Incentive-based demand response under incomplete information based on the deep deterministic policy gradient

Siyu Ma, Hui Liu, Ni Wang, Lidong Huang and Hui Hwang Goh

Applied Energy, 2023, vol. 351, issue C, No S0306261923012023

Abstract: Incentive-based demand response (IBDR), as an important measure to encourage the users to participate in the demand-side management, is commonly modeled as the Stackelberg game with the complete information. However, it is difficult to acquire the users' complete information due to privacy protections. In this paper, a Markov decision process (MDP) game model is proposed to address IBDR under the incomplete information, which is based on a deep deterministic policy gradient algorithm (DDPG). Considering differences on the users' load, the K-means method is used to classify different users according to the daily load rate and the peak-to-valley difference, such that different types of user load will have different incentive prices to participate in demand responses. The proposed DDPG algorithm can improve the calculation efficiency of the Nash equilibrium solution of the IBDR under the incomplete information, as it can deal with the multi-dimensional continuous state and action spaces. Simulation results show that the proposed approach can achieve the Nash equilibrium under the incomplete information and has the higher calculation accuracy and the lower calculation time in comparison to the Q learning method.

Keywords: Deep deterministic policy gradient (DDPG); Deep reinforcement learning; K-means; Incentive-based demand response; Incomplete information; Stackelberg game (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (5)

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DOI: 10.1016/j.apenergy.2023.121838

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