Wind Energy Infrastructure Appraisal: Price Based Demand Response Case

International Journal of Energy Engineering 2013, 3(4): 217-226 DOI: 10.5923/j.ijee.20130304.02

Wind Energy Infrastructure Appraisal: Price Based Demand Response Case

Obio Etete B.1, Okon Bassey B.2,*

1MSc Engineering, Postgraduate with the School of Electrical and Electronic Engineering, The University of Manchester, Faculty of Physical Science, United Kingdom; M13 9PL

2PhD Student with the School of Construction and Project Management, UCL Bartlett Faculty of the Built Environment London, WC1H 0QB, United Kingdom

Abstract Energy demand within the built environment has gradually enhanced electricity production primarily from the conventional thermal power p lant, which is a major source of CO2 emission. From the sustainability perspective, renewable energy if properly integrated into the energy mix is capable of solving the world energy crisis thus mit igating greenhouse gas emission. Favorable energy policies have stimulated the large penetration of renewable energy mostly in the form of distributed generation with wind energy having a greater percentage comparatively. Th is research is tailored on the use of price based demand response (PBDR) as an active network management (ANM) tool for optimising the distribution network with large penetration of wind generation. PBDR model involves the use of dynamic electricity pricing based on the real time price of electricity production. In this study, a 132/33KV five bus distribution test system was used alongside the UK wind and load (industrial, commercial and domestic economy) profiles, extracted from the data base of center for sustainable electricity and d istributed generation. Intermittent nature of the wind resources over a period o f one year (8760 hours) was considered during this investigation. Findings revealed huge benefits and value added potentials on the applied model and the entire results are very promising.

Keywords Demand Response, Distribution Network, Management, Renewable Energy, Sustainability

1. Introduction The quest for satisfying the world energy demand while

reducing the world carbon foot print, has led to several investigation in the area of power generation. Most of this research is geared towards finding a more sustainable, economic and optimum way of generating electricity from a zero carbon foot print source such as tidal, wind, solar among others. In meeting these huge objectives of energy demand as well as mit igating greenhouse gas (GHG) emission, renewable energy will have to be largely integrated into the overall energy mix as a sustainability measure.

This increase in renewable energy will yield a reduction in the capacity of thermal power plant serving the domestic, industrial, heat and transportation sectors. In achieving this increase in the amount o f renewable, favorab le energy policies need to be in itiated. Report from[1] shows that about 70 governments around the world have introduced strategic plans to drive the deployment of renewable energy with the aim of mit igating climate change, improving energy security and also to encourage energy independency. Also,[2] reports

* Corresponding author: [email protected] (Okon Bassey B.) Published online at http://journal.sapub.org/ijee Copyright © 2013 Scientific & Academic Publishing. All Rights Reserved

that about 55 countries globally are providing incentives in the form of d irect capital, grants or rebate, investment subsidy, investment tax cred its and import duty.

Among all the form of renewable energy, wind has witnessed a cumulative growth over the past decade[3] thus calling for the need to investigate its impacts on the power system. From economic perspective, it is cost effective to connect wind generation at the d istribution part of the network since its connection cost is proportional to the voltage level. Th is economy important has resulted to an increase in the amount of wind generation connected to the distribution network.

Traditionally, distribution network were designed to cope with unidirectional power flow, but with large penetration of wind generation, the power flow now becomes multi-direct ional. Since wind resources is intermittent in nature, there exist a possibility of the amount of generation surpassing the available demand (load) as well as demand exceeding generation typically when the wind output is zero. This unbalance between supply (generation) and demand may result in voltage and thermal constraints of the distribution network being violated. Therefore, in order to ensure that the integrity of the distribution network is not jeopardized and guaranteeing security of supply, a paradigm shift is require from the passive network management (PNM) to an active network management (ANM) approach. Some of

218 Obio Etete B. et al.: Wind Energy Infrastructure Appraisal: Price Based Demand Response Case

the ANM techniques include, coordinated voltage control, dynamic line rat ing, network topology management, storage, active and reactive power control of generator unit and demand side management. These approach if not adopted will result in upgrading of the network infrastructure with a cost which will later be transferred by the distribution network operators (DNO) to the electricity consumers.

Based on this,[4] indicate that over the years so much emphasis has been placed on managing the uncertainty surrounding wind generation through supply-side reserve. In order to reduce the additional cost associated with network reinforcement and increasing generation reserved, demand response (DR) becomes a better option. From[5] point of view, DR is simply a means of restraining the load or flattening the demand profile by deliberately shift ing the load from peak period to off-peak period in order to alleviate the network capacity during the peak hours. On the other hand, demand response can however be considered as a reaction to the disparity in electricity price or an incentive payment to the consumers by electric utility or electric grid operators[6]. Also,[7] outline the benefits associated with demand response in managing network with large penetration of wind generation. Further discussion from[7] shows that there are two essential DR programs; price-based (stability-based) demand response and incentive based (economic-based) programs.

