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Effective subsidy for each offshore wind farm auctioned in Europe Panel a assumes grid connection costs should be paid for by the developer and are thus considered part of the wind farm. Panel b assumes these should be socialized and considered part of the overall grid infrastructure. Each marker shows the effective subsidy for each wind farm at the planned date of operation. The lines show the effective subsidy linear regression against time across all countries, covering all bids, and recent bids from 2015 onwards. The shaded areas depict ±1 standard deviation for each regression. Wholesale electricity prices are assumed to remain constant in real terms when calculating the support level from each CfD. Other price scenarios are shown in Supplementary Fig. 3. Source data
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Offshore wind energy development has been driven by government support schemes; however, recent cost reductions raise the prospect of offshore wind power becoming cheaper than conventional power generation. Many countries use auctions to provide financial support; however, differences in auction design make their results difficult to compare. Here,...
Citations
... However, direct use of renewable electricity for heating via heat pumps is much more efficient than producing hydrogen from renewable sources and using it in a boiler [39] 14 . Furthermore, renewable electricity generators have experienced a significant cost reduction in recent years which has made them competitive with conventional electricity generators [79]. In comparison, currently, the cost of producing hydrogen from renewable sources is more than three times higher than producing hydrogen from natural gas with carbon capture and storage [11,26,75]. ...
This paper uses a whole-system approach to examine different strategies related to the future role of the gas grid in a low-carbon heat system. A novel model of integrated gas, electricity and heat systems, HEGIT, is used to investigate four key sets of scenarios for the future of the gas grid using the UK as a case study: a) complete electrification of heating; b) conversion of the existing gas grid to deliver hydrogen; c) a hybrid heat pump system; and d) a greener gas grid. Our results indicate that although the infrastructure requirements, the resource mix, and the breakdown of costs vary significantly over the complete electrification to complete conversion of the gas grid to hydrogen spectrum, the total system transition cost is relatively close. This reduces the significance of total system cost as a guiding factor in policy decisions on the future of the gas grid. Furthermore, we show that determining the roles of low-carbon gases and electrification for decarbonising heating is better guided by the trade-offs between short-and long-term energy security risks in the system, as well as trade-offs between consumer investment in fuel switching and infrastructure requirements for decarbonising heating. Our analysis of these trade-offs indicates that although electrification of heating using heat pumps is not the cheapest option to decarbonise heating, it has clear co-benefits as it reduces fuel security risks and the dependency on complementary carbon capture and storage infrastructure. Combining different strategies, such as grid integration of heat pumps with increased thermal storage capacity and installing hybrid heat pumps with gas boilers at the consumer side, were demonstrated to effectively moderate the infrastructure requirements and reduce consumers' costs and the reliability risks of widespread electrification. Further reducing the strain on the electricity grid can also be accomplished by complementary options at the system level, such as partial carbon offsetting using negative emission technologies and partially converting the gas grid to hydrogen.
... If the market price is higher, the government receives part of the difference from the producers. First bids range around € 120/MWh ( [Jansen et al. (2020)]). The government expects the CfD to limit the exposure to electricity price volatility for producers of renewable energy, reduce financial risk for projects and therefore encourage investment in the production of renewable energy ( [Higgins & Foley(2014)]). ...
... This policy instrument shares price risk between firms and governments more evenly and still seems to drive down the costs for OWP. The bid price halved between 2015 and 2017 which suggests that the policy of reducing risks for developers and producers is highly effective, for instance making access to capital cheaper ( [Jansen et al. (2020)]). But developments in technology are also relevant (larger turbines, more efficient installation at sea), and the eagerness from developers to offer competitive bids is increasing, so several independent factors are at play driving down bid prices (see Appendix Figure A2). ...
This paper analyses how institutional constellations and their associated mode of risk allocation are reflected in the choice of policy instruments for the promotion of offshore wind power. Using the Varieties of Capitalism framework we expect that governments in Liberal Market Economies (LME) tend to use policy instruments that privatise investments and risk-taking, while those in Coordinated Market Economies (CME) use policy instruments that facilitate investments and shared risk-taking in the earlier, more riskful phase of technological development. We test our expectations through a longitudinal comparative analysis of the use of policy instruments and the deployment of offshore wind power in Denmark, the United Kingdom, Germany and the Netherlands between 1990 and 2020.
