Smart Energy Management Using Agent Based Control

This project investigates the application of autonomous agents systems and artificial intelligence (AI) to control energy flow in distributed power generation, energy storage devices and flexibles loads. The overall goal is to minimize energy and reduce peak demands.

Lay Summary:
The reliability and security of the generation, transmission and delivery of electrical energy is a priority theme worldwide. An upward trend in energy demand combined with the potential for the mass adoption of new technologies such as electrical vehicles and micro-generation presents significant challenges that our existing infrastructure cannot handle. An upgraded, smarter grid is therefore considered essential and governments around the world are investing heavily in the research and development of new technology in this area. The potential applicant would do research into methods of creating gatekeepers (agents) within a smart grid, at the neighborhood level, that work to ensure energy takes the optimal path between micro-generators, loads, and distributed storage. The proposed activity will build on existing work to deliver new research highly relevant to worldwide energy security.

Outline of Plan of Work:
Following a literature review of the best technical approaches to energy management in the context of the smart grid, the research work will target the following three tasks:
• Identify requirements for the power electronics hardware and optimal agent-based control strategies, and hence select a set a potential technical solutions. As the control approach will seek to minimise a cost function to determine optimal energy flow, the constituent elements of this cost function will also be determined.
• Develop models and simulations to investigate the performance of potential technical solutions. These simulations will demonstrate the consequence of different combinations of energy storage, loads and micro-generation in real-time when using optimal agent-based control strategies to determine energy flow.
• Develop lab-based hardware prototypes to demonstrate smart energy management in a smart grid, including the optimal control of a distributed system of loads, storage and micro-generation.