advantage over a BS that is

powered by conventional grid, e.g., the BS can balance its energy requirement in such

a way that use of RES is maximized and electricity from smart grid is controlled

as per requirement. This demands priori control over a number of variables such as

price schedules, energy flow from RES, battery charging discharging, traffic intensity

variations etc. This has been shown in [67], where the authors proposed a power

management algorithm to minimize power cost for an RES enabled BS in a smart

A similar scenario has been tackled in [68], by describing an energy management

scheme for a micro BS, in which renewable energy and SG power is adaptively used

as per user requirement and smart grid tariff. A micro BS has been chosen, because

its traffic based consumption has a significant portion of BS power consumption.

This problem is also formulated as a stochastic power management problem, which

is solved to minimize the energy cost of the BS.

Authors in [69], have tried to address the issue of reducing power consumption

of a BS in a rural area, where grid power is highly unreliable, by optimizing the

cooling of the shelter. The authors have made simulations of the BS load comprising

1) transceiver equipment load, 2) cooling load, and 3) battery charging losses, as well

as the power sources comprising a DG and a PV array. Authors demonstrated that

under separate cooling environments for BS equipment and the batteries, a 3 kW PV

panel can significantly reduce DG running hours (to 3 hrs a day) for a total load of

1.4 kW.

2.3.2 Optimization of Radio Resources

Green (harvested) energy maximization is also shown possible by the intelligent use

of radio resources and traffic management. Strategies developed to this end involve

scheduling of traffic in a green BS, regulating transmission power to maximize green

energy usage, employing efficient beam forming techniques, and cell size adaptation

as per RES availability. Adjusting power output results in increase and decrease of

cell size, addressed as cell zooming [70], where the cell size zooms in and out according

to traffic to conserve energy and maximize the use of green energy while maintaining

required QoS. Authors in [71] have proposed an energy-efficient resource allocation in

OFDMA systems with hybrid energy harvesting BS. Temporal and spatial diversity

in traffic also provides opportunities to optimize the radio resources. Uncertainties in

the traffic, energy harvested from renewable sources, as well as the utility pricing, have

led to development of energy efficient scenarios tackled through different heuristics

[67, 71]. Other techniques of radio resource management involve adjusting output

power according to the energy available, called cell breathing, during peak hours of

the traffic [72] and energy efficient system design like cross-layer scheduling amongst

BS modules [73].

Authors in [74], consider the problem of minimizing the average grid power con-

sumption of a Green BS down-link in scheduling N users with average delay con-

straints. The existence of a power optimal policy under delay constraints for multiple

users is proven by scheduling the users and allocating the optimal transmission rate

for the chosen user. The power consumption of different modules such as feeder ca-

bles, base band unit, rectifiers etc. is taken into consideration, however, only power

amplifier (PA) is modeled because it is linked to transmission power. In [30], au-

thors attempt to minimize energy cost while maximizing user sum-rate, by devising

multiuser down-link zero-forcing (ZF) beam-forming and power control policies, for

efficient transmission in a renewable energy enabled BS. The BS is on-grid and also

supported with RES through the associated batteries. Both energy-cost-minimization

and sum-rate-maximization problems are formulated separately. Comparison of off-

line and on-line policies show off-line policies to perform better for both cases.

In one energy management scheme for fully green BSs described in [75], the cov-

erage/capacity of the BS is adapted according to the energy stored in batteries, the

forecasted weather data (solar/wind) and the historical power consumption pattern.

The adaptation of the coverage and/or capacity of the BS is performed by controlling

the energy required to provide coverage to mobile users through the pilot channels for

continuous user connectivity, as well as the energy required to service the user traffic

at any given instant.

2.3.3 Battery/ Storage Optimization

Storage operation is a key issue in efficient utilization of energy produced through

renewable sources, especially for large capacity applications. In a green BS, the

harvested energy is stored in a battery bank of limited capacity, which must be used

in such a way that the BS energy requirement is met efficiently. This means the energy

drained from battery is not more than the energy stored. Also, the battery should

not be overcharged to avoid damage. In [69], authors show that a single tenancy BS

is typically backed by a 48V, 600 AH battery rack. The battery is operated at 60%

depth of discharge and takes 4 hours to reach its full capacity when charged at a rate

of capacity/10.

Simulation of battery behavior and estimation of battery’s health is an important

aspect of research as discussed in [76]. Optimization of an off-grid hybrid PV-wind-DG

system with different battery technologies (lead-acid, Li-ion and redox-flow batteries)

has been carried out in [77], with the main objective to operate each battery in a way

that minimizes the ageing for all batteries. Authors in [77] have demonstrated an

off line energy management strategy that decreases energy billing cost significantly

if the BS is equipped with a battery whose capacity and charging/discharging rates

are kept above certain threshold. In [78], performance analysis of an energy storage

system for stand alone BSs has been carried out, which is based on Lithium Polymer

(LiPo) cell model whose parameters have been identified through a combination of

least mean square and genetic algorithms.

The battery’s state of charge (SOC) and/or discharge are the key features opti-

mized in a battery bank used to store energy harvested from nature. Thus optimiza-

tion of battery operation is a distinct research area as noted through above examples.

Other features of interest related to storage are the types of batteries/ storage-devices

and battery capacity sizing. The state of storage/battery is a direct measure of the

harvested energy. Based on the battery’s state of charge, algorithms are designed to

optimize the BS’s traffic in terms of throughput, coverage etc. When the battery’s

SOC goes below a predefined threshold, and the BS’s power consumption is signifi-

cant, different energy management strategies may be adopted, such as, Reduce user

QoS by decreasing throughput, increasing the data delay, switch to non-renewable

energy sources till battery is sufficiently charged and Reduce the coverage area of the

BS to save power.

2.3.4 Multi-cell Cooperation

Multi-cell cooperation is a broad mechanism which entails traffic load sharing between

adjacent or close proximity cells to minimize the network power consumption and/or

maximize the use of green energy. Multi-cell cooperation is particularly applicable

for heterogeneous networks employing macro and micro cells or similar cell types.

Authors in [79], discuss pros and cons of large versus small cell size deployment for

energy efficient radio access architectures for green radio. An overview of multi-cell

cooperation has been presented in [80], which broadly classifies it as traffic-intensity-

aware multi cell cooperation and energy-aware multi cell cooperation. In the former,

the network is adapted by switching off cells with lesser traffic and off loading traffic

to neighboring cells, whereas, in the latter the users are served by (off-grid) BSs pow-

ered by RES whenever possible. The various aspects of these approaches are covered

elaborately by the authors in the above stated paper as well as in [81]. Scheduling

of cell sizes, like dividing a macro cell into micro cells, or shutting down micro cells

by extending coverage (cell zooming) with macro cell when traffic is low, is another

way of multi-cell cooperation to optimize BSs’ energy usage [82]. Cell zooming, i.e.,

extending coverage of a cell to cover the cells that have been switched off, has been an-

alyzed in [83]. Closely associated to cell-splitting is cell-on-edge deployment scheme,

which reduces network energy consumption as compared to uniformly distributed

configuration [84]. The basic approach in multi cell cooperation is to adjust the cells

in one of the following ways:

� Switching off BSs while associating users with neighbor cells by extending their

coverage area. This requires centralized channel state information and traffic

load information of every cell.

� Expanding coverage of BSs powered by RES while constricting the coverage

area of BS powered by the grid, also called cell breathing [85]. This requires

awareness of energy sources of a cell and awareness of stored energy at a BS.

In [64], authors have proposed a scheme to optimize the utilization of green (solar)

energy during the peak traffic hours, i.e., day times, when solar energy is available,

through cell zooming. Their algorithm aims to reduce grid consumption in such traffic.

The model assumes that BSs can adapt coverage area by varying power level of pilot

signal, with an upper bound on power. More users will be served when coverage is

large, however, more energy will be consumed. Further, the BSs always have data

transmission in each time slot, at equal rate to all users. Thus, the number of users

determine traffic volume at each BS.

Energy Cooperation Scenario : BS1 has more harvested energy and less consumption, whereas its vice versa for BS2

Smart Grid Or

Central Controller

Core Network

ConsumptionLevel

EnergyLevel

ConsumptionLevel

BS1 BS2

Figure 2.3: Energy cooperation in green energy harvesting networks.

In [86], authors have proposed an energy aware cell size adaptation strategy to

optimize the utilization of green energy in cellular networks by minimizing the max-

imal energy depleting rate of the low-power BSs powered by renewable sources. In a

heterogeneous multi-cell cooperation of low-power (green) BS (LBSs) and high-power

(grid) BSs (HBSs), authors proposed a scheme with an objective to minimize the

energy depletion rate of low-power BSs through cell breathing, thereby covering more

users with green energy.

A comprehensive analysis of energy dynamics of green cellular HetNets is pre-

sented in [87]. A generalized energy model has been studied as it evolves (from

harvesting to consumption) in the green cell. In proposed HetNet model, the macro

BS is powered by the grid and provides general coverage in its area, whereas three

(micro/pico/femto) cells provide coverage to high user density buildings and areas

within the macro BS’ coverage.

2.3.5 Energy Cooperation

In energy conservation cases discussed above, it is quite evident that an RES enabled

BS that is not fully loaded or is in idle state, may have energy available from its RES,

which can be made available for the neighboring BSs. Such an arrangement is called

energy cooperation and requires both energy and data control amongst network ele-

ments. In such schemes, the nodes may provide the data transmission service, whereas

the power grid is used to transfer the harvested energy between nodes. The BSs also

act as energy storage devices, which cooperate with each other for their energy needs.

Smart grids offer the perfect environment for energy cooperation between the cellular

BS, where BSs not only cooperate in traffic sharing by on/off switching but also in

energy sharing when surplus green energy is available. The common objectives re-

lated to energy cooperation are maximizing the use of renewable energy, minimizing

use of grid energy, load sharing between the cooperating BSs, and minimizing the

GHG emissions. Thus, energy cooperation entails certain desirable features in the

network, which are:

� Energy storage capacity at the BS.

� An energy aware processing capability at the BS.

� A central control/management unit in the network.

� Smart grid or physical connectivity between BSs.

As stated above, the objective of energy cooperation in networks is the optimal

scheduling of energy sources with intelligent utilization of energy generated from RES.

This has been shown in [39], in which an on line data and energy aware algorithm

makes use of data admission control to allocate energy for each node according to

its traffic. A similar results have been achieved in [37], by using the energy state

Table 2.1: Energy cooperation scenarios for cellular BSs enabled with renewableenergy sources.

With central controller [36], [37], [88]Powered by SmartGrid

No central controller [50], [89], [90]

With central controller [39]No Smart Grid No central controller [38], [40], [91]

information of the network. This is done by developing an off-line, an on-line and

their hybrid algorithms for optimal utilization of energy, through bi-directional flow

of energy between thee nodes and uni-directional between bae station to grid. The

said model is also restricted to a pair of BS.

Authors in [40], consider two BS, cell a having i number of users and cell b having

k users. The aim is to maximize the sum rate of all users by optimizing the quantum

and direction of energy to transferred at a given instant between the two BS. However,

it does not consider time varying prices. Bi-directional energy cooperation between

grid and network is also realizable for green cellular networks powered by smart

grids. For example, in [88], authors aim to find optimal energy management strategy

to minimize the energy cost by energy exchange between the BSs as well as with the

utility. It is assumed that the space and time dependent energy-buying and energy-

sharing costs and energy-selling prices are made known in advance. Authors propose

a strategy in which the instantaneous energy requirement of every BS is fulfilled while

meeting BS battery constraints.

In another work, authors have proposed a joint energy and spectrum cooperation

scheme between different cellular systems to reduce their operational cost [38]. The

underlined theme is to meet the energy needs of one BS by borrowing energy from

other BSs, interconnected in the network through power cables. Energy cooperation

is also shown in conventional grids or off-grid BSs through wireless power transfer.

Authors in [91] introduced the concept of energy cooperation where a user wirelessly

BS Type NTRX Pmax[W]

P0[W ] ∆p Psleep[W ]

Macro 6 20 130 4.7 75.0RRH 6 20 86 2.8 56.0Micro 2 6.3 54 2.6 39.0Pico 2 0.13 6.8 4.0 4.3Femto 2 0.15 4.8 4.8 2.9

Table 2.2: Dynamic and static power components for different BS types, includingthe sleep mode [1].

transmits a portion of its energy to another energy harvesting user. Other papers that

investigate energy cooperation include [89], which employs bi-directional energy flow

in smart grid in conjunction with CoMP. In [90], authors studied joint communication

and energy cooperation between cellular BSs powered through smart grid, for energy

cost savings. Similarly, in [36], authors have studied energy cooperation amongst

green BSs in multi-tier cellular networks. Again smart grid is employed and proposed

algorithm achieves similar grid-energy saving results as others.

Table 4.3 highlights the salient features of RES enabled BSs cooperating with each

other in sharing the surplus green energy. Smart grid adds an advantage to these BSs

in facilitating energy transfer.

2.3.6 BS Sleep (On/Off) Mechanism

A cellular BS is always consuming energy whether it is serving any traffic or not. The

consumption level of energy ranges from a base-line for no traffic to full traffic load

level. However, in a report from the Energy Aware Radio and NeTwork tecHnologies

(EARTH) 1 project of EU, authors quantify three types of energy consumption levels

for different types of BSs, as shown in Table 2.2 : Pstatic shown as P0, Pmax and

Psleep [1]. The energy consumption of a BS has a static value regardless of load

(Pstatic/P0) and a dynamic value that varies between zero and Pmax (when BS is

1www.ict-earth.eu

operating at max capacity load). However, Psleep has been additionally defined as

the power consumption when the BS is not transmitting anything and is considered

asleep. NTRX denotes the number of transmitters per node, and ∆p denotes the

slope with which power varies according to increase in traffic load. Consequently,

on/off switching of BSs or putting BSs to sleep to conserve energy, has emerged as a

promising research topic. A detailed example of this mode is presented in chapter 5.

One of the key findings of EARTH project is that a BS is transmitting much below

its capacity most of the time. Also, the call/data traffic at any typical BS demon-

strates significant temporal and spatial fluctuations in user concentration. Fluctua-

tions in network traffic are encountered at different times of the day such as during

the morning office hours, when activity is more as compared to evening hours. In

addition, traffic is more dense in urban areas where people mostly visit and work, as

compared to urban and rural areas. However, the cell is optimized for peak traffic in

a predefined coverage area. That means at off-peak hours, these cells can save energy

by switching off the BS radio resources to exploit the lean traffic. Research shows

that significant energy saving can be made by switching off BS at certain periods [92].

In times when call traffic is low the network operators can employ BS on/off switch-

ing or sleep mechanisms to save power [93]. The users of a BS may be associated

to another BS when certain pre-selected criterion is met, e.g., BS-to-user distance or

data rate. This will result in traffic concentration in fewer BSs, allowing some BSs

to be switched off. There are two fundamental aspects associated with the sleeping

of BSs.

� How and when to associate users from one BS to another BS, without compro-

mising users’ QoS? [35]

� How to perform the on/off switching mechanism of a BS; centrally or locally?

A comprehensive analysis of BS on/off switching is presented in [62], which sug-

gests a design principle on the concept of network-impact, introduced in the paper

as a metric for on/off switching decision making. Both user association and on/off

switching mechanism have been addressed in this work. This work shows that the

amount of energy saved is dependent upon the traffic ratio of mean and variance, and

BS deployment. Results shows that the proposed algorithm can save up to 80% of

network energy.

