Sizing battery energy storage and PV system in an extreme fast charging station considering uncertainties and battery degradation. (1st May 2022)

• Record Type: Journal Article
• Authors: Rehman, Waqas ur; Bo, Rui; Mehdipourpicha, Hossein; Kimball, Jonathan W.
• Is Part Of: Applied energy. Volume 313(2022)
• Publication Date: 2022-05-01
• Subjects: Battery energy storage -- Demand charges reduction -- Extreme fast charging of EVs -- Energy arbitrage -- Energy and power sizing -- PV system -- Uncertainty modeling; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2022.118745
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Novel mixed integer linear programming formulations are proposed and solved. Energy storage and PV system are optimally sized for extreme fast charging station. Robust optimization is used to account for input data uncertainties. Results show a reduction of 73% in demand charges coupled with grid power imports. Annual savings of 23% and AROI of ∼70% are expected for 20 years planning period. Abstract: 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.