A Next Generation Smart Energy Technology Ms. Aurobi Das, Dr. V. Balakrishnan

A Next Generation Smart Energy Technology

Ms. Aurobi Das

Professor in Computer Science & SAP-SOA Consultant

Affiliation(s): Anna University, IIIT , IISWBM- CU, BEE (Ministry of Power-G.O.I)

& ISI dauroin@gmail.com

Dr. V. Balakrishnan Professor in Business Administration

Affiliation(s): FDPM(IIM-Ahmedabad),Former Director-Anna

University research_job@hotmail.com

doi: 10.4156/aiss.vol2.issue2.14

Abstract This paper has focused on the integration of Hybrid Renewable Energy, specifically the solar

energy resources into conventional electric grid and deployment of smart architecture of hybrid energy system in the user-centric pervasive computing concept in the context of Kyoto Protocol for sustainable development of the rural and urban sector. A concept of next generation mobile smart-grid city for efficient real-time collaborative use of renewable and non-renewable energy sources at smart user-centric device for sustainable green environment in the context of climate change is proposed.

Keywords: Enterprise Resource Planning, Systems Applications Products in Data Processing, Service-Oriented-Architecture, Hybrid Renewable Energy System, Acceptance Index, Next Generation

Smart-Grid, Pervasive Computing, NASA Surface Meteorology and Solar Energy Data 1. Introduction

This paper has focused on the integration of renewable energy, specifically the solar energy

resources into conventional electric grid and deployment of smart architecture of hybrid energy system in the user-centric pervasive computing concept in the context of Kyoto Protocol for sustainable development of the rural and urban sector. A concept of next generation mobile smart-grid city for efficient real-time collaborative use of renewable and non-renewable energy sources at smart user-centric device for sustainable green environment in the context of climate change is proposed. A proposed research background is discussed with an Energy Optimization Model for accessing Market Potential Mappings (MPMs)of solar energy resources specifically through calculating Acceptance Index(AI), considering socio-economic and environmental parameters of utility consumers utilizing NASA Solar Meteorology and Surface Energy Data in the context of East India as an experiment for application of pervasive computing technologies in energy & utility sector for sustainable rural and urban development in the era of future next generation smart-grid. 2. Pervasive Computing

Pervasive Computing in the context of Renewable Energy Integration into Electricity Grid –Smart-

Grid: Pervasive computing is a rapidly developing area of Information and Communications Technology (ICT). The term refers to the increasing integration of ICT into people’s lives and environments, made possible by the growing availability of microprocessors with inbuilt communications facilities. Pervasive computing has many potential applications, from health and home care to environmental monitoring and intelligent transport systems. The idea that technology is moving beyond the personal computer to everyday devices with embedded technology and connectivity as computing devices become progressively smaller and more powerful. Also called ubiquitous computing, pervasive computing is the result of computer technology advancing at exponential speeds - a trend toward all man-made and some natural products having hardware and software. Pervasive computing goes beyond the realm of personal computers: it is the idea that almost any device, from clothing to tools to appliances to cars to homes to the human body to your coffee mug, can be imbedded with chips to connect the device to an infinite network of other devices. The goal of

118

Advances in Information Sciences and Service Sciences Volume 2, Number 2, June 2010

pervasive computing, which combines current network technologies with wireless computing, voice recognition, Internet capability and artificial intelligence, is to create an environment where the connectivity of devices is embedded in such a way that the connectivity is unobtrusive and always available. This paper provides an overview of application of pervasive computing in energy and environment for sustainable development. 3. Renewable Energy Scenario and Sustainability and Climate Change

Energy is the most basic need of the people in the world, and it is needed more than ever, while a

majority of world energy supplies are provided by fossil fuels, which are causing environmental problems. Long term projections indicate that world energy demand may increase drastically, with most of this increase taking place in developing countries with the growing gap between demand and supply, diminishing indigenous oil and gas reserves, rising energy cost and security concerns along with hydropower, solar energy, biomass and wind power assumed as sustainable energy options for the developing countries [4, and 5]. In last few years, new and renewable energy technologies are required to play a greater role in the future energy mix. Renewable energy sources can create a significant impact in the generation of grid electricity because of the progress made in biomass power, wind power and small hydro power in the last few years. Research and development exercises over the years have established solar energy as a potential source of renewable energy. Implication of solar power plants in regions of high insolation is the most promising option for an environmentally compatible electricity supply strategy [1, 2, and 3]. A distinguished feature of renewable energy sources is their distributed and localized nature. Some renewable can be used as a primary source in a centralized electricity grid, e. g. large-scale hydro. Some are suitable as the sole source of electricity for (usually small scale) applications remote from a grid (‘autonomous’ or ‘stand-alone systems), especially with suitable energy storage in the system, e. g. photovoltaic powered lighthouses using battery storage. But almost all renewable energy sources can contribute significant electricity as distributed inputs to a winder grid. Many environmental issues have risen in public and political consciousness in the last 50 years. In particular, global concern for sustainable development and climate change led to international concern, expressed most notably by the United Nations Framework Conventional on Climate Change (FCCC 1990) and its associated Kyoto Protocol (1997). The Kyoto Protocol is a protocol to the United Nations Framework Convention on Climate Change (UNFCCC or FCCC), an international environmental treaty with the goal of achieving "stabilization of greenhouse gas concentrations in the atmosphere at a level that would prevent dangerous anthropogenic interference with the climate system”.Almost all countries have agreed to accept obligations under the FCCC to reduce, or at least reduce the increase of, their greenhouse gas emissions and to report on their progress for this. Since the principle source of greenhouse gas emission is CO2 from burning fossil fuels, this constitutes an increase both to use energy efficiently, and to substitute renewable energy for fossil energy. The energy supply sector has good potential to reduce Green House Gas (GHG) emissions in both heat and electricity generation using proven low-carbon technologies at low carbon prices. New technologies such as Carbon capture and storage (CCS) [which is a theoretical approach to mitigating the contribution of fossil fuel emissions to global warming, based on capturing carbon dioxide (CO2) from large point sources such as fossil fuel power plants. It can also be used to describe the scrubbing of CO2 from ambient air as a geo-engineering technique. The carbon dioxide might then be permanently stored away from the atmosphere], solar power and ocean energy are needed to make deeper cuts [6, 7, and 8]. 4. Proposed Research in Renewable Energy Resources-Solar Energy to Develop a Smart Energy Scenario

