Abstract
Transmission Expansion Planning (TEP) aims at identifying a list of new assets to be installed on the transmission grid to meet the long-term forecasted demand while ensuring a safe supply over the entire planning horizon. As TEP is a Mixed Integer Non-Linear Problem (MINLP) with a huge search space, in the last years several modern heuristic algorithms were proposed to deal with its challenging characteristics. In this way, this paper describes and evaluates the impact and implementation of four operators that can be easily incorporated in any evolutionary algorithm, namely: Neighborhood Search for Local Improvement (NSLI), Diversity Control (DC), Elitist Reproduction (ER) and Boundary Local Search (BLS). The impact of these operators is assessed and discussed over a hundred simulations using a traditional Genetic Algorithm (GA) and a well-known test system, the RTS 24-bus. Regarding the results, the NSLI and the BLS operator considerably improved the GA performance in solving the TEP problem regarding both the final value of the objective function and the diversity of solutions.
J. Manuel Colmenar
Associate Professor
My research interests are focused on metaheuristics applied to optimization problems. I have worked on different combinatorial optimization problems applying trajectorial algorithms such us GRASP or VNS. Besides, I am very interested in applications of Grammatical Evolution, specifically in model and prediction domain, as alternative to machine learning approaches.
Abraham Duarte
Full Professor
Abraham Duarte is Full Professor in the Computer Science Department at the Rey Juan Carlos University (Madrid, Spain). He has done extensive research in the interface between computer science, artificial intelligence, and operations research to develop solution methods based on Computational Intelligence (metaheuristics) for practical problems in operations-management areas such as logistics and supply chains, telecommunications, decision-making under uncertainty and optimization of simulated systems.