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EnglishThis 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. - ELETTRONICO. - (2021), pp. 13-38.
| Titolo: | Metaheuristics for Transmission Network Expansion Planning | |
| Autori: | ||
| Data di pubblicazione: | 2021 | |
| Titolo del libro: | Transmission Expansion Planning: The Network Challenges of the Energy Transition | |
| Abstract: | This chapter presents the characteristics of the metaheuristic algorithms used to solve the trans...mission 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. | |
| ISBN: | 978-3-030-49427-8 978-3-030-49428-5 | |
| Appare nelle tipologie: | 2.1 Contributo in volume (Capitolo o Saggio) |
File in questo prodotto:
| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| Chapter2_final_sent_31March2020.pdf | 2. Post-print / Author's Accepted Manuscript | Non Pubblico - Accesso privato/ristretto | Embargo: 20/11/2022 Richiedi una copia |
http://hdl.handle.net/11583/2854112
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