首页|iPlugie: Intelligent electric vehicle charging in buildings with grid-connected intermittent energy resources

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

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  • NSTL
  • Elsevier
Today'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.

Electric vehiclesChargingOptimizationSmart grid

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

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Department of Computer Science, California State University

School of Informatics, The University of Edinburgh

Department of Industrial and Systems Engineering, Auburn University

School of Electrical and Computer Engineering, Technical University of Crete

Lawrence Berkeley National Laboratory

Center for the Built Environment, University of California

College of Business Administration, California State University

Department of Informatics, Aristotle University of Thessaloniki

Department of Electrical Engineering and Computer Sciences, University of California

Imperial College

Institute of Transportation Services, University of California

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2022

10.1016/j.simpat.2021.102439

Simulation modelling practice and theory

Simulation modelling practice and theory

EISCI
ISSN：1569-190X
年,卷(期)：2022.115
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