Demand Forecasting of Electricity and Optimal

Demand Forecasting of Electricity and Optimal Locationing of Transformer Locations Using Geo-Spatial Techniques: A Case Study of Districts of Bihar, India Prerna R. & Prasun K. Gangopadhyay

Applied Spatial Analysis and Policy ISSN 1874-463X Volume 8 Number 1 Appl. Spatial Analysis (2015) 8:69-83 DOI 10.1007/s12061-014-9121-3

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Author's personal copy Appl. Spatial Analysis (2015) 8:69–83 DOI 10.1007/s12061-014-9121-3

Demand Forecasting of Electricity and Optimal Locationing of Transformer Locations Using Geo-Spatial Techniques: A Case Study of Districts of Bihar, India Prerna R. & Prasun K. Gangopadhyay

Received: 9 May 2014 / Accepted: 9 September 2014 / Published online: 19 September 2014 # Springer Science+Business Media Dordrecht 2014

Abstract In the present study an attempt has been made to identify the demand of each household in the study area, in order to divide the villages into pockets of high, moderate and low priority for provision of electrification. This division is based on a “Priority Index” that has been designed in a manner to address the variations in electricity demand also considering the socio-economic background of the household dwellers. Parameters that were applied in order the assess the socio-economic status were, monthly income/ expenditure, rate of literacy, occupational structure, number of school going children, size of household and level of poverty i.e. identification of APL/BPL households. Out of these, the most significant contributors were included in the computation of a bias free Priority Index. The spatial division (as per priority) was helpful in proposing potential locations for the placement of new transformers, providing higher spatial coverage with planned energy allocation and minimized transmission loss. According to this methodology, a holistic categorization was achieved which closely identified the pockets needed to be given highest priority for providing electricity, without compromising on greatest spatial coverage. The average percentage of households that remained uncovered across the five villages studied was 9.68 % only, promising a reasonably high coverage. Keywords Demand forecasting . Priority Index . Transformers . RGGVY

P. R. (*) Department of Natural Resources, TERI University, 10 Vasant Kunj, New Delhi 110070, India e-mail: [email protected] P. R. e-mail: [email protected] P. K. Gangopadhyay The Energy and Resources Institute (TERI), Darbari Seth Block, IHC Complex, Lodhi Road, New Delhi 110003, India Present Address: K. Gangopadhyay P. R. National Centre for Antarctic and Ocean Research, Headland Sada, Vasco-da-Gama, Goa 403804, India

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P. R., P.K. Gangopadhyay

Introduction The use of Geographic Information Systems (GIS) in the planning and development sector has, over the years, gained importance and popularity due to its high efficiency and robustness. There have been several instances where GIS and remotely sensed data have been brought together in order to identify pockets for intensifying electrification, delineating routes for placing transformer lines, grid network analysis and so forth. Rural electrification is one such domain of the planning sector that needs continual observation and management so as to aid in timely development and distribution. The development of any rural area depends on a multitude of factors and for a rapidly progressing country like India, it becomes prerogative for every rural community to be provided with all basic requirements, in order to utilize its resources in maintaining itself and commit in the betterment of the entire economy. One of these rudimentary necessities of life is the access to electricity. Over the years, it has been observed that the people, who belong to rural India, have diversified so much in terms of their lifestyle that the need for a reasonable supply of electricity is a must so as to be able to maintain their daily lifestyles. The era of oil lamps and incandescent lighting sources has been replaced with energy efficient bulbs for e.g. CFLs, mechanized tools for irrigation, television sets, dish antennas etc. showing the transition of rural lifestyle. This clearly indicates the upliftment of the marginal classes but at the same time, also lays more stress on the government to cater to these growing demands. Several initiatives have been taken by the government from time to time aiming at the reduction of power distribution inequalities in the economy coupled with intensive electrification of rural sections. One such widespread programme of the Indian government is the Rajiv Gandhi Grameen Vidyutikaran Yojana (RGGVY) which aims at providing electricity to all rural households of India and free of cost service connection for Below Poverty Line (BPL) households (RGGVY at a glance 2005). Also, one of the main objectives of the scheme is a minimum daily supply of 6–8 h of electricity in the RGGVY network with the assurance of meeting any deficit in this context by supplying electricity at subsidized tariff as required under the Electricity Act, 2003. Unfortunately, due to several shortcomings, these primary objectives were noticeably absent in the villages selected for the purpose of the study. Some of the prime reasons for this lacuna between demand and supply are: (1) the load of the transformers is often exceeded by superfluous usage; (2) inadequate knowledge of the village community to understand the concept of optimum usage of resources; (3) heavy practice of hooking/thieving of electricity; (4) poor maintenance of electrical apparatus. These factors together lead to the absence of electricity, by incapacitating the transformers installed to meet the total load of all households in a village even for the minimum duration of 6–8 h. Premature breakdown of transformers mostly because of the disproportionate load on them is the most rampant hindrance noticed in most villages. Due to the above, such programmes are unable to fetch rightful capability. Previous researchers have also identified certain more concrete and generalized factors that act as deterrents in the functioning of these distribution transformers. Bhalla (2000) has outlined that in order to reduce Transmission and Distribution (T and D) losses, it is essential to first understand the magnitude of technical and commercial losses. The prominent reasons for technical losses in India, according to