In [16], a network model has been developed for a green LTE cellular network

in which the BSs are enabled with RES and utility is provided by a smart grid,

along with standby generators. The energy cost optimization has been performed

by considering a stochastic network model having variable user traffic and renewable

energy, while maintaining QoS constraint. Furthermore, BS sleep mechanism is also

incorporated whenever traffic state so permits. The aim is to optimize the sleep cycle

of a BS as well as procurement of energy from the smart grid or the generator, i.e.,

procure only when RES at a BS is unable to meet the demand.

The authors in [94] have also shown that energy can be saved by adopting power

aware strategies for on/off switching of BS in LTE, based on transmission power re-

quirements. Instead of distance, their simple model depends on using BS transmission

power requirements as a metric for ranking of BS for switching OFF priority. Authors

proposed two criterion for selecting a BS to be switched off: 1) power-aware strategy

which finds total power consumption of each BS and switching off the one having

highest power consumption. This has been represented as P(b) =∑N

i=1 P(b)i and 2)

Power based ranking strategy called power ratio based strategy, relates the UE (user

equipment) directly with its serving BS (b) and inversely with the power consumption

of its neighboring BSs (b′) that are sharing the load, and is mathematically expressed

as C(b) =∑N

i=1 P(b)i /P

(b′)i .

In [95], authors introduce a dynamic programming algorithm that determines

which BSs are to be put sleep, considering the evolving state of cellular traffic and

the harvested energy at each BS, while determining the optimum utilization of RBs for

the active traffic. The network’s QoS is maintained by minimizing the call rejection

probability and the running call’s hand-off probability. Near optimum performance

is achieved by the algorithm and significant energy savings are made by sleep mode

of operations as compared to network energy consumption when it does nothing to

maximize the usage of the harvested energy.

Another scenario that has been investigated by the authors in [96], involves dif-

ferent operators sharing BSs during off-peak hours. It is a known fact that BSs of

multiple users are densely packed in urban city centers to satisfy the high traffic load

in these areas. Often BSs are co-located on a roof top or stand within meters of each

other. Sharing BSs in such locations can make substantial savings in energy if the

operators agree to do so. The authors estimate that this BS cooperation can save

as much as 85% of the total energy consumed during off-peak hours in dense urban

areas, which is considered 35% over and above the savings operators would make if

they acted on their own.

2.3.7 Resource Optimization of Cellular Networks in Smart

Smart grid is a topic of extensive research in green communication. Smart grids rely

on communication infrastructures for information sharing regarding power tariff, sur-

plus energy etc, and support distributed generation of energy Fig. 2.4. These features

make green cellular BSs, having renewable energy sources, an obvious choice for its

utilization in energy resource optimization. A green cellular network, having utility

provided through a smart grid, can incorporate controlled electrical energy flow from

both the sources. Management of energy resources in such a scenario considers dif-

ferent uncertain parameters like power availability from renewable source, the traffic

dependent power consumption of network, and cost schedule of power from the electri-

cal grid. Consequently, different energy management strategies have been formulated

to cater for this scenario-i.e., cellular network powered by smart grid and renewable

energy sources (RES). A very promising area of research for green cellular networks

powered by SGs is energy cooperation amongst the BSs. The common objective re-

lated to energy cooperation is minimizing the overall energy cost by maximizing the

use of renewable energy and minimizing the use of grid energy in correlation varying

traffic. Another objective is maximizing/balancing user throughput in the cells by

load sharing between the cooperating BSs.

The bi-directional flow of energy in a SG is made possible by the availability

of communications infrastructure in parallel to the power grid. Deploying a mini

SG between green BSs has certain dynamics that deal with efficient energy harvest-

ing/storage, distributed generation of energy with demand side management, and

design and optimization of renewable sources. An architecture of a micro-grid of

RES enabled BSs has been presented in [48], which allows for a more efficient use of

electricity resources.

2.4 Summary

The futuristic wireless technologies of 5G are going to bring dramatic performance

improvements in terms of data rates, network capacity, latency, cost and coverage. In

order to achieve these desired goals, technological improvements are underway at all

tiers as well as the search for new innovative solutions. Densification and diversifica-

tion of the radio access network will requite new models to make them economical and

energy efficient such as dynamic and adaptable allocation of resources. With smart

Figure 2.4: A smart grid incorporates distributed generation of electricity with de-mand side management, facilitated by ICTs.

grid and renewable energy systems also maturing, a new paradigm of green com-

munication is emerging that aims to improve energy efficiency of cellular networks

comprising macro, micro, femto and pico base station transceivers. The industry is

also aware that technology improvements must not be at the cost of adverse climatic

effects, thus renewable energy based solutions to meet the power demands are order of

the day. Renewable energy systems, particularly solar and wind, are a viable option

for a cellular BS as well as the whole network. RES can be employed for not only

cutting down on the harmful GHGs but also for decreasing the energy cost of tradi-

tional networks. A BS is the most energy hungry element in a cellular network and

consumes upto 60% of the energy consumed by a network of macro BSs. Renewable

energy is not only feasible for stand alone BSs that are off the grid but equally feasible

for BSs deployed in a network supported by the grid energy. It is also seen that small

cells like femto and micro cells need lesser energy. Thus, small cells are more feasible

for the RES to power them. However, for proper integration of RES into the present

networks, proven system designs are required that can readily replace the diesel gen-

erators. Sizing, interfaces, infrastructure etc. need to be defined and standardized

so that RESs can be easily installed and replaced when needed. Smart grids with

distributed generation of green energy can provide clean and cheap power to the cel-

lular networks, thereby, decreasing the energy cost and reducing the harmful GHGs.

Smart grids have special significance for cellular BSs in terms of facilitating energy

exchange between them. Smart grids also provide flexibility in energy price forecast-

ing which allows for scheduling of energy sources to maximize the use of green energy.

Energy cooperation is not only possible between the BSs but also between green

networks and local community where utility supply is non-existent or intermittent.

Intelligent controllers are required that can control the energy flow between different

modules. Researchers have come up with the optimal energy management strategies

to use renewable energy in their systems under various scenarios that make use of

centralized or decentralized controllers. Such controllers will make the BSs smart

energy wise and provide effective demand side management in the emerging smart grid

environment. Incorporating these controllers into existing hardware is a challenge for

the system designers. The essence of research in the are of RESs has been positive and

proven empirically as well as theoretically. What is required is a joint policy statement

by the key industry players to integrate PV-wind energy system solutions at new sites

and gradually replace the generator sets at the older ones. Standardization of the

RES is a serious challenge for their practical implementation. Greening of cellular

networks through increased energy efficiency as well as incorporation of renewable

energy is a growing research area. These aspects of RESs have good prospects when

co-joined with other emerging trends in wireless networks, such as cognitive radios,

cooperative relaying and cloud radio access network (C-RAN).

Chapter 3

Modelling RES for Energy Sharing Microgrid

3.1 Introduction

Electricity is the most widely used form of energy that is required to sustain the

modern living. In some developing and most underdeveloped countries, a significant

portion of population is deprived of a reliable supply of electricity. On the other hand,

these countries do enjoy fairly healthy coverage of wireless cellular communications

through the companies operating different networks. That means a large deployment

of cellular base stations in areas even short of electrical power. For countries that are

deprived of this form of energy, a set of RES enabled base stations can provide some

relief to the local community through energy sharing. Each year, 300 to 400 million

people are added to the subscriber base of mobile users, as a result of some 120,000

base stations being erected world wide to cater for the increasing demand [97].

Ninety nine percent of the BS sites are powered by or have a back up of diesel

generator. Diesel generators have been favoured traditionally due to its low invest-

ment cost, but considering the perpetual requirement of the sites for energy, and the

decreasing cost of wind and solar energy systems, the return of investment (ROI)

equation is changing in favour of RES in the long run [98]. The technology advance-

ments in deep cycle rechargeable batteries, MPPTs (maximum power point trackers),

bi-directional inverters, charge controllers etc. have resulted in the development of

proven systems [25]. In one study carried out to power a GSM BS with solar and wind

hybrid system, with a stand-by diesel generator, it is concluded that an autonomous

BS powered by such a power source, will pay off in two to four years with good windy

and sunny conditions [47].

A BS is well suited for renewable energy sources (RES) because power generation,

storage and load are all located at one place. Especially for remote/off-grid sites that

enjoy good sunshine and wind, RES offer a good alternate to diesel generators, eco-

nomically and environmentally [99]. Other than running cost, storage, transportation

and theft of diesel also add to expenses of the operator. But the most compelling

reason, perhaps, is the harmful effect on climate caused by the GHG emissions at-

tributed to diesel generators. System models developed show that for a stand alone

base station the hybrid PV-Wind-generator system can provide the optimal mix of

renewable and non-renewable energy. A green energy harvesting system is a function

of different factors, which are:-

� The amount of natural energy such as solar irradiation and wind speed available

throughout the year.

� The availability of conventional sources such as diesel generator and grid.

� The energy consumption of the system to be powered with RES.

� The energy storage capacity or the size of the battery bank.

A smart-grid/ microgrid offers inherent advantage of enabling distributed genera-

tion and facilitating energy cooperation between energy harvesting nodes as demon-

strated in [48]. Different energy management schemes have been proposed in literature

for both on-line and off-line optimization of energy resources [49,50] in a smart grid.

The distributed generation of electricity at BS sites can be directed towards the local

grid station during off-peak hours when it may be lying surplus at the node. Thus a

microgrid of RES enabled BSs is a lucrative possibility offering a win win situation

to both the network operator as well as the utility.

In this chapter we will formulate a mathematical model to estimate the energy

consumption of a BS as a function of its user traffic. Based on this consumption

figure, which has a static and a dynamic part, we will model a PV-array and a wind

turbine for Islamabad region, to power the BS. An estimate of battery bank to store

the harvested energy will also be made. Based on these models, it will be shown that

the dynamic energy consumption of the BS and the fluctuating harvested energy,

yields surplus energy at some time periods of the day, which can be provided to the

local grid for the benefit of the local community.

3.2 Energy Consumption in Cellular Base Stations

A typical base station consists of many sub-systems, which consume energy, as shown

in Fig. 3.1. These sub-systems include baseband (BB) processors, transceiver (TRX)

(comprising power amplifier (PA), RF transmitter and receiver), feeder cable and

antennas, and air conditioner [100]. Their energy consumption ranges from a few

watts to kilo watts depending on the type of BS, with a macro BS consuming most

energy, as depicted in Table 3.1. Power consumption of different modules for some

known BS types is given in the Table 3.1, i.e., power consumption of radio equipment

comprising power amplifier (PA), BB, and RF transmitter/receiver modules, and the

auxiliary equipment comprising main supply, voltage converters, and cooling units

[101]. Whereas, the power consumption of the PA, RF and BB modules is fixed for a

particular type of BS, the consumption of power supply modules and air conditioning

is shown as a factor of the radio equipment’s consumption. It is evident from Table 3.1

that in case of a macro BS the PA alone accounts for 57% of total energy consumption,

which is reduced to 51% for remote radio head (RRH) configuration. Thus, significant

power dissipation takes place in the PA, which is directly associated with the traffic

load at the BS.

From the above discussion we may deduce that the energy consumption by a

BS can be bifurcated into static and dynamic types. The former accounts for basic

consumption, independent of traffic load, and is attributed to the cooling equipment,

power supplies, and RF/BB modules. While the latter is proportional to the traffic

load or data throughput of a BS and is attributed to the energy consumption in PAs.

Thus the total energy being consumed is the sum of the two types of consumptions

Base Station

Electronics

Air Conditioning

Power Supplies

Air Interface

Figure 3.1: Typical layout of power consuming modules of a cellular BS.

noted here. The traffic variations, thus can be a measure of the variation in energy

consumption of a BS. The power consumption of a BS b serving a system traffic load

of density ρ in time t, may be given as Pb = ρP tb , where P t

b is the power transmitted

per resource block by the BS in time slot t. Bifurcating this power into the above

stated static and dynamic components, which is expressed as:

Pb = Pdynamic + Pstatic, Pb = 4pPtb + Pstatic, (3.2.1)

where Pstatic is the fixed energy consumption component and 4p is the slope of the

load dependent power variable [93].

In terms of energy consumption in a cellular network, the energy efficiency of

a cellular BS has been broadly defined in literature as the ratio of BS’s output-

power/area-coverage/network-capacity to the total input power Pb or [93], :

ηb = Pt/Pb; = Tn/Pb; = Cn/Pb, (3.2.2)

where the first term (Pt/Pn) is the ratio of the transmitted power to the network

Table 3.1: Power consumption breakdown for different types of base stations

BSType

NTRX PA[W]

Others Total perTrx

Total forNTRX

Macro 6 128.2 12.9 29.6 16.5 225.0 W 1350 WRRH 6 64.4 12.9 29.6 16.5 125.8 W 754.8 WMicro 2 6.3 54 2.6 48.0 72.3 W 144.6 WPico 2 0.13 6.8 4.0 15.3 7.3 W 14.7 WFemto 2 1.1 0.6 2.5 20.0 5.2 W 10.4 W

power, whereas in the second term, which is a variant of first, Tn stands for power

required for certain area coverage and has unit of Watt−1. The third term (Cn/Pn) is

a further modification and accounts for the aggregate network capacity to the total

power consumed, having units of bits per second per watt [93].

3.2.1 Energy Consumption Model of a Base Station

The energy consumption of a BS is basically of static and dynamic nature as discussed

above. LTE base stations called evolved node base stations(eNBs) require relatively

less energy than there predecessors. A typical tri-sectorial base station can cover an

area of about 0.66 km2 having a range of 500 m. In suburban/rural areas this coverage

may grow to 7.8 km2 [48]. The EARTH project has quantified the power consumption

of different types of base stations [102]. According to EARTH report, the power

consumption is given in terms of the power consumed by the power amplifier (PA),

the radio frequency (RF) module and the baseband (BB) unit as well as the losses

incurred in feeder, DC power supplies, mains and cooling. The BS power consumption

for maximum load Pb, given that power consumption grows proportionally with the

number of transceiver chains NTRX , is then expressed by the following equation.

Pb = NTRXPPA + PRF + PBB

(1− σDC)(1− σMS)(1− σCool)(3.2.3)

where,

PPA =Pout

ηPA(1− σfeed)(3.2.4)

is the power consumption of the PA, which is equal to the ratio of power out of

the antenna feeder and PA efficiency plus feeder loss. At maximum load the power

consumption of eNB is shown to be 1350 W for three sectorial coverage each having

two transceivers in MIMO configuration. The power consumption figures for various

types of cellular BSs (macro, micro, pico, femto) is shown in Table. 3.1. The power

consumption for each element in a single transceiver configuration is depicted against

each type. The consumption incurred in DC-DC power supply, cooling and mains is

shown as a percentage of overall power consumption. The last column of the table

shows total consumption, which is equal to power consumption of a single transceiver

chain multiplied by the total number of transceivers employed.

The EARTH report also gives useful insight into the dynamic power consump-

tion of a BS, which is discussed in detail in next chapter. That is the relationship

between the BS traffic and power consumption of the base station, which forms the

fundamental basis of energy cooperation. Given the fact that the PA(power amplifier)

consumes between 55-60 % of total power, and that the PA consumption increases

linearly with traffic, the total consumption increases linearly with mobile traffic. The

power consumption can be described in terms of the static P0 and dynamic output

power Pout, which varies with traffic load and number of transceivers deployed. Thus

BS power consumption as fraction of traffic load/transceivers is given as:-

Pb = NTRX × (P0 + ∆pPout), 0 < Pout ≤ Pmax (3.2.5)

where no of transceivers can be six (3x2) maximum, and ∆p is the slope of the

load dependent power consumption given as 4.7 in the said report. For Pmax given

as 20 W per transceiver, we can thus find the maximum value of Pb to be 1344 W at

full traffic load and minimum value of 780 W for no traffic. We add another 1 kW to

these figures for air conditioner (one ton), given the hot climate of this region. The

BS power consumption, therefore, fluctuates between 2.35 kW for max load and 1.78

kW at no traffic load in this case. These basic estimates provide the figures for energy

consumption assessment for a cellular base station. At 2.35 kW power consumption,

the daily energy consumption is 56.4 kWh, which is the case for BS operating at

maximum traffic throughout the day.

