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dc.contributor.authorZheng, Zedong
dc.contributor.authorYang, Shengxiang
dc.date.accessioned2019-04-10T08:17:04Z
dc.date.available2019-04-10T08:17:04Z
dc.date.issued2019-06
dc.identifier.citationZheng, Z. and Yang, S. (2019) A two-layer optimisation management method for the microgrid with electric vehicles. Proceedings of 2019 IEEE Congress in Evolutionary Computation, Wellington, New Zealand, June 2019.en
dc.identifier.urihttps://www.dora.dmu.ac.uk/handle/2086/17705
dc.descriptionThe file attached to this record is the author's final peer reviewed version.en
dc.description.abstractThe energy management of the microgrid (MG) with electric vehicles (EVs) is a large-scale optimization problem where the goal should take into account the performance and economic benefits of the power system while meeting the travel needs of EVs. Due to the development of vehicle to grid (V2G) technologies and demand response (DR), the relationship between EVs and MG becomes currently closer, which leads to a more complex situation. Therefore, the relationship of interest between MG and EVs has to be clarified to improve the performance of MG and EVs to achieve a win-win situation. This paper proposes a two-tier energy management strategy that considers the benefits for both MG and EVs. The first layer ensures the performance of the MG, while the second layer reduces the charging cost from the perspective of the car owners. In addition, based on the existence of uncertain parameters, mixed type variables and nonlinear constraints in the optimization problem, the differential evolution, stochastic search and greedy algorithm are used to analyze and find the optimal solution. Simulation results verify the effectiveness of the proposed strategy and solutions, which benefit both the MG and EV owners.en
dc.language.isoen_USen
dc.publisherIEEE Pressen
dc.subjectOptimization managementen
dc.subjectMicrogriden
dc.subjectElectrical vehicleen
dc.subjectTwo-layer optimizationen
dc.subjectDifferential evolutionen
dc.titleA two-layer optimisation management method for the microgrid with electric vehiclesen
dc.typeConferenceen
dc.peerreviewedYesen
dc.funderNo external funderen
dc.cclicenceN/Aen
dc.date.acceptance2019-03
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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