Author's personal copy Demand Forecasting and Transformer Locationing Using GIS Method

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his study, are: (a) inadequate investment on T and D causing overloading of the distribution system without commensurate strengthening and augmentation; (b) haphazard growth of distribution systems with short sighted supply goals; (c) large scale rural electrification through long 11 kV and Low Tension Lines; (d) too many stage of transformations; and (e) poor quality of equipment used in agricultural pumping. On the other side, commercial losses are spurred mainly by theft/pilferage accounting for a substantial part of high T and D losses in the country. As per Bhalla (2000), nonconsumers avail unauthorized supply by hooking or tapping the bare conductors of L.T. feeders, while the bonafide consumers cause damage by willfully tampering the measuring equipment installed at their premises. Due to absence of realistic mechanisms of measuring T and D losses, it becomes all the more difficult to estimate revenue requirements for meeting the loop-holes existing in the energy dissipation network. It is evident from the above discussion that the major reason for poor performance of energy facilitation programmes is the miscalculation of the electricity demand of the area being served and application of geospatial tools is one of the ways to bridge this gap. If more and more methodologies can be developed and applied intelligently, the issue of pre-installation planning can be catered for, thereby increasing the life of the transformers and allowing the people to access an undisrupted flow of electricity where supply is adequate. In the light of the above, this study makes an attempt to assess the merit of the RGGVY programme in five blocks of Gaya and Nawada districts in the state of Bihar, India and also propose a geospatial methodology for identifying the most crucial pockets in a village requiring greater attention in terms of energy supply. Primarily, the methodology follows a thorough door-to-door survey in the villages to identify the reasons for poor performance of the installed transformers and also to acquire, as precise as possible, electricity demand per household. In this way, a demand forecast of the entire village has been done to calculate regions of priority which forms the base for the identification of potential locations of new transformers. The positions and capacity of the new transformers have been suggested in congruence with the overall energy requirement of the village, while being aimed at minimizing T and D losses. Also, the effectiveness of employing GIS tools resulting in better planning and reduction in total cost of implementation/maintenance has been highlighted in the study.

Previous Work There are ample studies that have been conducted to portray the advantages of applying geo-spatial techniques in rural electrification planning and network constructions but not many have applied their methods at the grass root level i.e. they have been limited to the district or state level. Nevertheless, such works have proved quite beneficial in constructing the methodology followed in this study and are discussed briefly hereinafter. Barnard (2008) utilized various factors for the identification of “the best path for an extension or new network”, the important ones being – roads, land cover, household positions and slope, which were combined to obtain a suitability raster with pixel values rating equal or near to zero. The conclusion of the study showed that the implementation of GIS and spatial analysis was highly appreciable in finding areas