The power consumption of a BS Pb, estimated above, has a constant (static)

consumption of 1.78 kW, which goes up to 2.35 kW, with the slope noted as δ(t) in

equation (3.2.5). The traffic intensity or the number of users associated with a base

station/network is a function of area-population Pop and the number of network

operators in the area are given as Nop. Then the total subscribers available to the

particular network’s eNBs are Pop/Nop. Ideally every NWO gets equal share, but

realistically the share is unequally distributed, with some having more subscribers

than others. The energy consumption profile varies with user traffic throughout the

day between the following two states of energy consumption:

� High activity state during peak hours of the day, consuming maximum energy.

� Low activity state during off peak hours of the day, having surplus energy.

3.2.2 Spatial and Temporal Load Variations

The traffic intensity associated with any BS may be modeled for some area having

a known population Pop. The number of network operators in the area are given as

Nop. Then the total subscribers available to a particular BS are proportionate to the

population and number of operators in any area – i.e., Pop/Nop. Ideally every network

operator gets equal share, but realistically the share is unequally distributed, with

some having more subscribers than others, which results in spatial load variations

[103].

The power consumption of a BS (Pb), estimated above, comprises two types of

power consumptions. One is the constant consumption independent of the traffic

00 hrs

10 hrs

12 hrs

14 hrs

16 hrs

18 hrs

20 hrs

22 hrs

24 hrs

City Centre

University Campus

Residential

Figure 3.2: Traffic load fluctuations throughout the day in different areas of a cellularnetwork result in fluctuating energy consumption.

load, on account of baseband unit and other components. The other is the power

consumption related to the traffic load that increases linearly with the allocation

of resource blocks to active traffic, which keeps varying throughout the day. This

variation in traffic load, which is a function of time, is termed as temporal variation.

Thus all BS are likely to have different instantaneous traffic-loads due to the temporal

and spatial variations in their traffic, which forms the basis of different energy sharing

strategies.

3.2.3 Traffic Pattern Based Energy Consumption

In order to know the fluctuations in the load or power consumption of a BS, we need

to determine its traffic profile. It is evident from section 3.2.1 that the instantaneous

power consumption of a BS is directly proportional to its traffic. The variation in

power consumption of PA is defined by ∆p in equation (3.2.5) indicating the dynamic

part of Pb. Consequently, the energy consumption of a BS keeps varying according to

the 24 hr temporal fluctuations in the user traffic. The traffic pattern is, therefore,

determined by (i) the minimum and maximum values of P tdynamic and (ii) the peak and

lean traffic hours. Using these values, we can generate the pattern curve representing

traffic of a BS as per the expression described in [104], as follows:-

W hb = (

%max − %min2

W ob = (

%max + %min2

f(b, t) = W hb Cos(2π

t− tpT

) +W ob +W t

(3.2.6)

where %max and %min are the maximum and minimum traffic loads of a BS, which

can vary from BS to BS depending on its hardware configuration. For the case of the

BS considered in section 3.2.1, these values may be estimated as %max = 2.35kW and

%min = 1.78kW . W hb is the height of the sinusoidal wave and W o

b is the DC offset. t is

the present time slot and tp is the peak-traffic-hour time slot. T is the total number

of time slots, which can be fixed for a day or a week or any predefined period. W tp

denotes a Poisson random variable.

The cellular BS is designed to handle peak traffic that occurs at a particular time

of the day, depending on area. There is also a corresponding lean time with least

traffic load and in between the traffic load is either decreasing or increasing. Thus

it is appropriate to model traffic load as sinusoidal variation. The BS traffic profile

generated on the basis of empirical data in [105] also shows the same and is very close

to our traffic load pattern. Furthermore, since the Poisson model is the most widely

used model to describe the user traffic arrival in cellular base stations [101, 106],

we apply Poisson probability distribution to the basic pattern generated above to

have varying patterns for different BSs. W pt is Poisson distributed traffic with mean

λ. Consequently, the energy consumption profile varies throughout the day between

the two states of energy consumption i.e., high activity state during peak hours,

consuming maximum energy, and low activity state during off-peak hours, having

surplus energy.

As a result, we get traffic pattern for the BSs deployed in a particular region, based

on the variables described above. The twenty four hour traffic profile of different

locations are shown in Fig. 3.2, which have been generated by using equation (3.2.6)

for three different locations i.e., a community centre, a residential complex and a

university campus. Based on the the results for the traffic based load and the size of

renewable energy harvesting systems, estimated next, we may observe surplus energy

during lean traffic period with capacity energy generation from solar and wind. This

surplus energy can be made available to the community as discussed in proceeding

paras.

3.3 Design and Optimization of Hybrid RES

Research has shown that hybrid energy systems can significantly reduce total life

cycle cost of stand alone power supplies in many situations [24]. Similarly in the

case for cellular BSs, the hybrid of both wind/solar and conventional energy gives the

most optimal solution to give energy efficient solutions; as investigated by researchers

in [11, 47, 69, 71, 77, 107]. The fundamental steps involved in equipping a BS with

renewable energy sources can be described as following:

� Feasibility study involves gathering site parameters, particularly average an-

nual solar insolation and wind speed as well as possibility of installation of

systems. Meteorological data generation is required for any feasibility study.

� System modeling/sizing of PV arrays, wind turbines, and battery bank is the

main task, which must be carried out very carefully. Different sizing methods

can be used for solar panels and wind turbines, which must ultimately search

for an optimum combination of two parameters, i.e., system reliability and

system cost [108]. For a BS, reliability is of paramount importance, therefore,

the system must be designed carefully with sufficient battery backup. Sizing

of PV-wind hybrid systems may be performed by different methods such as

the monthly average method, the most unfavorable month method, the loss of

power supply probability method, and software simulations.

� System evaluation is carried out using simulation software, which can perform

power reliability analysis and system cost analysis for renewable energy systems.

Several software tools are available for designing of renewable energy hybrid

systems, such as HOMER (hybrid optimization model for electric renewables)

that uses hourly simulations for arriving at optimum target.

� System optimization aims at (1) optimization of system capacity of RES,

i.e., size of PV array and the wind turbine as well as the battery bank and (2)

optimization of energy consumption within system in order to maximize the use

of green harvested energy. Different optimization techniques are employed to

achieve the stated goals [9].

A detailed description of the above stated and other related characteristics for op-

timum configuration of hybrid stand-alone solar/wind power systems is presented

in [24] and [108].

3.3.1 Subsystems of Hybrid Solar/Wind Enabled BS

A hybrid solar/wind based power system comprises PV array, wind turbine, battery

bank, charge controller, inverter, cabling, and other devices (such as fuses etc.). The

basic layout of a BS employing conventional as well as renewable energy sources

is shown in Fig. 3.3. In a green BS, the energy harvested from nature is mostly

either from sun or wind or combination of the two, with a battery bank to store

and regulate the green energy. The heart of the system is a hybrid controller that

HYBRID

CONTROLLER

INVERTER

Figure 3.3: Basic layout of a typical BS powered by main grid, a back-up generator,and renewable energy sources of solar and wind.

performs charging/discharging of the battery bank and also controls the flow of energy

between system components. Batteries are indispensable for the success of RES

because the energy harvested from sun and wind can be unstable and unavailable

at times. Consequently, batteries are invariably used to store energy for off-times as

well as to regulate the supply of energy to the system. A brief description of the

fundamental systems of BS powered by hybrid energy source is given below.

� Wind Turbine: The wind turbine is described as comprising two main com-

ponents, i.e., a rotor assembly of blades and coupling, and a permanent magnet

(AC/DC) generator with associated electronics such as AC/DC converter [109].

Vertical axis or horizontal axis wind turbines may be employed each having its

own merits/demerits. The electric power generated from a wind generator is

given as Pw = 12× ρ × Ce × Aw × V 3, where ρ is the air density, Ce is the

coefficient of wind turbines performance, Aw is the area traversed by wind and

V is the wind speed. It is obvious that wind speed V is the variable on which

the instantaneous output of a given turbine depends.

� Photo-voltaic Panels: The PV systems include PV module, mounting hard-

ware, DC charge controller, and installation. The electric power generated from

a PV array is given as Ppv = A×ηm×Pt×ηPC× I, where A is the total area of

PV array, ηm is the module efficiency (0.15 normally), Pt is the packing factor,

ηPC is power conditioning efficiency, and I is the hourly irradiance (kWh/m2).

Given the low efficiency of PV cells (approximately 15%), large panel sizes are

required to completely serve a reasonable load.

� Energy Management Unit: This module is required to control the flow of

energy that can be as simple as a charge controller or something quite complex

that maintains causal knowledge of wind and solar availability. Several man-

agement strategies may be implemented through algorithms developed for this

purpose may be stored in embedded systems.

� Battery Bank: The harvested energy is stored in battery bank and is used to

power the systems in a regulated manner. The capacity of the battery bank is

based on the BS load and the voltage produced. The higher the voltage rating

the lesser the line losses and vice versa. Similarly the type of batteries and the

depth of discharge also matter. Many researchers have focused on optimizing

battery charging/ discharging operations in green BS. [110,111].

� AC/DC Bus Bar: Bus bars are used for connectivity of AC and DC power

with different components of the system such as battery bank, air conditioner,

radio devices, charge controller etc. The length of cables and current ratings

determine the proper wire gauge, to minimize losses. Two bus bars are required

for the respective loads. An inverter of proper rating is also used to convert the

DC power from the battery bank for AC loads, and may come equipped with

charge controllers and net meters.

� Mechanical Assembly: A housing and support structure is required to in-

stall the wind turbine and the PV panels. The design needs to have minimum

maintenance requirements since BS could be located at remote locations. Both

horizontal axis and vertical axis wind turbines have been modeled, for both in-

tegrated or separate installation. Robust non-corrosive structures are employed

that can withstand weather effects for years to come.

3.3.2 Modelling Energy Sources for Powering a BS

Our preference is to use the sustainable green energy as much as possible. In order to

get an estimate of the size of PV-array and wind turbine required to sustain the load,

we have to perform some basic calculations for these systems. These calculations have

been described in Appendix to this chapter and their results are presented here. Not

every parameter such as line losses and battery charging losses are catered for since

we are only interested in general relative figures for BS load and energy generation

system. To power the BS with RES we need to consider the average daily energy

consumption of the BS as per section 3.2. Based on this requirement and the yield

of solar and wind energy at site, we can decide on the size of PV array and wind

turbine (WT). In order to regulate the energy harvested from PV array/wind turbine,

it is stored in batteries for smooth supply, thus a battery bank of sufficient size is

mandatory. In case the harvested energy is not enough to charge the batteries to their

required level, power is provided from a standby generator. On the other hand, if

batteries are fully charged and harvested energy is surplus, we may share this energy

with other consumers.

The individual output of the PV model and wind turbine model are presented in

the appendix along with the details of their calculations. By combining the electrical

energy output of the two systems i.e., the wind turbine and the PV-array, we get the

total harvested energy available to the BS. The combined yield is shown in Fig. 3.4,

which can be seen to be maximum 90 kW in June and 50 kW in December. Thus

our systems are sufficient to support the operations of a LTE BS consuming 2 kW

average power. The RES systems can be accordingly designed for higher or lower

capacity base stations.

Figure 3.4: Combined average daily yield of the 7.8 kW PV panels and 7.5 kW windturbine at BS site for Islamabad.

3.4 Green Energy Sharing in Microgrid Without

Cooperation

A rural/sub-urban area having N number of BSs operated by different NWOs is

considered. For example, in Pakistan all rural towns and villages are serviced by

4/5 licensed NWOs operating in the country. In order to seek positive dividends for

both the network operators and the utility, a microgrid scenario is present for energy

sharing from RES enabled BSs. The microgrid enables energy flow from the BS back

to the grid in a net-metering arrangement (Fig. 3.5). The key elements of our system

model are the following:-

� Energy harvesting base stations.

� A battery bank to store energy being harvested from the RES.

� Net metering for energy buy back by the utility.

The spatial and temporal diversity between the BS is on account of the number of

subscribers associated and the instantaneous user traffic, respectively. Due to this

diversity the traffic-load of each BS is different, which may be denoted as Ltn, and can

be determined trough equations described in section 3.2.1. This load is met from the

RES on site Et,gn as a preference and the utility Et,u

n , when available. In the absence

of both, a stand-by diesel generator is employed to power the BS, which is considered

same as utility since either of two is used. The energy balance, at BS n in slot t,

between load and generation can be expressed as:

Et,gn + Et,u

n ≥ Ltn (3.4.1)

The above equation tells us that a site may have surplus energy when the energy

available is more than the BS’ instantaneous load i.e., Et,gn > Ltn. At times of off-peak

traffic hours such as night time, the ’energy surplus’ may be made available to the

community through the grid, which can be substantial if all BSs in the community

contribute their surplus energy.

PANELS

TURBINE

ANTENNAE

PANELS

TURBINE

ANTENNAE

Figure 3.5: Energy cooperation between two RES enabled BSs and Communitythrough a microgrid in a rural area

3.4.1 Mathematical Modelling

Our objective is to minimize the conventional energy cost by using as much green

energy as possible while maximizing the energy sold back to the local grid. At all

times the energy consumed by a BS cannot be more than the energy harvested or

taken from utility/generator. Mathematically we can express this as follows:-

t,gn ,E

t,un ,E

T∑t=1

N∑n=1

t,un ) + Cg(E

t,gn )− Cv(Et,v

(3.4.2)

subject to:

C1 : Et,gn , Et,u

n , Et,vn ,≥ 0, ∀ n, t

C2 : Et,gn ≤ Eg,max, Et,u

n ≤ Emax,un , ∀n

C3 : Et,gn + Et,u

n = Ltn + Et,vn , ∀n

C4 : Etn.E

tn = 0 , ∀n, t

(3.4.3)

where Et,vn is the surplus energy sold back to the utility through the local grid

station. Cu, Cg, Cv are the arbitrarily assigned cost in order to effect the green

energy policy of the model. The C1 and C2 constraints ensure the minimum and

maximum limits of decision variables. The second constraint ensures that the energy

procured from RES or the DG is not more than the maximum energy generated

by these sources. The third constraint maintains the energy balance at BS, so that

energy used is not more than the consumption of BS plus energy given back to grid,

at any time t. The fourth constraint ensure that a BS may not sell energy to another

BS or itself.

3.5 Solution and Results for Microgrid of Green

The results of our energy sharing algorithm for the RES enabled BSs are discussed

here. The algorithm generates the energy generation and energy consumption profiles

for each BS by varying the relevant parameters discussed above. Based on these, it

estimates the net surplus energy to be sold back to the grid. The traffic based energy

consumption and RES based generation profiles for each BS are simulated for a finite

time horizon equally divided in one hour slots t. For each instant t, the difference in

generation and consumption will tell us if there is any ’surplus’ that could be provided

to local grid through net-metering.

Figure 3.6: Traffic-load generation for four BS, showing spatial and temporal diversitybetween sites.

3.5.1 Power Generation vs Power Consumption

The BS energy consumption is based on its traffic load. The Traffic pattern for

four BSs is generated by Poisson random variable, shown in Fig. 3.6, showing 24hr

energy consumption for each site. The energy consumption profile varies with user

traffic throughout the day between the following two states of energy consumption

i.e., high activity state during peak hours of the day, consuming maximum energy,

and low activity state during off-peak hours, having surplus energy. Since mobile

traffic arrival in a cell is given by Poisson distribution, we apply Poisson probability

distribution to the basic pattern generated to have varying patterns for different

BSs. W pt is Poisson distributed traffic with mean λ. We simulate spatial diversity

by varying the dynamic power consumption values between 1.5 to 2.4 kW, and the

temporal diversity between BS is shown by varying peak traffic time tp for each site;

thereby generating some diversity in their traffic patterns.

