A dual-level stochastic fleet size and mix problem for offshore wind farm maintenance operations. Issue 2 (3rd May 2021)

• Record Type: Journal Article
• Authors: Stålhane, Magnus; Bolstad, Kamilla Hamre; Joshi, Manu; Hvattum, Lars Magnus
• Is Part Of: Infor. Volume 59:Issue 2(2021)
• Page Start: 257
• Page End: 289
• Publication Date: 2021-05-03
• Subjects: Stochastic programming -- hierarchical planning -- multilevel planning -- mixed-integer programming -- integer L-shaped method -- multi-horizon; Operations research -- Periodicals; Electronic data processing -- Periodicals; Systems engineering -- Periodicals; Systems engineering; Electronic data processing; Periodicals; 003.05
• DOI: 10.1080/03155986.2020.1857629
• Languages: English
• ISSNs: 0315-5986

Abstract

Abstract: This paper studies the strategic problem of finding a cost optimal fleet of vessels to support maintenance operations at offshore wind farms. A dual-level stochastic model is formulated, taking into account both long-term strategic uncertainty and short-term operational uncertainty in a single optimization model. The model supports wind farm owners in making strategic decisions regarding the number, placement, charter length, and types of vessels to charter, to meet maintenance demands throughout the lifetime of a wind farm. To evaluate the quality of strategic fleet size and mix decisions, the model also considers the operational decisions of how to utilize the fleet to support maintenance operations. The model accounts for strategic uncertainties that have not been considered in previously developed optimization models for offshore wind, such as uncertainty related to long-term trends in electricity prices and subsidy levels, the stepwise development of wind farms, and technology development in the vessel industry. To solve the proposed stochastic programming model we have developed an ad hoc integer L-shaped method, with customized optimality cuts. The computational experiments show that the proposed method outperforms solving the deterministic equivalent using a commercial MIP solver.