Price-based demand response involves the changes in consumer consumption rate as a response to the variation in electricity price. Furthermore, PBDR can be sub divided into time-of-use (TOU), crit ical-peak pricing (CPP) and real time pricing (RTP). Incentive-based demand response are basically incentive oriented programs where by the customers derive some economic benefits by participating in the program. This could involve a penalty if the terms of agreement are being violated by the part icipating consumer. Typical of the incentive-based program includes direct load curtailment (DLC), interruptible/curtailable program (ICP). Also, in the ancillary services market (ASM) program, demand bidding (DB), emergency demand response (EDR), and the capacity market (CP) is within th is group. Real time pricing has been identified as the most effective DR program suitable for optimum operation of the power system which should be incorporated in the electricity market[7, 8].

Although, real-time pricing of electricity is not fu lly implemented at the moment, studies carried out show that consumer are likely to adjust their electricity consumption if aware of the electricity price ahead of time. In order to incorporate the consumer into the day-to-day operation of the power system, s mart meters have been roll out to grant the consumers access to the price of electricity in real time. It is evident that with the introduction of s mart meters, consumers will be more likely to response to the variation in the electricity price provided they are g iven the real time price signal. In that manner, this will result in shift ing their consumption demand from peak period to off peak period depending on the real time price of electricity in the energy market[9-11].

In this paper, the price-based DR is modelled to accommodate large penetration of wind generation in a distribution network while ensuring that the system constraints are respected. The concept is based on the principle of elasticity of demand by which an increase in electricity price causes a corresponding decrease in demand and vice versa, within a predefined elasticity of demand[12]. This demonstrates that with the introduction of demand response into the electricity market, optimum utilisation of wind energy can be achieved thus serving the cost associated with network reinforcement.

2. Sustainability in Energy Management Sustainability concept is increasingly becoming a beacon

for energy management with emphasis on the shift from the conventional (fossil fuel) to the renewable energy sources. Wind, solar, tidal, b io-mass and the geo-thermal energy among others are the common examples of sustainable energy sources with nearly or zero carbon foot print index. Against this backdrop,[13] noted that it will be more economically and technically feasible for man to benefit from the most abundant energy source of all, the wind and sun. These energy sources are not only inexhaustible, but, moreover, they are completely non-polluting, clean and sustainable.

Sustainable energy expresses the energy source that, in its production or consumption, has minimal negative impacts on human health and the healthy functioning of vital ecological systems, including the g lobal environment. It is an accepted fact that renewable energy is a sustainable form of energy, which has attracted more attention in recent years[14]. Extant literatures from researchers in the energy management fields’ also revealed further sustainable energy status worldwide. This is contained in the ‘‘Renewables for power generation: status and prospects’’ report. The authors maintain that, at approximately 20%, renewables are the second largest power source after coal (39%) and ahead of nuclear (17%), natural gas (17%) and oil (8%) respectively.

Classically, from 1973 to 2000 renewable energy grew at 9.3% a year, and it was predicted that this would increase by 10.4% a year in 2015. As a result, promoting innovative renewable applicat ions and reinforcing the renewable energy technologies market will contribute to preservation of the ecosystem by reducing emissions at local and global levels. But, this investigation from[14,15] claim that wind power grew fastest at 52% and should be multiply seven times by 2015, overtaking biopower and thus helping in the reduction of greenhouse gases, GHGs, emissions to the environment. This will also contribute to the amelioration of environmental conditions by replacing conventional (fossil) fuels with renewable energies that produce no air pollution or GHG emissions when put to use. Based on this argument, it becomes more expedient to address the energy resources scenario from the sustainability perspective.

Indeed, sustainability integration into energy management in the 21st century has brought a lot of transformation within

International Journal of Energy Engineering 2013, 3(4): 217-226 219

the energy domain. Many models have been developed by researchers in the energy sectors to tackle the palpable energy crisis trends, hence promoting clean and green energy. This development in recent time has birthed a model as displayed in Fig. 1. Such development is lauded in engineering the sustainability of energy prospect thus, promoting the social, economic and environmental (trio) values for sustainable development goals[16].

