A Systematic Framework for Projecting the Future Cost of Offshore Wind Energy

Abstract

Various analyst groups have published cost forecasts for fixed-bottom and floating offshore wind energy. These projections are useful for understanding the economic viability of offshore wind and informing investment and research and development decisions. However, the methodologies used to estimate these future costs are often not transparent and fail to characterize the uncertainties in the forecast. In this report, the authors introduce the F orecasting O ffshore Wind R eductions in C ost of E nergy (FORCE) model, which we use to estimate cost reductions of fixed-bottom and floating offshore wind technologies through 2035 and associated uncertainties. The model is available on GitHub for use by the broader analyst community.The FORCE model is based on an empirically derived learning rate for project capital expenditures in combination with assumptions of expected global deployment. We use historical project cost data to derive learning rates for fixedbottom and floating wind technologies and establish bounds on these estimates to provide scenario ranges for the learning rate. These bounds reflect the uncertainty in historical cost data. We prescribe current and future values of operational expenditures and capacity factors from literature to derive a total cost of energy because available project data to derive learning rates for these two parameters are very limited. Further, we define a set of fixed-bottom and floating reference sites to characterize how costs vary by spatial conditions.

Potential for large-scale deployment of offshore wind-to-hydrogen systems in the United States

“…The fixed-bottom sites, New York Bight and Gulf of Mexico, demonstrate lower LCOE compared to their floating counterparts. Floating technology with its greater uncertainty around capital, installation and maintenance costs, contributes to increased LCOE [30]. Between the fixed-bottom sites, the New York Bight exhibits lower LCOE than the Gulf of Mexico, partially due to its higher wind capacity factor.…”

“…ORBIT operates as a design trade-off tool integrated with HOPP, evaluating different wind plant design and installation choices. The tool does not predict costs through time, so we adjusted the ORBIT results using a multiplier to align the offshore wind plant costs with future projections [30] while preserving the flexibility to replace components for different system configurations. The multiplier represents the ratio of expected wind plant capital costs from the nationwide analysis by Fuchs et al [18] to the ORBIT total capital cost at each site.…”

Great Lakes Wind Energy Challenges and Opportunities Assessment

“…Summary of cost modeling process in NRWAL. Image based on Beiter et al (2016, 2020) BOS = balance of system; FLORIS = FLOw Redirection and Induction in Steady StateThe methodology outlined in the following sections is consistent with NREL's recent offshore wind energy cost assessments in New York State, Puerto Rico, Hawaii, Oregon, and California (NYSERDA 2022a;Duffy et al 2022;Shields et al 2022; Musial et al 2021;Beiter et al 2020).Note that this assessment presents unsubsidized LCOE values without policy incentives. Costs associated with bulk power system upgrades, which may be required to interconnect large generation projects, are also not included.…”

“…ORBIT 15 is used to confirm and update the spatial cost relationships in NRWAL by modeling wind turbine installation strategies for different technologies (Nunemaker et al 2020). FORCE 16 model is used to calculate cost reductions over time resulting from supply chain maturity and technology innovations (Shields et al 2022).…”

Scenario Analysis of Offshore Wind-Power Systems under Uncertainty

“…Investment-cost reduction is modelled by the offshore wind-power learning rate [75,76], which is considered fixed and equal to 9%. This data was combined with scenarios about the offshore wind-power installed capacity in Europe in 2030 [77], which may be 40.5, 70.2, and 98.93 GW.…”