Optimal Placement of Electric Vehicle Charging and Discharging Stations-Connected to Electrical Distribution Networks in the Presence of Renewable Energy Sources Considering Price-Based Demand Response Programs

Subject Areas : Power Engineering

Majid Farjamipour ¹ , Mojtaba Shivaie ²

1 - Department of Electrical Engineering, Shahrood University of Technology, Shahrood, Iran
2 - Department of Electrical Engineering, Shahrood University of Technology, Shahrood, Iran

Received: 2024-04-30 Accepted : 2024-08-13 Published : 2025-09-21

Keywords: Charging and discharging stations, Electric vehicle, Improved genetic algorithm, Price-based demand response programs, Renewable energy sources.,

Abstract :

In today’s industrial world, the development of electric vehicles has made energy storage resources play a significant role in the efficient operation of electrical distribution networks. On this basis, the optimal placement of charging and discharging stations associated with electric vehicles has emerged as a technical challenge for network operators. In this paper, therefore, with a new perspective, the placement problem of charging and discharging stations pertaining to electric vehicles in the presence of renewable energy sources is modeled. In the proposed model, technical objectives including minimization of losses and voltage drop, as well as economic objectives comprising minimization of the cost of power purchased from the network and maximization of the profit derived from selling power to the network by electric vehicles are tajen into account. In addition to this, existing limitations at the capacity of charging stations and the total power that can be charged and discharged at any moment are applied as constraints of the proposed model. The model also considers a peak-shaving strategy for load management on the consumer-side according to price-based demand response programs. The nonlinear power consumption problem and the participation of electric vehicles in providing network power are formulated as a mixed-integer problem and are solved using an improved genetic algorithm, as well as the results obtained are compared with those determined by a particle swarm optimization algorithm. The simulation of the proposed model is conducted using MATLAB software on a standard 69-node network, and the calculated results demonstrate its effectiveness and profitableness.

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