Metaheuristic Algorithm‐Based Optimal Energy Operation Scheduling and Energy System Sizing Scheme for PV‐ESS Integrated Systems in South Korea

Abstract

To efficiently utilize the power generated by a photovoltaic (PV) system, integrating it with an energy storage system (ESS) is essential. Furthermore, maximizing the economic benefits of such PV‐ESS integrated systems requires selecting the optimal capacity and performing optimal energy operation scheduling. Although many studies rely on rule‐based energy operation scheduling, these methods prove inadequate for complex real‐world scenarios. Moreover, they often focus solely on determining the ESS capacity to integrate into existing PV systems, thereby limiting the possibility of achieving optimal economic benefits. To address this issue, we propose an optimal energy operation scheduling and system sizing scheme for a PV‐ESS integrated system based on metaheuristic algorithms. The proposed scheme employs a zero‐shot PV power forecasting model to estimate the potential power generation from a planned PV system. A systematic analysis of the installation, operation, and maintenance costs is then incorporated into the economic analysis. We conducted extensive experiments for comparing economic benefits of various scheduling methods and capacities using real electrical load data collected from a private university in South Korea and estimated PV power data. According to the results, the most effective metaheuristic algorithm for scheduling is simulated annealing (SA). Additionally, the optimal PV system, battery, and power conversion system capacities for the university are 13,000 kW each, 10% of the PV system capacity, and 60% of the battery capacity, respectively. The estimated annual electricity tariff calculated from the data used in the experiment is $3,315,484. In contrast, SA‐based scheduling in the optimal PV‐ESS integrated system achieved annual economic benefits of $875,000, an improvement of approximately 7% over rule‐based scheduling of $817,730.