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dc.contributor.authorXu, J
dc.contributor.authorLi, K
dc.contributor.authorAbusara, M
dc.date.accessioned2021-09-17T12:05:47Z
dc.date.issued2021-03-24
dc.description.abstractMicrogrids with energy storage systems and distributed renewable energy sources play a crucial role in reducing the consumption from traditional power sources and the emission of CO2. Connecting multi microgrid to a distribution power grid can facilitate a more robust and reliable operation to increase the security and privacy of the system. The proposed model consists of three layers, smart grid layer, independent system operator (ISO) layer and power grid layer. Each layer aims to maximise its benefit. To achieve these objectives, an intelligent multi-microgrid energy management method is proposed based on the multi-objective reinforcement learning (MORL) techniques, leading to a Pareto optimal set. A non-dominated solution is selected to implement a fair design in order not to favour any particular participant. The simulation results demonstrate the performance of the MORL and verify the viability of the proposed approach.en_GB
dc.description.sponsorshipEuropean Commissionen_GB
dc.identifier.citationVol. 12654, pp. 684 - 696en_GB
dc.identifier.doi10.1007/978-3-030-72062-9_54
dc.identifier.urihttp://hdl.handle.net/10871/127102
dc.language.isoenen_GB
dc.publisherSpringer Verlagen_GB
dc.rights© Springer Nature Switzerland AG 2021en_GB
dc.subjectMulti-microgriden_GB
dc.subjectMulti-objective reinforcement learningen_GB
dc.subjectIndependent system operatoren_GB
dc.subjectMarket operatoren_GB
dc.subjectPareto Fronten_GB
dc.titleMulti-objective Reinforcement Learning Based Multi-microgrid System Optimisation Problemen_GB
dc.typeConference paperen_GB
dc.date.available2021-09-17T12:05:47Z
dc.identifier.isbn9783030720612
dc.identifier.issn0302-9743
dc.descriptionThis is the author accepted manuscript. The final version is available from Springer Verlag via the DOI in this recorden_GB
dc.identifier.journalLecture Notes in Computer Scienceen_GB
dc.rights.urihttp://www.rioxx.net/licenses/all-rights-reserveden_GB
dcterms.dateAccepted2021
exeter.funder::European Commissionen_GB
rioxxterms.versionAMen_GB
rioxxterms.licenseref.startdate2021-03-24
rioxxterms.typeJournal Article/Reviewen_GB
refterms.dateFCD2021-09-16T13:20:33Z
refterms.versionFCDAM
refterms.panelBen_GB


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