Optimized Scheduling of Heating System Electrical Load Using an Improved Particle Swarm Optimization Algorithm Considering Power Quality

This paper proposes an optimized scheduling strategy for renewable energy-based heating in Inner Mongolia, aimed at improving economic feasibility and meeting heating load demand. The proposed strategy aims to meet electricity supply requirements while maximizing economic benefits and minimizing environmental impact. First, a control strategy for the renewable energy heating system is developed, and a wind-solar-storage hybrid system model is constructed. Next, considering the volatility and uncertainty of wind and solar power, a power system simulation is conducted using the IEEE 33-node distribution network, incorporating heating load demand. The power variation data of each device, ensuring compliance with the voltage requirements of the distribution network, is used as the initial input for the optimization algorithm. Lastly, a multi-objective optimization scheduling model for grid-connected microgrids is proposed, integrating operational and environmental costs, and solved using an improved particle swarm optimization algorithm.