Multi-objective planning of charging stations and shunt capacitors considering Driving Range-based Traffic Flow in distribution networks

Abstract

The global transition towards Electric Vehicles (EVs) necessitates the widespread deployment of Electric Vehicle Charging Stations (EVCSs), which, while enhancing energy security and reducing carbon emissions, also pose significant challenges to the distribution network (DN). Key concerns include voltage deviations and increased active power losses due to the high penetration of EVCSs. This study proposes a comprehensive multi-objective optimization (MOO) framework for the optimal integration of EVCSs and shunt capacitors (SCs) within the DN, while simultaneously considering the dynamics of the transportation network (TN). To ensure an efficient and sustainable charging infrastructure, a Driving Range-based Traffic Flow Capturing (TFC) model is employed to optimize EV traffic flow coverage, accounting for EV battery constraints and strategic station placement. The proposed framework aims to minimize active power loss (APL) and voltage deviation (VD) in the DN while maximizing EV flow in the TN. To solve this complex multi-objective problem, a novel hybrid metaheuristic algorithm (HGCO) is developed by integrating Grey Wolf Optimization (GWO) with Cuckoo Search Optimization (CSO). Objective function normalization is applied to balance the conflicting goals between the DN and TN. The proposed framework was tested on a 33-bus DN and a 25-node TN under three different planning approaches: one focusing on the DN, another on the TN, and a third on their integrated operation. In the integrated planning case, the system recorded an APL of 161.3842 kW and captured 39.45% of electric vehicle flow when battery constraints were applied. Upon removing these constraints, performance improved significantly, with the APL decreasing to 148.5903 kW and EV flow capture rising to 50.52%. Simulation results demonstrate that the proposed HGCO algorithm effectively balances power system reliability and transportation service efficiency. This integrated planning approach highlights the importance of coordinated EVCS and SC placement in realizing a resilient, efficient, and sustainable electric mobility infrastructure.