Preference based multi-objective reinforcement learning for multi-microgrid system optimization problem in smart grid
dc.contributor.author | Xu, J | |
dc.contributor.author | Li, K | |
dc.contributor.author | Abusara, M | |
dc.date.accessioned | 2022-01-05T14:21:06Z | |
dc.date.issued | 2022-02-22 | |
dc.date.updated | 2022-01-05T13:44:17Z | |
dc.description.abstract | Grid-connected microgrids comprising renewable energy, energy storage systems and local load, play a vital role in decreasing the energy consumption of fossil diesel and greenhouse gas emissions. A distribution power network connecting several microgrids can promote more potent and reliable operations to enhance the security and privacy of the power system. However, the operation control for a multi-microgrid system is a big challenge. To design a multi-microgrid power system, an intelligent multi-microgrids energy management method is proposed based on the preference-based multi-objective reinforcement learning (PMORL) techniques. The power system model can be divided into three layers: the consumer layer, the independent system operator (ISO) layer, and the power grid layer. Each layer intends to maximize its benefit. The PMORL is proposed to lead to a Pareto optimal set for each object to achieve these objectives. A non-dominated solution is decided to execute a balanced plan not to favor any particular participant. The preference-based results show that the proposed method can effectively learn different preferences. The simulation outcomes confirm the performance of the PMORL and verify the viability of the proposed method. | en_GB |
dc.description.sponsorship | European Commission | en_GB |
dc.description.sponsorship | UKRI | en_GB |
dc.description.sponsorship | Engineering and Physical Sciences Research Council (EPSRC) | en_GB |
dc.description.sponsorship | National Natural Science Foundation of China | en_GB |
dc.description.sponsorship | Royal Society | en_GB |
dc.identifier.citation | Published online 22 February 2022 | en_GB |
dc.identifier.doi | 10.1007/s12293-022-00357-w | |
dc.identifier.grantnumber | GP ENF5.10 | en_GB |
dc.identifier.grantnumber | MR/S017062/1 | en_GB |
dc.identifier.grantnumber | 2404317 | en_GB |
dc.identifier.grantnumber | 62076056 | en_GB |
dc.identifier.grantnumber | IES212077 | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/128296 | |
dc.identifier | ORCID: 0000-0002-4195-5079 (Abusara, Mohammad) | |
dc.language.iso | en | en_GB |
dc.publisher | Springer | en_GB |
dc.rights | © The Author(s) 2022. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. | |
dc.subject | Multi-Microgrid | en_GB |
dc.subject | preference model | en_GB |
dc.subject | multiobjective reinforcement learning | en_GB |
dc.subject | independent system operator | en_GB |
dc.subject | market operator | en_GB |
dc.subject | Pareto optimal | en_GB |
dc.title | Preference based multi-objective reinforcement learning for multi-microgrid system optimization problem in smart grid | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2022-01-05T14:21:06Z | |
dc.identifier.issn | 1865-9284 | |
dc.description | This is the final version. Available on open access from Springer via the DOI in this record | en_GB |
dc.identifier.eissn | 1865-9292 | |
dc.identifier.journal | Memetic Computing | en_GB |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_GB |
dcterms.dateAccepted | 2022-01-04 | |
rioxxterms.version | VoR | en_GB |
rioxxterms.licenseref.startdate | 2022-01-04 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2022-01-05T13:44:19Z | |
refterms.versionFCD | AM | |
refterms.dateFOA | 2022-03-02T12:51:20Z | |
refterms.panel | B | en_GB |
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Except where otherwise noted, this item's licence is described as © The Author(s) 2022. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.