Bi-Objective Optimization Of Location And Capacity Of Distributed Generation

The quality, reliability and security of electricity supply have become increasingly demanding in modern society. Because the shortcomings of large power systems can not meet such request, distributed generation(DG) holds more and more shares in power system, thus it has become a new trend in power source. And more and more attention has been paid to DG all over the world. Combination of large power systems with DG can improve the economical and security of the system.The conception of DG is introduced in this dissertation, and the influences of the operation of distribution system are also analyzed in detail. It discusses and concludes the DG influence on power loss and voltage profile considering three related factors: the interconnected location, injected capacity and power factor. The regulations show that the rational planning scheme for DG in the distribution network is of great significance for bringing the benefits of DG into full play and restraining the negative effects brought by DG. A model of bi-objective optimal allocation is established, where the active power loss minimization and index of static voltage stability minimization are taken into account. Based on the factor of self-adaptive weight and self-adaptive penalty function, a self-adaptive genetic algorithm is proposed, which makes the bi-objective optimal allocation of DG model transform into a single objective model. By using of the proposed method the multidirectional property of searching can be ensured and the defect of fuzzy membership algorithm, namely the overlong computing time, can be avoided. During searching process the self-adaptive penalty function can effectively utilize the available information in infeasible solution and appropriately punish the infeasible solution.The proposed method is simulated and analyzed by using the 33 nodes distribution system. The simulation results show that the proposed algorithm is an effective method for bi-objective optimal allocation. The solutions can be obtained with the method, and the model is able to enhance power system voltage stability during the economical operation. The model and method provide decision-makers with more flexible configuration schemes of DG, and have practical significance.