Optimum optimization of DSTATCOM distribution and optimal allocation and DGs by using innovative algorithms in fuzzy framework abstract
One of the best solutions to improve the performance and reliability of distribution networks is the use of distributed generation and reactive power compensation devices. Considering the many technical and economical benefits of distributed generation and reactive power compensators, the use of these resources in various levels of power networks has increased significantly. Studies have shown that the installation location of these resources has a significant impact on the reduction of losses, increased loading capacity and improved voltage profile of distribution networks. Given this, the location of the installation of these resources should be determined on the basis of a detailed study taking into account technical and economic criteria. Another effective tool for improving the voltage stability and reducing losses in distribution networks is optimal reconfiguration of these networks. The simultaneous use of optimal reconfiguration along with the optimal allocation of distributed generation and reactive power compensation devices will improve the many of the technical and economic index of the distribution networks. In this regard, this research propose a new model to solve the problem of network reconfiguration along with the optimal allocation of distributed generation sources and reactive power compensation devices. The model has been formulated as a multi-objective optimization aimed at minimizing the cost of investment, reducing the power loss and improving the network voltage stability index. For this purpose, the photovoltaic and
DSTATCOM have been selected as distributed generation and reactive power compensation sources, respectively. To solve this problem, the multi-objective particle swarm optimization (MOPSO) algorithm and the fuzzy criterion are used to select the best response from the optimal Pareto front. The performance of the proposed model is evaluated by applying it to the IEEE 33 Bus system. Simulation results indicate that optimal reconfiguration along with optimal allocation of energy sources and reactive power supplies will significantly improve voltage stability and reduce power losses.