Distributed power generation planning for distribution network using chimp optimization algorithm in order to reliability improvement

The most important challenges facing system designers in radial distribution networks include low voltage profiles, high current values, very high power losses and voltage drops. Therefore, the use of Distributed Generations (DGs) improves the technical and economic performance of distribution systems. In this article, in order to identify the optimal location of DGs, increase voltage stability and improve reliability in 119-bus distribution network, a comparison is made of the results of Chimp Optimization Algorithm (ChOA) and other algorithms. The objective functions implemented in ChOA include the minimization of investment and operation costs to avoid economic losses and the minimization of Energy Not-Supplied (ENS) to prevent network blackouts. The ChOA method performs better than algorithms such as the Whale Optimization Algorithm (WOA), the Gray Wolf Optimization (GWO) Algorithm, Social Spider Algorithm (SSA), and Imperialist Competitive Algorithm (ICA). The simulation results show that by considering DGs using ChOA in 119-bus distribution networks, the value of reliability improvement is reduced by 61.70%. The results show that the value of ENS using ChOA compared to WOA, GWO, SSA and ICA algorithms is improved by 2.60%, 4.63%, 6.37% and 9.67%, respectively. The proposed model shows that reliability improvement is better than other algorithms in distribution networks.