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dc.contributor.authorCasals, L. C.
dc.contributor.authorCorchero, C.
dc.contributor.authorOrtiz, J.
dc.contributor.authorSalom, J.
dc.contributor.authorCardoner, D.
dc.contributor.authorIgualada, L.
dc.contributor.authorCarrillo, R. E.
dc.contributor.authorStauffer, Y.
dc.date.accessioned2021-12-09T14:04:26Z
dc.date.available2021-12-09T14:04:26Z
dc.date.issued2019
dc.identifier.citation2019 16th International Conference on the European Energy Market (EEM), pp. 1-5
dc.identifier.urihttps://yoda.csem.ch/handle/20.500.12839/367
dc.description.abstractThis study shows the results of the SABINA H2020 project, which analyzes the effect of two level optimization algorithms to increase the consumption of renewable power sources and reduce greenhouse gas emissions. First, at building level, a building algorithm maximizes the self-consumption of generated energy by its own renewable power sources. Second, at district level, a market integrated district algorithm takes into account aspects related to the electricity grid, such as the electricity generation mix and the prices of electricity and ancillary services, and aggregates the energy flexibility forecast of buildings to minimize the overall CO2 emissions while ensuring a cost reduction to prosumers.
dc.subjectRenewable Energy;Optimization;Aggregator;Demand Response;Emissions mitigation
dc.titleHow Building and District Algorithms Enhance Renewable Energy Integration in Energy Markets
dc.typeProceedings Article
dc.type.csemdivisionsDiv-V
dc.type.csemresearchareasDigital Energy
dc.identifier.doihttps://doi.org/10.1109/EEM.2019.8916457


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