Presenting a Differential Evolutionary Algorithm for Solving the Stochastic Problem of Reconfiguration of the Distribution Network and Optimal Allocation of Distributed Generation Units (Wind Turbine)

Reconfiguration of the distribution network as well as the optimal use of distributed generation resources in the distribution system are very effective methods to reduce losses and improve the voltage profile or in other words power quality in the electricity distribution system. In recent years, researchers have paid attention to the use of distributed production resources. The use of these resources has several advantages, the most important of which are the reduction of network losses and the increase of voltage stability. In this study, a differential evolutionary algorithm is presented to solve the desired optimization problem to reduce losses and bus voltage deviation. On the other hand, since the system load is always changing and is not constant, therefore, for the simulation results to be close to the real conditions of the distribution network, it is suggested in this study that the uncertainty of the consumption load should also be modeled and applied to the optimization problem. The mentioned problem has different discrete and continuous variables, which necessitates the use of algorithms that can search in discrete and continuous spaces. Therefore, to overcome this issue and apply different constraints to the problem, the differential evolutionary algorithm has been used. The mentioned method has been tested on a standard 33-bus test network and the results have been compared in three different scenarios. In the second and third scenarios, reconfiguration of the distribution network has been resolved in the absence/presence of scattered production units (wind turbines) respectively. The results of the proposed method have been compared with other references. The results show that the proposed differential evolutionary algorithm performs better than the other two algorithms and has achieved better results.