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Decision Optimization Model of Incentive Demand Response Based on Deep Reinforcement Learning
Author:
Affiliation:

1.NARI Group Corporation (State Grid Electric Power Research institute), Nanjing 211106, China;2.State Key Laboratory of Smart Grid Protection and Control, Nanjing 211106, China

Abstract:

With the advancement of the electricity market reform in China, electricity side market is gradually opening up. The electricity retailers can aggregate a large number of distributed loads to participate in the demand response in the electricity market environment. In this paper, the modeling and solving methods of incentive demand response based on deep reinforcement learning are proposed to maximize the comprehensive profits of both retailers and customers. Firstly, the demand response models for retailers and customers are established. By introducing the time-price elasticity, the recent customer response model is improved, which takes the customer response to the subsidy price difference between adjacent periods into account. Then, based on the Markov decision process framework, an optimization model of subsidy price decision is constructed, and a solution algorithm based on deep Q-learning network is designed. Finally, simulation calculation is performed using one retailer and three different types of customers as examples. By analyzing the convergence of the algorithm and comparing the optimization results of different models and parameters, the rationality of the improved model and the effectiveness of the generated strategy are verified, and the impact of incentive demand response on the retailer and customers is analyzed.

Keywords:

Foundation:

This work is supported by National Key R&D Program of China (No. 2018AAA0101504) and State Grid Corporation of China (No. 5700-202019364A-0-0-00).

Get Citation
[1]XU Hongsheng, LU Jixiang, YANG Zhihong, et al. Decision Optimization Model of Incentive Demand Response Based on Deep Reinforcement Learning[J]. Automation of Electric Power Systems,2021,45(14):97-103. DOI:10.7500/AEPS20200208001
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History
  • Received:February 08,2020
  • Revised:January 19,2021
  • Adopted:
  • Online: July 21,2021
  • Published: