Towards Real-time Microgrid Power Management using Computational Intelligence Methods

Microgrids are an emerging technology which promises to achieve many simultaneous goals for power system stakeholders, from generator to consumer. The microgrid framework offers a means to capitalize on diverse energy sources in a decentralized way, while reducing the burden on the utility grid by generating power close to the consumer. As a critical component to enabling power system diversity and flexibility, microgrids encompass distributed generators and load centers with the capability of operating islanded from or interconnected to the macrogrid. To make microgrids viable, new and innovative techniques are required for managing microgrid operations given its multi-objective, multi-constraint decision environment. In this article, two example computational intelligence methods, particle swarm optimization (PSO) and ant colony optimization (ACO), for application to the microgrid power management problem are introduced. A mathematical framework for multi-objective optimization is presented, as well as a discussion of the advantages of intelligent methods over traditional computational techniques for optimization. Finally, a three-generator microgrid with an ACO-based power management algorithm is demonstrated and results are shown.