Energy produced from renewable energy resources such as solar specifically for example, is stored

in battery before consumption, which is a solar module, which will be able to supply energy uninterupted. The main advantage of the system is that energy production’s independence from electricity network. The proposed research is to investigate solar irradiance in a particular location and to build solar module at that location of maximum irradiance and to measure the solar energy produced

119

A Next Generation Smart Energy Technology Ms. Aurobi Das, Dr. V. Balakrishnan

and simultaneously send that estimated solar energy to a computer through wireless connection or cable. The system will be able to monitor . 5. Changing Smart Energy Scenario - A Literature Review

Visions are: (i) “Use of advanced technologies to improve the performance of electric utility

systems to address the needs of society.” (ii) “A fully automated power delivery network, ensuring a two-way flow of electricity and information between the power plant and appliance, and all points in between. Its distributed intelligence, coupled with broadband communications and automated control systems, enables real-time transactions and seamless interface among people, buildings, industrial plants, generation facilities and the electric network.” - U.S. Department of Energy Grid 2030. (iii) “Its foundation is new distributed data communication, computing, and control technologies – efficient transfer of data and control from/to/among many field units.”

Figure 1. Changing Consumer Landscape of Smart Energy

6. Keys to Achieving the “Smart Grid” The keys to achieve the “Smart Grid” are: (i) Architecture: An integrated approach to

deploy technology, new technology on the system must conform to this architecture, should allow for efficient and economic development of new applications; (ii) Communications: Effective, two-way communication with all parts of the Electric Grid are essential to achieving the vision, handle very large amounts of data points to glean information; and (iii) Technology: Utilize technological enhancements in equipment and methodologies that are cost effective, innovative, interchangeable, and reliable to provide the performance enhancing capabilities throughout the electric grid.

6.1 Technology Overview

Fig.1, Fig.2, Fig. 3 are case examples of Next Generation Smart-Grid for Smart-Energy Technology [12] & Table 1 is illustrating Smart-Grid evolving changes.

120

Advances in Information Sciences and Service Sciences Volume 2, Number 2, June 2010

Figure 2. Smart Appliances in Next Generation Smart-Grid

Figure 3. Enterprise Network for Smart-Energy

6.1.1. “Smart Grid”: Evolving Changes

Table 1. Smart-Grid Evolving Changes

121

A Next Generation Smart Energy Technology Ms. Aurobi Das, Dr. V. Balakrishnan

7. Proposed ERP-SAP-SOA Basic Concepts in the Context of Smart Energy

Enterprise-wide resource planning (ERP) is a planning philosophy enabled with software that

attempts to integrate all the business processes of different departments and functions across a company onto a single computer system that can serve particular needs of different departments. ERP combines all the business requirements of the company together into a single, integrated software program that runs off a single database so that the various departments can more easily share information and communicate with each other. Out of more than 100 ERP providers worldwide, SAP-AG, Oracle, JD Edwards, PeopleSoft and Baan –collectively called the “Big Five” of ERP software vendors – control more than 62 percent of the ERP market share (Mabert et al., 2001). Established in Germany in 1972, SAP AG, with 33 percent market share, is the major ERP package vendor for the Fortune 500 companies. Powered by the SAP NetWeaver technology platform to drive innovation and enable business change, Systems Applications Products in Data Processing (SAP) software helps enterprises of all sizes around the world improve customer relationships, enhance partner collaboration and create efficiencies across their supply chains and business operations. Service Oriented Architecture (SOA) is a design for linking business and computational resources (principally organizations, applications and data) on demand to achieve the desired results for service consumers (which can be end users or other services)[11]. 8. Research & Development on Comprehensive Technological Aspects (i) Distributed Generation and Grid Optimization: Integration of fluctuating and dispersed energy sources into National Conventional Electricity Grid; Co-generation of heat/cold/electricity; advanced demand-side management, power electronics; related information technology; demand forecasting; (ii) System Technology for the Off-grid Applications: Advanced maintenance and diagnostics concepts; electricity storage systems; theft protection; (iii) Transregional Electricity and Gas Transport and Global Link: High-voltage direct-current transmission; high temperature super-conductors; (iv) Heat Storage: Phase change materials; sorbents; long-term storage concepts; (v) Storage of High-Exergy Energy: Hydrogen and fuel cell technology; new electro-chemical storage(batteries); storage based on superconductors; (vi) Energy Meteorology: Spatially resolved prognosis of renewable energy potentials; local forecast of energy fluxes (e.g. wind speeds); (vii) R & D on the Sustainability Effects of the Large-Scale Application of Renewable Energies: Possible recycling of wind turbine blades; minimization or reuse of emissions during biomass conversion; reduction of energy payback times of renewable systems; (viii) R & D on Standardization of Renewable Energy Products; (ix) R & D on Solar City Planning. 9. Information Architecture of Next-Generation Smart-Grid

A clarification of information architecture is necessary as it is related to services. The

different types of information can be described in terms of three layers: physical data (sources), domain (service) data, and semantic data. Physical data is the persistent enterprise data implies the data related to generation, transmission and distribution of existing conventional power plants and non-conventional hybrid renewable systems, existing utility consumers, their demand patterns etc. in the context of Hybrid Energy Business Model. Domain data is the classes that encapsulate information needed to implement services. This data is used in the service implementation, which uses the classic object/relational mapping to describe all of the information that is used in the implementation of a service. Semantic data is the information exchanged between service consumers and providers through service interfaces. Semantic data is a normalized view of the common data from all of different services. The mapping between these three layers represent the mining of data for different purposes where an centralized server is maintained for holding the huge multidimensional database of these real-life data of consumers and service providers etc.

