An Adaptive Hybrid Meta-heuristic Approach for Transmission Constrained Multi-objective GEP

Abstract

Meta-heuristic methods are characterized by their combination of both mathematical optimizations with heuristic concepts. The combination of both concepts helps to suppress the limitations associated with either deterministic or heuristic approaches while taking advantage of their individual strengths. This paper presents a novel Adaptive Hybrid Meta-heuristic approach for solving the highly dimensional and complex Transmission Constrained Multi-Objective Generation Expansion Planning (TC-MOGEP). The algorithm combines both evolutionary and swarm intelligence meta-heuristic techniques in its formulation. The proposed algorithm is tested on the IEEE six-bus test system in three scenarios. In Scenario A, both system contingencies and reserve margin requirements are ignored, Scenario B takes into account N-1 contingency while ignoring reserve margin requirements and Scenario C considers both N-1 contingency and reserve margin requirements. The obtained results are compared to those obtained by other researchers in the area. The proposed adaptive hybrid metaheuristic approach gives better expansion plans for most of the considered system load levels; thus it can be confidently applied in solving the power system expansion optimization problems.