A fluid vacation model for renewable-powered electric vehicle charging systems: Performance evaluation and cost-optimal battery sizing

IF 12.2 1区工程技术 Q1 ENERGY & FUELS

Applied Energy Pub Date : 2026-05-01 Epub Date: 2026-02-20 DOI:10.1016/j.apenergy.2026.127537

A.P. Nikhil, T.G. Deepak

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引用次数: 0

Abstract

The widespread adoption of electric vehicles (EVs) and the global transition toward renewable energy have created an urgent need for sustainable, reliable, and cost-efficient EV charging infrastructures. Renewable-powered EV charging stations (EVCSs), although environmentally attractive, are highly vulnerable to the intermittency of renewable generation and the limited capacity of local battery energy storage systems. These challenges can result in charging interruptions, queue congestion, and degraded quality of service, particularly in off-grid or weak-grid environments.

This paper develops a unified analytical framework to evaluate the performance and economic trade-offs of renewable-powered EVCSs under energy uncertainty. The proposed model is based on a Markov Fluid Vacation Queue (MFVQ), which jointly captures stochastic renewable energy harvesting, finite-capacity battery storage dynamics, and random EV arrivals. The battery energy buffer is modeled as a continuous fluid queue driven by renewable inflow and charging demand, while EVs form a finite-capacity queue with stochastic arrivals and charging times. Energy shortages are explicitly represented through service interruptions in off-grid systems and grid-assisted operation in hybrid on-grid configurations.

Using the proposed framework, we quantify how renewable variability and battery capacity affect EV waiting delays, service reliability, and energy availability. These results are then used to develop a cost-optimal battery sizing methodology that balances battery investment cost against performance degradation due to energy scarcity and EV queue congestion. Numerical case studies based on realistic system parameters demonstrate that the proposed MFVQ-based approach provides practical design insights and enables informed decision-making for the planning and operation of off-grid and hybrid renewable-powered EV charging stations.

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可再生电动汽车充电系统的流体休假模型：性能评估和成本最优电池尺寸

电动汽车（EV）的广泛采用和全球向可再生能源的过渡，迫切需要可持续、可靠和经济高效的电动汽车充电基础设施。可再生能源电动汽车充电站（evcs）虽然具有环境吸引力，但极易受到可再生能源发电的间歇性和当地电池储能系统容量有限的影响。这些挑战可能导致收费中断、队列拥塞和服务质量下降，特别是在离网或弱网环境中。本文建立了一个统一的分析框架来评估能源不确定性下可再生能源电动汽车的性能和经济权衡。所提出的模型基于马尔科夫流体休假队列（MFVQ），该模型联合捕获随机可再生能源收集、有限容量电池存储动态和随机电动汽车到达。将电池能量缓冲区建模为一个由可再生能源流入和充电需求驱动的连续流体队列，而电动汽车形成一个具有随机到达和充电时间的有限容量队列。能源短缺通过离网系统中的服务中断和混合并网配置中的电网辅助运行来明确表示。利用提出的框架，我们量化了可再生能源可变性和电池容量如何影响电动汽车的等待延迟、服务可靠性和能源可用性。然后，这些结果用于开发成本最优的电池尺寸方法，以平衡电池投资成本和由于能源短缺和电动汽车队列拥堵而导致的性能下降。基于实际系统参数的数值案例研究表明，所提出的基于mfvq的方法提供了实用的设计见解，并为离网和混合可再生能源电动汽车充电站的规划和运行提供了明智的决策。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Applied Energy 工程技术-工程：化工

CiteScore

21.20

自引率

10.70%

发文量

1830

审稿时长

41 days

期刊介绍： Applied Energy serves as a platform for sharing innovations, research, development, and demonstrations in energy conversion, conservation, and sustainable energy systems. The journal covers topics such as optimal energy resource use, environmental pollutant mitigation, and energy process analysis. It welcomes original papers, review articles, technical notes, and letters to the editor. Authors are encouraged to submit manuscripts that bridge the gap between research, development, and implementation. The journal addresses a wide spectrum of topics, including fossil and renewable energy technologies, energy economics, and environmental impacts. Applied Energy also explores modeling and forecasting, conservation strategies, and the social and economic implications of energy policies, including climate change mitigation. It is complemented by the open-access journal Advances in Applied Energy.