122

Advances in Information Sciences and Service Sciences Volume 2, Number 2, June 2010

Figure 4. A Next-Generation Smart-Grid Perspective of SOA

[Source: Adopted from Applied SOA: Service-Oriented Architecture and Design Strategies by Michael Rosen, Boris Lublinsky , Kevin T. Smith , Marc J. Balcer ]

Fig. 4 depicts a Next-Generation-Smart-Grid perspective of SOA and the numbered circles in this figure correspond to the numbered list as below. SOA needs to describe the aspects of services within an enterprise such as : (1) A definition of services, the granularity, and types of services; (2) How services are constructed and used; (3) How existing packaged and legacy systems are integrated into the service environment; (4) How services are combined into processes; (5) How services communicate at a technical level (i.e., how they connect to each other and pass information); (6) How services interoperate at a semantic level( i.e., how they share common meanings for that information);(7)How services align with the business strategy and goals; (8) How to use the architecture .

In the case of Hybrid Energy Business Model, utility consumers is supposed to get service from a National Grid where hybrid renewable energy systems are integrated to National Conventional Grid or decentralized distributed hybrid renewable energy resources are forming a network to export extra renewable power to the National Grid (See Fig. 5 & 6). Providers are renewable development agencies, state electricity boards, which can be assumed to be controlled by a National Control Centre or Area Control Centres.

10. Decentralized Distributed Hybrid Renewable Energy Systems at Sagar Island of Sundarban of 24 Pgs South of West Bengal - Eastern Part of India

Source: WBREDA

123

A Next Generation Smart Energy Technology Ms. Aurobi Das, Dr. V. Balakrishnan

11. Proposed Hybrid Renewable Energy Systems in Sagar Island to be Connected to Eastern Regional Grid Through 132 KV Transmission Line of West Bengal State Electricity Distribution Company Limited(WESEDCL)

Figure 5. Design of Proposed Hybrid Renewable Energy Systems

12. Proposed Methodology Incorporating NASA Surface Meteorology and Solar Energy Data on Solar Energy Resources[9],

Calculating Acceptance Index (AI) based on the algorithm as given in Section 15, for Solar Photo Voltaic (SPV) System and Comparing SPV with any competing conventional system for the best suitable energy system and Design and Deployment of SAP-SOA-EP to integrate the Solar Energy System into Conventional Electricity Smart Grid in the context of Smart Energy System under pervasive computing technology is the principle behind this proposed research

The proposes methodology is considering following parameter such as: (a) Identification of parameters such as: (i) Economic: Cost of product, maintenance and operating cost, prevailing subsidy, tax benefits, benefit due to absence/lesser amount (than fossil-fuel-run equivalent system) of social/scarcity/opportunity cost, resale value, etc. – all in annualized quantities; (ii) Social: Energy habit of the customer, social custom, aesthetic value of the product, customers goodwill for reasons such as lowering of pollution by use of these “green systems”, political goodwill/propaganda, population density & accessibility of the location, grid connectivity, etc. and (iii) Environmental: Availability of solar radiation and other environmental conditions that would significantly affect the performance of the SPV system in consideration. (b) Quantification of each parameter should also take care of three levels of awareness of the target population, such as, (i) Totally un-aware; (ii) Aware but not yet totally realized; and (iii) Totally aware and realized. (c) A reasonable time frame based on: (i) Estimated product-life; (ii) The life of the technology; and (iii) Other factors (viz. the dynamic nature of the above mentioned parameters, the replacement frequency that depends on general habit of the users[8] etc.)

13. Overview of SOA Implementation Methodology

The methodology consists of the following major activities: (i) SOA Reference Architecture:

Define the important aspects of the SOA reference architecture, in particular what a service is, the types of services and their relationships, design and implementation concepts and processes, and relationships to other architectures and communications; (ii) Business Architecture Definition: The first step is to define the enterprise business architecture. This influences the processes, services, information, and enterprise solutions that will be built; (iii) Service Identification: Define a set of services within the enterprise context that supports the business architecture. The overall set of services makes up the service inventory; (iv) Service Specification: Create service contracts that can be used at

124

Advances in Information Sciences and Service Sciences Volume 2, Number 2, June 2010

design time for the selection of appropriate services in solutions. The service specification includes the service interface as well as other usage and dependency information; (v) Semantic Information Model Definition: Creates an enterprise information model that defines the shared semantics of processes and services; (vi) Service Realization: Design and implement services; (vii) Implementation of Service-Oriented Solutions: Build Enterprise solutions from services. These service-oriented solution are influenced by the application architecture, where the process is iterative and incremental starting by creating a high-level business architecture and service inventory and then by going about implementing the first set of services to support specific business goals and then after updating SOA architecture, business architecture, service inventor standards, governance, and the like, the next set of services can be started to build. 14. Services Provided by Smart-Grid in Comparison to Traditional Grid