Given the size of solar panels and the wind turbine, we can simulate the power

5 10 15 200

Time in hours

Load Shedding Indicator

Figure 3.7: Example of grid power outage simulated for a day.

generated at each site with associated randomness in wind speed. The combined

average power obtained from solar and wind sources designed above is shown in Fig.

3.4; for a day. Since the four BSs are collocated, the availability of solar and wind

energy will practically be the same at each site. Also, since solar energy is only

available in day time, the combined yield is significantly higher in daylight hours. For

any shortfall utility and standby diesel generator fill the gap. In order to effect the

net metering or energy buy back to grid, we have introduced power outage as shown

in Fig. 3.7.

The overview of energy generated vs energy consumed is shown in Fig. 3.8 for each

BS. The graph above the red line indicates the surplus energy in the corresponding

time slot. As stated above, due to the combined yield of the wind-turbine and the

PV-panels, we have surplus energy generated during day light hours that can be made

available to the local grid through net-metering.

3.5.2 Power Sharing with Community in Microgrid

The algorithm determines the surplus harvested energy in each time slot t at each BS

n i.e., Et,gn − Ltn. As discussed the amount of surplus energy available is dependent

on instantaneous traffic and energy generation at each BS. Where there is no wind

or solar energy available, the diesel generater provides the required power. The net

surplus energy thus available to community at the tth time slot is Π, which is given

Πt = max

N∑n=1

[Et,gn − Ltn

]), ∀t (3.5.1)

s s Fig

The simulation results of the energy sharing algorithm are shown in Fig.3.9. The

share of energy from each BS to the grid station is shown in green, which can be

substantial if all surplus energy from every BS is combined. It can be seen that

maximum energy surplus is available in daylight hours due to availability of both

solar and wind energy, which can be used to serve the local community through the

local grid station in a microgrid scenario. The more the number of RES enabled BS

the more the energy available for community. Also, the net saving is significant if both

wind energy and solar energy is used rather than either of them being deployed solely.

Thus, we may conclude that powering cellular BS with renewable energy sources is

beneficial for both the network operator as well as the utility, especially in areas facing

grid power outages. However, a stand by generator may still be required to make up

for the unavailability of stored renewable energy at any instant.

3.6 Summary

In this chapter a macro BS consuming 2.35 kW of power, including air-conditioning,

was considered for installation of RES of a wind turbine and a PV array. Based on this

load and the solar insolation and wind conditions for Islamabad region, calculations

were made for suitable size of solar panels and a wind turbine. This resulted in a

PV array and a wind turbine with peak capacity of 7.8 kW and 7.5 kW, respectively.

The dynamic and total load as a function of traffic fluctuations were also estimated

for N number of BSs. The yield of the two renewable energy sources was simulated

and it was realized that given the variance in the load as well as energy harvested,

the instantaneous energy state reveals surplus green energy at some instants. It

was assumed that all network operators are willing to cooperate and there is pricing

agreement between utility and operators to buy/sell energy. Also a microgrid is in

place with a central controller for bi-directional energy flow. This model is particularly

suited for application at sites that are off-grid and have good windy conditions such

as the coastal area and mountain ranges. Thus we concluded that a BS equipped with

RES of solar and wind can yield surplus energy that may be given to local community

through the local grid station in a microgrid scenario.

3.A Solar Energy System Model

Solar irradiation is the solar energy available at a site, indicated in kW/m2, whereas,

solar insolation is the energy available over a period of time. The solar insolation

data for Islamabad is shown in Fig. 3.10(a). The average daily insolation for each

month is given in kWh/m2/day, e.g., it is lowest in December (4.79kWh/m2/day)

and maximum in May (6.63kWh/m2 per day)1. The total electrical energy for a

period (a day in our case) produced by the solar panels of certain size is a function

of the daily solar insolation (I), given in kWh/m2 per day, area of the panels (A)

in m2, photovoltaic (PV) module efficiency or solar yield (ηm) and the performance

ratio or coefficient for losses PR (taken as 0.75). The electrical energy generated in

kWh from PV modules is then given by the following expression.

Epv = A× ηm × PR× I (3.A.1)

where Epv is in kW and the solar yield (ηm) is given as ratio of wattage (in kW)

and area (in m2) of the panel, which is equal to 0.16 = 0.26/1.63 in our model .

Using the above expression, we can find the size of panels to produce 56 kW of

energy required to power the BS, which comes out to be 80m2 approximately for an

insolation value of 5.5kWh/m2. For a single panel of 1.63m2, this translates into

49 panels. This is a very large number and not feasible. We must reduce the PV-

array size and compensate the deficiency with a wind turbine. For an average daily

insolation of worst month of 4.79kWh/m2 per day in December, the electrical energy

yield of a 50m2 panel comes out to be 29 kWh per day approximately. Whereas,

for the month of May this figure increases to 40 kWh per day. The average daily

energy available from the stated panel size is shown for each month for Islamabad

in Fig. 3.10(b), which can be seen to be ranging between 30 to 40 kWh for a day,

1www.solarelectricityhandbook.com/solar-irradiance.html

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Avg Value 4.81 5.06 5.69 6.35 6.63 6.56 5.77 5.52 6.25 6.63 5.91 4.75

Avg Monthly Insolation

(a) Average solar insolation figures for Islamabad, measured in kWh/m2 onto a solar panel facing56 degree south for optimal year round performance .

KWh/day

(b) Electrical energy yield for a day calculated for a PV panel array of 50m2 having a peakcapacity of 7.8 kWp

Figure 3.10: Solar energy data for Islamabad area

approximately. Thus for 50m2 PV-array, given the size of one panel as 1.63m2 and

260 W power (24V nominal voltage), the total number of panels required are 30

(connected in series parallel), having a total wattage of 7.8 kW peak and 5.85 KW

(7.8× 0.75) nominal.

The minimum solar energy yield is 29 kWh per day in the month of December.

In order to power the above stated load of 56 kWh with solar panels only, we need to

almost double the size of solar panels, so that enough power is generated to charge the

batteries fully and subsequently power the BS. However, instead of making the panels

unreasonably large, we can also make use of a wind turbine. Wind turbine will be a

good edition because it is also a clean source of energy and available at night, unlike

solar. However, wind is more unpredictable than solar energy. Quisque ullamcorper

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quam, ac pulvinar elit purus eget enim. Nunc vitae tortor. Proin tempus nibh sit

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3.B Wind Energy System Model

On one hand wind energy is available throughout the day in certain areas, unlike

solar energy, while on the other hand, wind can be highly unstable. Different sizes

of horizontal axis wind turbines and vertical axis wind turbine are employed for the

generation of electricity from wind. There are different methods of calculating the

annual energy output (AEO) of a wind turbine, such as (i) swept area method (ii)

manufacturers estimates (iii) and power curve method. A Wind Turbine is described

by its power curve showing output power vs wind speed. Other factors describing a

WT are peak power (e.g 1800W) and rated power (e.g 1000W), which is given at a

rated wind speed (e.g 8 m/s), and also rated turbine speed in RPM. The problem

however is that a wind turbine operates at the rated power for a low percentile due

to large variations in wind speed. Wind is empirically known to follow a Weibull

probability distribution. The wind speed probability distribution for any region is

described by the spread of Weibull curve given by its (i) shape parameter k, and

the (ii) average wind speed. The Weibull distribution of wind for the Islamabad

region of Pakistan2 is shown in Fig. 3.11(a), having an average yearly wind speed

of 5 m/s and a ’k’ parameter value equal to 1.80 [112]. Based on this probability

distribution, we can generate hourly wind generation for a day. Consequently, we can

have the electrical energy available on an hourly basis from a wind turbine of a given

size. The electrical energy generated by any WT is a function of the area A, in m2,

swept by its blades. Other factors are the wind speed v, air density ρ and turbine

efficiency coefficient Cp. The generated energy is then given as:-

Ewt = 1/2× ρ× v3 × Cp × A (3.B.1)

For a particular size of a wind turbine the output can be known for various wind

speeds. Taking lead from the work done in [47], the BWC-Excel-R/48 7.5 kW wind

turbine is considered, which gives 48 VDC and is suitable for BS application. Ac-

cording to the above equation, adjusted by the manufacturer for the said turbine, the

average power output, for an average wind velocity of 5 m/s, site elevation of 500

m, and area ’k’ factor of 1.8, is 1.71 kW and daily energy output of 41 kWh. This

figure goes up to 66 kWh daily if the average wind speed increases to 7 m/s. Thus

we get lot of power variation for the same WT with variation in wind speed. Based

on the average monthly wind data for Islamabad, and other parameters for our area,

the daily average energy output for each month is shown in Fig. 3.11(b). Quisque

2www.windfinder.com/windstatistics/Islamabad-Rawalpindi-airport.

(a) Average monthly wind speed (m/s) data for Islamabad.

(b) Average daily electrical energy yield from the 7.5 kW BWC-Excel-R/48 wind turbine

Figure 3.11: Wind energy data for Islamabad region (source: Islamabad meteorolog-ical department and)

ullamcorper placerat ipsum. Cras nibh. Morbi vel justo vitae lacus tincidunt ultrices.

Lorem ipsum dolor sit amet, consectetuer adipiscing elit. In hac habitasse platea dic-

tumst. Integer tempus convallis augue. Etiam facilisis. Nunc elementum fermentum

wisi. Aenean placerat. Ut imperdiet, enim sed gravida sollicitudin, felis odio placerat

quam, ac pulvinar elit purus eget enim. Nunc vitae tortor. Proin tempus nibh sit

amet nisl. Vivamus quis tortor vitae risus porta vehicula.

3.C Battery Bank Modeling

A battery bank is invariably used with communication systems to ensure 100% re-

liability of the system. Ideally, one would like to have a battery bank large enough

to power the BS for considerable time/hours in case of power outage. The size of

the battery bank is a function of two main factors; how much is the load? and, how

long do you want to power it? Thus for a given load (2.35 kilo Watt in our case) the

size will depend on how long we want the backup. Of course, the longer the battery

backup, the larger the size and higher the cost. Other than cost, housing is also an

issue in confined places like a cellular base station. Another important aspect to note

is that it would take longer to charge a larger bank then a smaller one. These factors

determine the impact that size would have on the system and therefore have to be

kept in mind while sizing the battery bank.

Based on the calculations made in section 3.2.1, the maximum energy consumption

figure for the a macro BS is approximately 56 kWh daily [101]. Considering a 48 V DC

system powering the system, the size requirement for the battery bank is 56 kWh/48V

= 1175 Ah. The lifetime of a battery depends upon its charging/discharging cycles

and is different for different types of batteries. An other consideration in prolonging

the battery life is to safeguard it from over charging and over discharging. However,

the batteries are not to be discharged below 50% of their maximum state of charge

to preserve their life. If discharged below a certain level, it’s life deteriorates fast.

As a rule of thumb, it is considered that a battery may not be discharged below

50% of its capacity. Therefore, our battery bank should have double the capacity,

112 kWh or 2350 Ah, to power the BS for 24 hrs. Different types of batteries been

developed, however, we consider rechargeable 12 V 180 Ah Deep cycle gel lead-acid

battery, which is widely used and available in developing countries like Pakistan. It

is reasonable to have a standby power for 24-48 hrs. In order to generate this much

backup capacity for the BS, we require a total of quantity eight batteries connected

in series parallel (4x2) configuration i.e., four (04) batteries in series for 48 V output,

and then two such parallel sets. The combined output of such a battery bank would

be 48 V × 360 Ah or 17.28 kWh, which is sufficient to power the BS for three days.

If the RES outage is more, a standby diesel generator would be turned on.

The energy flow w.r.t batteries state of charge (SOC) can be investigated for dif-

ferent cases such as (i) fully charged (ii) below threshold and (iii) in-between threshold

and full capacity. We are not modeling the various states of battery bank here and

assume a simple linear input output relationship between harvested energy and bat-

tery back-up capacity. That means when harvested energy is more than the BS load

it is considered surplus. In good windy and sunny conditions a battery bank would

be amply recharged to provide regulated power to the BS. However, in night time,

with no wind, the battery would start depleting. We have restricted backup to 48

hours, because as the size of the bank increases so does the cost. In our model, a total

of 24 batteries in series-parallel combination are considered (8x3) each of 6 volts and

390 ampere hour to cater for the load of 56 kilowatt hour per day. Depending on the

site - we have an average wind of 5 m/s, which is sufficient to run the wind turbine

– total outage of renewable sources should not prevail for long. So 12 hours backup

is considered. Quisque ullamcorper placerat ipsum. Cras nibh. Morbi vel justo vitae

lacus tincidunt ultrices. Lorem ipsum dolor sit amet, consectetuer adipiscing elit. In

hac habitasse platea dictumst. Integer tempus convallis augue. Etiam facilisis. Nunc

elementum fermentum wisi. Aenean placerat. Ut imperdiet, enim sed gravida sollic-

itudin, felis odio placerat quam, ac pulvinar elit purus eget enim. Nunc vitae tortor.

Proin tempus nibh sit amet nisl. Vivamus quis tortor vitae risus porta vehicula.

Chapter 4

Sustainable Energy Cooperation in Cellular

Networks

4.1 Introduction

Reducing the increasing cost of energy and the associated GHG emissions can be

achieved by adopting energy efficient network topologies/schemes and incorporation

of renewable energy sources. In a cellular network, all elements, such as back haul,

switching centres, base stations and mobile terminals may be subjected to energy effi-

cient designs and strategies [13]. However, as noted earlier, the base station consumes

approximately 60% of the networks energy due to which it is a prime target for said

improvements [14], as well as employment of renewable energy systems as discussed

in last chapter.

Indeed it has been shown in literature that equipping BS with solar panels and

wind turbine is technically and financially viable in the long run [47, 113–115]. This

is especially true for a remote and off-grid sites, which are mostly powered by the

conventional diesel generator. The research models show that regulated supply of

energy can be obtained from these sources through a battery bank. A sizeable battery

bank is required to not only provide back up power but also ensure hundred percent

reliability of energy supply. For example, in one case of a base station equipped with

RES and powered through the smart grid, energy cost minimization is done through

efficient scheduling of renewable energy sources through an energy management unit

(EMU) that also controls the flow of energy from the site (battery bank) to the

grid [50].

In the last chapter we modeled the temporal fluctuation in energy consumption of

an LTE base station. Based on the BS’s energy consumption, and wind/solar resource

available at the site, we subsequently modeled the size of pv-array and wind turbine

required to power the BS with green harvested energy. We also modeled the traffic

pattern of six base stations over a day, divided into 24 time slots. It was shown that,

given the variation both in traffic and the energy harvested from solar and wind, we

end up having surplus green energy at some periods of time. This energy can be

sold back to the grid or an arrangement can be made to power the local community

in microgrid scenario. The incorporation of renewable energy cuts down the cost of

conventional energy supply and reduces the harmful GHG emissions.

A network of green BSs also allows for reduction in the overall energy consumption

of cellular networks through different topology management schemes such as ’energy

cooperation’ between the nodes. There are two basic premises for energy cooperation,

either the BSs have different traffic loads or they have different capacities of energy

sources. One such scenario could be a case of energy harvesting BSs sharing their

surplus energy with non-harvesting BSs. Another scenario could be a heterogenous

network with low powered BSs getting green energy from high powered BSs equipped

with PV-array and wind turbine. In all cases energy cooperation is made possible

through an energy transfer mechanism, which can be a smart grid or a dedicated

network for energy transfer with a centralized/decentralized control mechanism.

