A wind farm is a collection of large scale (usually > 1MW) wind turbines generally located across wide and uneven terrain in order to capture sufficient wind resources to generate a source of electrical energy. The electric power networks of such farms serve to electrically connect all the turbines in the farm back to a central substation, which is in turn connected to a load, often via an existing electricity distribution or transmission network. While optimisation methods currently exist for the design of cable networks in off-shore wind farms, which primarily aim to reduce installation cost and energy loss, the design for onshore farms is usually achieved manually and iteratively, and can often result in a suboptimal design. This paper offers a Genetic Algorithm based optimisation method for onshore applications, and demonstrates how an optimal wind farm cable network design solution can be reached in terms of minimum cost, minimum power losses and maximum reliability. The algorithm developed performs the required calculations and demonstrates that an optimised solution has been reached. It is demonstrated that this method provides faster calculations than the manual method and can be used for any standard on-shore wind farm layout design, utilising components as desired by the user such as underground or overhead cables and single or triple-core cables. Index Terms — Energy networks, genetic algorithms, multi-objective optimization, wind energy.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.