The ‘smart-grid’ is the best thought of as a megatrend within electricity markets, rather than a particular set of technologies. In today’s context, the whole electricity-value chain from centralized power plant to building is subject to only selective central information –gathering and control. In a smart-grid, operators will have a full grid perspective set out on their computer banks, all the way from the power plant to the many devices involved in end-use of electricity. They will be able to tune the whole system to minimize emissions and maximum efficiency. The operators’ own profits will increasingly come from these services, and less from generation. In the smart-grid, operators will enjoy automatic data collection through ‘smart meters’ in home which will transmit information either wirelessly or via the electricity cables themselves, and thereby smart-grid will be able to tune load by tuning end-use appliances off., or down, without inconvenience to the user. Individual end-users will be similarly empowered: they will be able to use smart-meter to adjust their microgeneration .In electricity grids today; there is relatively little embedded generation and energy storage. In smart-grids there will be plenty, all of it providing data allowing for central conditions. As electric vehicle proliferate, they will increasingly act as tunable mini-power plants within the smart-grid. Parked electric cars will be plugged into the grid, either charging or if charged-being used as paid sources of electricity. In this way, intermittency will be countered, redundancy will be added, peaks will be further shaved. The condition of the grid itself will be much better monitored in the context of local substations automation and remote controlling. The ever diminishing number of large power plants at the top end of the value chain will be fully integrated with micro-grids to provide only what energy is needed, and beyond a prudent reserve –no more . Smart-grid can be assumed as the electricity equivalent of the internet. Towards vision of value creation in the context of consumers concern, the whole house will be programmable to maximize cost and carbon savings. The electric car will be chargeable at night, when rate are low, for example. Household electrical devices will be programmable to turn off around times of peak demand, when utility prices are high. Carbon savings will be monitorable all along. [14]. Fig. 6 illustrates the Smart-Grid in comparison to Traditional Grid.

Figure 6. Traditional Grid versus Smart-Grid

125

A Next Generation Smart Energy Technology Ms. Aurobi Das, Dr. V. Balakrishnan

15. Algorithm for Acceptance Index

16. Challenges Faced by Power Companies ESCOs

Environmental legislation in most industrialized countries calls for an increase in the production of renewable energy. The decentralized nature of facilities that produce green energy (solar energy), where Green Energy or Sustainable Energy-is the energy already passing through the environment as a current or flow, irrespective of there being a device to intercept and harness this power; compounded by natural fluctuations in power output, make it difficult for a power company to gather, validate, and deliver power-capacity data. Hence, estimation of reasonably accurate capacity forecasts is a greater challenge along with the changes in government policies that result in changes to subsidies and tax breaks granted to producers of green energy. Enterprise services into composite application of mainstream power business enable a power company to reliably gather green-energy data from disparate sources and to embrace change in the fast-moving green-energy part of its business. A power company needs timely information on its current and anticipated future capacities of green energy – a task that is extremely difficult to fulfill with error-prone manual and semiautomatic methods. Data sources typically include SAP solutions as well as a number of non-SAP applications plus nonstandard databases run by the company’s smaller suppliers of green energy. They may even include arrays of solar panels operated by individuals who merely have a separate electricity meter that relays data to the power company in real time. Collecting complex data from disparate sources such as these and then aggregating and validating all relevant data requires a tremendous amount of effort plus time-consuming communication. Also green energy is popular; a growing number of customers are prepared to pay a premium for “clean” power. That’s why a power company wants to keep customer-facing information up-to-date at short refresh intervals without tying up scarce resources. To sharpen its competitive edge in a highly competitive and sometimes volatile market, a power company wants to deploy a solution designed to ensure the quality and currency of data; to automate the tasks of data gathering, aggregation, validation, and presentation; and to ensure sustained regulatory compliance.

16.1. Renewable Policy Design Should Reflect Five Fundamental Principles

The principles are : (i)The removal of non-economic barriers, such as administrative hurdles, obstacles to grid access, poor electricity market design, lack of information and training, and the

126

Advances in Information Sciences and Service Sciences Volume 2, Number 2, June 2010

tackling of social acceptance issues - with a view to overcome them – in order to improve market and policy functioning; (ii) the need for a predictable and transparent support framework to attract investments; (iii)the introduction of transitional incentives, decreasing over time, to foster and monitor technological innovation and move technologies quickly towards market competitiveness;(iv)the development and implementation of appropriate incentives guaranteeing a specific level of support to different technologies based on their degree of technology maturity, in order to exploit the significant potential of the large basket of renewable energy technologies over time; and (v) the due consideration of the impact of large-scale penetration of renewable energy technologies on the overall energy system, especially in liberalized energy markets, with regard to overall cost efficiency and system reliability[10].

17. Case Analysis -Energy Scenario at Sagar Island

The island is separated from the mainland by Hooghly and Muriganga rivers and surface transportation is a major bottleneck for development of this area. The journey to Sagar Island from Calcutta involves 90 Km of road journey and 6 Km of boat journey (up to Kachuberia). The total length of black topped, semi metal and unpaved kutcha roads are 35 Km, 49 Km and 464 Km, respectively.

A. Problem Statements:(i) To reduce the generation cost by adding auxiliary sources of energy; (ii) To eradicate the barriers of non-reliability due to unpredictable availability of renewable energy sources, where wind is more unpredicted in nature than solar; (iii) Biomass is available during monsoon but contains lots of moisture which affects the system, but availability of wind is better in monsoon;(iv) There is no optimized operating zone.

18. Case Analysis of Moushuni Solar PV Power Plant

West Bengal Renewable Development Agency (WBREDA) has set up a 53.5 kWp Solar PV Power

Plant with an integrated daytime water supply system at village Bagdanga of Moushuni Island. Total population of this island is about 20,000. Primarily 300 families will be benefited with this Power Plant (See Fig 7). Moushuni Island is a small picturesque island situated near Sagar Island and between Muriganga and Chinai rivers but very close to Bay of Bengal. (A 20 kW Biomass Gasifier System is recommended to give back-up to this Hybrid Renewables System for a reliable electric supply during shortage of intermittent renewable like solar, wind)

Figure 7. Hybrid Renewable Energy Systems operated at Moushuni Island

17.3. Proposed Main Activities for the above two Cases

(i) A Service-Oriented-Architecture (SOA) can be deployed for the proposed Hybrid Renewable Energy Systems integrating to the National Grid in Eastern Part of India, considering isolated Sundarban Islands, (here Sagar Island & Moushuni Island is considered) (ii) The similarity between the monthly availability of different renewable resources can be expressed in terms of correlation of hourly energy availability; (iii) Biomass supply chain should be maintained from energy security point

CCU: Charge Controlling Unit BB: Battery Bank INV: Inverter F: Feeder

127

A Next Generation Smart Energy Technology Ms. Aurobi Das, Dr. V. Balakrishnan

of view; (iii) Diesel back-up must be maintained to cater demand in case of shortage of estimated renewable energy .