Energy cooperation amongst cellular BS is an ongoing research topic as seen in

literature, with different aspects of this energy management scheme under study.

The common objective being determining the quantum and direction of energy to

transfer between the base stations. Different energy cooperation strategies have been

considered in literature in an effort to optimize the use of renewable energy in the

cooperating base stations. The inherent features of the smart grid such as demand

side management, intelligent scheduling and smart energy purchasing will potentially

reduce the energy costs [116], as well as facilitate energy cooperation in RES enabled

cellular networks. The bi-directional flow of energy and information in the grid makes

it possible to share energy between the nodes and maximize the use of clean energy

[88,90].

Some recent work pertaining to energy cooperation in cellular networks is sum-

marised in Table 4.1. The salient features of which were discussed in the literature

Table 4.1: Energy Cooperation in cellular base stations found in literature

Ref. No ofBSs

EnergySource

Centralized/ Decen-tralized

Algorithm On-line /Off-line

GHGCon-trol

[38] Two Grid Centralized Bi-Section Search[37] Two Grid +

RESHeuristic Greedy Both

[40] Two Grid +RES

Heuristic Algo-rithms

On-line

[39] Five RESOnly

Centralized Water Filling type On-line

[36] Multi-tier

Grid +RES

Centralized Water Filling type On-line Yes

[117] Five RESOnly

Centralized Water Filling type On-line

[118] N/A RESOnly

Heuristic, (Lya-punov)

On-line

[119] SmallCell

RESOnly

Both Heuristics On-line

[120] Any RESOnly

Yes Heuristics On-line

OurWork

Any Grid +RES +Gen

Yes Convexificationand Interior PointMethod

Both Yes

review carried out in Chapter 2. The table depicts certain aspects undertaken in this

research that were not covered earlier in this scheme, The main contribution of this

work is summarized as under:

1. In this work an energy cooperation framework is formulated for cellular base sta-

tions employing renewable energy sources. The traffic and energy aware model

ensures priority utilization of the sustainable energy thereby cutting down on

utility/DG cost. The proposed model gradually evolves from a simple scenario

of surplus green energy sharing to a complex mechanism of energy sharing along

with BS on/off switching in sleep mechanism. The Traffic Aware Sustainable

and Environment friendly Energy Cooperation (TASEEC) can handle N num-

ber of interconnected BSs, regulated through smart grid or a central controller.

The framework also handles net-metering and load-shedding in the network. An

arbitrary tariff is assigned to each type of energy, which forces the optimizer to

acquire energy from the most economical source. Thus, through TASEEC, an

environment friendly energy usage is ensured whereby both conventional energy

and GHG emission is reduced.

2. Due to the bilinear form of some constraints, the TASEEC becomes a non-linear

and a non-convex problem. This anomaly is removed by employing McCormick

envelopes to convexify the problem and transform it into a linear optimiza-

tion framework. Therefore, the linear optimization problem is then solved by

applying the interior point method.

3. The dynamic nature of the traffic and the harvested energy is exploited to formu-

late a sustainable energy cooperation policy. Extensive simulation is conducted

to show the positive results of energy cooperation.

4.2 System Model

We consider a cellular network of N macro BSs powered by utility with renewable

energy harvesting systems (solar/wind) installed on site, Fig.4.1. The (dynamic)

energy consumption of the BS is determined in terms of the traffic load of the BS.

The traffic fluctuation in 24 time slots is determined for each BS on the basis of peak

traffic hour and the max/min load value, using calculations shown in section 3.6. We

make all energy calculations in time slotted system, for each slot t (1 ≥ t ≤ T ), T

being the max number of time slots. The key features of our system model are the

following.

� Energy harvesting BSs through solar and wind energy.

� A battery bank for storing the harvested energy.

� Inter-connectivity between BSs for energy cooperation.

� Smart grid or Central controller for energy transfer.

� An Energy management unit for algorithm processing.

Figure 4.1: Energy cooperation amongst renewable energy harvesting base stationsentail flow of energy from one to another.

4.2.1 Energy Utilization and Generation at BSs

The conventional energy consumed by a cellular BS is from utility with diesel gener-

ator as a stand-by in case of power outage. This energy is designated as Eu and is

same for all BSs, having a unit cost of say α. In a time slotted unit such as ours, the

energy utilized by a BS n is given by Et,un . The total conventional energy consumed

by all active BSs in period T is then given as:-

ξu =T∑t=1

N∑n=1

Et,un (4.2.1)

In the absence of grid supply for any reason, the diesel generator is switched on.

The two supplies, utility and diesel generator are therefore used interchangeably and

not modeled separately. The total cost of the conventional energy is then unit cost

α multiplied with (4.2.1) or αξu. However, the constraint on this usage is that the

total used energy cannot be more than the max available energy.

T∑t=1

N∑n=1

Et,un ≤ Et,u

max (4.2.2)

The solar and wind energy systems modeled in the last chapter were of a larger

capacity than the required power of the BSs. This is because the average yield of

electrical energy from these systems is much less than their peak capacity. However,

it was noted that at times the combined yield of these RES was more than the

instantaneous load of the BS, Ltn, which would render surplus some of the energy

harvested from RES. The green energy harvested by BS n in time slot t is denoted as

Et,gn . Thus we have energy surplus at a BS when Et,g

n is > Ltn, which saves conventional

energy cost of the network. The total renewable energy harvested by the network can

be expressed as follows:-

ξg =T∑t=1

N∑n=1

Et,gn ≤ Et,g

max (4.2.3)

It may be noted that we may have a BS n whose harvested energy Et,gn is more than

its load Ltn, and another BS m where the harvested energy Et,gm is less than the BS

load Ltm. This leads to the possibility of energy cooperation between the two BSs,

described in section 4.2.3, below.

4.2.2 Energy Cost Model of RES Enabled Network

The cellular network powered by conventional energy(utility) furnishes an energy

consumption cost, which is equal to the unit cost of energy times the total energy

consumed. If α is the unit cast of energy procured from utility and ξu is the total

energy consumed, then the total cost of energy for the network is α × ξu. Similarly,

the cost of energy consumed from utility is then α×ξu for the network. The combined

green-energy harvested by the network is given as ξg, and the unit cost of this type

of energy is taken as β, giving us the total green-energy cost as β × ξg. In order

to ensure procurement of energy from renewable sources as a first priority, the value

of β is kept lower than α i.e., β << α. We assume that the capital expenditure

and operational expenditures of renewable energy systems have been adjusted in the

pricing formula. The energy cost saving Π of the network is then given as utility cost

minus the harvested energy cost or:-

Π = α× ξu − β × ξg (4.2.4)

Whereas the energy supply from utility is unlimited, the energy from RES is

obviously limited. The objective is then to minimize Π, that is to minimize the use

of utility, which comes from the fossil fuels, and maximize the green energy. The

calculations for energy consumption as well as energy generation profiles are made in

hourly time slots t, as discussed previously.

4.2.3 Energy Exchange and Energy States

The added advantage of RES enabled BS lies in energy cost savings by transferring

surplus energy from one BS to the nearby BS. For BS n having green energy surplus

and BS m having green-energy shortage, we wish to transfer energy from BS n to m.

We denote by Et,gn,m as the energy transferred from BS n to BS m in time t, which is

possible if the green energy harvested at BS n is more than its load, while it is less

than the load for BS m, at any time t. These two conditions can be expressed as

follows.

Et,gn > Ltn, and E

t,gm < Ltm (4.2.5)

Energy exchange, Et,gn,m or Et,g

m,n, between BSs is made possible by the availability

of a central controller in a microgrid or bi-directional flow of energy between nodes

made possible through smart grid (Fig.4.1). Else, the network may adjust the over

all energy bill with utility through net metering.

The instantaneous energy consumption of a base station is directly proportional

to the prevailing traffic load. In chapter 3 we modeled the 24 hr traffic variations of

a BS divided into hourly slots throughout the day. The diversity in the traffic load

of various BSs was achieved on account of different peak time Tp for each BS and the

maximum and minimum traffic loads i.e., %max and %min. The former gives temporal

diversity and the latter, the energy consumption diversity. Consequently, the variation

in the traffic profile of each BS forms the basis, which can be the incentive for energy

sharing between BSs. Thus we find the traffic load fluctuating between two states of

energy consumption in a 24 hr period:

� A state of high activity in peak hours of day, consuming maximum energy.

� A state of low activity in off-peak hours, consuming minimum energy.

4.2.4 Tariff

In order to prioritize renewable energy usage over GHG based energy, a tariff is

assigned to each energy type. While assigning tariff to different sources arbitrarily

only one condition is maintained that cost of Utility/DG is higher than that of RES

i.e., Costgenerator > Costutility > CostRES. This difference of cost between non-

renewable and renewable energy sources forms the basis of energy cooperation in

that a BS buys cheaper energy from a neighbour rather than the utility or turn on

the generator. For the network, the aim is to maximize its energy sold cost - sold to

utility and other BS:-

Energy cost to be maximized =N∑

n=1,n6=m

C(Etn,u) + C(Et

where C(Etn,u) expresses net-metering for selling surplus harvested energy to the

grid. Similarly, minimizing the procurement cost can be depicted by the following

expressions:-

Energy cost to be minimized =T∑t=1

N∑n=1

C(Et,gn ) + C(Et,dg

n ) + C(Etu,n) + C(Et

where C(Et,gn ), C(Et,dg

n ), C(Etu,n) and C(Et

m,n) is the cost of energy from RES, gen-

erator, utility and other BS, respectively.

4.3 Energy Cooperation in Green Cellular Net-

Energy cooperation entails sharing the load between nodes by providing energy from

one node to the other. The dynamic nature of traffic load Ltn and the harvested

energy Et,gn at a BS, produces different energy states amongst BSs making ’energy

cooperation’ viable. The power consumption of each BS, modeled in Ch 3, fluctuates

between a min and a max value depending on its configuration and traffic profile (e.g.

1.78 kW to 2.35 kW as per calculations in section 3.2.1), whereas, the RESs give

a mix of low or high power output depending on wind speed and solar irradiation.

Considering the energy consumption figures and the power available from the RES,

we see a case of surplus energy available in some slots of the day.

The surplus energy can be made available to a neighbouring BS not harvesting

enough clean energy at that time, or it can be sold back to the local grid station for

community. The energy transferred from BS n to m is denoted as Etn,m, and must be

less than the max available energy at BS n. The energy cooperation must also take

place while maintaining the energy balance, that is, total inflow should be equal to

total outflow, expressed as follows:-

Et,gn + Et,u

n +N∑m=1

Etm,n = Ltn +

N∑m=1

Etn,m, ∀n, t (4.3.1)

The instantaneous traffic load of the nth base station is Ltn given by equation (3.2.6)

in chapter 3. The left hand side is then the net energy flowing into the BS and the

right hand side is net energy flowing out for powering the BS or transferring surplus

energy to other BS m.

4.3.1 Mathematical Formulation and Solution Approach

Our objective is to minimize the energy cost of the network operator through energy

cooperation between BSs as well as maximize the use of green energy (Fig. 4.1). The

energy found surplus at any BS is either directed towards another BS that is in want

of energy or sold back to the grid. The generator is only employed if the renewable

harvested energy or that from utility, is not available. The energy cost minimization

optimization problem with energy cooperation is stated as follows:-

Given:

� The total number of BSs in the network.

� The traffic profile of each BS for any period T .

� The energy generation profile of each BS.

� The unit cost of energy from utility and RES.

Determine for each time period t of any finite time horizon T :

� The energy surplus, if any, in time slot t, at a BS.

� The energy deficiency of harvested energy at a BS.

� The amount of energy to be transferred from BS n to BS m.

� The net energy savings in network from energy cooperation.

Mathematically, for each BS the objective U(Et,gn , Et,dg

n , Etu,n, E

tn,u, E

tm,n, E

)is to

minimize the energy procurement cost while maximizing the energy sold cost, as

depicted in the following expression.

U(Et,g

n ,Et,dgn ,Et

u,n,Etn,u,E

tm,n,E

T∑t=1

N∑n=1

[C(Et,g

n ) + C(Et,dgn ) + C(Et

u,n)− C(Etn,u) +

N∑n=1,n6=m

C(Etm,n)− C(Et

](4.3.2)

where Et,gn , Et,dg

n , Etu,n, E

tn,u, E

tm,n, E

tn,m are the nth BS’ electricity generated from re-

newable energy sources, from diesel generator, electricity purchased from utility, elec-

tricity sold back to the utility, electricity purchased from the mth BS and electricity

sold to the mth BS, respectively.

The mathematical formulation of the problem can be written as follows:

t,gn ,E

t,dgn ,Et

u,n,Etn,u,E

tm,n,E

U(Et,g

n ,Et,dgn ,Et

u,n,Etn,u,E

tm,n,E

) (4.3.3)

subject to:

C1 : Etm,n, E

tn,m, E

t,gn , Et,u

n ≥ 0, ∀ m,n, t

C2 : Et,un ≤ Eu,max, Et

m,n ≤ Emaxn , ∀n

C3 : Etm,n.E

tn,m = 0 , ∀m,n, t

C4 : Et,gn + Et,u

n +N∑m=1

Etm,n = Ltn +

N∑m=1

Etn,m, ∀n, t

C5 : Etn,n = 0 ∀n, t

where Ltn is the load of the nth BS at the tth time slot.

The first and second constraints are box constraints. The third constraint binds

a BS to either sell or buy energy at any given time slot. The fourth constraint

ensures balance of energy in the network for each BS n. The fifth constraint stops

a BS from selling energy to itself. The problem (4.3.3) is a non-convex bi-linear

problem because of constraint C3. Therefore, we reformulate problem (4.3.3) as a

linear optimization problem by using McCormick envelopes. Constraint C3 from

(4.3.3) is the bi-linear function for energy. Function in the form f(x, y) = xy is a

bi-linear function. constraint C3 as a bi-linear function is written as f(Etmn, E

= Etm,nE

tn,m. For bi-linear products, which can be negative or positive, McCormick

envelopes are commonly used in order to have convex relaxation [121]. C3 is the

positive bi-linear product and its McCormick envelopes can be written as:

f(Etmn, E

tnm) = Et

mnEtnm

h(Etmn, E

tnm) = max{Emin

mn Etnm + Emin

nm Etmn − Emin

mn Eminnm ,

Emaxmn E

tnm + Emax

nm Etmn − Emax

mn Emaxni }

Eminmn E

tnm + Emin

nm Etmn − Emin

mn Eminnm ≤ CE

Emaxmn E

tnm + Emax

nm Etmn − Emax

mn Emaxnm ≤ CE

(4.3.4)

Where Eminmn , E

minnm and Emax

nm , Emaxmn are the upper and lower bounds of energy flow from

base station m to n and vice versa, respectively, and CE is convex envelope coefficient.

Now the problem has become a linear problem that can be solved using simplex or

interior point method [122]. The McCormick envelopes transform the bilinear prod-

uct into linear approximation. The linear programming transformation over convex

polyhedron using McCormick envelopes can be efficiently solved using average scale

linear programming interior point solvers with guaranteed convergence [122]. This

estimation will yield the best possible parameters for our energy cooperation model.

Interior point linear programming is a technique used to solve linear programming

problems, and involves following steps. Preprocessing is performed which ensures that

all variables are bounded below by zero, all constraints are equalities, fixed variables,

those with equal upper and lower bounds, are removed, the constraint matrix has

full structural rank, and columns of all zeros in the constraint matrix are removed.

