An Optimal Deployment of Fuel Cells in Distribution Systems by Means of A Genetic Algorithm

Technological problems of fuel cell generation systems have been overcome mostly; if fuel cell manufactures succeed in reducing the production cost, a number of fuel cells will be installed in distribution systems. Therefore, it is important to prepare a policy to evaluate the merit and demerit of fuel cell installations beforehand. In our previous study, we have proposed a framework to optimally introduce fuel cells into distribution systems. According to this framework, an optimal deployment planning of fuel cells has been formulated as a mixed integer programming problem and the authors have developed a solution algorithm based on the Branch and Bound method. For large scale systems, however, a combinatorial explosion makes the algorithm unable to search the optimal solution within a reasonable computation time. In this paper, a solution method using the genetic algorithm technique is proposed to find suboptimal solutions for large scale problems. Genetic algorithm is belonging to multi-agent search algorithms and shows a robust characteristic against a multi-modal objective function like this problem. The proposed algorithm exploits two kinds of techniques to improve its performance: The first is the diploid population and the second is the efficient initialization. The former contributes the preservation of genetic variety and the latter accelerates the speed of convergence. The proposed algorithm has been applied to various example systems and compared with a simulated annealing method to evaluate their performances with successful results.