Figure 1. Technology Push versus Market Pull concept

Contextually, from Fig. 1, it exp lains the concept of sustainable energy demand associated with the current trend in energy utilisation alongside the market forces. Technology push emanating from the renewable (wind) energy is a scenario in which the energy producer, seeing an advantage to the consumers that they do not see; produces wind energy which is cleaner and green for economic gains. In such situation, the demand for wind energy from the end users (consumers) becomes very high. The wind energy producer is in the business of fulfilling functions for the end users. Therefore, the producer uses unique methods and technology to better fulfill the function in ways even the end users may not initially recognise. As in the case of Fig. 1 also, the wind energy producer depends so much on the forecast based on the market demand fo r the energy consumption.

The concept of market pull in energy business simply expresses a situation in which the market demands for wind energy type, whereby the producers respond by producing and delivering more energy according to the demand rate. By so doing, the market desire for the wind energy demand is well considered. Also, the wind energy producer is in the business of responding to the targeted quantity of energy to satisfy a market-defined demand. Wind energy technology within the market pull scenario in terms of demand response depends on a perception of the amount of energy the end users want without violating the constraints of the distribution network[17 - 19].

Further analysis from Fig. 1 shows that, the market pull resulting from the end users aimed at sustainable development (reduction in the world carbon footprint) scheme. This will necessitate for the upgrade of the existing infrastructure (network) in order to promote optimum utilisation of the renewable energy source (wind) for economic benefits. But, the above idea could only be economically v iable if the costs associated with the

investment on the existing infrastructure can be deferred. Obviously, the demand response in this case will become a veritable tool fo r accommodating large penetration of wind energy thereby deferring the investment on network reinforcement. Th is approach certainly will drive home a more sustainable and best practice in managing the wind energy resource as well as mitigating GHG emission worldwide.

3. Network Reliability Analysis In this study, a network reliability analysis was employed

as indicated. For an existing distribution network with large penetration of wind generation (W G), the reliab ility of the system may be affected thus requiring an active control, ANM of the distribution network. Th is reduction in the system reliability is likely to reduce the allowable wind generation that can be connected to the network, therefore posing a setback on the global objective of achieving a low carbon economy.

The system in Fig. 2 shows a simple d istribution system which can be used to demonstrate the effect of voltage rise and its control. The network consist of an on-load tap changer (OLTC), a distribution line with impedance (Z), alongside active and reactive component of the load (PL, QL), where Pg is the active component of the power generated. Also, Qg is the reactive component of generated power (imported or exported), reactive compensator (QC) and a wind generation (W G) system.

Figure 2. Simplified system model for voltage rise[20]

VL ≤ V2 ≤ VU (1) V2 ≈ V1 + R (PG – PL) + (± QG - QL ± QC) X (2)

maxmax 2 1

gV VP

R−

≤ (3)

Where; V1 is the voltage at bus 1, V2 is the voltage at bus 2.

However, VL and Vu are the lower and upper voltage limits. Similarly, R denotes the line resistance and X the circu it reactance.

Eqn. (1) shows the allowable voltage range at bus 2 and eqn. (2) illustrates the calculation of the voltage at bus 2 for a system with W G. Considering a system operating at a unity power factor (± QG ± QC = 0) under a passive network management scheme, it can be adduced from eqn. (3) that, the reliab ility of the system will be affected. This is like to occur when generation is far greater than the availab le

DN WG Z = R + jX

PL, QL

Qg,

Pg

Comp Qc

1

2

OLTC

V2

V1


demand (PG >> PL) as a result of h igh penetration of wind. This scenario will limit the amount of wind generation that can be connected to the distribution network. In order to increase the WG capacity, the voltage (V2

max) needs to be actively control to ensure that the reliability o f the system is maintained. Increasingly, this can be achieved through the use of OLTC or reactive power management in Fig. 2.

Typically, a similar approached is adopted using price based demand response (PBDR) model as a control option to maintain the voltages in the system within the specified range. On the other hand, the benefit of DR goes beyond only maintaining the system reliability as shown in Fig. 3 where it is capable of reducing the electricity market p rice.

Figure 3. Effect of DR on the electricity market price[7]

The reliability of the distribution network with wind generation in terms of sustainability can also be presented mathematically in eqn. (4).

Reliab ility(R) (DN) = R (Ecv) x R (Sov) x R (Env) (4) Where: Env –Environmental values, Ecv – Economic values, Sov – Social values, DN – Distribution network Also for the DN at failu re condition;

R (DN Failing) = 1- Interference (DN) (5) Consequently, the mathematical expression in eqn. (4)

denotes the sustainability key parameters (social, economic and environmental) values in terms of the distribution network reliability. For a distribution system with large amount of wind generation if reliab le, will have a great economic value for the DNO in terms of the reduction in operation and maintenance (OM) costs. Also, considering the need to reduce the world carbon foot print as well improving the social welfare of all the stakeholders in the energy sector, the reliab ility of the system becomes imperative. From engineering sustainability point of view, the distribution network may witness some interference which is basically the difficult ies associated with the continuous operation of the system. If for any reason, the network constraints are violated, the infrastructure (distribution system) will no longer deliver satisfactory services, which could further lead to system failure if the interference persists.

