Ant colony optimization for microgrid multi-objective power management

Steadily increasing needs for electrical power, progress in power deregulation, tight construction constraints on new high voltage lines for long distance power transmission, and global environmental concerns have created increased interest in alternative energy (AE) generation. Hybrid combination of AE sources can significantly improve their reliability and better deliver power to customer loads without reliance on centralized electricity production. It is expected that alternative energy distributed generation (AEDG) microgrids that capitalize on diverse energy sources, are controlled in a decentralized way, and reduce the burden on the utility grid by generating power close to the consumer will penetrate the existing grid-infrastructure in the near future. This paper presents a framework for an intelligent supervisory controller that utilizes ant colony optimization (ACO) methods for AEDG microgrid dispatch control. The novelty of this work is the application of ACO to the rapid microgrid power management problem given complex constraints and objectives including: environmental, fuel/resource availability, and economic considerations. Given the compound nature of the multi-objective, multi-constraint energy management problem for integrated AEDG systems, this paper develops a constraint satisfaction problem (CSP) algorithm capable of finding Pareto optimal dispatch solutions. Microgrid power management control is not an easy problem, but its development is critical for widespread AEDG system implementation.