Coordinated Optimization Strategy for Load Aggregator in Distribution Network Considering Demand Response and Peak Regulation Incentive

Abstract

With the increasing proportion of flexible resources such as distributed generation, energy storage and demand response in new power systems, load aggregators, as an important subject connecting users and the market, face the challenges of complex load types, large differences in response capabilities, and high peaking costs. Therefore, this paper proposes a coordinated optimization strategy of load and electricity consumption considering aggregator load demand and peak load regulation incentive. Firstly, based on the operating characteristics of load equipment in multiple scenarios, the aggregator load is divided into three categories: energy storage type, elastic electrical equipment and inelastic electrical equipment, and the corresponding electricity cost model is established. Combined with utility theory and user subjective perception, a residential and industrial electricity comfort model is constructed. Secondly, a price-elasticity-based load potential assessment method is developed to quantify response envelopes under time-of-use tariffs, and a bi-level optimization model considering peak load regulation incentives is constructed. Next, in order to solve the bi-level non-convex optimization problem efficiently, a reinforcement learning solution framework based on a multi-agent deep deterministic policy gradient is proposed. The aggregator and user groups are modeled as collaborative agents respectively, and the global optimal strategy is realized by centralized training and decentralized execution. Finally, simulation results show that the proposed strategy can effectively guide the load from the peak period to the trough period, significantly improve the peak load shifting effect, improve the users’ electricity satisfaction, and reduce demand-side response cost.