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dc.contributor.authorSchellenberg, Christoph
dc.contributor.authorLohan, John
dc.contributor.authorDimache, Laurentiu
dc.date.accessioned2020-11-05T13:23:06Z
dc.date.available2020-11-05T13:23:06Z
dc.date.copyright2020
dc.date.issued2020-06
dc.identifier.citationC. Schellenberg, J. Lohan, L. Dimache, Comparison of metaheuristic optimisation methods for grid-edge technology that leverages heat pumps and thermal energy storage, Renewable and Sustainable Energy Reviews, Volume 131, 2020, 109966, ISSN 1364-0321, https://doi.org/10.1016/j.rser.2020.109966.en_US
dc.identifier.urihttp://research.thea.ie/handle/20.500.12065/3455
dc.description.abstractGrid-edge technology can unlock flexibility from consumers to contribute to meeting the growing need for flexibility in European energy systems. Furthermore, power-to-heat technology such as heat pumps and thermal energy storage has been shown to both decarbonise heat and enable the cost-effective integration of more renewable electricity into the grid. The consumer's reaction to price signals in this context presents the opportunity to simultaneously unlock operational cost reductions for consumers and localised implicit demand-side flexibility to benefit grid operators. In this paper, the prediction accuracy, run-time, and reliability of several (metaheuristic) optimisation algorithms to derive optimal operation schedules for heat pump-based grid-edge technology are investigated. To compare effectiveness, an optimisation effectiveness indicator OEI is defined. Particle Swarm Optimisation (PSO) and Genetic Algorithm (GA) were found to be most effective and robust in yielding quasi-optimal minima for the non-linear, multi-modal, and discontinuous cost function. GA optimisation with binary variables is 5–15 times more effective than with continuous variables. Using continuous variables, PSO is more effective than GA due to smaller optimisation error, shorter run-time, and higher reliability (smaller standard deviation). Simulated Annealing and Direct (Pattern) Search were found to be not very effective.en_US
dc.formatPDFen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofRenewable and Sustainable Energy Reviewsen_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Ireland*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/ie/*
dc.subjectFlexibilityen_US
dc.subjectGrid-edge technologyen_US
dc.subjectPower-to-heaten_US
dc.subjectMetaheuristic optimisationen_US
dc.subjectImplicit demand responseen_US
dc.subjectHeat pumpen_US
dc.subjectThermal energy storageen_US
dc.titleComparison of metaheuristic optimisation methods for grid-edge technology that leverages heat pumps and thermal energy storageen_US
dc.typeArticleen_US
dc.contributor.affiliationDepartment of Industrial and Mechanical Engineering, Galway-Mayo Institute of Technology
dc.description.peerreviewyesen_US
dc.identifier.urlhttps://doi.org/10.1016/j.rser.2020.109966en_US
dc.identifier.volume131en_US
dc.rights.accessOpen Accessen_US
dc.subject.departmentIndustrial and Mechanical Engineeringen_US


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Attribution-NonCommercial-NoDerivs 3.0 Ireland
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Ireland