18. Conclusion

A smart-energy market where an integrated renewable energy system into the smart-grid –a service

oriented architecture can be designed to assess market potential of conventional versus renewable via acceptance index which is accessible to a mobile device or any smart device into a single entry point to the utility customer for efficient reliable use of energy resources in the context of environmental pollution control scenarios reducing GHG in the context of Kyoto Protocol for rural and urban sustainable development. In addition, ‘Hybrid systems’ also have enormous potential for remote areas. Wind turbines, possibly with additional solar resources, are used in conjunction with back-up diesel generators, which operate as required. The renewable sources typically supply 60-85 % of energy. Existing renewable energy systems and DG sets are not adequate to fulfill the actual demand of electricity at Sagar Island. Hence, more Solar Photovoltaic (SPV) and wind energy is required to be installed and which would be Grid-Conned via Eastern Regional Grid to form an unified Smart-Grid System, through which extra renewable energy after fulfilling the consumers demand of Sagar Island will be exported to the conventional National Grid and vice versa when there will be shortage of hybrid renewable energy sources to satisfy the needs of the customers of Sagar Island. Also intermittent supply of SPV and wind energy and other renewable sources should be fully utilized . Deployment of SOA will be based on the topology of this Hybrid Renewable Energy Systems and also Grid-Interactive Solar Energy System or Solar-Wind integrated System, which may be distributed but can be mapped for feeding into the National Electricity Grid through proper deployment of SOA.

19. References [1] New Scientist , Annual Review of Energy and the Environment , Electrical Review, Modern

Power Systems [2] Refocus: Published for the International Solar Energy Society [3] Advances in Solar Energy : Published by the American Solar Energy Society Solar-derived

technologies (e.g. wind power and bio-mass) [4] Renewable and Sustainable Energy Reviews, Solar Energy, Wind Enrgy and Bio-Mass and Bio-

engineering [5] European Directory of Renewable Energy Supplies and Services, annual, ed. B. Cross, James and

James, London [6] www.iea.org/papers/2007/CCS_in_CDM.pdf [7] www.worldenergy.org/documents/stat2007 [8] www.ipcc.ch/pdf/presentations/poznan-COP-14/ralph-sims.pdf [9] www.eosweb.larc.nasa.gov/sse/RETScreen;www.nrel.gov/rredc/solar_resource.html; www.nrel.

gov/international/ra_india.html [10] www.iea.org/g8/2008/G8_Renewables.pdf [11] Enterprise SOA Development Handbook, Enterprise Services Enhancement Guide [12] www.ieeeusa.org/calendar/conferences/annualmeeting/2009/program/Presentations/SmartGrid.p

df [13] West Bengal Renewable Development Agency (www.wbreda.org) & West Bengal State

Electricity Distribution Company Limited( www.wbsedcl.in) [14] Aurobi Das, V. Balakrishnan, Energy Service Companies (ESCOs) to Optimize Power in

Peak Demand Period in Hybrid Energy System- An Impact on Climate Change, Proceedings of the 2nd IEEE Green Technology Coferernce, 2010 at Dallas, Texas.

20. Acknowledgments

Our sincere thanks to the National Renewable Energy Laboratory (NREL) and also NASA for incorporating NASA Surface Meteorology and Solar Energy Data. Our sincere thanks also to WBREDA

128

Advances in Information Sciences and Service Sciences Volume 2, Number 2, June 2010

for providing their resources for research purpose. & colleague Mr. S. Chakroborty for professional outlook.

Appendix A - Sample Results & Data Analysis Following is the surveyed and tested data for villages under Malda District of West Bengal, India for the project Specified in the years 2009, 2011 & 2021 respectively at each village at gram panchayet & block level of Malda in W.B. district, India. The Data Analysis are as : (i) Estimated Solar Radiation at a particular Latitude 25 and Longitude 87 provided as a sample at a particular point of the above mentioned Malda District along with, details of climate data at that location of earth throughout the year(See

Table A); (ii) Sheet 1: Socio-economic pattern of the same population of the above mentioned village; (iii) Sheet 2: Estimation of Latitude, Longitude, & Elevation in details for each block, gram panchayet under each village of the Malda ; (iv) Sheet

3 :Projected population & energy demand by that population with growth rate percentage in details; (v) Sheet 4: Efficiency Vs. kVA % of Inverter 1(INV1) & Inverter 2(INV 2) for Moushuni Island of Sundarban of 24 PGS South, of W.B. , India for

controlling off-grid infrastructure proposed to be connected to National Grid via Easter Regional Grid.

Table A. SAMPLE OF CLIMATE DATA ESTIMATION OF SOLAR RADIATION Sample of NASA Surface Meteorology and Solar Energy Data for Northeast India-Malda District of West Bengal

Unit Climate data location

Latitude °N 25

Longitude °E 87

Elevation m 37

Heating design temperature °C 12.70

NASA Surface meteorology and Solar Energy: RETScreen Data Cooling design temperature °C 31.26

Earth temperature amplitude °C 15.95

Frost days at site day 0

Month Air temperature Relative humidity

Daily solar radiation - horizontal Atmospheric pressure Wind speed Earth temperature Heating degree-

days Cooling

degree-days °C % kWh/m2 kPa /d m/s °C °C-d °C-d January 17.6 47.9% 4.25 99.3 1.7 19.3 27 237 February 20.7 43.5% 5.30 99.1 2.1 23.5 2 302 March 25.1 40.4% 6.27 98.7 2.1 28.8 0 465 April 26.6 58.0% 6.70 98.4 2.6 29.8 0 499 May 26.6 76.8% 6.51 98.2 3.0 28.8 0 517 June 27.6 82.9% 5.39 97.8 2.9 28.9 0 527 July 27.4 85.7% 4.39 97.9 2.5 28.3 0 538 August 27.4 84.9% 4.44 98.0 2.1 28.1 0 539 September 26.3 84.3% 4.12 98.4 2.0 27.0 0 493 October 24.2 76.9% 4.86 98.8 1.6 24.9 0 450 November 21.6 57.5% 4.72 99.2 1.6 22.3 0 359 December 18.9 49.3% 4.17 99.4 1.6 20.1 7 284 Annual 24.2 65.7% 5.09 98.6 2.1 25.8 36 5210 Measured at (m) 10.0 0.0

