Cost-Optimal Aggregated Electric Vehicle Flexibility for Demand Response Market Participation by Workplace Electric Vehicle Charging Aggregators

Yi-An Chen, Wente Zeng, Adil Khurram and Jan Kleissl ()
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Yi-An Chen: Center for Energy Research, University of California San Diego, La Jolla, CA 92093, USA
Wente Zeng: TotalEnergies Research & Technology, San Francisco, CA 94105, USA
Adil Khurram: Center for Energy Research, University of California San Diego, La Jolla, CA 92093, USA
Jan Kleissl: Center for Energy Research, University of California San Diego, La Jolla, CA 92093, USA

Energies, 2024, vol. 17, issue 7, 1-16

Abstract: In recent years, with the growing number of EV charging stations integrated into the grid, optimizing the aggregated EV load based on individual EV flexibility has drawn aggregators’ attention as a way to regulate the grid and provide grid services, such as day-ahead (DA) demand responses. Due to the forecast uncertainty of EV charging timings and charging energy demands, the actual delivered demand response is usually different from the DA bidding capacity, making it difficult for aggregators to profit from the energy market. This paper presents a two-layer online feedback control algorithm that exploits the EV flexibility with controlled EV charging timings and energy demands. Firstly, the offline model optimizes the EV dispatch considering demand charge management and energy market participation, and secondly, model predictive control is used in the online feedback model, which exploits the aggregated EV flexibility region by reducing the charging energy based on the pre-decided service level for demand response in real time (RT). The proposed algorithm is tested with one year of data for 51 EVs at a workplace charging site. The results show that with a 20% service level reduction in December 2022, the aggregated EV flexibility can be used to compensate for the cost of EV forecast errors and benefit from day-ahead energy market participation by USD 217. The proposed algorithm is proven to be economically practical and profitable.

Keywords: aggregated electric vehicle flexibility; model predictive control; demand charge management; day-ahead demand response energy market; cost optimization (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: 2024
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