Intelligent particle swarm optimization augmented with chaotic searching technique to integrate distant energy resources

Abstract

Summary This paper proposes a long-term framework for generation expansion and transmission expansion planning taking into account the renewable energy integration. To solve the problem, a hybrid technique is used. The mechanism of this technique is based on decomposing the original problem into master and slave subproblems where the master subproblem is solved using a heuristic optimization algorithm and slave subproblems are solved using general algebraic modeling system, which is a well-known software with powerful mathematical solvers. The proposed heuristic algorithm is a combination of the intelligent particle swarm optimization and chaotic searching technique. Finally, the proposed model is simulated using 3 case studies including 6-bus Garver test system, IEEE 24-bus, and modified IEEE 118-bus test systems to validate the effectiveness of the long-term planning framework while the simulation results are compared to those obtained from classic genetic algorithm (GA-Classic) and classic particle swarm optimization (PSO-Classic) to verify the efficiency of the technique used in this paper.