Village name Panchanandapur Jamai Para Peyari Pola Mangadpur Geographical Area sq. km Gram Panchayet name Panchanandapur Panchanandapur Panchanandapur Hamidpur Population - Male nos. Population - Female nos. Population - Total nos. 240 126 146 218 Population - School Going Children nos. 80 36 46 35 Population - SC nos. 126 101 95 Population - ST nos. Population - Lit. Male nos. 75 29 55 40 Population - Lit. Female nos. 46 21 44 21 Population - Lit. Total nos. 121 50 99 61 Population - Agri /lab nos. 45 23 29 42 Population - Fishing nos. 0 1 0 0 Population - Others nos. 0 2 0 1 Population - Working Total nos. 45 26 29 43 Total Income rs. 59000 32700 38800 63700 No of Families nos. 45 26 29 43 Avr. Income per Family rs. 1311 1258 1377 1481 Average Monthly Fuel Requirement a) kerosene litres 4.7 2.9 4.9 5.04 b) fuel wood kg 115.5 85.3 93.1 172.5 No. of Points required a) Fan nos. 55 47 30 71 b) Light nos. 113 60 59 103 c) TV nos. 18 19 3 34 Electrical Load a) Fan (@ 60 W each) kw 3.3 1.9 1.2 2.84 b) Light kw 11.3 6 5.9 10.3 c) TV kw 4.3 4.5 0.72 8.16 d) Total kw 18.9 12.4 7.82 21.3 e) Summer (3 Hrs/day for 6 months) kwh/month 9612 6696 4222.8 11502 f) Winter (3 Hrs/day for 6 months) kwh/month 9612 6696 4222.8 11502 h) Total (S:1602 ; W:1602) kwh/year 19224 13392 8445.6 23004

SHEET 1. ESTIMATION OF SOCIO-ECONOMIC PARAMETERS[14]

129

A Next Generation Smart Energy Technology Ms. Aurobi Das, Dr. V. Balakrishnan

BLOCK

GRAM PANCHAYET CHAR VILLAGE NASA Surface Meteorology and Solar Energy

Data ELEVATION(ft) LATITUDE LONGITUDE

KALIACHAK-II HAMIDPUR HARUTOLA 240 88?01'20.86"E 55'01.45"N 93 KALIACHAK-II HAMIDPUR KHATIAKANA 240 88?02'45.23"E 55'17.99"N 69 KALIACHAK-II HAMIDPUR MANGADPUR 240 87?57'15.32"E 57'08.45"N 97 KALIACHAK-II HAMIDPUR JANKI SORKAR 240 87?57'19.39"E 54'06.43"N 75 KALIACHAK-II HAMIDPUR SRIPUR 240 87?57'19.39"E 53'46.48"N 78 KALIACHAK-II HAMIDPUR HALLAS TOLA 240 87?57'19.39"E 52'16.43"N 88 KALIACHAK-II HAMIDPUR KATLAMARY 240 87?57'19.39"E 51'26.53"N 85 KALIACHAK-II HAMIDPUR JUGAL TOLA 240 87?52'15.37"E 55'01.40"N 92 KALIACHAK-II PANCHANANDAPUR SIBU TOLA 240 87?59'33.94"E 56'04.00"N 76 KALIACHAK-II PANCHANANDAPUR JAMAI PARA 240 87?57'39.98"E 57'05.08"N 84 KALIACHAK-II RAJ NAGAR NAYA GRAM 240 87?51'11.45"E 55'08.27"N 75 KALIACHAK-II RAJ NAGAR CHARBABU PUR 240 87?51'11.45"E 55'08.27"N 69 KALIACHAK-II RAJ NAGAR NAPITPARA 240 87?52'12.47"E 55'01.41"N 97 MANIKCHAK DAKSHIN CHANDIPUR JITENTOLA 240 87?53'12.36"E 03'47.50"N 95 MANIKCHAK DAKSHIN CHANDIPUR GUDURTOLA 240 87?57'20.36"E 13'01.55"N 85 MANIKCHAK DAKSHIN CHANDIPUR RAGHUNATHTOLA 240 87?57'25.26"E 15'06.48"N 79 MANIKCHAK DAKSHIN CHANDIPUR AAIKATTOLA 240 87?57'12.36"E 55'01.45"N 81 MANIKCHAK DAKSHIN CHANDIPUR MASTER TOLA 240 87?57'39.90"E 55'04.40"N 88 MANIKCHAK DAKSHIN CHANDIPUR JAGABANDHU 250 85?57'37.36"E 50'53.61"N 79 MANIKCHAK DAKSHIN CHANDIPUR JADUTOLA 250 86?55'42.46"E 55'43.55"N 75 MANIKCHAK DAKSHIN CHANDIPUR RAGHU TOLA 250 87?56'47.26"E 51'41.50"N 81 MANIKCHAK DAKSHIN CHANDIPUR BHABANI TOLA 240 87?57'88.06"E 00'42.74"N 84 MANIKCHAK DAKSHIN CHANDIPUR SADHUCHARANTOLA 240 87?57'12.36"E 05'41.75"N 97 MANIKCHAK DAKSHIN CHANDIPUR SAMASTIPUR 250 87?47'09.37"E 04'43.61"N 85 MANIKCHAK DAKSHIN CHANDIPUR PASCHIMNARAYANPUR 250 87?48'40.51"E 12'58.50"N 82 MANIKCHAK DAKSHIN CHANDIPUR KARTIK TOLA 250 87?48'40.51"E 12'58.50"N 83 MANIKCHAK DAKSHIN CHANDIPUR CHABILALTOLA 240 87?57'06.73"E 55'25.57"N 75 MANIKCHAK HIRANANDAPUR SONAR TOLA 240 87?58'13.61"E 49'26.38"N 90 MANIKCHAK HIRANANDAPUR NATHURAM TOLA 240 87?57'12.36"E 55'01.45"N 76 MANIKCHAK HIRANANDAPUR REKHA TOLA 240 87?51'11.26"E 55'31.35"N 77 MANIKCHAK HIRANANDAPUR LALMOHAN TOLA 240 87?58'42.66"E 55'21.55"N 78 MANIKCHAK HIRANANDAPUR RAMANANDA TOLA 240 87?52'32.46"E 55'31.35"N 94 MANIKCHAK HIRANANDAPUR NATUN TOLA 240 87?50'13.66"E ?52'22.45"N 85 MANIKCHAK HIRANANDAPUR RAGHUBIRTOLA 240 87?51'17.76"E 51'07.45"N 87 MANIKCHAK HIRANANDAPUR FULCHAND TOLA 250 87?55'16.39"E 11'56.40"N 79 MANIKCHAK HIRANANDAPUR SOMESWAR TOLA 250 87?51'12.52"E 04'40.70"N 78 MANIKCHAK HIRANANDAPUR SANKAR TOLA 240 87?57'12.36"E 55'01.35"N 87 MANIKCHAK HIRANANDAPUR FULUKTOLA 240 87?57'12.36"E 55'23.45"N 86 MANIKCHAK HIRANANDAPUR AMIRCHANDTOLA 240 87?57'12.36"E 51'01.45"N 85 MANIKCHAK HIRANANDAPUR BIPIN TOLA 240 87?51'08.26"E 45'01.34"N 83

