A GENETIC ALGORITHM APPROACH FOR ENERGY PLANNING IN DECENTRALIZED AREAS

Abstract

Renewable energy can be used for meeting the basic energy needs of developing countries. With the oil crisis that took place in 1970,many nations were forced to consider means of their fulfilling energy demand in the wake of increased oil prices. The worse hit were developing countries, which have limited foreign exchange resources for obtaining oil imports. Such countries were forced to realize the necessity of searching for alternative means to fulfil their energy demands. Research has shown that one such concept which has proved practical is the use of renewable sources of energy. These are normally found abundant in nature. Most developing countries have a rich supply of such resources in. the form of solar energy, wind energy, energy from biomass and energy in the form of flowing water in rivers and streams. The rural planner developing an energy plan for a particular area is interested in obtain the best possible mix of different energy resources which will satisfy different energy need of the rural population. This is to be done with respect to the resource availability through out the year and the demand of each resource for meeting The optimization usually involves the use of Linear programming. Recently there has been much work reported in the use of the Genetic Algorithm (GA) as a minimizing tool. Different areas of application have been explored but as far as by energy planning is concerned there does not appear to be any reported work. It was therefore felt suitable to investigate an energy planning exercise for rural area using Genetic Algorithms (GA). The present work is an attempt in this direction. In the present work two objectives have been investigated namely: 1. To develop a model for minimizing supply cost using GA and compare the results with classical linear programming approach. 2. To develop a GA program for energy resource allocation planning in a remote Indian village in related to various energy and application. It is seen that the Genetic Algorithm gives results that are sufficiently accurate in comparison with those of Linear Programming The optimization exercise using the Genetic. Algorithm are carried out for a typical Indian village with hypothetical data, and the results are computed using a developed program for the same. The present work is a pioneering effort in the use of Genetic Algorithms for decentralized energy planning in rural areas of a developing country.