Integration of Renewable Energy and Electric Vehicles into Existing Multi-Microgrid Systems: A Decarbonization Impact Analysis Using AI and ML Techniques

The proposed research project seeks to address the critical challenge of integrating renewable energy sources and electric vehicles (EVs) into existing multi-microgrid systems to advance global decarbonization goals. By leveraging the transformative potential of artificial intelligence (AI) and machine learning (ML), this project aims to develop innovative solutions that enhance energy sustainability, grid stability, and operational efficiency.

Key Objectives

1. Renewable Energy Integration

• Formulate strategies to integrate solar, wind, and other renewable sources into multi-microgrid systems.
• Overcome challenges such as variability, intermittency, and the impact on grid stability.

2. Electric Vehicle (EV) Integration

• Examine the implications of EV charging and Vehicle-to-Grid (V2G) technologies on grid performance.
• Develop optimized charging and discharging schedules to reduce grid stress and enhance renewable energy utilization.

3. Decarbonization Impact

• Quantify reductions in greenhouse gas (GHG) emissions due to renewable energy and EV integration.
• Establish carbon intensity metrics to evaluate decarbonization benefits.

4. AI and ML Applications

• Create AI models for accurate forecasting of renewable generation and EV charging demand.
• Use ML techniques for real-time optimization of power flow, load balancing, and emission reduction.

Supervisor Details

Dr Arunachalam Sundaram

Dr Omar Alani

Please use this Research Proposal, Personal statement and CV Guide when preparing an application.