Our results confirm the market oriented nature of policy instruments employed by the LME case of the United Kingdom throughout, while we witness initially lower levels of market orientation among the CME cases of Germany, Denmark and the Netherlands. Though the market orientation of Germany's policy instruments declined half way to build up domestic momentum, we generally see an increased use of market oriented policy instruments over time by the CME. Putting the trajectories together we witness an overall convergence in the use of policy instruments which we attribute to the liberalisation of the energy sector in the EU as well as to policy-learning effects. The results have generic relevance and can also be used to inform future national strategies and policies for deploying new low-carbon technologies, such as electrolysis for green hydrogen, which face similar risks and challenges.
... The design lifetime refers to the maximum expected operating lifetime of the wind farm in the planning stage, which is usually taken as a certain value range in engineering construction. The average design lifetime of onshore and offshore wind farm is generally 20 years and 25 years, respectively [11,12]. ...
... The newly added installed capacity of offshore wind turbines (OWTs) had a record year with more than 21 GW of grid-connected offshore energy generation, and the total installed capacity of OWTs exceeded 57 GW, accounting However, there are still many constraints on the development of OWP, whic offshore wind uncompetitive. The reasons are as follows: firstly, the construction offshore wind farms (OWFs) is more than onshore wind farms, owing to more c construction [11,12], a more severe environment (marine) [13], high requireme equipment material load and corrosion protection, and expensive materials and str [14]; Secondly, OWF has high operation and maintenance (O&M) costs [15,16], a hi ure rate due to high salt spray and humidity, a limited window for offshore ope high rental costs of O&M vessels, and high maritime safety risks [17][18][19]. Despite t tinuous optimization of OWT designs [20], the OWP Levelized Cost of Energy which represents the average lifecycle price per MWh of electricity generation for energy source) [21] has decreased to approximately 47% over ten years (2009 t [18,22]. ...
... For example, Kheirabadi et al. [46] conducted a summary and quan analysis of reducing wake interferences for wind farm power maximization and ev However, there are still many constraints on the development of OWP, which make offshore wind uncompetitive. The reasons are as follows: firstly, the construction cost of offshore wind farms (OWFs) is more than onshore wind farms, owing to more complex construction [11,12], a more severe environment (marine) [13], high requirements for equipment material load and corrosion protection, and expensive materials and structures [14]; Secondly, OWF has high operation and maintenance (O&M) costs [15,16], a high failure rate due to high salt spray and humidity, a limited window for offshore operations, high rental costs of O&M vessels, and high maritime safety risks [17][18][19]. Despite the continuous optimization of OWT designs [20], the OWP Levelized Cost of Energy (LCOE, which represents the average lifecycle price per MWh of electricity generation for a given energy source) [21] has decreased to approximately 47% over ten years (2009 to 2019) [18,22]. ...
The wake management of offshore wind farms (OWFs) mainly considers the wake effect. Wake effects commonly occur in offshore wind farms, which cause a 5–10% reduction in power production. Although there have been many studies on wake management, many methods are not accurate enough; for instance, look-up table and static wake model control methods do not consider the time-varying wake state. Dynamic wake management is based on the real-time dynamic wake, so it can increase the energy of the OWFs effectively. For OWFs, dynamic wake control is the main method of dynamic wake management. In this paper, the existing wake model and control progress are discussed, mainly emphasizing the dynamic wake model and the dynamic wake control method, solving the gap of the review for dynamic wake management. This paper presents a digital twins (DT) framework for power and fatigue damage for the first time.. The structure of this paper is as follows: (1) the mechanism of wind farm wake interference is described and then the dynamic wake model is reviewed and summarized; (2) different control methods are analyzed and the dynamic wake management strategies for different control methods are reviewed; (3) in order to solve the problems of dynamic wake detection and real-time effective control, the technology of DT is applied to the dynamic wake control of OWFs. This new DT frame has a promising application prospect in improving power and reducing fatigue damage.
... These data sheets summarize key figures regarding costs, electricity generation potentials, and life-cycle based greenhouse gas emissions. Current updates regarding wind power are based on inputs from SFOE, which have cross-checked using recent literature on current and future wind power costs and past and future technology development (Ren et al., 2017;Williams et al., 2017;Zerrahn, 2017;Ram et al., 2018;Jansen et al., 2020;Johnston et al., 2020;Soares-Ramos et al., 2020;Stehly, Beiter and Duffy, 2020;Beiter et al., 2021). Figures for biomass based, geothermal, nuclear, wave and tidal, and concentrated solar power are likely to be partially outdated, but can still be used as reference values, especially when it comes to prospective long-term estimates (i.e. ...
