Sizing Battery Energy Storage and PV System in an Extreme Fast Charging Station Considering Uncertainties and Battery Degradation

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.

Department(s)

Electrical and Computer Engineering

Comments

This work was supported by the U.S. Department of Energy, under Grant DE-EE0008449

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)

0306-2619

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2022 Elsevier, All rights reserved.

Publication Date

01 May 2022

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