Optimal charge-discharge scheduling of PV-battery storage system microgrid using a hybrid optimization algorithm

Abstract

Abstract In last few years, integrating renewable energy sources (RES) with traditional energy sources became prominent applications to diversify the energy mix in the modern power grids. Nevertheless, the incorporation of RES poses additional challenges to power distribution networks because of their inherent intermittency and their randomness nature. Furthermore, maintaining grid operational efficiency during peak demand periods requires effective peak load shaving, which remains a critical challenge. A promising remedy of these challenges is to use energy storage technologies such as battery systems, green hydrogen generation, thermal storage, etc. Thus, the battery stored energy can be dispatched during periods of low renewable generation and high load demand, thereby enhancing grid reliability and efficiency. Consequently, energy storage solutions emerge as a compelling alternative to traditional, expensive grid challenges, due to their flexibility, declining costs, and rapid deployment. This paper proposes a novel hybrid algorithm for optimizing the charging and discharging schedule of a PV-battery storage system connected to microgrid. The proposed Hybrid optimization technique combines two metaheuristic algorithms, Particle swarm optimization (PSO) and gray wolf optimization (GWO) for utilizing the available PV power optimally targeting the reduction of the peak power demand. Historical data of solar PV generation unit and power demand are collected from a substation in UK. The proposed approach introduces a new optimization strategy that maximizes the use of photovoltaic energy to charge the energy storage unit which contributes to maximizing the daily peak load reduction. The simulation results conducted for two seasonal scenario days with different PV generation profiles achieve power load reductions between 21.6% and 28.1%. The results also prove that the proposed algorithm outperforms the other well-known optimization techniques introduced in the literature in terms of optimization techniques performance metrics with ranges from 1 to 3%.