Investigations on the Optimal Planning of Electric Vehicle Charging Stations in coupled network using Meta Heuristic Techniques

Abstract

Growing oil prices, rapid depletion of fossil fuels, and emission of Green House Gases have compelled a shift from conventional combustion engine to Electric Vehicle (EV). It is anticipated that the share of EV is going to rise within a short time. However, driving range and charging time are issues that limit the share of EV. Providing proper charging infrastructure along the road side of urban roads and utilization of advanced technology in charging could tackle the above mentioned issues. There are many charging methods available for EVs, and one of them is DC Rapid Charging, which can quickly charge an EV battery. The performance of the distri bution system is impacted by increased power loss and voltage deviation caused by additional load brought on by EVs. Furthermore, positioning charging stations haphaz ardly throughout a power distribution network does great harm. In addition, planning of charging station considering only distribution networks is not a reliable approach. Moreover, the location of the charging station should offer convenience to the EV user in a given EVdriving range andbenefits the charging station owner. All of the aforemen tioned issues were the rationale for the thesis, which aims to plan charging stations in a coupled network involving power distribution network and road transportation network. Initially, a multi-objective approach for optimal planning of Rapid Charging Stations (RCSs) and distributed generators (DGs) was proposed. The method suggested aims to achieve reduced active power loss, EV user costs, and voltage deviation for effective RCSs and DGs planning. IEEE 33 bus power distribution network superimposed with a 25-node road transportation network was considered as the test system. Rao 3 algo rithm was applied for optimization, and the results were compared with PSO and JAYA algorithms. The number of charging connectors at charging station not only impacts the station installation cost but also waiting time. Hence, determination of optimal number of con nectors is necessary in optimal planning. Therefore, a two-stage optimal planning is proposed in this thesis to address the issues stated above. In the first stage, simultaneous optimal planning of RCSs and distributed generators is done to minimize active power loss, voltage deviation, EV user cost and to maximize voltage stability index. In the second stage, an optimal number of connectors was decided to minimize the installation cost and waiting time in queue at RCS. Here, M1/M2/C queuing model was considered to determine the waiting time. vi In addition, integration of D-STATCOMs was done along with charging station and DGs to improve the performance of distribution system through a two stage approach. In Stage 1, RCSs, DGs, and D-STATCOMs were planned optimally by improving voltage stability index, active power loss, voltage deviation, and EV user cost. RCS connectors count was identified in Stage 2 by reducing building cost and waiting time. Further, network reconfiguration was employed along with optimal planning of RCSs, DGs, and D-STATCOMs to improve the performance of the distribution system. Mini mization of active power loss, voltage deviation, EV user cost, waiting time, installation cost, and improvement of voltage stability index were considered in optimal planning. The proposed approach was tested using an IEEE 33 bus RDN coupled with transporta tion network. Daily load variation at buses and hourly charging probability of EVs were used in the analysis. The optimization problem was solved using the novel Multi Objective Rao 3 Algorithm (MORA), and the solutions validated using NSGA-II. The results demonstrate the effectiveness of the suggested strategy by MORA in determining optimal sizes and locations to benefit EV users, charging station owners, and distribution network operators