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

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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)

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.

Suggested Citation

  • Rishabh Ghotge & Yitzhak Snow & Samira Farahani & Zofia Lukszo & Ad van Wijk, 2020. "Optimized Scheduling of EV Charging in Solar Parking Lots for Local Peak Reduction under EV Demand Uncertainty," Energies, MDPI, vol. 13(5), pages 1-18, March.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:5:p:1275-:d:330540
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Athanasios Paraskevas & Dimitrios Aletras & Antonios Chrysopoulos & Antonios Marinopoulos & Dimitrios I. Doukas, 2022. "Optimal Management for EV Charging Stations: A Win–Win Strategy for Different Stakeholders Using Constrained Deep Q-Learning," Energies, MDPI, vol. 15(7), pages 1-24, March.
    2. Jaehyun Lee & Eunjung Lee & Jinho Kim, 2020. "Electric Vehicle Charging and Discharging Algorithm Based on Reinforcement Learning with Data-Driven Approach in Dynamic Pricing Scheme," Energies, MDPI, vol. 13(8), pages 1-18, April.
    3. Yirga Belay Muna & Cheng-Chien Kuo, 2022. "Feasibility and Techno-Economic Analysis of Electric Vehicle Charging of PV/Wind/Diesel/Battery Hybrid Energy System with Different Battery Technology," Energies, MDPI, vol. 15(12), pages 1-20, June.
    4. Giuseppe Barone & Giovanni Brusco & Daniele Menniti & Anna Pinnarelli & Gaetano Polizzi & Nicola Sorrentino & Pasquale Vizza & Alessandro Burgio, 2020. "How Smart Metering and Smart Charging may Help a Local Energy Community in Collective Self-Consumption in Presence of Electric Vehicles," Energies, MDPI, vol. 13(16), pages 1-18, August.
    5. Ahmad Almaghrebi & Fares Aljuheshi & Mostafa Rafaie & Kevin James & Mahmoud Alahmad, 2020. "Data-Driven Charging Demand Prediction at Public Charging Stations Using Supervised Machine Learning Regression Methods," Energies, MDPI, vol. 13(16), pages 1-21, August.
    6. Asaad Mohammad & Ramon Zamora & Tek Tjing Lie, 2020. "Integration of Electric Vehicles in the Distribution Network: A Review of PV Based Electric Vehicle Modelling," Energies, MDPI, vol. 13(17), pages 1-20, September.
    7. Viviani Caroline Onishi & Carlos Henggeler Antunes & João Pedro Fernandes Trovão, 2020. "Optimal Energy and Reserve Market Management in Renewable Microgrid-PEVs Parking Lot Systems: V2G, Demand Response and Sustainability Costs," Energies, MDPI, vol. 13(8), pages 1-24, April.
    8. Aritra Ghosh, 2020. "Possibilities and Challenges for the Inclusion of the Electric Vehicle (EV) to Reduce the Carbon Footprint in the Transport Sector: A Review," Energies, MDPI, vol. 13(10), pages 1-22, May.
    9. Maciej Ławryńczuk & Piotr M. Marusak & Patryk Chaber & Dawid Seredyński, 2022. "Initialisation of Optimisation Solvers for Nonlinear Model Predictive Control: Classical vs. Hybrid Methods," Energies, MDPI, vol. 15(7), pages 1-21, March.

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