Research on Ordered Power Usage Strategy for Decentralized Electric Heating Considering User Behavior Differences

The large-scale integration of electric heating devices under the “coal-to-electricity” policy can cause peak loads, affecting grid stability and power quality. To address this, this paper proposes a leader-follower game optimization for a “Photovoltaic + Thermal Storage Electric Heating” system, led by a Virtual Power Plant (VPP) and considering user behavior. Using a building thermal load model and K-Means clustering, users are grouped for better management. The bilevel model optimizes VPP pricing and user electricity plans, which is then solved as a Mixed-Integer Linear Programming (MILP) problem. Case studies show that the optimized pricing strategy reduces user costs, increases VPP profits, and improves photovoltaic utilization while ensuring orderly electricity use.