Optimal Operation of Park and Ride EV Stations in Island Operation with Model Predictive Control

Soichiro Ueda (), Atsushi Yona, Shriram Srinivasarangan Rangarajan, Edward Randolph Collins, Hiroshi Takahashi, Ashraf Mohamed Hemeida and Tomonobu Senjyu
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Soichiro Ueda: Faculty of Engineering, University of the Ryukyus, Senbaru Nishihara-cho, Nakagami 903-0213, Japan
Atsushi Yona: Faculty of Engineering, University of the Ryukyus, Senbaru Nishihara-cho, Nakagami 903-0213, Japan
Shriram Srinivasarangan Rangarajan: Department of Electrical and Electronics Engineering, Dayananda Sagar College of Engineering, Bengaluru 560078, India
Edward Randolph Collins: Department of Electrical and Computer Engineering, Clemson University, Clemson, SC 29631, USA
Hiroshi Takahashi: Fuji Electric Co., Ltd., Tokyo 141-0032, Japan
Ashraf Mohamed Hemeida: Department of Electrical Engineering, Aswan University, Aswan 82825, Egypt
Tomonobu Senjyu: Faculty of Engineering, University of the Ryukyus, Senbaru Nishihara-cho, Nakagami 903-0213, Japan

Energies, 2023, vol. 16, issue 5, 1-17

Abstract: The urgent need to reduce greenhouse gas emissions to achieve a decarbonized society has led to the active introduction of electric vehicles worldwide. Renewable energy sources that do not emit greenhouse gases during charging must also be used. However, the uncertainty in the supply of renewable energy is an issue that needs to be considered in practical applications. Therefore, in this study, we predicted the amount of electricity generated by renewable energy using model predictive control, and we considered the operation of a complete island-operated park and ride EV parking station that does not depend on commercial electricity. To perform appropriate model predictive control, we performed comparative simulations for several different forecast interval cases. Based on the obtained results, we determined the forecast horizon and we simulated the economic impact of implementing EV demand response on the electricity demand side. We found that without demand response, large amounts of electricity are recharged and a very high return on investment can be achieved, but with demand response, the return on investment is faster. The results provide a rationale for encouraging infrastructure development in areas that have not yet adopted electric vehicles.

Keywords: electric vehicle; microgrid; model predictive control; park and ride; renewable energy (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: 2023
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