which were supply deficit, showed overloaded demand, sparse capacity pockets, ideal spaces for new substations establishment and so forth. Another study by Kaijuka (2006) used a multitude of factors to formulate a network development plan for intensifying electrification along the existing routes. Some of these variables included population distribution and population density, location of demand centers i.e. schools and village trading centers, road network, health centers, and finally the projection of high demand centers or regions as an assimilation of the above variables. The entire study has been performed on the GIS platform enabling data interoperability and the effect of one variable over the other. The dynamism and robustness of GIS in managing numerous data sets and several available means to compare different parameters for studying energy alternatives for rural communities has been established by Dominguez and Pinedo-Pascua (2009) in their research work on Latin American countries. The above citations give an idea about the relevance of remote sensing and GIS techniques in dealing with the issues of rural electrification planning. However, a very small amount of work has been done in this field. There are several countries which could exploit the potentiality of such techniques for careful and systematic planning over time. It has also been advocated by many that rural energy issues cannot be handled by studying a particular attribute, rather an assortment of parameters have to be incorporated. Rural electrification can be modelled as a multifactorial task connected to a large number of variables: decision makers need to choose the appropriate options by considering not only the techno-economic competitiveness but also socio-cultural dynamics and environmental consequences, making the task intricate (Rahman et al. 2013). Multi-criteria analysis is one such common technique followed by researchers for holistically studying the parameters that contribute in the variation of electricity in a given region. Nerini (2012) in his work highlighted the importance of performing a multi-criteria analysis for choosing the right parameters to create a new aggregate index for identifying the appropriate electrification technology. It has also been shown by Munda and Russi (2005) that in the case of rural renewable energy policies, multicriteria analyses aid decision makers to assess the contribution of different parameters and makes planning more effective. The need for developing such exhaustive approaches to tackle the issue of rural electrification is prerogative. Since most failures occur due to the voids existing between demand and supply, such techniques can play a significant role in modulating pre-installation measures, such that no issues are encountered later.

Research Objectives The most important research question to be answered through this study was to gauge the level up to which geospatial techniques could be successfully employed in the domain of rural electrification. This was a challenging task as in most Indian villages, unplanned routes; scattered settlements and unequal demand of the households make it difficult to develop an approach which could provide electricity in an equitable manner. The second objective was the computation of an index for dividing the households as per priority (considering electricity demand cum socio economic factors); and finally


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to propose new transformer locations catering to the newly defined priority centers. These objectives have been summarized below: & & & &

Data collection at grass root level to assess the contributing factors in load distribution and understand the socio-economic background of the village community. Selection of the most significant contributors to be incorporated in the Priority Index (PI). Division of the village as per PI into high, moderate and low. Identification of new transformer locations in close proximity of high priority pockets while also attempting for maximum coverage.

Material and Methods Study Area The state of Bihar lies in the north eastern part of the Indo Gangetic plains. It is divided into 38 districts out of which two were selected namely Gaya and Nawada. The prime reason for the selection of these two districts was due to their enlistment in India’s 250 most backward districts in 27 states which are covered by Backwad Regions Grant Fund Programme (BRGF) launched in 2006 (Ministry of Panchayati Raj 2009). Both Gaya and Nawada have a poor percentage of electrified households with 24.4 and 12.9 % respectively (International Institute for Population Sciences 2010) hence posing another reason for their selection in the present study. Further, villages were identified from the two districts which fall under the umbrella of the RGGVY and according to ease of accessibility – five villages were short-listed to initiate the study. The villages are located between 24° 45′ and 24° 56′ North latitudes, and 84° 52′ and 85° 22′ East longitudes. The village names with their respective household counts are Ganjas (54); Paharpur (104); Nepa (128); Risaudh (159) and Repura (199), first four from Gaya district and the last from Nawada (Fig. 1). Data Used For the current study, high resolution images from Google Earth (DigitalGlobe and GeoEye imageries) were extracted and geo-referenced on WGS 1984 datum using ERDAS Imagine 9.1. Thereafter they were projected onto Universal Transverse Mercator (Zone 45N) coordinate system which provided the base for generation of digitized maps for the study area. Delineation of priority areas, location of transformers and street network was done using the base maps in ArcMap 9.3. Methodology The methodology adopted in the study has been summarized in the flow chart (Fig. 2) shown below:



