Optimized Scheduling of EV Charging in Solar Parking Lots for Local Peak Reduction under EV Demand Uncertainty

Rishabh Ghotge, Yitzhak Snow, Samira Farahani, Zofia Lukszo and Ad van Wijk
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Rishabh Ghotge: Department of Process and Energy, Faculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology, Mekelweg 5, 2628 CD Delft, The Netherlands
Yitzhak Snow: Department of Process and Energy, Faculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology, Mekelweg 5, 2628 CD Delft, The Netherlands
Samira Farahani: Department of Engineering Systems and Services, Faculty of Technology, Policy and Management, Delft University of Technology, Jaffalaan 5, 2628 BX Delft, The Netherlands
Zofia Lukszo: Department of Engineering Systems and Services, Faculty of Technology, Policy and Management, Delft University of Technology, Jaffalaan 5, 2628 BX Delft, The Netherlands
Ad van Wijk: Department of Process and Energy, Faculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology, Mekelweg 5, 2628 CD Delft, The Netherlands

Energies, 2020, vol. 13, issue 5, 1-18

Abstract: Scheduled charging offers the potential for electric vehicles (EVs) to use renewable energy more efficiently, lowering costs and improving the stability of the electricity grid. Many studies related to EV charge scheduling found in the literature assume perfect or highly accurate knowledge of energy demand for EVs expected to arrive after the scheduling is performed. However, in practice, there is always a degree of uncertainty related to future EV charging demands. In this work, a Model Predictive Control (MPC) based smart charging strategy is developed, which takes this uncertainty into account, both in terms of the timing of the EV arrival as well as the magnitude of energy demand. The objective of the strategy is to reduce the peak electricity demand at an EV parking lot with PVarrays. The developed strategy is compared with both conventional EV charging as well as smart charging with an assumption of perfect knowledge of uncertain future events. The comparison reveals that the inclusion of a 24 h forecast of EV demand has a considerable effect on the improvement of the performance of the system. Further, strategies that are able to robustly consider uncertainty across many possible forecasts can reduce the peak electricity demand by as much as 39% at an office parking space. The reduction of peak electricity demand can lead to increased flexibility for system design, planning for EV charging facilities, deferral or avoidance of the upgrade of grid capacity as well as its better utilization.

Keywords: electric vehicle; demand forecasting; peak shaving; smart charging; robust optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

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