Integrated Electricity-Gas System Optimal Dispatch Based on Deep Reinforcement Learning

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Detail(s)

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE Sustainable Power and Energy Conference
Subtitle of host publicationEnergy Transition for Carbon Neutrality, iSPEC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1082-1086
ISBN (Electronic)9781665414395
ISBN (Print)9781665414401
Publication statusPublished - Dec 2021

Publication series

NameProceedings - IEEE Sustainable Power and Energy Conference: Energy Transition for Carbon Neutrality, iSPEC

Conference

Title2021 IEEE Sustainable Power and Energy Conference (iSPEC 2021)
PlaceChina
CityNanjing
Period22 - 24 December 2021

Abstract

Optimal dispatch plays a significant role in the efficient work of Integrated Electricity-Gas System (IEGS). This paper employs the Deep Reinforcement Learning (DRL) method to solve the IEGS optimal dispatch problem to improve the efficiency of decision-making. Mathematical modeling for the power network, gas network, and coupling equipment in the IEGS is first conducted. The objective function and constraints of the IEGS optimal dispatch model considering the economy and low carbon emission are proposed. Then, the Soft Actor-Critic algorithm (SAC) is selected to solve the optimal dispatch problem. A case on a modified IEGS combining IEEE 9-node with Gas 9-node is introduced to verify the effectiveness of DRL on the optimal dispatch problem.

Research Area(s)

  • deep reinforcement learning, IEGS, optimal dispatch, Soft Actor-Critic Algorithm

Citation Format(s)

Integrated Electricity-Gas System Optimal Dispatch Based on Deep Reinforcement Learning. / Teng, Xinyuan; Long, Huan; Yang, Luoxiao.

Proceedings - 2021 IEEE Sustainable Power and Energy Conference: Energy Transition for Carbon Neutrality, iSPEC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 1082-1086 (Proceedings - IEEE Sustainable Power and Energy Conference: Energy Transition for Carbon Neutrality, iSPEC).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review