The Wind Farm Cable Routing problem plays a key role in offshore wind farm design. Given the positions of turbines and substation in a wind farm and a set of electrical cables needed to transfer the electrical power produced by the turbines to the substation, the task is to define a cable-connection tree that minimizes the overall cable cost. In the present paper we describe, implement and test five different metaheuristic schemes for this problem: Simulated Annealing, Tabu Search, Variable Neighborhood Search, Ants Algorithm, and Genetic Algorithm. We also describe a construction heuristic, called Sweep, that typically finds an initial high-quality solution in a very short computing time. We compare the performance of our heuristics on two datasets: one contains instances from the literature and is used as a training set to tune our codes, while the second is a very large new set of realistic instances (that we make publicly available) used as a test set. Some practical recommendations on the proposed heuristics are finally provided: according to our experiments, Variable Neighborhood Search obtains the best overall performance, while Tabu Search is our second best heuristic.

Heuristic algorithms for the Wind Farm Cable Routing problem

Cazzaro Davide;Fischetti Matteo
2020

Abstract

The Wind Farm Cable Routing problem plays a key role in offshore wind farm design. Given the positions of turbines and substation in a wind farm and a set of electrical cables needed to transfer the electrical power produced by the turbines to the substation, the task is to define a cable-connection tree that minimizes the overall cable cost. In the present paper we describe, implement and test five different metaheuristic schemes for this problem: Simulated Annealing, Tabu Search, Variable Neighborhood Search, Ants Algorithm, and Genetic Algorithm. We also describe a construction heuristic, called Sweep, that typically finds an initial high-quality solution in a very short computing time. We compare the performance of our heuristics on two datasets: one contains instances from the literature and is used as a training set to tune our codes, while the second is a very large new set of realistic instances (that we make publicly available) used as a test set. Some practical recommendations on the proposed heuristics are finally provided: according to our experiments, Variable Neighborhood Search obtains the best overall performance, while Tabu Search is our second best heuristic.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3347183
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