Fig. 1 Map of study area

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Data Collection – A questionnaire was prepared using which, data were collected from a sample of households in each village and a total of 195 households were questioned out of 644 approximating to 30 % of the total number of households. The households were thoroughly questioned regarding: (a) their daily consumption of electricity, (b) the kind of electrical appliances in their households, (c) the current status of electrical supply, (d) status of billing and installation of meters. The detailed questionnaire has been given in Annexure 1. The socio economic indicators aimed to be estimated through this survey can also be found in the questionnaire. Certain parameters such as household size, number of school going children, main occupation of the family, income status were considered to be influential factors, which could in some way effect the electrical demand of a household. These parameters were very subjective and a direct one-to-one association could not be identified i.e. it could not be assumed that all houses with large number of inhabitants had a higher electrical demand as opposed to a household with fewer members. After much deliberation, four parameters were chosen to be included in the computation of the “Priority Index”, discussed subsequently. Quantification of Area – Google earth imageries were used (post geo-referencing) for digitizing the household boundaries, street network and positions of the already existing transformers (if any). This data was projected on UTM projection (Zone 45N) at WGS 1984 datum, and the area for each property was calculated using appropriate tools in ArcMap 9.3. Computation of Priority Index (PI) – The division of villages into different priority zones (high, moderate and low) was required, so as to delineate which area of a village required more attention in


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Fig. 2 Methodology of the study

terms of providing access to electricity compared to others. A simple division of the village in terms of load could have also been done in a way that the households with maximum demand for electricity receive more priority when deciding the placement of transformers. The marginal households with hardly one bulb or one fan would have been left devoid of electricity simply because of their lesser electrical consumption, while the more affluent households (i.e. houses with TV, more number of electric appliances etc.) could have received greater access to electricity. Due to this shortcoming, the computation of a PI was done by incorporating four factors, namely - number of members in a household; number of school going children in a household; load of the household in terms of wattage; and area of the property in square meter. The formula for the PI designed by incorporating the above factors was: X M ði Þ S ði Þ LðiÞ AðiÞ HH ðiÞ ¼ þ þ þ M max S max L max A max

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where, HH is household; M(i), S(i), L(i) and A(i) is number of members, number of school goers, load and area for every ith household respectively; M max, S max, L max and A max is the maximum value of M, S, L and A in the dataset respectively. Load estimation – A sum of the total wattage of the electrical items installed in each household was taken to be the “load” of a household for instance if there are 3 bulbs of 100 W and a fan of 60 W, then the load of the household would be denoted as 360 W. Out of the 30 % of the total sample, 25 % were used to ascertain the values for the remaining households. Households of similar sizes (in terms of area) were allotted


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similar values of “electrical load” and demographic structure (number of members in the household and number of school going children). In this manner, the overall pattern of (a) electrical demand and, (b) socio-economic character of the village, was achieved. Such extrapolation helped in creating an inventory of the entire village for further analysis. Remaining 5 % of the data collected was used to validate the values of the households that were not in the initial sample i.e. the households for which extrapolation was performed. The degree of reliability of this approach was checked using the correlation of the previous data’s PI values and the new data’s PI values. Division of the villages as per PI – On the basis of the calculation, which returns a single value of each household, every village could be classified into three major classes, i.e. high, moderate and low values. The threshold value to delimit the three classes was not standard for all villages, as the range of the PI varied throughout the population. It was hence considered more appropriate to set limits of high, moderate and low categories individually for every village, depending on the range of PI values. Because the approach of transformer locationing and distribution was unique for every village, a standardized value could not be implemented to delineate the priority classes; hence a village-by-village approach was adopted.

Once the priority levels of the households were calculated using the aforementioned PI, it was easy to predict the potential positions to install new transformers. One such map has been shown (Fig. 3) where the village is divided into three categories as per priority i.e. high, moderate and low shown by red, orange and yellow respectively. These divisions have been arrived at by applying the method of Natural breaks (Jenk’s) in the dataset to find regions having similar values and applying class boundaries where the values of the preceding group of items and a subsequent group is wide enough. This classification of the households helped greatly in deciding positions of the new transformers

Results and Discussion It was of utmost importance to suggest transformer locations that justified the priority demarcation of the villages. Apart from that, allotment of desirable load capacity of the transformer was also of supreme importance. After working out various combinations, optimal positions of transformers were suggested giving maximum coverage. Using the buffer tool in the ArcMap environment, it was possible to assess the spatial coverage of every transformer. The extent of the transformer was determined in such a way, that the total energy demand of the houses falling under every transformer does not exceed its capacity. Also, in order to make the allocation as equitable as possible, the number of households falling under the umbrella of more than one transformer was kept to a minimum. An example of the transformer positioning for one of the villages with four 16 kVA transformers have been shown in Fig. 4. In Fig. 4, four 16 kVA transformers have been suggested to meet the demand of the entire village albeit, with this kind of system a few houses still remain uncovered.