When some singleton rows exist in the constrained matrix, the associated variables

are solved and rows removed. After the pre-processing a slack variable is added to

make primal problem and a dual of the primal problem are generated. Further-

more, optimality conditions for the linear program are generated by implementing

the complementarity and feasibility conditions. Then duality gap is quantified, which

measures the residual of the complimentarily position. Finally, both Primal and Dual

programs are solved simultaneously.

Our aim is to determine the instantaneous energy state at each BS site in order

to determine the flow of energy between sites or grid. There are thus three variables

that may determine the energy state of a BS at any given time.

� The instantaneous traffic load of the BS.

� The green power generation from the installed RES.

� The outage in utility due to load shedding.

In order to determine the results of our energy cooperation algorithm, the required

data is simulated according to the system configurations defined earlier in this disser-

tation. First of all the dynamic traffic load is generated as per the equations derived

in section 3.2.1. Traffic profile of six BSs N = 6 is simulated with different peak

times (Tp) and load; estimated through poisson distributions since traffic arrival at a

BS is shown to follow poisson probability curves. Based on these values, six different

24 hr traffic patterns are generated by using equation (3.2.6) as shown in Fig. 4.2.

Secondly, renewable energy generation is simulated for all six base stations by using

definitions described in previous chapter. Although the solar irradiation and wind

speed are considered same for all sites, algorithm simulates diversity in generation by

varying the size of solar panels and wind turbine. Lastly, we simulate some power

outage (load shedding), which allows the BSs to sell energy to utility through net

metering. Where level is ’0’, it means grid outage or load-shedding and when it is ’1’

it means grid power form utility/grid is available. The combined BS load and energy

profiles of all six BS is shown in Fig. 4.5; the load shown in red colour and generation

in black dashed lines. The twenty four hour time slot covers the whole day which

provides cyclic repetition of parameters. The hourly slot is considered mainly because

it is small enough to capture the variations in traffic as well as energy, in sufficient

detail, and large enough to make the computations easy. Further amplification of

time can be done, e.g., every minute or every five minutes. However, that would

not make any special bearing on result as prime focus of the paper is on energy cost

saving through energy corporation by sharing the surplus energy, which is effectively

shown in the results.

’sen

tility

4.3.2 Energy Cooperation Results

Given the electricity tariff of each source, the load/consumption profile, and the

energy generation profile of a BS, the algorithm finds the quantum and direction of

energy transfer between the nodes and with utility. When a BS is unable to meet

its energy demand from own RES or from a neighbouring BS, the deficiency is met

from utility/diesel generator. The net result of energy cooperation for N = 6 BS is

shown Fig. 4.4. Each BS either sells off energy or purchases it from outer sources in

accordance with its own energy state. The net cost savings are taken where the BS

is able to generate energy from own green sources or from that of a neighbour BS,

minimizing the usage of diesel generator/utility. The results of energy cooperation

show BS2, BS5 and BS6 purchasing green energy from other BSs, whereas BS1, BS3,

and BS4 are selling their surplus harvested energy to other three as well as to the

utility.

To analyze the impact of our energy cooperation mathematical model, we gener-

ated Poisson distributed traffic scenarios for base stations and applied Monte carlo

simulation technique. The temporal and spatial variation in the traffic-load is achieved

by assigning different values to peak-time and max/min load values in equation

(3.2.6). Traffic instances on each base station is generated randomly using poisson

distribution on the BS’ dynamic load. We use Pakistan’s capital Islamabad area’s av-

erage data of wind speed and solar insolation for renewable energy estimation analysis

as shown in Fig. 3.4. The results are simulated for two scenarios, (1) with base sta-

tion energy cooperation as mentioned in (4.3.4) and (2) without base station energy

cooperation as mentioned in (4.3.5). The comparison of the corresponding results

of these two scenarios, one with cooperation and other without cooperation, allows

us to estimate the net energy savings, and thus know the impact of our framework.

When there is no cooperation, all energy over and above the harvested energy is met

from utility or generator. However, under cooperation, the surplus harvested energy

at any BS is used before utility/generator due to the cheaper cost assigned to RES.

We have assigned a tariff of 2.5 cents/kWh for RES, 4 cents/kWh for utility and 6

cents/kWh for generator. Thus, network energy cost is saved when green energy is

shared between the BSs and use of utility or gen set is minimized.

minEt,rg

n ,Et,dgn ,Et

u,n,Etn,u∀n,t

Unc(Et,rgn , Et,dg

n , Etu,n, E

)subject to:

CC1 : Et,rgn , Et,dg

n , Etu,n, E

tn,u ≥ 0, ∀ n, t

CC2 : Et,un ≤ Eu,max, Et

m,n ≤ Emaxn , ∀n

CC3 : Et,rgn + Et,dg

n + Et,un = Ltn + Et

n,u , ∀n

(4.3.5)

Unc(Et,rgn , Et,dg

n , Etu,n, E

T∑t=1

N∑n=1

[C(Et,rg

n ) + C(Et,dgn ) + C(Et

u,n)− C(Etn,u)]

(4.3.6)

The results in table 4.2 analyze energy cost savings of network comprising {4,

8, 12, 16} BS, respectively, for every month. In each case, the average daily cost is

presented with non-cooperation scenario followed by the energy-cooperation scenario.

In all cases, it can be seen that the energy cost is more in former scheme and less in the

later scheme. This is because under non-cooperation, utility (which is more expensive

than RES) is the only alternate available if harvested energy is not enough. Whereas,

under energy-cooperation, the surplus harvested energy from another BS is procured,

if available, which is cheaper than utility. Consequently, the energy mix determines

the energy cost of the network, which is defined below for the two scenarios:-

� Non-cooperation scenario: Own harvested energy (cheaper) plus utility (expen-

sive), with generator as stand by.

� Energy-cooperation scenario: Own harvested energy (cheaper) plus surplus har-

vested energy from neighbor BS (cheaper) plus utility (expensive), with gener-

ator as stand by.

Table 4.3: Energy classification into Green and GHG components.

From coal/oil/gas firedplants

GHG Type

From Utility From Hydal/ Wind / SolarFarms

Green Type

From Installed RenewableEnergy Sources

Green Type

From Base Station From Installed Diesel Gener-ator

GHG Type

4.4 Energy Cooperation with GHG Penalty

The energy cooperation scheme described in last section 4.3, is extended to maximize

the green-energy utilization by imposing a penalty on GHG emitting energy sources

i.e., the GHG emitting utility component and the stand-by generator at each site.

The energy from utility (Et,un ) and that generated at the BS is bifurcated into two

components, green and GHG. For utility, power generated from fossil fuel (oil, gas)

based plants is classified as GHG, while that from wind-farms, hydal-power-stations

etc. is called green energy. Similarly, at BS, the energy harvested from RES is

green(Et,gn ) and that from DG is GHG (Et,G

n ). Other than this a BS may procure

surplus harvested energy from neighbour BS (Et,gm,n), which is also classified as green.

An arbitrary tariff is charged by utility as well as each BS, in $/kWh, like before,

RES price being cheaper than utility. Furthermore, a penalty tariff is imposed on

the GHG energy type so as to discourage its usage by the network. The energy-state

information sharing may be carried out between the nodes through a central controller

or the smart grid. The following table illustrates this classification.

4.4.1 Mathematical Formulation and Solution

Our earlier objective is modified so as to incorporate energy cooperation, with special

emphasis on of green energy by posting a penalty on the fossil based (GHG) energy

source. The energy cooperation between BSs is based on the instantaneous traffic

load and renewable energy generation at each node; as discussed earlier. The energy

cost minimization optimization problem with GHG penalty may be stated as follows:-

Given:

� The total number of BSs in the network.

� The tariff for each energy source.

� The traffic load (energy consumption) profile of each BS.

� The penalty cost of the GHG emitting source.

� The energy generation profile at each BS.

Determine for each BS n for each time slot t:

� The surplus renewable energy available at each BS.

� The energy procured from a neighboring BS.

� The energy procured from utility.

� The net energy sale/purchase energy cooperation.

Mathematically we formulation the problem in equations (4.4.1) and (4.4.2).

T∑t=1

N∑n=1

Green︷︸︸︷

C(Et,un )︸︷︷︸

Utility Cost

+ C(Et,gn )︸︷︷︸

Self Generation Cost

+N∑m=1

C(Etm,n)− C(Et

n,m)︸︷︷︸Energy Exchange Cost

GHG︷︸︸︷C(Et,u

n,G)︸︷︷︸Utility Cost

+ C(Et,gn,G)︸︷︷︸

Self Generation Cost

+N∑m=1

C(Et,Gm,n)− C(Et,G

n,m)︸︷︷︸Energy Exchange Cost

(4.4.1)

Coconstraints for (4.4.1)

C1 : Etm,n, E

tn,m, E

t,gn , Et,u

n ≥ 0, ∀ m,n, t︸︷︷︸Energy must be positive

C2 : Et,un ≤ Eu,max, Et

m,n ≤ Emaxn , ∀n︸︷︷︸

Maximum Possible Energy

C3 : Etm,n.E

tn,m = 0 , ∀m,n, t︸︷︷︸

Green simultaneous bidirectional energy flow constraint

C4 : Et,Gm,n.E

t,Gn,m = 0 , ∀m,n, t︸︷︷︸

GHG simultaneous bidirectional energy flow constraint

C5 : Et,gn + Et,u

n + Et,gn,G + Et,u

n,G +N∑m=1

(Etm,n + Et,G

Ltn +N∑m=1

(Etn,m + Et,G

)︸︷︷︸

Energy balance constraint

, ∀n

C6 : Etn,n = 0, ∀ n, t︸︷︷︸

Self sell/purchase constraint, green

C7 : Etn,n,G = 0, ∀ n, t︸︷︷︸

Self sell/purchase constraint, GHG

(4.4.2)

The constraints above are same as the one described for 4.3.5, with the addition

of a sperate element for GHG component such as Et,gn,G and Et,u

n,G for diesel generator

and fossil fuel based plants, respectively. Also, the problem in equation (4.4.2) is a

non-convex bilinear problem due to constraints C3 and C4. Again, both constraints

are linearized through McCormick envelopes as done in section 4.3.1. C3 and C4

as a bilinear function are written as f(Etm,n, E

tn,m) = Et

m,n.Etn,m and f(Et,G

m,n, Et,Gn,m)

= Et,Gm,n.E

t,Gn,m. C3 and C4 are the positive bilinear product and their McCormick

envelopes h(Etm,n, E

tn,m) and h(Et,G

m,n, Et,Gn,m) are written in (4.4.3) and (4.4.4).

h(Etm,n, E

tn,m) = max

{Eminm,nE

tn,m + Emin

n,mEtm,n

−Eminm,nE

minn,m , E

maxm,n E

tn,m + Emax

n,m Etm,n − Emax

m,n Emaxn,m

} (4.4.3)

h(Et,Gm,n, E

t,Gn,m) = max

{Emin,Gm,n Et,G

n,m + Emin,Gn,m Et,G

−Emin,Gm,n Emin,G

n,m , Emax,Gm,n Et,G

n,m + Emax,Gn,m Et,G

m,n − Emax,Gm,n Emax,G

} (4.4.4)

Using the McCormick envelopes for (4.4.3) and (4.4.4), the constraints in (4.4.1)

can be mathematically expressed as:-

Constraints for (4.4.1) using McCormick envelopes

C1, C2, C5, C6 and C7 of (4.4.2)

C8 : Eminm,nE

tn,m + Emin

n,mEtm,n − Emin

m,nEminn,m ≤ CE

C9 : Emaxm,n E

tn,m + Emax

n,m Etm,n − Emax

m,n Emaxn,m ≤ CE

C10 : Emin,Gm,n Et,G

n,m + Emin,Gn,m Et,G

m,n − Emin,Gm,n Emin,G

n,m ≤ CG

C11 : Emax,Gm,n Et,G

n,m + Emax,Gn,m Et,G

m,n − Emax,Gm,n Emax,G

n,m ≤ CG

In these equations, Eminm,n , E

minn,m and Emax

n,m , Emaxm,n represent the lower and upper

bounds on energy from BS m to n and vice versa, respectively. In a similar way we

expressed the McCormick envelope of constraint C4. CE and CG are convex envelope

coefficients for energy and GHG emissions. The linear programming transformation

over convex polyhedron using McCormick envelopes can be efficiently solved using

average scale linear programming interior point solvers with guaranteed convergence

[122].

4.4.2 Solution and Results

In order to solve problem (4.4.1) with McCormick envelop transformation, we apply

primal-dual interior point method based on iterative Mehrotra’s prediction-correction

[122–124]. The primal and dual for our environmental friendly energy cooperation in

cellular base station are:-

Primal : minEx∈Ψ

∆TaEx (4.4.5)

s.t AΘEx = B

Eminx ≤ Ex ≤ Emax

Dual : maxEx∈Ψ

∆Tb π (4.4.6)

s.t ATΘπ + S = C

S ≥ 0

where Ψ ∈ {Et,un , Et,g

n , Et,un,G, E

t,gn,G, E

tn,m, E

t,Gn,m} and Θ is the set of constraints

C1, C2, C3, C4, C5, C8, C9 , C10, and C11.

In this method, slack variables are added to approximate the optimization problem

to a sequence of subproblems. The search direction of the predictor and the corrector

are found using Cholesky decomposition in each iteration, and both work in sequence.

Initially, the optimizer uses first order derivatives, Γ1Ex,Γ1

S,Γ1π, to determine the search

direction of predictor. Then the step size is computed to find the centrality correction

µi. Subsequently, the correction is computed using the centrality term and the second

order derivative µi,Γ1Ex,Γ2

Ex,Γ1

S,Γ2S. This way complete information is given by the

sum of predictor and corrector. The pseudo code of the proposed method is given

in Algorithm 1. In this algorithm, the Eix, π

0x are the estimation of primal-dual

solution at the ith iteration. The first and second order derivatives of Ex,ΓS,Γπ

are Γ1Ex,Γ1

S,Γ1π and Γ2

Ex,Γ2

S,Γ2π, respectively. With this method we achieve the best

possible parameters for energy exchange in our energy cooperation problem.

4.4.3 Energy Cooperation with GHG Penalty Results

Our algorithm is versatile to cater for N number of BS and evaluate energy coopera-

tion for any definite time period T . However, we consider three BS and evaluate their

traffic load profile for 24 hrs, since load and RES calculations were done in Chapter 3

for 24 hr periods. The traffic pattern is generated using equations (3.2.6) defined in

Chapter 3, with Poisson random generation to achieve variation in traffic-loads. The

renewable energy harvested is also generated for three BS using variation in configu-

ration of solar panels and wind turbine defined in section 3.4. The 24 hr simulation

Algorithm 1 Proposed Algorithm.

1: E0x ≥ 0, S0

x ≥ 0, π0x ≥ 0, i = 0

2: while (Terminate if termination criterion satisfied) do3: yiS = ATΘπ

i + Si − C, yiEx= AΘE

ix −B

4: Find the First derivative of primal-dual trajectory5: Γ1

π = −(AΘSi−1Ei

xATΘ)−1

6: Γ1S = yis − AΘΓ1

7: Γ1Ex

= Eix − Si−1Ei

8: Computer centering parameterµi

9: Find the second derivative of primal-dualtrajectory using µi

10: Γ2π = −2(AΘS

i−1ATΘ)−1AΘΓ1π(Γ1

S − µi)/Si11: Γ2

S = AΘΓ2π

12: Γ2Ex

= −2AΘΓ1π(Γ1

S − µi)/Si − Si−1EixΓ

13: Construct Taylor polynomial and find stepsεEix, εSi

size using Γ1Ex,Γ2

Ex,Γ1

S,Γ2S

14: Compute search direction using step size, firstand second derivative

15: Check the feasibility16: if new solution is better than previous

solution then update Eix, S

0x, π

17: i = i+ 118: end while

of traffic load (consumption) and generation is depicted in Fig. 4.5, correspondingly

for each of the three BS. Where generation is less than consumption, the BS will have

to purchase energy from another source or turn on the standby generator. Where it

is more than the consumption, the BS can sell the surplus energy to another BS.

traffi

The Utility cost, Self-generation cost and Energy-exchange cost for green and

GHG types are considered separately in equation (4.4.1). These costs and the energy

state (load vs generation) of a BS control the sale/purchase of energy by each BS. The

results of energy sale/purchase are depicted in Fig 4.6, for both ’green’ and ’GHG’

energy types. The energy is either purchased from utility (Et,un ), or from another BS

(Etm,n), whereas energy sold to the other BS is the surplus harvested energy (Et

In all cases, BS prefer to utilize the green energy and are discouraged to use GHG

type due to the imposed penalty.

