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<p>This paper evaluates power system operations through the optimal coordination of grid-connected electric vehicles (EVs) and renewable energy resources using transactive control within an optimization framework. The objective function aims to minimize the costs of thermal generation, charging costs of electric vehicles, and load loss costs for the power system. Moreover, the constraints consist of power flow equations, governing equations for electric vehicles, thermal generation, energy storage systems, technical network constraints, and power balance constraints of the transmission network. The problem was formulated as a mixed-integer nonlinear programming (MINLP) challenge. Such issues are often time-consuming and can present challenges related to local optima. To obtain the global optimal solution efficiently and with minimal error, an equivalent mixed-integer linear programming (MILP) model was developed. This model accounts for various uncertainties, including demand, the number of EVs, wind speed and energy prices. The corresponding scenarios were defined based on the normal probability density function, with scenarios of higher probabilities extracted using the backward scenario reduction method. The proposed problem was then implemented on a standard 24-bus IEEE system in GAMS for performance evaluation. A comprehensive assessment of economic and environmental aspects is conducted. The results indicate an overall system cost reduction of 8.04% with uncertain wind and EV fleets in scheduling costs, and an EV fleet cost reduction of 12.21%, respectively.</p>

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Optimal Coordination of Grid-Connected Electric Vehicles Charging Stations and Renewable Power Generation with Transactive Control

  • 期刊论文

  • 发表日期：

    2025年8月28日

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  • Bhagyashri Govindrao Sherkhane,
  • Sudarshan L. Chavan

摘要

This paper evaluates power system operations through the optimal coordination of grid-connected electric vehicles (EVs) and renewable energy resources using transactive control within an optimization framework. The objective function aims to minimize the costs of thermal generation, charging costs of electric vehicles, and load loss costs for the power system. Moreover, the constraints consist of power flow equations, governing equations for electric vehicles, thermal generation, energy storage systems, technical network constraints, and power balance constraints of the transmission network. The problem was formulated as a mixed-integer nonlinear programming (MINLP) challenge. Such issues are often time-consuming and can present challenges related to local optima. To obtain the global optimal solution efficiently and with minimal error, an equivalent mixed-integer linear programming (MILP) model was developed. This model accounts for various uncertainties, including demand, the number of EVs, wind speed and energy prices. The corresponding scenarios were defined based on the normal probability density function, with scenarios of higher probabilities extracted using the backward scenario reduction method. The proposed problem was then implemented on a standard 24-bus IEEE system in GAMS for performance evaluation. A comprehensive assessment of economic and environmental aspects is conducted. The results indicate an overall system cost reduction of 8.04% with uncertain wind and EV fleets in scheduling costs, and an EV fleet cost reduction of 12.21%, respectively.