Development of Artificial intelligence system for integration of fast charging station under smart grid environment

Project Title

Development of Artificial intelligence system for integration of fast charging station under smart grid environment

Project Area of Specialization Electrical/Electronic EngineeringProject Summary

• In this project, we will use Artificial Intelligence techniques for the integration of Electric Vehicle Stations (EVs) into the smart grid.
• With the passage of time, the wider adoption of Electric Vehicles (EVs) is seen as a catalyst for the reduction of CO2 emissions and more intelligent transportation systems.
•  The Smart Grid, an electricity supply network that uses digital communications technology to detect and react to local changes in usage, is the foundation that will enable the adoption of EVs in the marketplace.
• In this project, we will use CAN (Control Area Network) for communication and control between the different systems and components.

Project Objectives

• Development of an Intelligent communication system between Smart Grid and Fast Charging Stations
• Virtual Smart Grid Development
• Intelligent Control System for Power Generation and Utilization
• Controlled Charging Mechanism  based on Load Demand
• Intelligent Integration of charging mechanism with the Smart Grid
• Power Quality Monitoring (V, I, Power factor)
• Use of CAN (Controller Area Network)

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Project Implementation Method

• A charger with a duty cycle control function is developed.
• A load variation graph (from the previous record) is set up by traffic load at different charging stations
• The data is sent from a server to the main computer (which is acting as a smart grid) using Wifi Module.
• Then the data are transmitted automatically to different charging stations using Controller area Network communication.
• The charging stations with changing their speed of battery storage relative to the predictable upcoming load relative to the graph.
• The overall impact of load variation on the grid could be countered easily by this method and energy utilization will be smart.

Benefits of the Project

Problem:

The large and variable load currents of ECSs can bring negative impacts to both EV-related power converters and power distribution systems if the energy flow is not regulated properly.

Impacts:

• System Instability.
• Instant variation in loads can cause a collapse of the system.
• Power Supply and Demand Issues.
• Tension in the system.
• Improper Load Determination causes inefficiency in the system.

Benefits:

• We can predetermine load variations, prepare the system and prevent from losing system stability.
• The severe impact of large-scale Electric Charging stations on the grid could be eliminated.
• Energy could be generated and utilized smartly.
• Power Supply and demand issues could be countered.
• Tension in the power system could be reduced.

Technical Details of Final Deliverable

• A charger with a duty cycle control function using the transistor switching is developed.
• By using the Image processing technique (By machine learning technique of AI) to measure vehicles at charging stations a load variation graph (from the previous record) is set up by traffic load at different charging stations
• The main control system is developed using AI to control charging at the charging stations. The data is sent from a server to the main computer (which is acting as a smart grid) using Wifi Module.
• Then the data are transmitted automatically to different charging stations using Controller area Network communication.
• The charging stations with changing their speed of battery storage relative to the predictable upcoming load relative to the graph.
• The overall impact of load variation on the grid could be countered easily by this method and energy utilization will be smart.

Final Deliverable of the Project HW/SW integrated systemCore Industry Energy Other Industries Transportation Core Technology Artificial Intelligence(AI)Other Technologies Clean TechSustainable Development Goals Affordable and Clean Energy, Industry, Innovation and InfrastructureRequired Resources

Item Name	Type	No. of Units	Per Unit Cost (in Rs)	Total (in Rs)
			Total in (Rs)	14560
Arduino Uno	Equipment	2	1500	3000
Arduino Atmega2560 16AU Microcontroller	Equipment	1	3500	3500
MCP 2515 CAN Bus	Equipment	3	800	2400
16*2 LCD Display	Equipment	1	500	500
I2C LCD module	Equipment	1	180	180
IRF-9530 MOSPHET	Equipment	2	100	200
2n2222 NPN transistor	Equipment	10	8	80
24V DC Bulb	Equipment	1	50	50
Bread Board	Equipment	1	250	250
Vero Board	Equipment	2	150	300
Connecting Wires	Equipment	2	200	400
Nodemcu ESP-8266 Wifi Module	Equipment	2	600	1200
24V DC Battery	Equipment	1	2000	2000
5V Charger	Equipment	1	500	500