SHEET 2. ESTIMATION OF LATITUDE, LONGITUDE, &ELEVATION [14]

130

Advances in Information Sciences and Service Sciences Volume 2, Number 2, June 2010

MALDA DISTRICT OF W.B.-INDIA POPULATION ENERGY DEMAND (kW)

BLOCK GRAM PANCHAYET CHAR VILLAGE GROWTH RATE 2009 2011 2021 GROWTH

RATE 2009 2011 2021

KALIACHAK-II HAMIDPUR HARUTOLA 2.58% 220 231.4984408 298.6585949 3.55% 17.58 18.8503352 26.71899443 KALIACHAK-II HAMIDPUR KHATIAKANA 316 332.5159422 428.9823454 20.02 21.46665021 30.4274328 KALIACHAK-II HAMIDPUR MANGADPUR 227 238.8643003 308.1613684 21.37 22.91420154 32.47923271 KALIACHAK-II HAMIDPUR JANKI SORKAR 222 233.6029721 301.3736731 13.13 14.07877708 19.95565398 KALIACHAK-II HAMIDPUR SRIPUR 211 222.02805 286.4407433 12.11 12.98507163 18.40540515 KALIACHAK-II HAMIDPUR HALLAS TOLA 174 183.0942214 236.2117978 10.11 10.84055113 15.36570158 KALIACHAK-II HAMIDPUR KATLAMARY 183 192.5646121 248.4296494 10.16 10.89416414 15.44169417 KALIACHAK-II HAMIDPUR JUGAL TOLA 284 298.8434418 385.5410953 19.01 20.38366735 28.89238249 KALIACHAK-II PANCHANANDAPUR SIBU TOLA 204 214.6621906 276.9379698 10.53 11.29090043 16.00403933 KALIACHAK-II PANCHANANDAPUR JAMAI PARA 122 128.3764081 165.6197663 10.75 11.52679769 16.33840672 KALIACHAK-II RAJ NAGAR NAYA GRAM 273 287.2685197 370.6081655 16.5 17.69229413 25.0775545 KALIACHAK-II RAJ NAGAR CHARBABU PUR 169 177.8328932 229.4241025 10.76 11.53752029 16.35360524 KALIACHAK-II RAJ NAGAR NAPITPARA 277 291.4775823 376.0383218 16.73 17.93891398 25.42712041 MANIKCHAK DAKSHIN CHANDIPUR JITENTOLA 2.05% 178 185.3728045 227.0785492 3.58% 14.71 15.78208892 22.43486977 MANIKCHAK DAKSHIN CHANDIPUR GUDURTOLA 158 164.5443995 201.5641055 12.73 13.65778328 19.41508445 MANIKCHAK DAKSHIN CHANDIPUR RAGHUNATHTOLA 309 321.7988573 394.1981557 24.29 26.06029504 37.04575029 MANIKCHAK DAKSHIN CHANDIPUR AAIKATTOLA 207 215.5739918 264.0744927 11.18 11.99481674 17.05111108 MANIKCHAK DAKSHIN CHANDIPUR MASTER TOLA 145 151.0059363 184.9797171 11.99 12.86385086 18.28647781 MANIKCHAK DAKSHIN CHANDIPUR JAGABANDHU 391 407.1953178 498.807375 27.17 29.15019416 41.43816531 MANIKCHAK DAKSHIN CHANDIPUR JADUTOLA 421 438.4379253 537.0790406 32.95 35.35145004 50.25349823 MANIKCHAK DAKSHIN CHANDIPUR RAGHU TOLA 157 163.5029793 200.2883833 8.93 9.580833045 13.61953685 MANIKCHAK DAKSHIN CHANDIPUR BHABANI TOLA 130 135.3846325 165.8438843 12.19 13.07842719 18.59150663 MANIKCHAK DAKSHIN CHANDIPUR SADHUCHARANTOLA 217 225.9881943 276.8317145 16.5 17.70254706 25.16487772 MANIKCHAK DAKSHIN CHANDIPUR SAMASTIPUR 339 353.0414648 432.4698213 23.03 24.70846417 35.12406872 MANIKCHAK DAKSHIN CHANDIPUR PASCHIMNARAYANPUR 344 358.248566 438.8484322 22.91 24.57971837 34.94105143 MANIKCHAK DAKSHIN CHANDIPUR KARTIK TOLA 232 241.609498 295.9675473 14.16 15.19200402 21.59604051 MANIKCHAK DAKSHIN CHANDIPUR CHABILALTOLA 283 294.7219308 361.0293788 20.97 22.49832799 31.98227186 MANIKCHAK HIRANANDAPUR SONAR TOLA 188 195.787007 239.8357711 9.84 10.55715534 15.00741798 MANIKCHAK HIRANANDAPUR NATHURAM TOLA 236 245.775179 301.0704361 13.35 14.32296989 20.36067379 MANIKCHAK HIRANANDAPUR REKHA TOLA 255 265.5621638 325.3091576 14.18 15.21346166 21.6265434 MANIKCHAK HIRANANDAPUR LALMOHAN TOLA 243 253.0651208 310.0004914 12.98 13.92600369 19.79637047 MANIKCHAK HIRANANDAPUR RAMANANDA TOLA 140 145.798835 178.6011061 6.75 7.24195107 10.2947227 MANIKCHAK HIRANANDAPUR NATUN TOLA 142 147.8816755 181.1525505 8.38 8.990748143 12.78070759 MANIKCHAK HIRANANDAPUR RAGHUBIRTOLA 157 163.5029793 200.2883833 8.93 9.580833045 13.61953685 MANIKCHAK HIRANANDAPUR FULCHAND TOLA 176 183.289964 224.5271049 9.23 9.902697537 14.07708008 MANIKCHAK HIRANANDAPUR SOMESWAR TOLA 192 199.952688 244.9386599 10.11 10.84683338 15.41920689 MANIKCHAK HIRANANDAPUR SANKAR TOLA 126 131.2189515 160.7409955 7.26 7.789120706 11.0725462 MANIKCHAK HIRANANDAPUR FULUKTOLA 211 219.7396728 269.1773814 11.23 12.04846082 17.12736829 MANIKCHAK HIRANANDAPUR AMIRCHANDTOLA 254 264.5207435 324.0334354 16.54 17.74546233 25.22588348 MANIKCHAK HIRANANDAPUR BIPIN TOLA 231 240.5680778 294.6918251 12.32 13.2179018 18.78977536

