Energy Management Strategy of Integrated Electricity-Heat Energy System Based on Federated Reinforcement Learning

被引：0

作者：

Wang J. ^{[1
]}

Wang Q. ^{[2
]}

Ren Z. ^{[1
]}

Sun X. ^{[1
]}

Sun Y. ^{[2
]}

Zhao Y. ^{[2
]}

机构：

[1] Economic Research Institute, State Grid Shaanxi Electric Power Company, Xi'an

[2] School of Electrical and Electronic Engineering, North China Electric Power University, Beijing

来源：

Shanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University | 2024年 / 58卷 / 06期

关键词：

deep deterministic policy gradient (DDPG); energy management; federated learning; integrated energy system (IES);

D O I：

10.16183/j.cnki.jsjtu.2022.418

中图分类号：

学科分类号：

摘要：

The energy management of the electricity-heating integrated energy system (IES) is related to the economic benefits and multi-energy complementary capabilities of a park, but it faces the challenges of the randomness of renewable energy and the uncertainty of load. First, in this paper, a mathematical model of the energy management problem for the electricity-heating IES is conducted, and each energy supply subsystem is empowered as an agent. Based on the deep deterministic policy gradient (DDPG) algorithm, a system energy management model is established that comprehensively considers the real-time energy load of the subsystem, the time-of-use pricing, and the output of each equipment. Then, the federated learning technology is used to interact with the gradient parameters of the energy management model of the three subsystems during the training process to synergistically optimize the training effect of the model, which can protect the data privacy of each subsystem while breaking the data barriers. Finally, an example analysis verifies that the proposed federated-DDPG energy management model can effectively improve the economic benefits of the park-level IES. © 2024 Shanghai Jiaotong University. All rights reserved.

引用

页码：904 / 915

页数：11

共 29 条

[1] SU Hulling, YANG Shihai, CHEN Mingming, Multi-objective optimal scheduling of integrated energy system considering energy efficiency, Proceedings of the CSU-EPSA, 34, 2, pp. 130-136, (2022)
[2] DONG Wenjie, TIAN Kuo, CHEN Yunfei, Et al., Evaluation method of comprehensive energy system based on game theory &. evidence theory under energy InternetCJ], Smart Power, 48, 7, pp. 73-80, (2020)
[3] ZHU Zhifang, XU Yuan, CEN Haifeng, Et al., Optimal configuration of park-level integrated energy system considering demand response, Smart Power, 50, 1, pp. 37-44, (2022)
[4] LI Chiyu, GAO Hongjun, LIU Youbo, Et al., Optimal sharing operation strategy for multi park-level microgrid, Electric Power Automation Equipment, 40, 3, pp. 29-36, (2020)
[5] XIONG Luolm, MAO Shuai, TANG Yang, Et al., Reinforcement learning based integrated energy system management: A survey, Acta Automatica Sin-ica, 47, 10, pp. 2321-2340, (2021)
[6] CARLI R, DOTOLI M., Decentralized control for residential energy management of a smart users mi-crogrid with renewable energy exchange, CAA Journal of Automatica Sinica, 6, 3, pp. 641-656, (2019)
[7] FARROKHIFAR M, AGHDAM F H, ALAHYARI A, Optimal energy management and sizing of renewable energy and battery systems in residential sectors via a stochastic MILP model, Electric Power Systems Research, 187, (2020)
[8] ALIPOUR M, ZARE K, ABAPOUR M., MINLP probabilistic scheduling model for demand response programs integrated energy hubs, IEEE Transactions on Industrial Informatics, 14, 1, pp. 79-88, (2018)
[9] MOSER A, MUSCHICK D, GOLLES M, Et al., A MILP-based modular energy management system for urban multi-energy systems: Performance and sensitivity analysis, Applied Energy, 261, (2020)
[10] YANG Jiahao, Calculation of cold-heat-electricity-gas probabilistic multi-energy flow in regional comprehensive energy system, Power System Technology, 43, 1, pp. 74-82, (2019)

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