Optimization of Active Distribution Network Scheduling Based on Stackelberg Game

This chapter explores the optimization of active distribution network (ADN) scheduling using Stackelberg game theory, focusing on the interaction between the ADN and load aggregators (LA). The study addresses the challenges posed by electric vehicles (EVs) and air conditioning loads on grid stability and proposes a strategy to minimize costs and maximize revenue for both parties. The Stackelberg game framework is detailed, highlighting the leader-follower dynamics where the ADN aims for peak shaving, valley filling, and revenue maximization, while the LA seeks to minimize electricity costs. The model incorporates constraints such as energy storage, power balance, and electricity price limits. The solution process involves a differential evolution algorithm, and the effectiveness of the model is demonstrated through a case study. The results show that the Stackelberg game approach leads to lower operational costs for the LA, reduced load peak-valley differences, and increased revenue for the ADN, ultimately contributing to a more stable and economically efficient power grid.