Lowering Operation and Maintenance (O&M) expenses is pivotal in order to increase the penetration of offshore renewables in the generation of electricity. The combined use of Monte Carlo simulation and optimization algorithms has been explored to support the assets management and propose improved solutions in an efficient and automated way. However, due to the lack of operational experience and historical data, validation of these models, intended in the commonly known sense of comparison against observed data is often not possible. This generates concern about their ability to fully grasp and interpret the complex dynamics of an offshore renewable energy system. This paper presents a method to effectively calibrate, verify and benchmark computational tools for O&M strategies and asset management of an offshore wind farm, as an alternative to validation in absence of real data. A case study is used to test the quality of the results and compare them against those provided by similar tools built for the same purpose. The evaluation functions for an optimization of the O&M strategies are then benchmarked against these outputs in order to ensure that the solutions are consistent within the overall characterization and optimization framework. The requirements for acceptability of the models performance, as well as guidelines for analogous verifications using similar models, are derived. Hence, this work provides a basis to benchmark future models and increase confidence and credibility in computational tools for the O&M planning of offshore renewables.
Verification and Benchmarking Methodology for O&M Planning and Optimization Tools in the Offshore Renewable Energy Sector
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Rinaldi, G, Pillai, AC, Thies, PR, & Johanning, L. "Verification and Benchmarking Methodology for O&M Planning and Optimization Tools in the Offshore Renewable Energy Sector." Proceedings of the ASME 2018 37th International Conference on Ocean, Offshore and Arctic Engineering. Volume 10: Ocean Renewable Energy. Madrid, Spain. June 17–22, 2018. V010T09A052. ASME. https://doi.org/10.1115/OMAE2018-77176
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