Research
Reinforcement Learning and Quantum Optimization for Microgrid Operations
I am currently engaged in research on microgrid operations, where we explore advanced methodologies such as reinforcement learning, stochastic dynamic programming, and quantum optimization. My research focused on these topics, using a three-phase representation of the electrical grid and integrating high penetration levels of renewable energy sources. This research aims to develop optimal strategies for microgrid management under uncertainty, improving system stability and efficiency.
Local Energy Markets and Peer-to-Peer Transactions
In collaboration with a doctoral project, I investigate the design and optimization of local energy markets. These markets enable peer-to-peer (P2P) transactions and collaborative systems with implicit information sharing among participants in subtransmission systems. Our research focuses on how peers can collaborate implicitly to maintain optimal utilization of the electrical grid, ensuring efficiency and reliability in distributed energy systems.
Communication Between Microgrids and Data Acquisition Systems for Microgrids
Another ongoing research project focuses on communication between microgrids, enabling transactions between nano and microgrids. This work involves the development of data acquisition systems and enabling communication technologies to facilitate seamless energy exchanges. By enhancing inter-grid communication, we aim to create resilient and adaptive decentralized energy systems.
Renewable Energy Integration and Three-Phase Grid Representations
My doctoral research included the integration of renewable energy into microgrids using a detailed three-phase representation of the electrical grid. This work addressed the challenges of incorporating distributed energy resources, such as solar and wind power, into traditional grid operations. Our research demonstrated the potential for significant improvements in grid reliability and efficiency through advanced optimization techniques.
Future Directions
In the future, my research will continue to explore the intersection of artificial intelligence, optimization, and energy systems. Key areas include the application of reinforcement learning for real-time microgrid operations, the development of collaborative energy markets, and advancing the communication capabilities of decentralized energy systems. By addressing these challenges, we aim to create sustainable and efficient energy solutions for the future.
