Optimized Scheduling of EV Charging in Solar Parking Lots for Local Peak Reduction under EV Demand Uncertainty
| Autor: | Samira S. Farahani, Rishabh Ghotge, Ad van Wijk, Yitzi Snow, Zofia Lukszo |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Control and Optimization
business.product_category Computer science 020209 energy Energy Engineering and Power Technology robust optimization 02 engineering and technology lcsh:Technology Automotive engineering Scheduling (computing) Electric vehicle 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Engineering (miscellaneous) electric vehicle demand forecasting peak shaving smart charging Energy demand Renewable Energy Sustainability and the Environment business.industry lcsh:T 020208 electrical & electronic engineering Demand forecasting Renewable energy Model predictive control Peaking power plant Parking lot business Energy (miscellaneous) |
| Zdroj: | Energies; Volume 13; Issue 5; Pages: 1275 Energies, Vol 13, Iss 5, p 1275 (2020) Energies, 13(5) |
| ISSN: | 1996-1073 |
| DOI: | 10.3390/en13051275 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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