Context-aware multi-agent model of microgrid optimization using fuzzy preferences evolutionary algorithm

This paper proposes a multi-agent system for energy resource scheduling of microgrid, which consists of integrated microgrids and lumped loads. Multiple objectives are considered for load balancing among the feeders, minimization of the operating cost, minimizing the emission, minimizing active power losses. The agent represents message of microgrid unit and constitutes an autonomic unit. By understanding properties of messages, the agent use projection-join closure to capture such message situations. The network is achieved by the evolution of the agent based on the semantic negotiation. Based on the objectives evaluated by membership functions respectively, we propose a new fuzzy preferences evolutionary algorithm to solve it. Simulation results demonstrated that the proposed method is effective in improving performance and management of micro-sources.