Optimal allocation of photovoltaic/wind energy system in distribution network using meta-heuristic algorithm

Abstract

In this paper, allocation of hybrid photovoltaic panels, wind turbines and battery storage (PV/WT/BA) system in distribution network is presented aimed active losses cost minimization, voltage profile enhancement and minimizing power purchased from the hybrid system by the network. Meta-heuristic improved whale optimizer algorithm (IWOA) is used to determine the optimal location and size of the PV/WT/BA system components as decision variables. The conventional WOA is inspired by social behavior and the hunting of humpback whales and in this study its performance is improved by using crossover and mutation operators of differential evolution (DE) method to avoid getting caught in local optimal and reinforcement to achieve global optimal. The methodology is implemented on IEEE 33 bus network considering seasonal variations. The results indicated that optimal determination of the decision variables in the network minimizes the active losses cost, voltage deviations and cost of power purchased from the hybrid system by the network using IWOA. The superiority of the IWOA is confirmed in benchmark test functions solution with very competitive results and also better results in statistic analysis and higher convergence speed and accuracy in comparison with the WOA, DE and particle swarm optimization (PSO). Also the results showed that the highest losses and better voltage are related to the summer and the lowest values are obtained in autumn season. The solar panels only participate in energy generation in spring and summer seasons, cost of power purchased by the network is highest in summer and lowest in autumn season. Moreover, the network is placed in stable current and voltage condition and the network power losses and voltage deviations are minimized.