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Fig. 3 The village of Nepa showing the division of the village as per PI

Although it is known that the buffers generated are not symbolic of the limit of the transformers in providing electricity; in such situations, it was considered best not to include the uncovered households because their inclusion under any of the transformers could cause exceedance of transformer capacity, thereby posing the threat of premature breakdown. In the current study, a buffer of 50 m has been taken as a standard for all transformers but it would not be appropriate to state that houses beyond this distance would be completely devoid of access to electricity. A summary of the number of transformers proposed for each village along with their capacities and projected coverage has been given in Table 1. Certain points have been considered for placement of transformers which are: (a) greater the distance from the transformer, greater the transmission loss; (b) greater the length of the wires for connections, greater the threat of theft of electricity by illegal consumers en-route; (c) smaller capacity transformers in a greater number are better that small number of large capacity transformers for the provision of “intensive



Fig. 4 Nepa village with new transformer locations showing maximum coverage

electrification”; (d) smaller capacity transformers (equal to or less than 25 kVA) are considered safer and easier to maintain as opposed to bigger transformers; (e) smaller capacity transformers can be placed closer to households due to their safety and small size, while bigger ones must be placed away from local regions of influence. Conclusions and Policy Implications In all five villages, the ratio between the households not covered to the total number of households goes to a maximum of 12.96 % indicating a good coverage, with an average of 9.68 % (Table 2). The spatial pattern of the village also seems to have a significant impact on the modeling of transformer load division, for example in village Risaudh more than 1/10th of the households were left out due to the linear pattern of its dwellings (Fig. 5). On the other hand, village Nepa, which is an equally large village with 128 households,


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Table 1 Village wise transformer distribution with capacity and the respective area covered Village name

No. of transformers proposed

Capacity of transformers (kVA)

Total Demand (watts) & area (m2)

Demand covered by transformers & area (m2)

Demand not covered by transformers & area (m2)

Ganjas

1

1×16

13124 6349.8

12638 5758.6

486 591.2

Paharpur

3

2×10 1×25

36369 14477.8

34474 13677.2

1922 800.6

Nepa

4

4×16

54862 16387.2

52622 15779.6

2240 607.6

Risaudh

4

3×16 1×25

73890 17462.3

66670 15463.2

7220 1999.1

Repura

5

3×10 2×16

62882 21702.2

56922 19406.9

5960 2295.3

showed a good coverage leaving out only 5.46 % of the households by virtue of its clustered/nucleated pattern. The transformers have been placed in such a way that they do not obstruct any movement i.e. they are not along any street or road and are positioned only in open spaces thereby causing no threat. It can be seen by the previously shown maps, that although the transformers are positioned in a manner by which they cover all high priority households, households with low priority have not entirely been neglected or avoided. They are also greatly covered showing that there is no bias in the positioning of the transformers. The primary advantage of the study is that it highlights the ways in which GIS and remotely sensed data can be utilized for effective planning in the electricity domain. Sarr (2006) had performed a similar study in Senegal to come up with a sustainable electrification program for the rural communities. It was concluded that GIS played a critical role in estimating the true demand on the system and for designing an optimal electrification model. Similarly, in the Indian context, such innovative methodologies need to be adopted at the earliest as there is huge scope for GIS implementation in the planning arena which is yet to be discovered. Another advantage of the study is that it has been

Table 2 Percentage and number of households not covered as per the proposed transformers Village name