Technically, failure in terms of service delivery of such WG system under failure defined a condition when the consumption rate of the wind energy resource is greater than

the available resources. Evaluating accurately, the area of interference represents the probability of failure from the engineering perspective. In reality, there is no WG when put to use without the resources and infrastructure not being interfered[21]. Then, the reliability of the distribution network analysis details are contained in the results and discussion of this paper.

4. Case Study Scenarios Two case study scenarios were adopted for this

investigation. These are the passive network management (PNM) and the price based demand response (PBDR) models.

In the PNM which is regarded as the base case (BC) scenario, it demonstrates a condition with which the distribution network is operated without any control. The algorithm displayed in Fig. 4 is applied on the test system under analysis to determine the optimum amount of WG that can be accommodated on the distribution network. The results obtained from the passive operation of the test system shows that much wind penetration is limited by this approach. This limitation is as a result of the vio lation of the voltage constraint in the network. In this flowchart also, it exp lains the step-by-step technique as applied during the study.

Price based demand response model as applied in this study has the capacity of maintaining the voltage within the specified range in the test system. As a consequence, the amount of W G that can be connected to the distribution network is increased without any violation of the network constraints. Based on the dynamic nature of this model, the PBDR is able to provide for the systems and sub-systems reliability as well as deferral of investment in the network infrastructure. The PBDR could offer CO2 emission reduction potential by supporting large penetration of WG in the distribution network thus promoting the sustainability goals. Although this model was used on a specific system, it can as well be applied to a standard distribution system universally for further investigation. The PBDR model employed in this examination is illustrated in Fig 5.

The schematic diagram of the 132/33KV test system applied in conducting this study is depicted in Fig. 6. The system consist of WG modelled as a negative load connected to bus 5 with the industrial, commercial and the domestic economy loads at bus 2, 3 and 4 correspondingly. Also, the allowable bus voltage range in the system is ±6% (0.94 ≤ V ≤ 1.06) of the nominal voltage and with a thermal limit of 30MW for the line connecting bus 5 and bus 3. The WG is assumed to be an induction generator operating at unity power factor and modelled as a negative load representing the ability for the generator to inject real power into the network while importing react ive power from the grid. In applying the PBDR model, it is only the domestic economy load that was assumed to be involved in the demand response program.

Table 1 indicates the assumed loads (MW) which are


produced to reflect the various factors (consumer behaviour, consumer classes, type and period of the day) that can influence loads magnitude in a typical power system. This is achieved by using a profile time mult iplier (PTM) obtained from the UK center for sustainable energy and distributed generation (SEDG) to normalise the real and reactive power values of the loads.

Table 1. Different loads and their corresponding profiles

Bus ID MW MVar Load Profile 2 20 6.57 Industrial 3 17 5.59 Commercial 4 10 3.5 Domestic economy

5. Methodology of Study The research employed a two stage methodologies;

literature survey and data collection from the UK generic distribution system (UKGDS) phase one models available on

sustainable energy and distributed generation (SEDG) data base. Information from these organisations were basically the wind and load (industrial, commercial, domestic economy) profiles. These statistics are available in the public domain but with restrict ion to researchers upon application.

The investigation was aimed at determining the optimum wind capacity that can be connected to an existing distribution network using PBDR as a control option. This also demonstrates the benefit of active network management in today’s power system operation. The 5 bus distribution test system used in the study was modelled with power world software as shown in Fig. 5 with system loads as contained in Table 1. The optimum W G capacity that the network can accommodate was determined through a load flow simulation using Matlab software (2011) version. This simulation was conducted for a year-round (8760hrs), taking into account the loads and wind PTM. However, ach ievable outcomes are presented in the results and discussion section.

Figure 4. Passive network management model


Figure 5. Price based demand response model


Figure 6. Distribution test system under study

6. Results and Discussion The results of wind energy infrastructure appraisal are

presented in Figs. 7 – 11. In a typical price based demand response program, the consumer should be able to shift their consumption rate from periods when the electricity price is high to when it is low as a response to real time price of electricity. Fig. 7 illustrates the variation in the domestic economy load as a response to the electricity price in order to ensure that the network constraints are not violated. This change in price accompanied by a comparable change in load is executed with some amount of elasticity of demand. Furthermore, an increase in the price from a based price of £45/MWh to about £58.5/MWh at periods 7854 to 7859hours of the year can be noticed from Fig.7.

