Neural Network Based Fea sible Region Approximation Model for Optimal Operation of Integrated Electricity and Heating System

IF 6.9 2区 工程技术 Q2 ENERGY & FUELS
Xuewei Wu;Bin Zhang;Mads Pagh Nielsen;Zhe Chen
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引用次数: 0

Abstract

This paper proposes a neural network based feasible region approximation model of a district heating system (DHS), and it is intended to be used for optimal operation of integrated electricity and heating system (IEHS) considering privacy protection. In this model, a neural network is trained to approximate the feasible region of the DHS operation and then is reformulated as a set of mixed-integer linear constraints. Based on the received approximation models of DHSs and detailed electricity system model, the electricity operator conducts centralized optimization, and then sends specific heating generation plans back to corresponding heating operators. Furthermore, subsequent optimization is formulated for each DHS to obtain detailed operation strategy based on received heating generation plan. In this scheme, optimization of the IEHS could be achieved and privacy protection requirement is satisfied since the feasible region approximation model does not contain detailed system parameters. Case studies conducted on a small-scale system demonstrate accuracy of the proposed strategy and a large-scale system verify its application possibility.
基于神经网络的电热一体化系统优化运行的可能区域近似模型
本文提出了一种基于神经网络的区域供热系统可行区域近似模型,旨在用于考虑隐私保护的综合供热系统的优化运行。在该模型中,训练神经网络来近似DHS操作的可行区域,然后将其重新表述为一组混合整数线性约束。电力运营商根据收到的DHS近似模型和详细的电力系统模型进行集中优化,然后将具体的供热计划发送回相应的供热运营商。此外,根据接收到的供热计划,为每个DHS制定后续优化,以获得详细的运行策略。在该方案中,由于可行区域近似模型不包含详细的系统参数,因此可以实现IEHS的优化,并满足隐私保护要求。在一个小规模系统上进行的案例研究证明了所提出的策略的准确性,并在一个大型系统上验证了其应用的可能性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
11.80
自引率
12.70%
发文量
389
审稿时长
26 weeks
期刊介绍: The CSEE Journal of Power and Energy Systems (JPES) is an international bimonthly journal published by the Chinese Society for Electrical Engineering (CSEE) in collaboration with CEPRI (China Electric Power Research Institute) and IEEE (The Institute of Electrical and Electronics Engineers) Inc. Indexed by SCI, Scopus, INSPEC, CSAD (Chinese Science Abstracts Database), DOAJ, and ProQuest, it serves as a platform for reporting cutting-edge theories, methods, technologies, and applications shaping the development of power systems in energy transition. The journal offers authors an international platform to enhance the reach and impact of their contributions.
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