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A platform for research: civil engineering, architecture and urbanism
Lifetime Extension, Repowering or Decommissioning? Decision Support for Operators of Ageing Wind Turbines
In Germany, more than one third of the installed wind energy capacity will leave the feed-in tariff funding between 2021 and 2025. Operators of affected turbines are therefore increasingly concerned with the design of profitable end-of-funding strategies. This requires feasibility analyses of both lifetime extension and repowering options and entails the subsequent challenge to determine the optimal lifetime extension and corresponding repowering timing. To support operators and other stakeholders dealing with wind turbines' end-of-life issues, this study presents a geographic information system that permits evaluating optimal end-of-funding strategies at different spatial scales reaching down to detailed analyses on individual turbine level. The decision support system processes topographic, wind, turbine, and finance data in an integrated system of resource simulations, spatial planning analyses and economic viability assessments. Case-study results show that a uniform end-of-funding strategy cannot be applied to all ageing turbines. Conducted sensitivity analyses rather indicate that the best strategy highly depends on various turbine-specific aspects, especially the location, type and maintenance costs as well as exogenous factors, including the developments of electricity spot market prices and tendered feed-in premiums. In light of latest trends regarding the exogenous factors, lifetime extension and repowering potentials increase. However, the results also indicate that dismantling, disposal and recycling of numerous ageing turbines will become a major challenge for the wind energy sector in the next decade. © 2019 Published under licence by IOP Publishing Ltd.
Lifetime Extension, Repowering or Decommissioning? Decision Support for Operators of Ageing Wind Turbines
In Germany, more than one third of the installed wind energy capacity will leave the feed-in tariff funding between 2021 and 2025. Operators of affected turbines are therefore increasingly concerned with the design of profitable end-of-funding strategies. This requires feasibility analyses of both lifetime extension and repowering options and entails the subsequent challenge to determine the optimal lifetime extension and corresponding repowering timing. To support operators and other stakeholders dealing with wind turbines' end-of-life issues, this study presents a geographic information system that permits evaluating optimal end-of-funding strategies at different spatial scales reaching down to detailed analyses on individual turbine level. The decision support system processes topographic, wind, turbine, and finance data in an integrated system of resource simulations, spatial planning analyses and economic viability assessments. Case-study results show that a uniform end-of-funding strategy cannot be applied to all ageing turbines. Conducted sensitivity analyses rather indicate that the best strategy highly depends on various turbine-specific aspects, especially the location, type and maintenance costs as well as exogenous factors, including the developments of electricity spot market prices and tendered feed-in premiums. In light of latest trends regarding the exogenous factors, lifetime extension and repowering potentials increase. However, the results also indicate that dismantling, disposal and recycling of numerous ageing turbines will become a major challenge for the wind energy sector in the next decade. © 2019 Published under licence by IOP Publishing Ltd.
Lifetime Extension, Repowering or Decommissioning? Decision Support for Operators of Ageing Wind Turbines
Piel, J.H. (author) / Stetter, C. (author) / Heumann, M. (author) / Westbomke, M. (author) / Breitner, M.H. (author)
2019-01-01
Journal of Physics: Conference Series 1222 (2019), Nr. 1
Article (Journal)
Electronic Resource
English
Economic viability assessment , Feasibility analysis , Decision supports , Electricity spot market , Konferenzschrift , Sensitivity analysis , Wind energy capacity , Exogenous factors , Artificial intelligence , Wind turbines , Integrated systems , Wind power , Decision support systems , ddc:530 , Lifetime extension , Costs
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