More accurate sizing of renewable energy sources under high levels of electric vehicle integration |
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| Affiliation: | 1. Laboratory of Electrical Engineering and Maintenance, Higher School of Technology, EST-Oujda, University of Mohammed I, Morocco;2. STIC Team, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco;3. Laboratory of Electrical Engineering and Maintenance, Higher School of Technology, EST-Fez, Sidi Mohamed Ben Abdellah University, Fez, Morocco;1. National Centre for Maritime Engineering and Hydrodynamics, Australian Maritime College, University of Tasmania, Locked Bag 1395, Launceston, Tasmania 7250, Australia;2. National Centre for Ports and Shipping, Australian Maritime College, University of Tasmania, Locked Bag 1397, Launceston, Tasmania 7250, Australia;3. UCL Mechanical Engineering, University College London, Torrington Place, London WC1E 7JE, United Kingdom;1. Research Center for Ocean Energy and Strategies, National Taiwan Ocean University, Keelung 202, Taiwan;2. Taiwan Ocean Research Institute, National Applied Research Laboratories, Kaoshiung 701, Taiwan;3. Department of Marine Environmental Informatics, National Taiwan Ocean University, Keelung 202, Taiwan;4. Department of Harbor and River Engineering, National Taiwan Ocean University, Keelung 202, Taiwan |
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| Abstract: | Electric vehicles (EVs) and distributed generation are expected to play a major role in modern power systems. Although many studies have introduced novel models to integrate distributed generation into high levels of EV-adoption scenarios, none has considered EV-embedded battery performance degradation and its economic effect on system planning. Based on well-established models and data to emulate the capacity fading of lithium-ion batteries, the current work presents a mixed-integer linear programming optimization framework with decision variables to size renewable energy resources (RESs) in modern microgrids. The objective function aims to minimize the total cost of the system while guaranteeing a profitable operation level of vehicle-to-grid (V2G) application, narrowing the gap between design stage and real-life daily operation patterns. Stochastic modeling is used to incorporate the effect of different uncertainties involved in the issue. A case study on a residential system in Okinawa, Japan, is introduced to quantitatively illustrate how a profitable V2G operation can affect RES sizing. The results reveal that accounting for the economic operation of EVs leads to the integration of significantly higher capacities of RESs compared with a sizing model that excessively relies on V2G and does not recognize battery-fading economics. |
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| Keywords: | Electric vehicle Lithium-ion battery Distributed generation Battery fading Optimization |
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