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Temperature-aware stochastic joint planning of fast charging stations and distributed energy resources capturing regulatory incentives
Journal article   Open access   Peer reviewed

Temperature-aware stochastic joint planning of fast charging stations and distributed energy resources capturing regulatory incentives

Iman Soltani, Ali Mokhtari, Amin Alavi-Eshkaftaki and Farhad Shahnia
Energy conversion and management. X, Vol.31, 102022
2026
DOI: https://doi.org/10.1016/j.ecmx.2026.102022
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Published1.80 MBDownloadView
Open Access CC BY V4.0

Abstract

Distribution system planning Electric vehicle fast charging station Regulation Stochastic optimization Temperature-dependent modeling
The decarbonization of transport and energy sectors requires rapid deployment of electric vehicle (EV) fast charging infrastructure integrated with distributed energy resources (DERs). Although joint planning of fast charging stations (FCSs), photovoltaics (PV), and energy storage systems (ESSs) has been widely studied, many existing frameworks rely on simplified operating assumptions and do not capture temperature-driven variations in asset performance or weather-load interactions. This paper proposes a two-stage stochastic planning framework that explicitly incorporates ambient temperature effects and aligns investment decisions with regulatory incentives. The model embeds temperature-dependent physical constraints for EV energy consumption, charging power limits, EV/ESS charging efficiency, ESS discharging efficiency, and ESS degradation. Two performance-based incentive mechanisms are incorporated to monetize system-supportive behavior, namely self-sufficiency and peak shaving. Uncertainty is modeled using a vine copula scenario generation method that preserves nonlinear and tail dependencies among solar irradiance, ambient temperature, and electrical load. Case studies on a modified IEEE 33-bus system across three climatic zones demonstrate the importance of temperature-aware planning. Under cold climate conditions, traditional planning models underestimate required FCS capacity by up to 67% and lead to substantial fleet energy deficits under realistic operation conditions. Repeating the study over a larger IEEE 69-bus system with 50% higher EV penetration verifies the scalability of the proposed technique and confirms the findings that even mild climate locations may experience significant fleet energy deficits when temperature effects are ignored, and that unserved energy may occur under extreme cold. The proposed framework yields investment plans that maintain operational adequacy and improve economic performance through effective incentive capture. [Display omitted] •A temperature-aware stochastic planning framework for FCSs with DERs is proposed.•Regulatory reward models monetize self-sufficiency and peak shaving.•A vine copula captures dependencies among weather variables and electrical load.•Traditional models significantly underestimate FCS needs in cold climates.

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UN Sustainable Development Goals (SDGs)

This output has contributed to the advancement of the following goals:

#7 Affordable and Clean Energy
#9 Industry, Innovation and Infrastructure

Source: SDGs in the Output

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