Research on solving the optimal sizing and siting of distributed generation

Abstract

Abstract. In this paper, a distributed network planning model is proposed with the goal of minimizing the sum of distributed power investment cost, network loss and interruption cost .In order to compare the performance of differential evolution algorithm (DE) and genetic algorithm (GA) in solving the optimal sizing and siting of distributed generation in distribution networks, the two algorithms were adopted to optimize the capacities and positions of DGs. Through analysis on a 10-bus test system, the study results show that the proposed model and algorithm can get reasonable planning scheme. And in solving simple optimization problems, both GA and DE Algorithms can get good results, but compare to DE, GA is of slow convergence speed and the convergence process is not quite stable. IntroductionThe optimal access problems of the distributed power, as a tool to promote the development of intelligent power grid, are gaining more and more attention from experts and scholars [1][2]. Distributed generation(DG)，a supplement to centralized generation, mainly includes wind power and solar power, small hydropower and miniature gas turbine, etc [3][4] .The combination of distributed generation and centralized generation can leverage their advantages .In fact, if DG is properly placed in a distribution network, it can reduce distributed power investments power losses and interruption cost for network enforcing. But, if it is not correctly applied, it can cause degradation of power quality and reliability , increase in system losses and costs [5] .The problem of DG sizing and siting is of great importance ,for that reason, optimization methods capable of indicating the best solution for a given distribution network have been extensively studied [6][7].So far, many methods for solving the problem of DG optimization exist in literature. Examples of those methods include Lagrange multiplier [8], genetic algorithm [9], the improved differential evolution algorithm [10], tabu search algorithm [11] and etc. Also, definitions of optimization vary among authors' opinions. The objectives can be the minimization of the system operation cost [8], maximization of the benefit/cost ratio [9], or minimization of network loss [11].Differential evolution algorithm (DE) and genetic algorithm (GA) are presented in this paper as the optimization techniques for the sizing and siting of DG in distribution network .In order to reduce cost in distribution network, distribution network planning model based on distributed power investment cost, network loss and the interruption cost is developed. Section 4 presents a brief introduction about load flow calculation issues. At last, an IEEE 10-bus application system is used to test our model, and the study results show that the proposed model and algorithms can get reasonable planning scheme, and the advantages and disadvantages of the two algorithms are discussed.