Scheduling optimization of park integrated energy system with a flywheel-based hybrid energy storage system and thermal power deep peak shaving

Abstract

As the penetration of renewable energy continues to rise in global power systems, energy storage technologies offer significant advantages in addressing the volatility of renewable energy and enhancing the operational stability of power systems. However, current approaches to utilizing energy storage as a flexibility resource often overlook the coordinated application of multiple energy storage systems for peak shaving and frequency regulation, as well as effective optimization scheduling across various energy forms. Therefore, this study introduces a flywheel-based hybrid energy storage system within PIES, coupling it with flexible thermal power to ensure stable system operation. Subsequently, by treating compensation revenue as opportunity cost, and a dual-objective economic and environmental function is formulated to achieve coordinated optimization scheduling of multiple energy forms within the PIES. After that, an improved multi-objective particle swarm optimization algorithm is then introduced to solve the complex multi-objective problems of the system. Finally, simulations are conducted in the actual system of three seasons and four Cases in northern China. The results indicate that this model and method can achieve resource complementarity among various energy forms in PIES, reduce reliance on external markets, and realize cost savings through more efficient energy management while ensuring the reliability of the system in responding to fluctuations.