DESIGN AND ANALYSIS OF ELECTRIC VEHICLE CHARGING STATION WITH INTEGRATED RENEWABLE ENERGY

Abstract

The major concern of metropolitan cities is reducing greenhouse gas emissions due to conventional engine-based vehicles. Excessive usage of such vehicles leads to health issues not only in humans but also worsens the ecological system of the earth. The release of various toxic oxides like CO2, NO2, SO2, etc. is one of the significant factors for global warming and change in climate. Researchers and policymakers worldwide advocate the implementation of an alternative mode of transport in the form of electric vehicles (EVs) to minimize the content of greenhouse gases. The technological advancement from conventional engine-based vehicles to EVs has numerous environmental and economic advantages, which include flexibility in fuels, easy charging, decent performance, and less reliance on fossil fuels. For the broad adoption of EVs, electric vehicle charging stations (EVCS) are inevitable. Inappropriate locations of EVCS impose negative impact on the efficiency of the electric grid. Therefore, this thesis focused on improving the grid’s efficiency by increasing its reliability, optimizing the benefits of EV users, and lowering the station development cost. These objectives can be realized by investigating the optimum locations and sizing of EVCS in the electrical grid network. This thesis comprehensively examines the impact of EV charging stations on distribution network operating parameters such as power loss, voltage stability index, voltage profile and reliability. Further, the single-objective formulation of EV charging stations, distributed generation sources and capacitors placement with power loss minimization as the objective function is illustrated in different chapters of this thesis. Distributed generation sources and XXIV capacitors are incorporated into the distribution network to minimize the power losses, maintaining the voltage profile and keeping the reliability of system within limits. The control and power management of EVs in grid-connected systems are the primary focus of researchers. However, one of the important aspects that must be addressed is an economic analysis that takes into account the power exchange with the grid. The fast adoption of EVs poses both constraints and opportunities for the current electricity system. A small grid-connected SPV and DG-based hybrid system with EVs is presented in this thesis for a charging station in North west region of Delhi, India. The main objective is to formulate a statistical model of a solar and diesel generator-based hybrid system with EVs and a backup grid. Furthermore, the purpose of this work is to reduce power interchange with the grid and utilize renewable energy sources to meet the load demand of EV load. The charging station placement problem's objective functions are highly non-linear in nature. The conventional optimization algorithms have limitations when it comes to solving this problem. As a result, the current work employs meta-heuristics to solve the charging station, distributed generation, and capacitor placement problems. A novel hybrid algorithm based on the combination of particle swarm optimization (PSO) and grey wolf optimization (GWO) is developed and used to solve the charging station, distributed generation, and capacitor placement problems. It is expected that combining PSO and GWO will improve solution quality and promote rapid convergence to the best solution. The proposed HGWOPSO is first tested on standard benchmark functions before being applied to the charging station placement problem in standard IEEE 33–bus and IEEE 69– bus distribution networks.