To address the limitations of traditional resource allocation methods
this study analyzes the process of optimizing resource allocation strategies for offshore wind farm construction under meteorological uncertainty. A multi-objective optimization framework is proposed
incorporating four key performance indicators: minimum total working time
resource allocation optimization rate
window period utilization rate
and cost-benefit ratio. A discrete-event digital simulation-based optimization model is developed to support this framework
enabling systematic evaluation of the benefits of resource allocation strategies through statistical analysis of these indicators.The Laotian Monsoon Wind Power Project is selected as a case study for simulation. Results demonstrate that the established model can effectively simulate the entire construction process of offshore wind farms
providing real-time visualization of meteorological conditions
wind turbine unit statuses
and personnel dynamics. It also accurately quantifies critical metrics including minimum working time
resource allocation optimization rate
window period utilization rate
and cost-benefit ratio. The model also to significantly enhances resource allocation during wind farm installation
improves overall construction efficiency
and provides valuable pre-construction planning support. These outcomes collectively contribute to achieving the dual goals of cost reduction and efficiency improvement in offshore wind farm development.
WU Y N, HU Y, LIN X S, et al.Identifying and analyzing barriers to offshore wind power development in China using the grey decision-making trial and evaluation laboratory approach[J]. Journal of cleaner production, 2018, 189: 853-863.
URSAVAS E.A benders decomposition approach for solving the offshore wind farm installation planning at the North Sea[J]. European journal of operational research, 2017, 258(2): 703-714.
WEI Y Z, ZOU Q P, LIN X H.Evolution of price policy for offshore wind energy in China: trilemma of capacity, price and subsidy[J]. Renewable and sustainable energy reviews, 2021, 136: 110366.
TYAPIN I, HOVLAND G, JORDE J.Comparison of Markov theory and Monte Carlo simulations for analysis of marine operations related to installation of an offshore wind turbine[C]//24th International Congress on Condition Monitoring (COMADEM). Stavanger, Norway, 2011.
TEZCANER ÖZTÜRK D, BARLOW E, AKARTUNALI K, et al. Robust rolling horizon optimisation model for offshore wind farm installation logistics[R]. Glasgow: University of Strathclyde, 2017.
MORANDEAU M, WALKER R T, ARGALL R, et al.Optimisation of marine energy installation operations[J]. International journal of marine energy, 2013, 3: 14-26.
SCHOLZ-REITER B, LÜTJEN M, HEGER J, et al. Planning and control of logistics for offshore wind farms[C]//Proceedings of the 12th WSEAS International Conference on Mathematical and Computational Methods in Science and Engineering. Faro, Portugal, 2010: 242-247.
AIT-ALLA A, QUANDT M, LÜTJEN M. Simulation-based aggregate installation planning of offshore wind farms[C]//Proceedings of the 12th WSEAS International Conference on Mathematical and Computational Methods in Science and Engineering (MACMESE’10). Faro: WSEAS Press, 2010: 242-247.
GOPALAN ACHARY VENKITACHALAM G. Modelling the installation of offshore wind farms: defining the installation of offshore wind submarine power cables using a discrete event simulation based logistics model[D]. Delft: Delft University of Technology, 2020.
WU M N, GAO Z, ZHAO Y N.Assessment of allowable sea states for offshore wind turbine blade installation using time-domain numerical models and considering weather forecast uncertainty[J]. Ocean engineering, 2022, 260: 111801.
TEOTÓNIO C, FORTES P, ROEBELING P, et al. Assessing the impacts of climate change on hydropower generation and the power sector in Portugal: a partial equilibrium approach[J]. Renewable and sustainable energy reviews, 2017, 74: 788-799.
YANG Z H, LIN Y F, DONG S.Weather window and efficiency assessment of offshore wind power construction in China adjacent seas using the calibrated SWAN model[J]. Ocean engineering, 2022, 259: 111933.