The net result of energy cooperation, with GHG penalty, for the three BSs, is

shown in Fig.4.7(a), 4.7(b), 4.7(c). In the figures the energy procured from renewable

sources is shown in green, while the energy procured from GHG emitting sources is

shown in red. The difference in the traffic load and the energy generation of each

BS has resulted in a mix of the BSs energy consumption profiles. The overall result

being maximization of renewable energy usage amongst the base stations. The energy

demand is met from a mix of energy sources, in varying proportions, in following order

of priority:-

1. Green energy generated from own RES (Et,gn ).

2. Green energy purchased from neighbour BS (Et,gm,n).

3. Green energy purchased from utility (Et,u).

4. GHG energy generated from own Diesel Generator (Et,Gn ).

5. GHG type energy purchased from utility (Et,Gu ).

To analyze the robustness and sensitivity of proposed framework. We use Pakistan’s

capital Islamabad average data of wind speed and solar insolation for sensitivity

analysis as shown in Fig. 3.4. Base station dynamic load is generated using Poisson

distributed. Monte carlo simulation is performed by generating 10,000 different traffic

0 5 10 15 20 250

1.4BS 1

Green PurchaseGHG PurchaseGreen GeneratedGHG Generated

0 5 10 15 20 250

3.5BS 2

0 5 10 15 20 250

2.5BS 3

Figure 4.7: Energy cooperation results in the realm of GHG penalty showing prefer-ence for energy procurement from Green sources.

patterns using Poisson distribution for different number of base stations and different

months of the year. Table 4.4 depicts the statistical analysis of the proposed frame-

work in terms of mean, standard deviation, and 95/99% confidence intervals (CI) for

the different months (February, May, September, and November) and different num-

ber of BSs (N = 4, 8, 12, and 16). The results clearly emphasize the effectiveness of

proposed method. It is observed that the mean, standard deviation, and confidence

intervals are increasing with the number of BSs. From Fig. 3.4 it can be noticed that

average solar insolation is high in the month of May and September when compared

to February and November. On the other hand, average monthly wind speed is high-

est in the month of May. Thus, we can see highest mean and confidence interval for

the month of May.

4.5 Summary

Equipping the cellular BSs with RES not only reduces their CO2 footprint but also

make them energy cost efficient in the long run. Using the results of previous chapter

in which we sized the PV array and a wind turbine for a macro BS, we formulated an

energy cooperation scheme for co-located BSs. Two cases of energy cooperation were

formulated. In the first, the surplus green harvested energy was shared between the BS

depending on their instantaneous energy state. In the second case, the GHG sources

were assigned a penalty so that a BS is more inclined to purchase green energy. The

algorithm so designed was simulated for the 24hr load profile of each BS as well the

energy harvested from the two RES. Based on these, the energy cooperation algorithm

ensures optimization of the harvested energy. The green energy maximization policy

so introduced ensured optimum utilization of total harvested energy in the network,

thereby reducing the use of utility and generator to minimal. Introducing energy

cooperation between green BS is especially feasible with the futuristic smart grid

offering bi-directional flow of energy and information.

Chapter 5

Traffic Aware Energy Cooperation with BS Sleep

Mechanism

5.1 Introduction

The futuristic wireless technologies of 5G are going to bring dramatic performance

improvements in terms of data rates, network capacity, latency, cost and coverage.

In order to achieve these desired goals, technological improvements are underway

at all tiers as well as the search for new innovative solutions. Densification and

diversification of the radio access network will require new models to make them

economical and energy efficient such as dynamic and adaptable allocation of resources.

With smart grid and renewable energy systems also maturing, a new paradigm of

green communication is emerging that aims to improve energy efficiency of cellular

networks comprising macro, micro, femto and pico base station transceivers. The

industry is also aware that technology improvements must not be at the cost of

adverse climatic effects, thus renewable energy based solutions to meet the power

demands are order of the day.

The renewable energy sources enabled cellular BSs can benefit in terms of cli-

mate friendly energy conservation by employing energy optimization schemes such

as ’energy cooperation’. In the last two chapters we modeled the renewable energy

sources for powering a cellular BS and maximized their utilization through energy

cooperation between the network nodes. In off-peak hours these green BS generate

surplus energy that can be sold back to the grid, as demonstrated in chapter 3. More-

over, the energy exchange between the BSs was also shown as a viable management

scheme for energy conservation in general and reducing GHG emitting energy sources

in particular. Smart grid is ideally suited for the exchange of energy between the

cellular elements due to bi-directional flow of information and energy. A central con-

troller in a microgrid is also envisaged a viable option for two way flow of energy and

information.

It is shown through different studies such as the one done in [4], that a BS mostly

performs well below its capacity for approximately 80% of the time. A BS is optimized

to handle maximum traffic but such traffic occurs only at a small percentage of

the day. Furthermore, the new standard for self organizing networks (SON) being

gradually introduced by 3GPP (third generation partnership project) is allowing for

more network adaptability according to the environment. This has lead to the notion

of putting some BSs to ’sleep’ during the off-peak hours, and shifting their traffic

to the neighboring BSs. Putting BSs in sleep mode requires on/off switching of BS,

which entails remotely switching off a BS at lean time and then switching it ’on’ when

the traffic increases. The ’sleep’ mechanism thus requires two types of operations to

be controlled. These are:-

� A mechanism to switch-off and switch-on a BS remotely.

� A mechanism to handover the users from off BS to other BS.

Furthermore, for green BS i.e., BS powered by RES such as solar and wind,

the sleep mode discussed above provides added opportunity for energy conservation.

For a green BS that is harvesting energy through its wind turbine/solar panels, the

harvested energy mostly lies surplus in case the BS is in sleep mode. Thus, traf-

fic load cooperation between the ’sleeping’ and active ’macro’ BS of a network is a

very promising solution for overall energy conservation and green energy utilization.

We will explore the said scenario for an ’umbrella’ network of BS and evaluate the

net energy savings by switching off the BS and transferring their traffic load to the

macro BS and their surplus energy to utility. Optimization of the network resources

is carried out by estimating the best number of sites to be switched off, through an

especially designed algorithm.

Lately, LTE networks have been subjected to sleep mode of operations for energy

savings ( [125], [126], [127], [128], [129], [130]). Exploiting the DTX and RTX in 4G

NW, Wang et al. [127] have proposed a novel energy saving scheme for LTE networks

in which the number of active sub-frames in a frame are optimally selected, which

results in a reduction in energy of up to 90%, as shown, at low traffic load. In [128],

authors incorporate sleep nodes while optimizing the antenna parameters such as tilt,

beam-width, transmit power etc. under the constraints of SINR, spectral efficiency

and user throughput. Authors in [129] consider both traffic load and the average

distance of its users in deciding which eNBs to switch off in an LTE network. Their

proposed scheme is shown to out perform random switching-off of eNBs, in terms of

overall energy saving. Sleep mode can also be used by utilizing the CoMP feature

in LTE [126]. The net power saving in switching off the BSs and extra power used

in CoMP, can be maximized by selecting an optimized set of points for coordination.

Another optimal energy saving framework through BS sleeping in LTE NW is pre-

sented in [125]. The stochastic analytical framework modifies the theoretical model to

account for the effect of BS sleeping, wile maintaining the outage probability metrics

such as SINR, constant.

In heterogenous networks the user association between small cells access points

(SAPs) used for point coverage, and the broader coverage macro cells, when smaller

(pico/micro) are put to sleep, is a key issue. The models proposed put the light

loaded point coverage eNBs to sleep to conserve energy and associate their users

with central macro cells [ [131], [132], [125], [133], [134]]. In [132], the sleep mode

of SAPs is investigated in relation to the trade-off between energy consumption and

false alarm rate. In another HetNet model the femto cells are switched off when

traffic load ia low, which is then handled by the macro BS [131]. The base station

loads are determined through the continuous time Markov Decision Process, where

states represent loads and switching is added to the state space as a new dimension.

The cost function is defined as an increasing function of energy consumption and a

decreasing of throughput.

5.2 System Model

5G networks are envisaged to be dense heterogeneous networks, comprising taller

antenna macro cell sites, combined with local cell sites for point coverage. We consider

an ’umbrella network’ of BSs that has a central macro BS with wide coverage and

within that coverage we have N number of micro BSs with localized coverage as shown

in Fig. 5.1. The BSs are equipped with RES such as wind-turbine and solar-array

to harvest green energy from natural resources. The central macro BS is designated

with symbol M , which is different from micro BSs n (n = 1....N). Here, N = 8 means

that there are eight micro BS plus one macro BS and N = 12 means twelve micro

and one macro; so on and so forth. We assume that any BS n can be switched off, for

multiple time slots t, when traffic is lean. The traffic load (Ltn) of the switched-off BS

is directed towards the Macro BS M , whose load increases as more traffic is handled

by it, which means that while the static power consumption will not be effected, the

dynamic power consumption of the central BS will increase. We shall observe that

this increase in load is considerably less than the power saved by switching off BS n.

Additionally we invoke energy transfer from the switched off BS to utility, resulting

in significant savings in conventional energy usage.

5.2.1 Energy Saving in Sleep Mode

As discussed earlier, the EARTH project report [101], has analyzed the power con-

sumption of a BS in different modes of operation. These modes are listed in Table 2.2

for Macro, RRH, Micro, Pico and Femto base stations. as stated earlier, the power

consumption of a BS is bifurcated into Static and Dynamic types. The static power

consumption per transceiver is designated P0 and is fixed for each BS type. Whereas,

the dynamic power consumption is a function of number of transceivers (NTRX) and

Figure 5.1: A heterogeneous umbrella network of N micro BS, enabled with RES.

power out (Pout) per transceiver. Particularly, the report also shows power consump-

tion in ’sleep’ mode (Psleep), when the radio equipment of the BS is switched off. The

said table shows the power consumption in sleep mode for the five BSs. The power

consumption of any generic BS b, including dynamic and static power consumption

can be expressed by the following expression [101].

Pb = NTRX × (P0 + ∆pPout), 0 < Pout ≤ Pmax (5.2.1)

where, ∆p is the slope of the power transmitted and is equal to 4.7 for macro BS,

given in the report. Thus for a macro BS operating 6 transceivers, the maximum

power consumed is 1344 W (6 x (130 + 4.7 x 20)) at full load, and the minimum is

780 W (6 x (130 + 4.7 x 0)) at no traffic. Whereas, the consumption in sleep mode

Psleep, is having a value of 75 W, which is the power consumed by the BS once it is

put to sleep. We can see that Psleep (75 W) is much less than 780 W consumed at

no traffic. Therefore, a BS when put to sleep will save significant amount of energy

than the one operating even at zero traffic. We need to exploit this characteristic by

putting to sleep (switch off) BSs having low traffic and diverting their load to other

BSs, thereby saving significant energy of the network.

5.2.2 Traffic Load Sharing in Sleep Mode

All micro BSs are at a distance an from the central macro BS, which are predeter-

mined. The micro BSs are divided into concentric circles around the macro BS such

that all BS fall into any one of these circles Fig. 5.1. Any micro BS n in the umbrella

network has rn radius of coverage and an instantaneous load given as Ltn. The macro

BS providing umbrella coverage is designed to take the extra load of a switched off

micro BS. The mechanism for putting a BS to sleep by switching it off remotely and

then switching it on is not discussed here as it is beyond the scope of our problem,

which concerns energy/load balancing and optimization. The amount of energy re-

quired by the macro BS to service the load of the switched off micro BS is directly

proportional to its distance an from the switched off BS n. If BS n is switched off

then macro BS will take care of its traffic and its surplus energy can be sold back

to grid. The macro BS transmits with more power as compared to switched off BS

because of the greater distance involved. The increase in the power of the macro BS

due to the traffic load of BS n can be given as follows:-

Pmacro = PmacroΓn (5.2.2)

Where Γn =(anrrn

)αis the traffic cooperation multiplying factor for the nth micro

BS put off. Here α is channel path loss, an is the distance from macro to micro and

rn is the radius of the nth base station.

5.2.3 Network Energy Model

The energy requirement of the network is fulfilled from utility ’U’ and the energy

harvested from nature ’G’ through solar panels and wind turbines. The energy re-

quirement is met from the harvested energy (solar/wind) primarily, and the deficit

is met from utility. The central macro BS is equipped with both PV-array and

wind-turbine , whereas the micro sites are powered by PV-panels mostly. Thus the

total energy inflow for the system model is the sum of the two and can be expressed

mathematically as :

Etu,M + Et

g,M − EtM,u +

N∑n=1

[Etu,n + Et

g,n + Etd,n − Et

u,n] (5.2.3)

Where Etu,M , Et

g,M and EtM,u is energy consumed by macro BS from utility, harvest

energy and surplus sold back, respectively. Similarly, Etu,n, Et

g,n, Etd,n, and Et

n,u is the

micro BSs energy consumption from utility, renewables, diesel generator and energy

sold back, respectively. The aforesaid energy consumption given by equation (5.2.3)

is utilized in servicing the traffic load of the HetNet serviced by a macro BS with

associated N micro BS i.e., LtM as well as∑N

n=1 Ltn. The Load (power consumption)

of the micro BS, or any BS for that matter, comprises a static and dynamic part as

discussed previously. We, therefore, may bifurcate the micro BS load as static and

dynamic i.e.,T∑t=1

N∑n=1

(Lt,dynamicn + Lstaticn ) (5.2.4)

It is pertinent to remind ourselves here that dynamic load is on account of the

traffic and when the traffic of one BS is handed over to another, it is the dynamic

load that gets transferred. Therefore, once we switch off a micro BS and hand over its

traffic load to the central BS, the energy consumption of the central BS will increase

by an amount equal to the dynamic load of the switched off BS. This distribution of

Dynamic Load BS 2

Dynamic

Static

Load Base

Base Station

Dynamic Load BS 1

Station 2

Station 1

Base Load

Distribution

Dynamic

Static

Figure 5.2: The BS load can be bifurcated into static and dynamic load, whereas thebase load is the minimum load when BS is put to sleep. When micro BS 1 and 2 areswitched off their corresponding dynamic load gets added to the dynamic load of thecentral macro BS.

load is depicted in Fig for better comprehension. Therefore, the increase in the traffic-

load of the central macro BS due to switching off of N micro BS is mathematically

expressed as:-

LtM +N∑n=1

(1− xtn)[Lt,dynamicn Γn] (5.2.5)

where, LtM is the load of the macro BS in kW and xtn is the decision variable for

each BS that can be either 0 or 1. If BS n is asleep its value is ’0’ and if it is active

xtn = 1. Since the central BS is more distant than the micro BS vis-a-vis the traffic,

the dynamic load of the switched off BS (Ldynamicn ) is multiplied by the factor Γn to

compensate for this power increase. The total energy outflow of the network can thus

be expressed as:-

LtM︸︷︷︸Macro

+N∑n=1

(Lt,dynamicn + Lt,staticn )︸︷︷︸Micro On

+N∑n=1

(1− xtn)[

Traffic Cooperation︷︸︸︷Lt,dynamicn Γn +Lsleepn ]︸︷︷︸Micro Off

(5.2.6)

where LSleepn is the energy consumption in sleep mode for those BS that have been

put to sleep. In order to ensure energy balance in the system, equation (5.2.3) must

be equal to equation (5.2.6). The net energy saving from switching off BS n will be

the difference of ’energy utilized by micro BS (Et,un + Et,g

n )’ and the ’extra energy

consumed by the macro BS’ given by equation (5.2.2). The extra load carried by

the macro BS is significantly less than the energy saved from putting a BS to sleep.

