Bi-level stochastic energy trading model for technical virtual power plants considering various renewable energy sources, energy storage systems and electric vehicles

dc.contributor.authorGough, M.
dc.contributor.authorSantos, Sérgio F.
dc.contributor.authorJavadi, M.S.
dc.contributor.authorHome-Ortiz, J.M.
dc.contributor.authorCastro, R.
dc.contributor.authorCatalão, J.P.S.
dc.date.accessioned2023-07-17T13:47:11Z
dc.date.available2023-07-17T13:47:11Z
dc.date.issued2023-09-15
dc.description.abstracthe ongoing transition of the energy system towards being low-carbon, digitized and distributed is accelerating. Distributed Energy Resources (DERs) are playing a major role in this transition. These DERs can be aggregated and controlled by Virtual Power Plants (VPPs) to participate in energy markets and make full use of the potential of DERs. Many existing VPP models solely focus on the financial impact of aggregating DERs and do not consider the technical limitations of the distribution system. This may result in technically unfeasible solutions to DERs operations. This paper presents an expanded VPP model, termed the Technical Virtual Power Plant (TVPP), which explicitly considers the technical constraints of the network to provide operating schedules that are both economically beneficial to the DERs and technically feasible. The TVPP model is formulated as a bi-level stochastic mixed-integer linear programming (MILP) optimization model. Two objective functions are used, the upper level focuses on minimizing the amount of power imported into the TVPP from the external grid, while the lower level is concerned with optimally scheduling a mixture of DERs to increase the profit of the TVPP operator. The model considers three TVPPs and allows for energy trading among the TVPPs. The model is applied to several case studies based on the IEEE 119-node test system. Results show improved DERs operating schedules, improved system reliability and an increase in demand response engagement. Finally, energy trading among the TVPP is shown to further reduce the costs of the TVPP and power imported from the upstream electrical network.pt_PT
dc.identifier.citationGough, M., Santos, S. F., Javadi, M. S., Home-Ortiz, J. M., Castro, R., & Catalão, J. P. S. (2023). Bi-level stochastic energy trading model for technical virtual power plants considering various renewable energy sources, energy storage systems and electric vehicles. Journal of Energy Storage, 68(107742), 1-14. https://doi.org/10.1016/j.est.2023.107742. Repositório Institucional UPT. http://hdl.handle.net/11328/4950pt_PT
dc.identifier.doihttps://doi.org/10.1016/j.est.2023.107742pt_PT
dc.identifier.issn2352-152X
dc.identifier.urihttp://hdl.handle.net/11328/4950
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.relationM. Gough is the recipient of a PhD scholarship from the Fundação para a Ciência e a Tecnologia (FCT), with reference number UI/BD/ 152279/2021. J.M Home-Ortiz Home-Ortiz acknowledges FAPESP for the funding provided through grants 2019/01841-5, 2019/23755-3, and 2015/21972-6. Also, M.S. Javadi acknowledges FCT for his contract funding provided through 2021.01052.CEECIND.pt_PT
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S2352152X23011398pt_PT
dc.rightsopen accesspt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectAggregationpt_PT
dc.subjectBi-level mixed-integer linear programmingpt_PT
dc.subjectDemand responsept_PT
dc.subjectDistributed energy resourcespt_PT
dc.subjectVirtual power plantpt_PT
dc.titleBi-level stochastic energy trading model for technical virtual power plants considering various renewable energy sources, energy storage systems and electric vehiclespt_PT
dc.typejournal articlept_PT
degois.publication.firstPage1pt_PT
degois.publication.lastPage14pt_PT
degois.publication.titleJournal of Energy Storagept_PT
degois.publication.volume68pt_PT
dspace.entity.typePublicationen
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