Day-Ahead Optimal Scheduling of Multi-Energy System Considering Hybrid Pumped-Battery Storage

Abstract

Multi-energy complementary systems are a promising solution for the integration of diverse energy sources, serving as a vital means to embrace renewable energy. Nevertheless, its intricate internal energy composition within it present significant hurdles when aiming for optimal optimization. Given the natural complementary characteristics of different sources, wind power, solar power, hydropower, lithium battery storage and pumped storage are taken as an essential component of a complementary power generation system. Using the aforementioned framework, a day-ahead optimization model is established, considering various power constraints. This work core aim is to diminish the output power fluctuations of the complementary system, then the optimal model is subsequently solved by the particle swarm intelligent optimization algorithm. Simulation results demonstrate that the proposed model effectively tracks the desired objectives, mitigates the impact of variable wind and solar power on the electrical power system, boosts the usage of renewable energy, reduces curtailment of wind and solar power, and offers significant environmental benefits.