Pricing Strategy for Regional Integrated Energy System Considering Privacy Based on Deep Reinforcement Learning

Xiong Wu; Bingwen Liu; Shengqi Yuan; Binrui Cao; Ziyu Zhang; Yanhong Hu

doi:10.17775/CSEEJPES.2021.08300

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Pricing Strategy for Regional Integrated Energy System Considering Privacy Based on Deep Reinforcement Learning

Regular Papers | 更新时间：2026-10-16

- Pricing Strategy for Regional Integrated Energy System Considering Privacy Based on Deep Reinforcement Learning
- Pricing Strategy for Regional Integrated Energy System Considering Privacy Based on Deep Reinforcement Learning
- 中国电机工程学会电力与能源系统学报(英文) 2025年11卷第5期页码：2399-2412
- 作者机构：
  
  School of Electrical Engineering Department, Xi'an Jiaotong University,Xi'an,China
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2021.08300
  中图分类号：
- 纸质出版：2025-09-01
- 稿件说明：
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Xiong Wu, Bingwen Liu, Shengqi Yuan, 等. Pricing Strategy for Regional Integrated Energy System Considering Privacy Based on Deep Reinforcement Learning[J]. 中国电机工程学会电力与能源系统学报(英文), 2025,11(5):2399-2412.

Xiong Wu, Bingwen Liu, Shengqi Yuan, et al. Pricing Strategy for Regional Integrated Energy System Considering Privacy Based on Deep Reinforcement Learning[J]. CSEE Journal of Power and Energy Systems, 2025, 11(5): 2399-2412.
Xiong Wu, Bingwen Liu, Shengqi Yuan, 等. Pricing Strategy for Regional Integrated Energy System Considering Privacy Based on Deep Reinforcement Learning[J]. 中国电机工程学会电力与能源系统学报(英文), 2025,11(5):2399-2412. DOI： 10.17775/CSEEJPES.2021.08300.

Xiong Wu, Bingwen Liu, Shengqi Yuan, et al. Pricing Strategy for Regional Integrated Energy System Considering Privacy Based on Deep Reinforcement Learning[J]. CSEE Journal of Power and Energy Systems, 2025, 11(5): 2399-2412. DOI： 10.17775/CSEEJPES.2021.08300.

摘要

Abstract

With deregulation of the energy market

the pricing strategy of energy sellers in a regional integrated energy system (RIES) can affect the interests of all participants in the market and the operation of the system. This paper proposes a pricing strategy for integrated energy service providers in RIES based on a deep reinforcement learning (DRL) algorithm considering privacy protection. The transaction process between the integrated energy service provider (IESP) and user aggregators (UAs) in RIES is modeled as a Stackelberg game. IESP serves as the leader in making retail prices

and different UAs serve as followers in optimizing their energy consumption strategies. Considering UAs' strategies are temporally coupled

a Markov decision process (MDP) is designed differently from existing studies. Case studies demonstrate that the proposed method is accurate and stable when solving a Stackelberg equilibrium without privacy leakage. The obtained pricing strategy avoids unreasonable pricing and guarantees the revenue of IESP and the energy demand of UAs.

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references

20. D. Cao, J. B. Zhao, W. H. Hu, F. Ding, Q. Huang, Z. Chen, and F. Blaabjerg. ( 2020, Jun. ). Model-free voltage regulation of unbalanced distribution network based on surrogate model and deep reinforcement learning . [Online ] . Available: https://arxiv.org/abs/2006.13992.

28. J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov. ( 2017, Aug. ). Proximal policy optimization algorithms. [Online ] . Available : https://arxiv.org/abs/1707.06347.

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