Optimal Placement of Electric Vehicle Charging Stations in the Active Distribution Network - Muhammad Zulqarnain Zeb; Kashif Imran; Abraiz Khattak; Abdul Kashif Janjua; Anamitra Pal; Muhammad Nadeem; Jiangfeng Zhang; Sohail Khan (IEEE Access, 2020)
Electrification of the transportation sector can play a vital role in reshaping smart cities. With an increasing number of electric vehicles (EVs) on the road, deployment of well-planned and efficient charging infrastructure is highly desirable. Unlike level 1 and level 2 charging stations, level 3 chargers are super-fast in charging EVs. However, their installation at every possible site is not techno-economically justifiable because level 3 chargers may cause violation of critical system parameters due to their high power consumption. In this paper, we demonstrate an optimized combination of all three types of EV chargers for efficiently managing the EV load while minimizing installation cost, losses, and distribution transformer loading. Effects of photovoltaic (PV) generation are also incorporated in the analysis. Due to the uncertain nature of vehicle users, EV load is modeled as a stochastic process. Particle swarm optimization (PSO) is used to solve the constrained nonlinear stochastic problem. MATLAB and OpenDSS are used to simulate the model. The proposed idea is validated on the real distribution system of the National University of Sciences and Technology (NUST) Pakistan. Results show that an optimized combination of chargers placed at judicious locations can greatly reduce cost from $3.55 million to $1.99 million, daily losses from 787kWh to 286kWh and distribution transformer congestion from 58% to 22% when compared to scenario of optimized placement of level 3 chargers for 20% penetration level in commercial feeders. In residential feeder, these statistics are improved from $2.52 to $0.81 million, from 2167kWh to 398kWh and from 106% to 14%, respectively. It is also realized that the integration of PV improves voltage profile and reduces the negative impact of EV load. Our optimization model can work for commercial areas such as offices, university campuses, and industries as well as residential colonies.