A reinforcement and imitation learning method for pricing strategy of electricity retailer with customers’ flexibility

The effective pricing of retail broker in competitive electricity market constitutes a key problem toward four goals: (1) the maximization of the broker's economic benefits; (2) the balance between customers’ energy supply and demand; (3) the realization of the energy supply and demand flexibility potential of customers; (4) the constraint that prevents the retail prices from too high or too low. Unfortunately, few studies can achieve four goals simultaneously. Moreover, the complicated electricity trading environment with continuous states and actions also increases the difficulty of learning optimal pricing strategy. To solve these problems, a reinforcement and imitation learning approach is proposed to develop the optimal pricing strategy of retail broker in this paper. Specifically, the proposed approach consists of a demand prediction method to predict customers’ energy demand and supply volume, a self-generated expert knowledge imitation learning mechanism to instruct the agent to imitate given expert policy with generated expert knowledge, and an action policy learning method. Different from existing studies, our approach achieves all four goals and exploits the generated transition tuples fully to learn a more effective pricing strategy. The proposed scheme is verified by experiments using real-world market data, the experimental results illustrate our proposed approach gains 9.71%, 3.32%, and 15.94% higher economic profits than three state-of-the-art pricing strategies, respectively. Meanwhile, the total needed computation time for our method to learn an effectiveness pricing strategy is only 4102 s. The results show that our method gains the highest economic profits for the broker with acceptable computation cost. Moreover, the changing curves of customers’ consumption/production habits demonstrate that the proposed method could achieve demand/supply response of customers.