Offshore Wind Market Report: 2021 Edition

Musiał, W.; Spitsen, Paul; Beiter, Philipp; Duffy, Patrick; Marquis, Melinda; Cooperman, Aubryn; Hammond, Robert; Shields, Matt

doi:10.2172/1818842

Cited by 35 publications

(20 citation statements)

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“…This result is not surprising because the underlying data-both the cost rates in ORBIT, which are used to establish the baseline results, and the learning rates derived from historic project data-are based on insights from the European market. The North America fixed-bottom costs from Wiser et al (2021) are higher than those predicted by the FORCE model, which could reflect uncertainty about the growth of a new domestic industry; however, recent power offtake agreements in the United States have suggested that these projects can be cost-competitive (or, in some cases, cheaper than) European projects (Musial et al 2021;Beiter et al 2021a). As such, it is encouraging that the FORCE forecast aligns with the expert predictions for the European market.…”

Section: Projected Capital Costsmentioning

confidence: 94%

“…The rate of decrease in CapEx depends on the deployment rate of fixed-bottom and floating wind. We used the fixedbottom deployment rate through 2035 from Musial et al (2021), which leads to a total global deployment of 276 GW at the end of 2035. We linearized this deployment projection to provide a smoother CapEx trajectory.…”

Section: Project Average Capex Into the Futurementioning

confidence: 99%

“…Offshore wind energy project costs were highly scattered (and, on average, increased) until 2014, before beginning to consistently decrease between 2015 and 2021 (Musial et al 2021). Limiting the available data set to the post-2014 window reduces the number of projects to 56.…”

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confidence: 99%

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

Shields¹,

Beiter²,

Nunemaker³

2022

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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.

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Section: Projected Capital Costsmentioning

confidence: 94%

Section: Project Average Capex Into the Futurementioning

confidence: 99%

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

Shields¹,

Beiter²,

Nunemaker³

2022

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“…A growing international focus on the "blue economy" represents a changing industrial landscape for our seas, which is exemplified by the rapid growth of offshore wind throughout the world (Global Wind Energy Council, 2020;Musial et al, 2021). Forecasting of ocean wave conditions is a fundamental requirement for the operation and maintenance of offshore and coastal infrastructure.…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Spatiotemporal Machine Learning Framework for the Prediction of Nearshore Wave Conditions

Chen

Ashton

Steele

et al. 2023

Artificial Intelligence for the Earth Systems

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The safe and successful operation of offshore infrastructure relies on a detailed awareness of ocean wave conditions. Ongoing growth in offshore wind energy is focused on very large-scale projects, deployed in ever-more challenging environments. This inherently increases both cost and complexity, and therefore the requirement for efficient operational planning. To support this, we propose a new machine learning framework for the short-term forecasting of ocean wave conditions, to support critical decision-making associated with marine operations. Here, an attention-based Long Short-Term Memory (LSTM) neural network approach is used to learn the short-term temporal patterns from in-situ observations. This is then integrated with an existing, low-computational cost spatial nowcasting model to develop a complete framework for spatio-temporal forecasting. The framework addresses the challenge of filling gaps in the in-situ observations, and undertakes feature selection, with seasonal training datasets embedded. The full spatio-temporal forecasting system is demonstrated using a case study based on independent observation locations near the southwest coast of the United Kingdom. Results are validated against in-situ data from two wave buoy locations within the domain and compared to operational physics-based wave forecasts from the Met Office (the UK’s national weather service). For these two example locations, the spatio-temporal forecast is found to have the accuracy of R2 0.9083 and 0.7409 in forecasting 1 hour ahead significant wave height, and R2 0.8581 and 0.6978 in 12 hour ahead forecasts, respectively. Importantly, this represents respectable levels of accuracy, comparable to traditional physics-based forecast products, but requires only a fraction of the computational resources.

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“…At these levels for ongoing wind deployment, decarbonization targets will likely not be achieved. Yet, land-based and offshore wind can potentially be deployed at even greater scale throughout the United States, especially as the technology progresses (Wiser et al 2021;Musial et al 2021), making it a key player in transforming the energy sector.…”

Section: Introductionmentioning

confidence: 99%

Wind Energy Supply Chain Deep Dive Assessment

Baranowski

Cooperman

Patrick

et al. 2022

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The report "America's Strategy to Secure the Supply Chain for a Robust Clean Energy Transition" lays out the challenges and opportunities faced by the United States in the energy supply chain as well as the federal government plans to address these challenges and opportunities. It is accompanied by several issue-specific deep dive assessments, including this one, in response to Executive Order 14017 "America's Supply Chains," which directs the Secretary of Energy to submit a report on supply chains for the energy sector industrial base. The Executive Order is helping the federal government to build more secure and diverse U.S. supply chains, including energy supply chains.To combat the climate crisis and avoid the most severe impacts of climate change, the U.S. is committed to achieving a 50 to 52 percent reduction from 2005 levels in economy-wide net greenhouse gas pollution by 2030, creating a carbon pollution-free power sector by 2035, and achieving net zero emissions economy-wide by no later than 2050. The U.S. Department of Energy (DOE) recognizes that a secure, resilient supply chain will be critical in harnessing emissions outcomes and capturing the economic opportunity inherent in the energy sector transition. Potential vulnerabilities and risks to the energy sector industrial base must be addressed throughout every stage of this transition. development of this report-including federal agencies, state and local governments, U.S. industry, national labs, researchers, academia, and non-governmental organizations. DOE also issued a request for information (RFI) to the public on energy sector supply chains and received comments that were used to inform policy strategies and recommendations in this report. The authors particularly acknowledge the below contributors and reviewers, Dr. Tsisilile Igogo a detailee at the DOE's Office of Policy for her guidance and leadership, and the 17 organizations that submitted wind-specific input to DOE's Supply Chain Request for Information for their valuable insight. Other experts and individuals who provided valuable insight are

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Offshore Wind Market Report: 2021 Edition

Cited by 35 publications

References 0 publications

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

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

A Real-Time Spatiotemporal Machine Learning Framework for the Prediction of Nearshore Wave Conditions

Wind Energy Supply Chain Deep Dive Assessment

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