Additional saving is made, which can be substantial, in buy-back of the harvested

energy from the switched-off BS by utility, which is designated as Etn,u, where n, u

means from BS n to utility.

5.3 Problem Formulation

Our objective is to minimize the energy consumption of the umbrella network by

switching off the lean-traffic BSs as well as cut down the energy provided by utility

by transferring the green energy harvested by the switched off BSs. The temporal

fluctuations in the BS traffic serves as the basis for decision on switching off a BS. In

any time slot t, the BS having least traffic load can be switched off. Its traffic load is

shifted to the macro BS and the harvested energy is sold back to the grid. The energy

cost minimization optimization problem for energy cooperation with sleep mode is

stated as follows:-

Given:

� The N number of micro BSs in the network.

� The finite period traffic profile of each BS.

� The finite period energy generation profile of each BS.

� The energy cost of utility and RES.

� The distance between the macro BS and micro BSs.

Determine for each time period t of a finite time horizon T :

� The energy surplus, at BS n, to be transferred to utility.

� The micro BS n to be switched off.

� The increase in the energy consumption of macro BS.

� The net energy saving in network from energy cooperation.

u,M ,Etg,M ,Et

M,u,Etu,n,E

tn,u,E

tg,n,E

td,n,X

T∑t=1

u,M) + C(Etg,M)− C(Et

M,u)N∑n=1

u,n) + C(Etg,n) + CEt

d,n − C(Etu,n)]](5.3.1)

subject to:

For Macro BS M

C1 : Et,gM + Et,u

M + Et,dM = LtM +

N∑n=1

(1− xtn)[Lt,dynamicn (Γn) + Lsleepn

], ∀n, t

For Micro BS, n=1 to N

C2 : Etu,n + Et

g,n + Etd,n = Et

n,u +X tnL

tn + (1− xtn)LSleepn ∀n, t

C3 : LtM +N∑n=2

(1− xtn)[Lt,dynamicn (Γn)

]≤ LMax

1 ∀m, t

C4 : Etu,n.E

tn,u = 0 , ∀n

C5 : Et ≥ 0

C6 : X tn ∈ {0, 1} , ∀n, t

Constraint C1 is the energy balance equation for the macro BS, which is denoted

with subscript 1. Similarly, C2 is the energy balance equation for the micro BS n.

C3 puts a limit on the traffic load that can be taken up by the macro BS i.e., the

combined load of macro and the asleep BS cannot be more than Lmax1 . C4 ensures

flow of energy either to or from utility. It is non linear in nature which is solidified as

linear by applying McCormick envelopes as discussed in the last chapter. C5 forbids

energy taking a negative value. The micro BS can either have value 0 (off) or 1 (on),

as per C6.

5.4 Simulation and Results

The traffic profiles of all the base stations, as well as their RES generation, are

generated for a 24 hr period as done previously. At each instant t, the algorithm

determines which all BS are to be put off to achieve the most optimized solution.

Simulation is carried out for HetNets of various sizes comprising a central macro BS

Pre-Processing

Branch and Bound

Pre-processing for Relaxed Conditions

Transfer load of selected Micro to Macro BS Share harvested energy of Off BS to Utility Get Linear programming solution

Initial Conditions X* , E*

Get Solution for Relaxed Problem

Pre-Processing for MILP • Analyze linear inequalities

• Eliminate futile candidates

Apply rounding heuristic to get feasible solution

Prune Xnt

E* < E X* < X

E < E*

Is Termination Criteria satisfied

Stop YES

Figure 5.3: Flow chart for solving the mixed integer linear problem (MILP) program-ming algorithm.

and N micro BS. The algorithm performs a thousand monte carlo simulations per

configuration and determines the number of micro BS to be put to sleep as per the

constraints earlier determined. The main constraint being the traffic load capacity

of the macro BS, which determines the number of BS that can be switched off. The

traffic-load of the macro BS increases as more micro BS are put to sleep, whereas,

the conventional energy cost decreases on account of the these asleep BSs and their

harvested energy is sold back to the utility. In Fig. 5.4 and 5.4, the BSs on/off state

is shown for different sized networks having 8,12,16 and 20 micro BSs respectively.

In all cases the BS are put to sleep in lean traffic hours and their number differs not

only from network to network but also throughout the year.

The net energy saving graphs in Fig. 5.6, show the results for four micro BS and

one macro as per our model umbrella configuration. The negative values show revenue

from BS sleep mode and buy back of surplus energy by utility. We have similarly

simulated results for N = 8, 12, 16, 20 BS and are depicted in Figs. 5.7(a), 5.7(b),

5.8(a) and 5.8(b), respectively. The figures show two types (bars) of energy savings

for each month. The white bar shows energy cost (+ive) or revenue (-ive) when

the network is operating normally and there is no sleep mechanism in operation.

In this case the surplus harvested energy, if any, is sold back to the utility by the

network operator. Whereas, the black bar shows the network energy cost/revenue

when BS are put to sleep and their traffic load is transferred to the macro BS, whereas,

the harvested energy, now wholly surplus, is sold back to the grid. In Fig 5.6, for

example, the network is making revenue in most of the months, depicted with negative

values. Since harvested energy is surplus in both cases, both bars are negative showing

revenue in almost all months except Nov and Dec. As the size of the HetNet keeps

increasing from 4 to 20, the energy savings keep reducing. This is because the capacity

of the macro BS to take on additional load is limited, thus in large configurations the

relative energy saving is reduced. However, in all cases, the net energy saving is more

in case BS are put to sleep than the case when no sleep mechanism is employed.

5.5 Summary

A novel energy saving scheme has been described for HetNets having RES by exploit-

ing energy and traffic cooperation in the network. An umbrella network consisting

of a central macro BS and surrounding micro BSs was modeled for this scenario. It

is viable to put some BS to sleep during lean traffic hours in order to conserve net-

work energy. Furthermore, if the BS put to sleep is harvesting energy from renewable

sources such as solar and wind, the energy can be sold back to utility to generate

revenue. The energy cooperation scheme developed in chapter 5 is particularly fea-

sible for a heterogenous network of green BS, where some BS are put in the sleep

mode and their traffic-load is shared by the central BS. In asleep BS all the energy

stored/harvested is surplus, which may be transferred to another active BS or sold to

utility. As the size of the network increased, the net savings reduced but nevertheless

remained more than non-cooperation scenario. That is because the traffic load and

therefore the energy consumption of the central BS increases as traffic is assigned to

it up to its maximum capacity. The model is formed under the assumptions that

the user association/dis-association is seamless and that on/off switching is instan-

taneous. The energy cooperation scheme developed in this chapter is particularly

suitable for HetNets of green BSs, where certain selected BS are put in sleep mode

and their traffic-load is shared by the central BS.

DecNovOctSepAugJulJunMayAprM

arFebJan2520

Time Slots

(a) BS on/off state for N=8

arFebJan2520

Time Slots

(b) BS on/off state for N=12

Figure 5.4: Results of BS sleep algorithm showing number of BS awake (vertical bars)over a 24 hr period for different sized HetNets.

arFebJan2520

Time Slots

(a) BS on/off state for N=16

arFebJan2520

Time Slots

(b) BS on/off state for N=20

Figure 5.5: Results of BS sleep algorithm showing number of BS awake (vertical bars)over a 24 hr period for different sized HetNets.

-12000

-10000

4000C, N=4NC, N=4

Figure 5.6: Monthly energy costs for network comprising 4 sites.

10000C, N=8NC, N=8

(a) Net cost savings for N=8

14000C, N=12NC, N=12

(b) Net cost savings for N=12

Figure 5.7: Results of net energy savings for different network sizes for each month.The negative bars show revenue and the positive bars indicated expenditure.

2C, N=16NC, N=16

(a) Net cost savings for N=16

2.5C, N=20NC, N=20

(b) Net cost savings for N=20

Figure 5.8: Results of net energy savings for different network sizes for each month.The negative bars show revenue and the positive bars indicated expenditure.

Chapter 6

Conclusion and Future Work

6.1 Conclusion

The rising energy consumption and the consequent increase in GHG emissions are

forcing world wide adaptation of non-fossil fuel based energy sources. Renewable

energy systems such as wind turbines and PV arrays are a viable option for a cellular

BS as well as the whole network. RES can be employed for not only cutting down on

the harmful GHGs but also for decreasing the energy cost of traditional networks when

used intelligently. A BS is the most energy hungry element in a cellular network and

consumes up to 60% of the energy consumed by a network of macro BSs. Renewable

energy is not only feasible for stand alone BSs that are off the grid but equally feasible

for BSs deployed in a network supported by the grid energy. It is also seen that small

cells like femto and micro cells need lesser energy and thus are more feasible for the

incorporation RES. However, for their proper integration into the present networks,

proven system designs are required that can effectively replace the diesel generators.

Sizing, interfaces, infrastructure etc. need to be defined and standardized so that

they can be easily installed and replaced when needed.

Power generated at a BS through renewable energy sources can be particularly

useful if utilized in a network of co-located BS. It is often seen that different ser-

vice providers are co-located at remote sites or along national highways due certain

geographical advantages. For such BS equipped with RES, other than conventional

ones such as diesel generator, they can efficiently be interconnected in a local net-

work to augment each other in power use. The cellular network traffic at each node

varies both temporally and spatially, offering different load situation for each service

provider. Thus, a node with less load may have redundant power available that it can

lend to its neighbors. The load sharing can be controlled through a central control

station, which receives both load-information and power from each BS and directs the

surplus power of one node towards the other. Such energy sharing in an off-grid sce-

nario can relieve each service provider from expensive diesel generated power to some

extent. This type of energy sharing is already a topic of much research, particularly in

smart grid scenarios. Energy cooperation mechanism has been discussed in this paper

which has much scope for study and implementation, especially in smart grid envi-

ronment. Other schemes like on/off switching and multi-cell cooperation also benefit

with the employment of renewable energy. In stand-alone BSs, co-located at a remote

site, it especially holds lucrative potential vis-a-vis alternate energy co-sharing. With

physical interconnection and necessary software/hardware upgrade, this research has

rich potential undertaking and should prove enticing for future researchers.

The advance LTE networks with incorporated RES and powered by smart grid of-

fer many new avenues of energy efficiency strategies and schemes. The new paradigm

of RES enabled cellular networks in SG involve different technologies that are mak-

ing green communication possible. The three distinct spheres of technology are then

renewable energy systems, smart grid and wireless networks (radio and network). As

the new paradigm find its way into field, many of these technologies will merge or

adapt to the new scenario. Smart grids with distributed generation of green energy

can help distribute clean and cheap power to the cellular networks thereby decreasing

the energy cost and reducing the harmful GHGs. Smart grids have special significance

for cellular BSs in terms of facilitating energy exchange between the nodes. Smart

grids also provide flexibility in energy price forecasting which allows for scheduling of

energy sources to maximize the use of green energy. Energy cooperation is not only

possible between the BSs but also between green NWs and local community in areas

where utility supply is non-existent or intermittent.

We have demonstrated an optimal energy management strategy to use renew-

able energy in cellular systems under varying scenarios that make use of centralized

or decentralized controllers. Intelligent controllers that control the energy flow be-

tween different modules can be incorporated with algorithms developed here. Such

controllers will make the BSs smart energy wise and provide effective demand side

management in the emerging smart grid environment. Incorporating these controllers

into existing hardware is a challenge for the system designers. Energy cooperation is

a viable strategy for optimization of RES as demonstrated in this research. A net-

work/group of RES enabled BS has shown to reduce the conventional energy usage

in the network. For this, three progressive scenarios were developed:-

1. A microgrid of RES enabled base stations sharing their surplus harvested energy

with local grid by exploiting the temporal and spatial variations amongst the

nodes.

2. A cellular network of green BS, cooperating with each other to use the surplus

green energy at a BS instead of using utility or employing the DG. A special

case was developed where a penalty was imposed on GHG emitting sources such

as coal fired plants.

3. An umbrella network of heterogenous BS employing sleep mechanism to con-

serve energy and sharing the surplus energy of the ’Off’ BS with utility under

a traffic cooperation scheme.

6.2 Future Work

The essence of such research, presented here, has been positive and proven empirically

as well as theoretically. What is required is a joint policy by the key industry players

to integrate PV-Wind energy system solutions at the newer sites and gradually replace

the generator sets at the older ones. Standardization of the RES is a serious challenge

for their practical implementation. Greening of cellular networks through increased

energy efficiency as well as incorporation of renewable energy is a growing research

area. These two aspects have good prospects when co-joined with other emerging

trends in wireless networks, such as cooperative relaying and cloud RAN or C-RAN.

6.2.1 Cooperative Relays

In conventional networks it is difficult to extend the range/ coverage to the distant

users due to impeding network characteristics such as path loss, signal fading etc.

Increasing transmission power is not always feasible due to negative effects such as

co-channel interference and increased power consumption. Cooperative relays allow

extension of coverage of coverage by creating a virtual MIMO system [135]. Relaying

is achieved either by installing fixed relays within the network or by making use of

other users as relays, as and when required [136]. The relays are more complex than

mere repeaters as they support complex algorithms and advanced functions. Whether

fixed or mobile, cooperative relaying techniques involve scheduling, routing and data

storing algorithms, amongst others, which require much research before they can be

integrated into networks [137]. The fixed relays can be investigated from the point

of view of incorporating renewable energy sources, which will allow them to function

independent of grid and at remote locations.

6.2.2 Energy Cooperation in C-RAN

Cloud RAN is an evolving concept that aims to maximize the network’s capacity to

use its resources. The RAN deployments for 5G networks will pose serious challenges

and host a combination of technologies. 5G Networks will have large number of

nodes with non-uniform sites. Sharing of resources between nodes and carriers will

be required. C-RAN envisages such sharing of resources to optimize the network.

This sharing mechanism thus developed will also give opportunity to share energy

between the nodes. An RES enabled 5G network can make use of information sharing

in cloud to share the surplus harvested energy between nodes. The C-RAN thus

offers advantages in terms of shared protocols and on-line data taht can allow energy

cooperation schemes such as the one developed here.

In this work, we have formulated and evolved an optimization strategy based

on energy cooperation, to increase the green energy usage and reduce the energy

costs of BS that were traditionally relying on grid and diesel generator. While RES

can supplement the energy requirements of a single BS, it can also be employed in

network configuration for the benefit of all BSs under different power sharing and

energy reduction schemes. The main purpose of energy cooperation in a SG of green

cellular NWs is sharing of green energy and cutting down on grid power. Energy

cooperation requires both energy and data flow amongst the NW elements, which is

possible through a SG. The application of this scheme in conjunction with network

sleep mechanism was a novel strategy employed for the first time in this research.

Energy cooperation in green cellular NWs is an active research topic, especially in

the realm of SG powered NWs. However, the unstable nature of renewable sources

like wind and solar energy call for efficient energy storage and diffusion solutions.

The variables associated with renewable sources and their integration issues into

mainstream cellular applications, in terms of cost and infrastructure, demands models

based on feasibility studies and optimization techniques explored thus far, so that an

RES enabled BS becomes a common sight.

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