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Assessing offshore wind potential

Abstract Quantifying wind potential is a pivotal initial step in developing and articulating a state’s policies and strategies for offshore wind industry development. This is particularly important in the Great Lakes States where lessons from other offshore environments are not directly applicable. This paper presents the framework developed for conducting a preliminary assessment of offshore wind potential. Information on lake bathymetry and wind resources were combined in simulating alternative scenarios of technically feasible turbine construction depths and distance concerns by stakeholders. These yielded estimates of developable offshore wind areas and potential power generation. While concerns about the visibility of turbines from shore reduce the power that can be generated, engineering solutions that increase the depths at which turbines can be sited increase such potential power output. This paper discusses the costs associated with technical limitations on depth and the social costs related to public sentiments about distance from the shoreline, as well as the possible tradeoffs. The results point to a very large untapped energy resource in the Michigan’s Great Lakes, large enough to prompt policy action from the state government.
- Michigan State University United States
- Michigan State University United States
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).27 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
