Two-Timescale Voltage Regulation for Coupled Distribution and Flash-Charging-Enabled Public Transit Systems Using Deep Reinforcement Learning

doi:10.1109/TTE.2024.3519215

科研成果详情

题名	Two-Timescale Voltage Regulation for Coupled Distribution and Flash-Charging-Enabled Public Transit Systems Using Deep Reinforcement Learning
作者	Wang，Ruoheng 1; Bi，Xiaowen2 ; Bu，Siqi 3; Long，Meng 4
发表日期	2024
发表期刊	IEEE Transactions on Transportation Electrification
摘要	As one of the most promising charging solutions for electric buses (EBs), the pantograph charger features fully automated high-power charging, allowing it to cater for EB flash charging at bus stops. By “charging less but more often,” the technical viability and economic competitiveness of EBs can be greatly enhanced. However, such a charging arrangement inevitably threatens secure operation of distribution networks (DNs), which is already complicated by the growing renewables. Hence, this paper proposes a data-driven two-timescale voltage regulation method to tackle the operation challenge in coupled distribution and flash-charging-enabled public transit systems (CDFPTSs). Concretely, remotely controlled switches (RCSs) and soft open points (SOPs), both functioning like “valves” to optimize power flow distribution, are coordinated by multiple intelligent agents to essentially enhance voltage security heavily impacted by abrupt and excess EB charging demand. Due to the different operation timescales of RCSs and SOPs, a two-timescale Markov game is dedicatedly formulated to enable a model-free and decentralized control of agents for accelerating decision-making and reducing communication reliance. An action-persistence multi-agent soft actor critic (AP-MASAC) algorithm is proposed to effectively handle the hybrid action space of RCSs and SOPs, ensure their operational constraints, and more importantly, mitigate non-stationary issues appearing in two-timescale learning to further boost regulation performance. Numerical results reveal that AP-MASAC can outperform various benchmarks in voltage regulation tasks for the CDFPTS, especially in relieving voltage violations in a data-driven manner.
关键词	action persistence flash-charging electric bus power transportation nexus soft actor critic (SAC) soft open point (SOP) Two-timescale learning voltage regulation
DOI	10.1109/TTE.2024.3519215
URL	查看来源
语种	英语English
Scopus入藏号	2-s2.0-85213020580
引用统计
文献类型	期刊论文
条目标识符	https://repository.uic.edu.cn/handle/39GCC9TT/12164
专题	北师香港浸会大学
通讯作者	Bu，Siqi
作者单位	1.Department of Electrical and Electronic Engineering,The Hong Kong Polytechnic University,Kowloon,Hong Kong 2.Guangdong Provincial Key Laboratory IRADS,Department of Statistics and Data Science,BNU-HKBU United International College,Zhuhai,China 3.Department of Electrical and Electronic Engineering,Shenzhen Research Institute,Research Centre for Grid Modernization,International Centre of Urban Energy Nexus,Centre for Advances in Reliability and Safety,Research Institute for Smart Energy,Policy Research Centre for Innovation and Technology,The Hong Kong Polytechnic University,Kowloon,Hong Kong 4.National Center for Applied Mathematics,Chongqing Normal University,Chongqing,China
推荐引用方式 GB/T 7714	Wang，Ruoheng,Bi，Xiaowen,Bu，Siqiet al. Two-Timescale Voltage Regulation for Coupled Distribution and Flash-Charging-Enabled Public Transit Systems Using Deep Reinforcement Learning[J]. IEEE Transactions on Transportation Electrification, 2024.
APA	Wang，Ruoheng, Bi，Xiaowen, Bu，Siqi, & Long，Meng. (2024). Two-Timescale Voltage Regulation for Coupled Distribution and Flash-Charging-Enabled Public Transit Systems Using Deep Reinforcement Learning. IEEE Transactions on Transportation Electrification.
MLA	Wang，Ruoheng,et al."Two-Timescale Voltage Regulation for Coupled Distribution and Flash-Charging-Enabled Public Transit Systems Using Deep Reinforcement Learning". IEEE Transactions on Transportation Electrification (2024).