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

Yang Zhang, Qingyu Yang, Donghe Li, Dou An

Abstract:

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.