Optimal battery energy storage system scheduling in grid-connected photovoltaic systems based on metaheuristic algorithms

Introduction. The integration of battery energy storage systems (BESS) with photovoltaic (PV) systems has become crucial for managing renewable energy intermittency and optimizing economic benefits in modern power grids. However, the complexity of battery scheduling optimization involving multiple conflicting objectives necessitates advanced computational approaches beyond traditional optimization methods. Problem. Current battery scheduling strategies often fail to adequately balance economic optimization with battery degradation costs, leading to suboptimal performance and reduced system profitability. The challenge lies in developing robust optimization algorithms that can handle the non-linear, multimodal nature of the battery scheduling problem while considering realistic operational constraints and long-term economic viability. Goal. To evaluate and compare the performance of three metaheuristic algorithms – particle swarm optimization (PSO), modified PSO with mutation operators, and grey wolf optimizer (GWO) – for optimal battery scheduling in grid-connected PV systems, with emphasis on economic viability and comprehensive degradation cost considerations. Methodology. The study employs mathematical modeling of battery dynamics, economic objective functions incorporating degradation costs, and realistic system constraints. Three metaheuristic algorithms are implemented and tested using real PV generation and load consumption data overextended periods. Performance evaluation includes convergence analysis, economic metrics, and battery utilization patterns with detailed cost structure analysis. Results. Simulation results demonstrate that GWO achieves superior economic performance with net losses of 2.86 million INR compared to 5.96 million INR for standard PSO, representing a 52 % improvement in economic outcomes. All algorithms show satisfactory convergence properties within 50 iterations, with degradation costs representing approximately 21 % of total system costs, highlighting their critical importance in optimization decisions. Scientific novelty. The study provides the first comprehensive comparative analysis of these three metaheuristic algorithms specifically for BESS scheduling with detailed degradation cost modeling, revealing the critical importance of balanced optimization approaches that consider both short-term arbitrage benefits and long-term degradation impacts. Practical value. The research demonstrates that aggressive battery cycling strategies are not economically viable under current market conditions when degradation costs are properly accounted for, providing valuable insights for BESS deployment and operational strategies in renewable energy systems and highlighting the need for additional revenue streams for economic viability. References 23, tables 2, figures 7.