SHEET 3. PROJECTED POPULATION WITH GROWTH RATE & ENERGY DEMAND

Day Time

(Hour) Efficiency(%)-INV1 kVA-INV1 % kVA-INV1 Efficiency (%) -INV2 kVA-INV2 % kVA-INV2

3-Apr 5.3 67.774 6.786 45.2405 67.992 12.5903 83.9538 8.3` 79.957 7.5578 50.3857 82.976 14.5621 97.0812 9.3 65.15 6.2244 41.496 79.099 14.2388 94.9255 10.3 57.749 5.8161 38.7742 80.278 14.145 94.3 11 51.824 5.0084 33.3894 74.042 13.7138 91.4258

4-Apr 5.3 66.178 10.3358 68.9053 58.155 7.2822 48.548 7.3 85.069 13.0411 86.9409 81.362 9.5998 63.9987 9.3 89.436 12.5172 83.448 103.648 8.888 59.2538 10.3 90.312 11.6505 77.6704 60.735 7.6706 51.1374 11 83.298 10.04 66.9338 53.859 6.7847 45.2318

5-Apr 5.45 63.633 6.8665 45.77702 68.082 7.5336 50.224 6.45 83.839 8.7679 58.4532 83.345 9.1175 60.7834 8.45 77.569 8.0596 53.7313 87.508 9.3333 62.2226 9.45 76.231 8.4691 56.4611 80 8.9536 59.6906 10.45 53.231 5.9871 39.9144 68.418 7.8084 52.0562

6-Apr 5.3 80.128 10.8139 72.0927 63.685 6.7552 45.0351 6.3 97.801 12.5623 83.7489 75.259 8.6124 57.4165 10.3 42.311 5.8824 39.216 100.385 10.3265 68.8436 11 39.138 5.3557 35.7048 89.383 9.522 63.48

7-Apr 5.3 76.743 12.2203 81.4689 71.508 7.6719 51.1465 11 71.101 11.3311 75.5409 55.892 7.3033 48.6891

8-Apr 5.3 57.51 5.9733 39.8224 115.227 11.2761 75.1742 6.3 76.614 7.4097 49.398 145.943 13.9404 92.936 7.3 81.37 7.9344 52.896 119.25 11.5979 77.3193 8.3 81.845 7.8201 52.1344 123.563 11.6498 77.6659 9.3 79.265 7.6375 50.9169 102.011 9.2616 61.744 10.3 51.824 5.3577 35.7184 99.137 9.2616 61.744

9-Apr 5.3 48.935 10.1605 67.7372 42.625 6.969 46.46 8.3 60.026 11.8783 79.1889 55.88 8.5311 56.8745 9.3 58.646 11.656 77.7069 55.88 8.3701 55.8013 10.3 55.196 10.8229 72.1532 47.768 7.671 51.1402 11 49.677 10.26 68.4 40.357 6.5586 43.7246

10-Apr

5.4 113.629 9.7668 65.1124 106.885 7.7631 51.754 8.3 130.281 11.6109 77.4064 122.493 9.2058 61.372

9.3 132.497 11.937 79.58 113.071 8.418 56.12 10.3 115.351 10.4683 69.788 86.615 6.9799 46.5328

SHEET 4. EFFICIENCY VS. KVA % FOR INV1 & INV2 FOR MONTH OF APRIL OF MOUHUNI ISLAND OF SUNDARBAN OF W.B., INDIA

131