This chapter presents the characteristics of the metaheuristic algorithms used to solve the transmission network expansion planning (TNEP) problem. The algorithms used to handle single or multiple objectives are discussed on the basis of selected literature contributions. Besides the main objective given by the costs of the transmission system infrastructure, various other objectives are taken into account, representing generation, demand, reliability and environmental aspects. In the single-objective case, many metaheuristics have been proposed, in general without making strong comparisons with other solution methods and without providing superior results with respect to classical mathematical programming. In the multi-objective case, there is a better convenience of using metaheuristics able to handle conflicting objectives, in particular with a Pareto front-based approach. In all cases, improvements are still expected in the definition of benchmark functions, benchmark networks and robust comparison criteria.
Metaheuristics for Transmission Network Expansion Planning / Chicco, Gianfranco; Mazza, Andrea - In: Transmission Expansion Planning: The Network Challenges of the Energy Transition / Lumbreras S., Abdi H., Ramos A.. - ELETTRONICO. - Springer : Springer, 2021. - ISBN 978-3-030-49427-8. - pp. 13-38 [10.1007/978-3-030-49428-5_2]
Metaheuristics for Transmission Network Expansion Planning
Chicco, Gianfranco;Mazza, Andrea
2021
Abstract
This chapter presents the characteristics of the metaheuristic algorithms used to solve the transmission network expansion planning (TNEP) problem. The algorithms used to handle single or multiple objectives are discussed on the basis of selected literature contributions. Besides the main objective given by the costs of the transmission system infrastructure, various other objectives are taken into account, representing generation, demand, reliability and environmental aspects. In the single-objective case, many metaheuristics have been proposed, in general without making strong comparisons with other solution methods and without providing superior results with respect to classical mathematical programming. In the multi-objective case, there is a better convenience of using metaheuristics able to handle conflicting objectives, in particular with a Pareto front-based approach. In all cases, improvements are still expected in the definition of benchmark functions, benchmark networks and robust comparison criteria.File | Dimensione | Formato | |
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Chapter2_final_sent_31March2020.pdf
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https://hdl.handle.net/11583/2854112