Joint optimization of electric bus charging and energy storage system scheduling
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| Název: | Joint optimization of electric bus charging and energy storage system scheduling |
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| Autoři: | Zhong, Lingshu, 1990, Zeng, Ziling, 1995, Huang, Zikang, Shi, Xiaowei, Bie, Yiming |
| Zdroj: | Frontiers of Engineering Management. 11(4):676-696 |
| Témata: | energy storage, mixed integer nonlinear programming, public transit, Monte Carlo simulations, electric vehicle |
| Popis: | The widespread use of energy storage systems in electric bus transit centers presents new opportunities and challenges for bus charging and transit center energy management. A unified optimization model is proposed to jointly optimize the bus charging plan and energy storage system power profile. The model optimizes overall costs by considering battery aging, time-of-use tariffs, and capacity service charges. The model is linearized by a series of relaxations of the nonlinear constraints. This means that we can obtain the exact solution of the model quickly with a commercial solver that is fully adapted to the time scale of day-ahead scheduling. The numerical simulations demonstrate that the proposed method can optimize the bus charging time, charging power, and power profile of energy storage systems in seconds. Monte Carlo simulations reveal that the proposed method significantly reduces the cost and has sufficient robustness to uncertain fluctuations in photovoltaics and office loads. |
| Přístupová URL adresa: | https://research.chalmers.se/publication/541538 |
| Databáze: | SwePub |
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Nájsť tento článok vo Web of Science | Abstrakt: | The widespread use of energy storage systems in electric bus transit centers presents new opportunities and challenges for bus charging and transit center energy management. A unified optimization model is proposed to jointly optimize the bus charging plan and energy storage system power profile. The model optimizes overall costs by considering battery aging, time-of-use tariffs, and capacity service charges. The model is linearized by a series of relaxations of the nonlinear constraints. This means that we can obtain the exact solution of the model quickly with a commercial solver that is fully adapted to the time scale of day-ahead scheduling. The numerical simulations demonstrate that the proposed method can optimize the bus charging time, charging power, and power profile of energy storage systems in seconds. Monte Carlo simulations reveal that the proposed method significantly reduces the cost and has sufficient robustness to uncertain fluctuations in photovoltaics and office loads. |
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| ISSN: | 20960255 20957513 |
| DOI: | 10.1007/s42524-024-3102-2 |