Joint Price and Power MPC for Peak Power Reduction at Workplace EV Charging Stations
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866918440041709568 |
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| author | Cambronne, Thibaud Bobick, Samuel Zeng, Wente Moura, Scott |
| author_facet | Cambronne, Thibaud Bobick, Samuel Zeng, Wente Moura, Scott |
| contents | Demand charge, a utility fee based on an electricity customer's peak power consumption, often constitutes a significant portion of costs for commercial electric vehicle (EV) charging station operators. This paper explores control methods to reduce peak power consumption at workplace EV charging stations in a joint price and power optimization framework. We optimize a menu of price options to incentivize users to select controllable charging service. Using this framework, we propose a model predictive control approach to reduce both demand charge and overall operator costs. Through a Monte Carlo simulation, we find that our algorithm outperforms a state-of-the-art benchmark optimization strategy and can significantly reduce station operator costs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_12703 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Joint Price and Power MPC for Peak Power Reduction at Workplace EV Charging Stations Cambronne, Thibaud Bobick, Samuel Zeng, Wente Moura, Scott Systems and Control Demand charge, a utility fee based on an electricity customer's peak power consumption, often constitutes a significant portion of costs for commercial electric vehicle (EV) charging station operators. This paper explores control methods to reduce peak power consumption at workplace EV charging stations in a joint price and power optimization framework. We optimize a menu of price options to incentivize users to select controllable charging service. Using this framework, we propose a model predictive control approach to reduce both demand charge and overall operator costs. Through a Monte Carlo simulation, we find that our algorithm outperforms a state-of-the-art benchmark optimization strategy and can significantly reduce station operator costs. |
| title | Joint Price and Power MPC for Peak Power Reduction at Workplace EV Charging Stations |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2507.12703 |