A mathematical program for global optimization of the cable layout of Offshore Wind Farms (OWFs) is presented. The model consists on a Mixed Integer Linear Program (MILP). Modern branch-and-cut solvers are able to solve large-scale instances, defined by more than hundred Wind Turbines (WTs), and a reasonable number of Offshore Substations (OSSs). In addition to the MILP model to optimize total cable length or initial investment, a pre-processing strategy is proposed in order to incorporate total electrical power losses into the objective function. High fidelity models are adapted to calculate cables current capacities, spatial currents. The MILP model is embedded in an iterative algorithmic framework, consisting in solving a sequence of problems with increasing size of the search space. The search space is defined as a set of underlying candidate arcs. The applicability of the method is illustrated through 10 case studies of real-world large-scale wind farms. Results show that: (i) feasible points can quickly be obtained in minutes, (ii) points near the global optimum with an imposed maximum tolerance, are calculable in reasonable computational time in the order of hours, and (iii) the proposed method compares favorably against a state-of-the art method available in literature.
A state-of-the-art review of the optimization of electrical cables in offshore wind farms (OWFs) is presented in this paper. One of the main contributions of this paper is to propose a general classification of this problem, framed in the general context of the OWFs design and optimization (OWiFDO). The classification encompasses two complementary aspects. First, the optimum sizing of electrical cables, with the three main approaches used nowadays, static-rated sizing, dynamic load cycle profile, and dynamic full time series, is conceptually analyzed and compared. The latest techniques and advances are described, along with the presentation of potential research areas not thoroughly addressed today, such as dynamic cable rating, and cable's lifetime estimation under time-varying conditions. Second, the network optimization of large OWFs is thoroughly presented, dividing the problem with a bottom-top approach: cable layout of the collection system, wind turbines (WTs) allocation to offshore substations (OSSs), number and location of OSSs, and interconnection between OSSs and onshore connection points (OCPs). A comparison among different methods is performed, taking into consideration the main engineering constraints. Global optimization, specifically, binary programming (BIP) or mixed-integer linear programming (MILP), is envisaged as the best way to tackle this topic. The full combinatorial problem is found to be better addressed following a top-bottom approach, combining exact formulations with high-level heuristics, or holistically with evolutionary algorithms.
A MILP program for integrated global optimization of electrical cables systems in Offshore Wind Farms (OWFs) is presented. Electrical cables encompass the cable layout in collection systems to interconnect Wind Turbines (WTs), and transmission systems to couple Offshore Substations (OSSs) to the Onshore Connection Point (OCP). The program is solved through a modern branch-and-cut solver, demonstrating the ability to tackle large-scale instances with hundreds of WTs and several OSSs. The model supports as objective function the initial investment plus economic losses due to total electrical power losses. The importance and functionality of incorporating electrical losses is demonstrated, along with the need to simultaneously optimize the cable layout, OSSs location, and transmission cables. The method is tested for three case studies. The results show that (i) points near the global optimum, with an imposed maximum tolerance, are calculable within reasonable computational time and effort, and (ii) the integrated model can be much more efficient than a benchmark approach based on enumeration, i.e., exhaustive evaluation of all possible optimization problems derived from unique OSSs locations.
Users may download and print one copy of any publication from the public portal for the purpose of private study or research. You may not further distribute the material or use it for any profit-making activity or commercial gain You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.
A parallel-based algorithmic framework for automated design of Offshore Wind Farms (OWF) collection systems is proposed in this paper. The framework consists basically on five algorithms executed simultaneously and independently, followed by a combined analysis aiming to generate the best results in terms of different objective functions. The main inputs of the framework are the location coordinates of the Wind Turbines (WT) and the Offshore Substation (OSS), wind power production time series, and the set cables considered for the collection system design. Four heuristics and one metaheuristic algorithm are considered. The heuristics are based on modified versions of well-known graph-theory algorithms: Kruskal (KR), Prim (PR), Esau-Williams (EW), and Vogel’s Approximation Method (VAM); all of them coded in a unified framework with quartic time complexity. The metaheuristic is built upon a Genetic Algorithm (GA) designed using a hierarchical-restricted penalization system. Comparisons between all of these methods are performed from different perspectives, taking into consideration the particular constraints treated for OWF practical applications. In general, primals from heuristics lead to faster and better results when only a single cable is available, and provide collection systems with lower electrical power losses for multiple cables choice, whilst the GA shows better results when the initial investment is prioritized and several cable types are considered.
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.
hi@scite.ai
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.