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The rapid growth of integrating electrical vehicles (EVs) into the distribution network has introduced complexities and power flow inefficiencies. To address these challenges, optimal renewable energy resources (RERs) integration along with applied demand-side management (DSM) contribute to managing load profiles and generation thus reducing costs. This should be smartly attained through selecting efficient optimization techniques to improve power quality, voltage profile, and reliability. This paper aims to investigate the effect of integrating EVs and applying peak load shifting (PLS) as a DSM strategy with the optimal allocation of distributed energy resources, specifically wind and photovoltaic (PV) systems, as distributed generators (DGs) on distribution networks. Taking into consideration the stochastic behavior of RERs, EVs demand elasticity of charging and discharging scenarios and load variance. The main objective of this work focuses on power loss reduction and implementing PLS to flatten the load profile and form a new loadability to reduce costs. The study is demonstrated on a typical IEEE 69-bus system, considering the load, EVs, and RERs profiles during weekdays in winter and summer seasons. The study examines the optimal size and location of combining two DGs (wind and PV), in addition to incorporating bidirectional plug-in hybrid electric vehicles into the system. The study utilizes the Zebra optimization algorithm (ZOA), in comparison with the Whale optimization algorithm (WOA), Grey wolf optimization algorithm (GWO), and Genetic algorithm (GA). The latter is employed only as a reference for comparison. For each season, the simulation is divided into two parts, each part consists of four cases. Part (1) is simulated assuming constant power integration for the RERs while part (2) considers their stochastic behavior. Also, optimal charging strategies for EVs are examined for cost-effectiveness during high penetration levels for the IEEE 123-bus system. The results demonstrated the effectiveness of the proposed algorithm in reducing power loss. Moreover, shifting peak hours flattens the load profile, thereby reducing costs and power loss across the distribution network. Furthermore, the performance of the ZOA dominates the WOA, GWO, and GA.
Eissa et al. (Tue,) studied this question.