Optimizing integrated energy systems using a hybrid approach blending grey wolf optimization with local search heuristics

Abstract

Integrated energy systems play a crucial role in the transition to sustainable energy solutions, requiring a delicate balance between economic efficiency and reliability. Energy storage and demand response are pivotal components for managing power fluctuations and optimizing consumption. This paper introduces a hybrid approach that combines Grey Wolf Optimization (GWO) with Local Search Heuristics (GWOLSH) to achieve optimal energy system configurations. The approach formulates a hybrid energy storage capacity allocation method that considers the needs of wind power companies and users. It begins by constructing load-shifting and load-reduction models based on flexible user-side load response characteristics. In a comparative analysis, GWOLSH exhibits superior performance over WSO and PSO, evidenced by achieving a lower cost reduction of 330,595 USD (GWOLSH) compared to 344,974 USD (WSO) and 350,694 USD (PSO), along with higher percentage improvements in system stability (0.38 % for GWOLSH, 0.39 % for GWO, and 0.40 % for Particle swarm optimization (PSO)) and greater user satisfaction levels (0.8985 for WSOLSH, 0.8930 for GWO, and 0.8928 for PSO). This hybrid configuration proves effective in enhancing both economic efficiency and system reliability, offering a promising solution for maintaining a consistent power supply amid fluctuations, identifying energy system configurations that enhance economic efficiency while bolstering system reliability to ensure a consistent power supply amid fluctuations.