Three-Stage Deep Reinforcement Learning for Privacy-and Safety-Aware Smart Electric Vehicle Charging Station Scheduling and Volt/VAR Control
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Sangyoon | - |
dc.contributor.author | Choi, Dae-Hyun | - |
dc.date.accessioned | 2023-11-08T08:42:38Z | - |
dc.date.available | 2023-11-08T08:42:38Z | - |
dc.date.issued | 2024-03 | - |
dc.identifier.issn | 2327-4662 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/68553 | - |
dc.description.abstract | This paper proposes a three-stage privacy-and safety-aware deep reinforcement learning framework for coordinating smart electric vehicle charging stations (EVCSs) integrated with a photovoltaic system/energy storage system (ESS) and Volt/VAR control in a power distribution system. The proposed framework aims to maximize the EVCS profit and minimize the network real power loss while ensuring zero ESS state of charge (SOC) and voltage violation as well as preserving the privacy of the EVCS net load schedule data. In Stage 1 with 30-min resolution, each charging station operator (CSO) agent of the EVCS performs day-ahead profitable real power charging/discharging of the ESS without violating its SOC constraint via a safety layer during training. In Stage 2, using the -differential privacy method, the CSO agents encrypt the EVCS net load schedule data delivered from Stage 1. In Stage 3 with 5-min resolution, the distribution system operator agent conducts real-time reactive power charging/discharging of the ESSs to minimize the real power loss while removing voltage violations completely via iterative safe exploration of the agent with iteration penalties during training. The proposed framework was assessed on the IEEE 33-bus system for its privacy preserving and safety performances. IEEE | - |
dc.format.extent | 12 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | Three-Stage Deep Reinforcement Learning for Privacy-and Safety-Aware Smart Electric Vehicle Charging Station Scheduling and Volt/VAR Control | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/JIOT.2023.3319588 | - |
dc.identifier.bibliographicCitation | IEEE Internet of Things Journal, v.11, no.5, pp 8578 - 8589 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 001203463700086 | - |
dc.identifier.scopusid | 2-s2.0-85173024945 | - |
dc.citation.endPage | 8589 | - |
dc.citation.number | 5 | - |
dc.citation.startPage | 8578 | - |
dc.citation.title | IEEE Internet of Things Journal | - |
dc.citation.volume | 11 | - |
dc.type.docType | Article | - |
dc.publisher.location | 미국 | - |
dc.subject.keywordAuthor | Data privacy | - |
dc.subject.keywordAuthor | deep reinforcement learning | - |
dc.subject.keywordAuthor | Electric vehicle charging station | - |
dc.subject.keywordAuthor | Power distribution | - |
dc.subject.keywordAuthor | Privacy | - |
dc.subject.keywordAuthor | privacy preserving | - |
dc.subject.keywordAuthor | Reactive power | - |
dc.subject.keywordAuthor | safety | - |
dc.subject.keywordAuthor | Schedules | - |
dc.subject.keywordAuthor | Training | - |
dc.subject.keywordAuthor | Volt/VAR control | - |
dc.subject.keywordAuthor | Voltage control | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
84, Heukseok-ro, Dongjak-gu, Seoul, Republic of Korea (06974)02-820-6194
COPYRIGHT 2019 Chung-Ang University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.