Optimal Placement of Electric Vehicle Charging Stations in a Distribution Network With Randomly Distributed Rooftop Photovoltaic Systems

Abstract

The increasing number of electric vehicles (EVs) in today's transport sector is gradually leading to the phasing out of petroleum-based vehicles. However, the rapid deployment of EVs largely depends on the coordinated and fast expansion of EV charging stations (EVCSs). The integration of EVCSs in the modern distribution network characterized by increased penetration of randomly distributed photovoltaic (PV) systems is challenging as they can lead to excessive power losses and voltage deviations beyond acceptable limits. In this paper, a hybrid bacterial foraging optimization algorithm and particle swarm optimization (BFOA-PSO) technique is proposed for the optimal placement of EVCSs into the distribution network with high penetration of randomly distributed rooftop PV systems. The optimization problem is formulated as a multi-objective problem minimizing active and reactive power losses, average voltage deviation index, and maximizing voltage stability index. The IEEE 69 node distribution network is used as the case network. The simulation is done using MATLAB to integrate the EVCSs in five cases of randomly sized and placed PV systems in the distribution network. For all five cases, a minimal increase in power losses is recorded with minor changes in the voltage deviation and stability indices due to the placement of the EVCSs. But for the voltages of nodes 29 to 48, the other node voltages remain unchanged upon placement of the EVCSs. The largest increase in power losses due to the EVCSs being brought into the network with PVs was noticed in case 3 (from 142.27kW, and 62.90kVar to 147.65kW, and 72.48kVar).

Hybrid genetic algorithm-simulated annealing based electric vehicle charging station placement for optimizing distribution network resilience

“…In order to strategically deploy EV charging stations in distribution networks with randomly distributed rooftop solar panels, this study suggests the hybrid optimization algorithm BFOA-PSO. The study results show the algorithm's efficiency in reducing power losses and preserving voltage stability, which helps ensure that EVs are easily integrated into contemporary distribution networks 48 .…”

“…In 47 optimizes grid stability and cost savings by combining PSO with stochastic modeling, a technique that takes random elements into account. To optimize the placement of EV charging stations with solar panels, 48 uses a hybrid BFOA-PSO method, which combines PSO and Bacterial Foraging Optimization. Although renewable energy is mentioned in the literature, it doesn't go into great detail about how solar power and EVCS work together.…”

Multi objective queue theory based optimal planning of rapid charging stations and distributed generators in coupled transportation and distribution network

“…In Reference 9, optimal placement of CS and solar power DGs has been done by considering the CS investor decision index, land cost index and EV population index. In Reference 10, the authors used a hybrid meta‐heuristic algorithm to identify CS locations with randomly distributed PVs in DS. These authors considered the minimization of power loss, voltage deviation, and voltage stability index (VSI) as objectives.…”

“…In the literature, [7][8][9][10][11][12] the authors considered only distribution system, [15][16][17][18][19] authors considered coupled network for CS placement. Most of the articles considered the minimization of power loss and voltage deviation (VD) in optimal planning of RCS.…”

Performance of Gradient-Based Optimizer on Charging Station Placement Problem

“…A multi-objective formulation of the charger placement problem is presented in [42] with cost, reliability, power loss as the objective function and the Teaching-Learning-Based Optimization (TLBO) was used for searching the optimal solution. A scheme for the placement of Level 1, Level 2 and Level 3 chargers in the active distribution network is presented in [43], with the installation cost and power losses as objective functions. Here, the PSO is implemented as a search strategy.…”