Incentive-based demand response for smart grid with reinforcement learning and deep neural network
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lu, Renzhi | - |
dc.contributor.author | Hong, Seung Ho | - |
dc.date.accessioned | 2021-06-22T10:22:30Z | - |
dc.date.available | 2021-06-22T10:22:30Z | - |
dc.date.created | 2021-01-21 | - |
dc.date.issued | 2019-02 | - |
dc.identifier.issn | 0306-2619 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/3489 | - |
dc.description.abstract | Balancing electricity generation and consumption is essential for smoothing the power grids. Any mismatch between energy supply and demand would increase costs to both the service provider and customers and may even cripple the entire grid. This paper proposes a novel real-time incentive-based demand response algorithm for smart grid systems with reinforcement learning and deep neural network, aiming to help the service provider to purchase energy resources from its subscribed customers to balance energy fluctuations and enhance grid reliability. In particular, to overcome the future uncertainties, deep neural network is used to predict the unknown prices and energy demands. After that, reinforcement learning is adopted to obtain the optimal incentive rates for different customers considering the profits of both service provider and customers. Simulation results show that this proposed incentive-based demand response algorithm induces demand side participation, promotes service provider and customers profitabilities, and improves system reliability by balancing energy resources, which can be regarded as a win-win strategy for both service provider and customers. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER SCI LTD | - |
dc.title | Incentive-based demand response for smart grid with reinforcement learning and deep neural network | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Hong, Seung Ho | - |
dc.identifier.doi | 10.1016/j.apenergy.2018.12.061 | - |
dc.identifier.scopusid | 2-s2.0-85058442884 | - |
dc.identifier.wosid | 000458712500073 | - |
dc.identifier.bibliographicCitation | APPLIED ENERGY, v.236, pp.937 - 949 | - |
dc.relation.isPartOf | APPLIED ENERGY | - |
dc.citation.title | APPLIED ENERGY | - |
dc.citation.volume | 236 | - |
dc.citation.startPage | 937 | - |
dc.citation.endPage | 949 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Energy & Fuels | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Energy & Fuels | - |
dc.relation.journalWebOfScienceCategory | Engineering, Chemical | - |
dc.subject.keywordPlus | ENERGY MANAGEMENT | - |
dc.subject.keywordPlus | ELECTRICITY PRICE | - |
dc.subject.keywordPlus | POWER MANAGEMENT | - |
dc.subject.keywordPlus | MODEL | - |
dc.subject.keywordPlus | MICROGRIDS | - |
dc.subject.keywordPlus | ALGORITHM | - |
dc.subject.keywordPlus | SYSTEM | - |
dc.subject.keywordPlus | MARKET | - |
dc.subject.keywordAuthor | Artificial intelligence | - |
dc.subject.keywordAuthor | Reinforcement learning | - |
dc.subject.keywordAuthor | Deep neural network | - |
dc.subject.keywordAuthor | Incentive-based demand response | - |
dc.subject.keywordAuthor | Smart grid | - |
dc.identifier.url | https://www.sciencedirect.com/science/article/pii/S0306261918318798?via%3Dihub | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
55 Hanyangdeahak-ro, Sangnok-gu, Ansan, Gyeonggi-do, 15588, Korea+82-31-400-4269 sweetbrain@hanyang.ac.kr
COPYRIGHT © 2021 HANYANG 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.