Total number of households

Total number of households not covered

Percentage of households not covered

Ganjas

54

07

12.96

Paharpur

104

07

6.73

Nepa

128

07

5.46

Risaudh

159

17

10.69

Repura

199

25

12.56



Fig. 5 Risaudh village showing its 10.69 % households not covered due to its linear spatial pattern

conducted at the household level. This implies that the “bottom up” approach has been applied truly. Extending a study at the grass root level is the most preliminary step that must be taken for performing socio economic research. The main objective of the study was to identify potential locations which can be used for placing transformers. This has been accomplished by identifying which portions of the village community needed to be given priority in terms of the “priority index”. Such other indices or parameters can be constructed for other electrification programmes as well. Overall, this project is sufficient to assess the demand of a village for electricity and decipher ways to provide access. If such works can be usefully implemented by the planning organizations, new pathways for planning can be paved. Acknowledgments The authors would like to express their gratitude to the colleagues in TERI for theirs intellectual contributions and the people of the villages for their valuable support and cooperation.


Annexure 1

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References Barnard, J. (2008). Using GIS in electrification and network planning. Eskom Distribution, GIS Technical, 2008, 25–31. Available at: http://www.eepublishers.co.za/images/upload/GIST%20-%20Using%20GIS% 20in%20electrification.pdf. Bhalla, M. S. (2000) Transmission and Distribution Losses (Power), In Proceedings of the National Conference on Regulation in infrastructure Services: progress and way forward, The Energy and Resources Institute, New Delhi, India. Available at: http://www.teriin.org/index.php?option=com_ publication&task=details&sid=355. Dominguez, J., & Pinedo-Pascua, I. (2009) GIS Tool for Rural Electrification with Renewable Energies in Latin America, Proceeding GEOWS ’09 Proceedings of the 2009 International


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Conference on Advanced Geographic Information Systems & Web Services, pp. 171–176, ISBN: 978-0-7695-3527-2, doi:10.1109/GEOWS.2009.25. Available at: http://dl.acm.org/citation.cfm? id=1510683. International Institute for Population Sciences (IIPS). (2010) District Level Household and Facility Survey (DLHS-3), 2007-08: India. Bihar: Mumbai: IIPS. Available at: http://www.rchiips.org/pdf/rch3/report/bh. pdf. Kaijuka, E. (2006). GIS and rural electricity planning: a case study Uganda, IT Power – Uganda. ATDF Journal, 2(2), 23–28. Available at: http://www.atdforum.org/IMG/pdf/GISUnganda.pdf. Ministry of Panchayati Raj. (2009) A Note on the Backward Regions Grant Fund Programme, 8th September 2009, National Institute of Rural Development, pp. 13–14. Available at: http://www.nird.org.in/brgf/doc/ brgf_BackgroundNote.pdf. Munda, G., & Russi, D. (2005) Energy Policies for Rural Electrification: A Social Multi-criteria Evaluation Approach, European Commission – Join Research Centre (EC-JRC), Italy and Universitat Autonoma de Barcelona Dept. of Economics and Economic History and Institute for Environmental Sciences and Technologies, Spain, 26/2005 – UHE/UAB – 10.01.2005. Available at: http://www.h-economica.uab.es/ wps/2005_01.pdf. Nerini, F., Francesco, (2012) Possibilities of Rural Electrification in the Brazilian Amazon: A multi-criteria analysis to compare the most promising technological solutions, Masters’ Thesis, Erasmus Mundus Master’s Program in Environmental Pathways for Sustainable Energy Systems (SELECT). Available at: http://www.diva-portal.org/smash/get/diva2:608450/FULLTEXT01.pdf. Rahman, M. M., Paatero, J. V., & Lahdelma, R. (2013). Evaluation of choices for sustainable rural electrification in developing countries: a multi-criteria approach. Energy Policy, 59, 589–599. doi:10. 1016/j.enpol.2013.04.017. Available at: http://www.sciencedirect.com/science/article/pii/ S0301421513002565. RGGVY. (2005) Rajeev Gandhi Grameen Vidyutikaran Yojana at a glance, Rural Electrification Corporation Ltd., Ministry of Power, Government of India. Available at: http://rggvy.gov.in/rggvy/rggvyportal/index. html Sarr, O. (2006) The Republic of Senegal Uses GIS for Rural Electrification, Agence Senegalaise D’electrification Rurale (ARES), Article posted on ESRI ArcNews. Available at: http://www.esri.com/ news/arcnews/summer02articles/republic-of.html.