This increase in price is typically during periods when the wind generation is zero, hence resulting in a corresponding reduction in demand. Similarly, when generation exceeds the available demand, the the electricity price decreases in order to encourage more consumption from the consumers. Hence, this situation is obsevered during period 7860 to 7865 when the price is reduced to about £26.93/MWh. In some case, storages facility can be used to accommodate the excess generation which can later be used by the consumers for economic gains.

Figure 7. Typical periods of PBDR control option

Fig. 9 depicts the system bus voltages at different W G capacity resulting from the application of the passive network management (PNM) model shown in Fig.4. This result shows that with passive operation of the distribution network, maximum utilisation of wind resources can be limited as a consequence of the network voltage rising above

WG


the statutory predefined range. Further analysis from Fig. 9 signifies that the voltage at bus 5 was above 1.06 at 10MW wind penetration. This limitation is what constitutes the need for an active network management such as the PBDR model to support the deployment of the wind energy for a sustainable development. In addition, this implies that the optimum wind generation capacity that can be connected to the test system is 5MW. From Fig. 8 also, it is evident that

that the maximum line flow (9.675MW < 30MW) has not been exceeded, hence, the distribution network still has the capability of supporting more wind generation if the voltage issue is determine.

To clear the network constraint, the PBDR model was applied at every W G capacity and a load flow simulat ion was carried out to check if there is any violation in the voltage and thermal constraints.

Figure 8. Wind generation capacity associated with PNM

Figure 9. System voltage profile with PNM


Evident of this finding is demonstrated in Fig.11 that the voltage in the system was maintained at the defined range (0.94 ≤ V ≤ 1.06) while increasing the amount of wind generation that can be connected to the test system.

Plots in Fig. 10 are very clear that the PBDR model was able to ensure that the upper and lower voltage limits in the network was not violated. Since the thermal limit of the test system is 30MW, this shows that the optimum wind generation that can be connected to the system is 30MW. As such, about 30MW wind generation capacity is achieved against the 5MW obtained in the passive operation of the test system. This simple model, provide a proof of concept that demand response has the potential of supporting the deployment of wind energy resources for the achievement of sustainability objectives. Obviously, with this outcome, there is guarantee on the entire systems infrastructure in terms of reliab ility, security of supply alongside other economic, social and environmental benefits associated with the renewable energy drive.

Figure 10. System voltage profile with PBDR

Figure 11. Wind generation capacity associated with PBDR

0.97

0.98

0.99

1

1.01

1.02

1.03

1.04

1.05

1.06

1.07

GSP BUS 2 BUS 3 BUS 4 BUS 5

Vol

tage

(pu)

System bus

5MW

10MW 15MW 20MW 25MW 30MW 35MW

1.0371

1.0596 1.0599 1.06 1.06 1.06 1.06

0.9481 0.9481 0.9481 0.9481 0.9481 0.9481 0.9481

0

5

10

15

20

25

30

35

40

0.88

0.9

0.92

0.94

0.96

0.98

1

1.02

1.04

1.06

1.08

5 10 15 20 25 30 35

Net

wor

k fl

ow (M

W)

Net

wor

k vo

ltage

(pu)

Distributed Wind Generation Capacity (MW)

Network Min. Voltage Network Max. Voltage

Network Max. flow


7. Summary and Conclusions In this study, thorough examinations have been conducted

on the wind energy management primarily on distribution network infrastructure through PBDR technique. To achieve an effective price based demand response program, prompt availability of real-time electricity price signal to the consumer is paramount. Although, the reliability of the auxiliary infrastructures, such as smart meter (SM) was not covered during this analysis as well as the optimum elasticity of demand that can be tolerated by all the stakeholders in the electricity market. This could form a basis for further studies in relation to the valid ity of the PBDR model as indicated.

Indeed, discoveries hitherto from this investigation will equip the industry and academic community on new ideas in tackling technology push and market pull (paradigm shift) regarding energy management. Remarkab ly, the results culminating from this analysis will form the basis for future studies concerning the wind energy management in view of sustainability pursuit towards sustainable development.

ACKNOWLEDGEMENTS The authors hereby acknowledge the support of the

University of Manchester for provid ing the enabling environment and facilit ies used during the course of this study. Also, credence goes to the UK center for sustainable energy and distributed generation (SEDG), for provid ing the statistics in the public domain for this investigation.

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Wind Energy Infrastructure Appraisal: Price Based Demand Response Case

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