Renewable Energy based Grid Connected Electric Vehicle Charging System

Recent advancements in electric vehicles (EV) give rise to increasing demand for EV charging stations/ facilities. However, power grid resources are limited as compared to this increasing demand for EV charging. In addition, rapid EV charging may hit the loading limits of certain line segments in a

Project Title

Renewable Energy based Grid Connected Electric Vehicle Charging System

Project Area of Specialization

Electrical/Electronic Engineering

Project Summary

Recent advancements in electric vehicles (EV) give rise to increasing demand for EV charging stations/ facilities. However, power grid resources are limited as compared to this increasing demand for EV charging. In addition, rapid EV charging may hit the loading limits of certain line segments in a distribution network which can possibly lead to voltage drops at certain nodes.
Nowadays, research in the domain of EV charging optimization is very much encouraged all over the world due to its direct impact on environmental targets at national and international levels as well as saving natural resources, such as fossil fuel, that are fast depleting.
Keeping in view the limited resources of the power grid and land resources, it is imperative to explore the area of renewable energy sources in connection with EV charging systems. This will not only significantly reduce the burden on the power grid but also solve problems of the distribution network in limited land resources by balancing the local energy production and consumption through energy storage and optimal allocation.
Proposed Methodology
Phase I 

• Review of different studies and research work pertaining to the subject work
• Study of various optimization algorithms along with different renewable energy sources to select the fit-for-purpose algorithm to carry out the proposed with the selected renewable energy source

Phase II

• Development of relevant MATLAB/Python codes for the proposed optimization algorithm
• Analysis of results generated by using different problem sets
• Comparison of results of this research work with previous studies in this area

Project Objectives

The objectives of this research are:

• To develop the mathematical model of the renewable energy-based grid-connected electric vehicle charging system for cost minimization
•  To implement a systematic analytical approach for solving the planning related issues of this system using a new optimization technique
•  To benchmark the proposed technique with Particle Swarm Optimization (PSO)

The scope of this study is concentrated to the following areas:
• Multiple renewable energy resources i.e. Solar Energy and Wind Energy is considered for the purpose of this research 
• G2V (Grid vehicle) Technology is considered during the course of this research while V2G (Vehicle to the grid) Technology is not considered
• Sale of excess energy from the renewable energy resource to the power grid is considered whereas the sale of excess energy from electric storage units (ESUs) to the grid is not considered

Project Implementation Method

In order to realize the problem statement mentioned in Project Summary, Particle Swarm Optimization (PSO) will be implemented to optimize the number of PV arrays and the number of ESUs for a renewable energy (solar & wind) based grid connected electric vehicle charging station having electric storage units with the objective of profit maximization for the commercial charging station.
In addition, to make sure the realistic perspective of the optimized solution, real-world parameters for various electrical components as well as actual meteorological data has been considered.

This research focuses on the optimal planning of electric vehicle (EV) charging system by taking into account renewable energy resource (PV energy), electric storage units (ESUs) and the grid connected to the EV charging system.

First of all, mathematical models of each of the electrical component of the charging system will be defined so that further calculations can be carried out on these models.
An EMA (Energy Management Algorithm) will be designed so that the flow of energy to/ from various electrical components of the charging system can be controlled with the help of a central controller embedded with this EMA.
Finally, optimization techniques will be applied to the system to obtain optimal results pertaining to the desired objectives.

Benefits of the Project

•  This research work will positively impact the global challenge of reducing carbon footprint by sharing the EV charging load between the power grid and renewable energy sources
• As well as reducing the burden on the distribution networks which are already near to saturation
• The power generated in excess of the EV demand can be provided to the power grid thereby creating more opportunities for future loads.
• EV charging system with renewable energy sources may serve the energy requirement of remote islands where existing grid power is not sufficient to fulfil the EV demand.

Technical Details of Final Deliverable

The proposed EV charging system shall significantly contribute in following areas:
• Profit generation for the EV charging station owners.
• Reduction of burden transmission and distribution networks resulting from increased EV demand.
• Reduction in line losses due to locally generated renewable energy to meet the EV charging demand.
• Also, the power generated in excess of the EV demand can be provided to the power grid thereby creating more opportunities for future loads.
• Reduction in carbon-footprint due to rely on renewable resources on priority basis.
• EV charging system with renewable energy sources may serve the energy requirement of remote islands where existing grid power is not sufficient to fulfill the EV demand.

Final Deliverable of the Project

Software System

Core Industry

Energy

Other Industries

Education , Transportation

Core Technology

Artificial Intelligence(AI)

Other Technologies

Clean Tech

Sustainable Development Goals

Affordable and Clean Energy, Industry, Innovation and Infrastructure, Climate Action

Required Resources

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