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iPlugie: intelligent electric vehicle charging in buildings with grid-connected intermittent energy resources

Panagopoulos Aris-Athanasios, Christianos Filippos, Katsigiannis Michail, Mykoniatis Konstantinos, Chalkiadakis Georgios, Pritoni Marco, Peffer Therese, Panagopoulos Orestis P., Rigas Emmanouil, Culler David E., Jennings Nicholas, Lipman Timothy

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URIhttp://purl.tuc.gr/dl/dias/8509A08C-AE63-4D5C-8651-9256D2FF9700-
Identifierhttps://doi.org/10.1016/j.simpat.2021.102439-
Identifierhttps://www.sciencedirect.com/science/article/pii/S1569190X21001350-
Languageen-
Extent17 pagesen
TitleiPlugie: intelligent electric vehicle charging in buildings with grid-connected intermittent energy resourcesen
CreatorPanagopoulos Aris-Athanasiosen
CreatorΠαναγοπουλος Αρης-Αθανασιοςel
CreatorChristianos Filipposen
CreatorΧριστιανος Φιλιπποςel
CreatorKatsigiannis Michailen
CreatorΚατσιγιαννης Μιχαηλel
CreatorMykoniatis Konstantinosen
CreatorChalkiadakis Georgiosen
CreatorΧαλκιαδακης Γεωργιοςel
CreatorPritoni Marcoen
CreatorPeffer Thereseen
CreatorPanagopoulos Orestis P.en
CreatorRigas Emmanouilen
CreatorCuller David E.en
CreatorJennings Nicholasen
CreatorLipman Timothyen
PublisherElsevieren
Content SummaryToday’s energy market is increasingly integrating time-varying tariffs, peak demand charges, and/or export tariffs. In this context, intelligent charging scheduling can considerably reduce the plug-in electric vehicle (PEV) charging cost. This is especially the case as more and more PEVs are charged in buildings that are also equipped with grid-connected intermittent energy resources (IERs) (e.g., photovoltaic systems and wind turbine generators). In this work, we propose a novel and complete intelligent PEV charging scheduling system (tailored for domestic settings) that can account for peak demand charges, time-varying tariffs, and/or export tariffs, appropriately considering both potential IER generation and the rest of a building’s consumption. The backbone of our approach builds on adaptive model predictive control, and includes an efficient depth-first-search-based PEV charging planning algorithm that we propose. Importantly, our approach does not rely on a simplified linear modeling of the charging dynamics, which is a typical and limiting assumption of such systems. We evaluate our approach with real data, considering both solar and wind IER generation capacity, to show that it can reduce the cost of charging by up to 45% and 35% in the United States and the United Kingdom domestic settings, respectively, compared to standard PEV charging practices.en
Type of ItemPeer-Reviewed Journal Publicationen
Type of ItemΔημοσίευση σε Περιοδικό με Κριτέςel
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2024-02-02-
Date of Publication2022-
SubjectElectric vehiclesen
SubjectChargingen
SubjectOptimizationen
SubjectSmart Griden
Bibliographic CitationA.-A. Panagopoulos, F. Christianos, M. Katsigiannis, K. Mykoniatis, G. Chalkiadakis, M. Pritoni, T. Peffer, O. P. Panagopoulos, E. S. Rigas, D. E. Culler, N. R. Jennings, and T. Lipman, “iPlugie: intelligent electric vehicle charging in buildings with grid-connected intermittent energy resources,” Simul. Modell. Pract. Theory, vol. 115, Feb. 2022, doi: 10.1016/j.simpat.2021.102439.en

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