Sizing Battery Energy Storage and PV System in an Extreme Fast Charging Station Considering Uncertainties and Battery Degradation
This paper presents mixed integer linear programming (MILP) formulations to obtain optimal sizing for a battery energy storage system (BESS) and solar generation system in an extreme fast charging station (XFCS) to reduce the annualized total cost. The proposed model characterizes a typical year with eight representative scenarios and obtains the optimal energy management for the station and BESS operation to exploit the energy arbitrage for each scenario. Contrasting extant literature, this paper proposes a constant power constant voltage (CPCV) based improved probabilistic approach to model the XFCS charging demand for weekdays and weekends. This paper also accounts for the monthly and annual demand charges based on realistic utility tariffs. Furthermore, BESS life degradation is considered in the model to ensure no replacement is needed during the considered planning horizon. Different from the literature, this paper offers pragmatic MILP formulations to tally BESS charge/discharge cycles using the cumulative charge/discharge energy concept. McCormick relaxations and the Big-M method are utilized to relax the bi-linear terms in the BESS operational constraints. Finally, a robust optimization-based MILP model is proposed and leveraged to account for uncertainties in electricity price, solar generation, and XFCS demand. Case studies were performed to signify the efficacy of the proposed formulations.
W. u. Rehman et al., "Sizing Battery Energy Storage and PV System in an Extreme Fast Charging Station Considering Uncertainties and Battery Degradation," Applied Energy, vol. 313, article no. 118745, Elsevier, May 2022.
The definitive version is available at https://doi.org/10.1016/j.apenergy.2022.118745
Electrical and Computer Engineering
Keywords and Phrases
Battery energy storage; Demand charges reduction; Energy and power sizing; Energy arbitrage; Extreme fast charging of EVs; PV system; Uncertainty modeling
International Standard Serial Number (ISSN)
Article - Journal
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01 May 2022