This report is an extension or continuation of the technology monitoring of the Swiss Federal Office of Energy (Bauer et al., 2017, 2019) and covers – after electricity production – two additional technological areas: Hydrogen (production, storage and transport, conversion with CO2 into methane) and electricity storage (batteries, compressed air and pumped hydro storage, re-electrification of
hydrogen in fuel cells). In addition, an update of costs and potentials of electricity production with photovoltaic systems and wind turbines in Switzerland is provided. Fact sheets regarding further technologies are provided in the appendix.
The cost analysis for the various energy technologies was performed before the summer of 2021, so that the increase in commodity prices and the market uncertainties since the fall of 2021 have not been taken into account in the present study. This applies in particular to the power generation costs of natural gas-fired power plants (section 21.5), for which a natural gas price for large consumers in
Switzerland of 5-7 Rp./kWh (natural gas) was assumed until 2050, based on the IEA scenarios.
The report contains an overview of the current state of the art and the expected future development of hydrogen and electricity storage technologies. It also provides information on the corresponding costs and their development up to 2050, as well as life-cycle greenhouse gas emissions. System aspects – i.e. answering the question what role hydrogen and electricity storage will play in the overall
energy system of the future – are not subject of this report. For this, we refer to the recently published Energy Perspectives (Kirchner et al., 2020) and similar analysis (Panos et al., 2021).
... Ocean surface winds are also important in their own right. Recently, offshore wind power generation has become increasingly efficient for sustainable development (Jansen et al., 2020), and offshore wind estimation is critical for this production. Furthermore, ocean surface winds determine air-sea fluxes, such as momentum flux, heat flux, and gas flux (Cronin et al., 2019). ...
Ocean surface wind and wave information is important in a wide variety of areas, such as coastal disaster reduction, offshore structure design, and atmosphere-ocean flux estimation. This study proposed a new method for ocean surface wind estimation from surface wave spectrum information measured by small GPS buoys. The concept of this method relies on the assumption that the high-frequency part of the ocean-wave spectrum is proportional to u∗f^−4 where u∗ is the friction velocity and f is the frequency. The determination algorithm for the coefficient of f^−4 was optimized in this study. The wind direction was determined by the wave cross-spectrum, assuming that the wind direction aligns the propagation direction of the high-frequency part of the wave. The proposed wind estimation method was applied to bay and open ocean observations, and the performance of the proposed wind estimation method was similar between the bay and the open ocean. The proposed method improves the wind estimation especially in coastal areas and at high wind speeds in the open ocean compared with the previous method. The performance of the method of the previous study differs between the bay and open ocean due to their spectral shape differences. High-quality wind and wave information can be obtained using the proposed method. If the mass deployment of small drifting buoys covered the global ocean, the information based on the proposed method could be considerably powerful, and could compensate for the weakness of satellite-based wind and wave estimations.
... Offshore wind energy is not more cost-effective than conventional power generation and other solutions to decarbonization [16]. In order to compete with other renewable energy sources, offshore wind energy still needs to strengthen the economic competitiveness which can be measured by comparing levelised cost of energy (LCOE) [17][18][19]. As one of the principal contributors of total cost of offshore wind energy, Operation and Maintenance (O&M) costs have been estimated to occupy around 14%-30% of life cycle cost (LCC) [20,21]. ...
... In constraint (16), the intermediate binary variables indicate whether the different type of maintenance action is performed on component at turbine in the th maintenance cycle. Constraint (17) determines the decision variable triggering preventive replacement is higher than the decision variable triggering major repair. Constraint (18) determines the threshold of the number of aged components in the farm must be a positive integer. ...
While offshore wind energy is showing enormous potential, effective approaches to enhance its economics are being sought at the same time. The design of maintenance strategy is a type of strategic decision-making for offshore wind farms, aiming to improve energy production and reduce maintenance expenses. As a complicated and challenging task, the maintenance decision-making is confronted with various types of uncertainty in the model. The presence of uncertainty affects the estimation of maintenance performance, and renders the determined maintenance decisions sub-optimal or even inappropriate. In this paper, the authors propose an integrated decision-making framework incorporating i) a maintenance model which is applied to estimate maintenance performance, including maintenance costs and production losses, ii) a probabilistic uncertainty modelling approach which is used to characterize different types of uncertainty and a Monte Carlo method is adopted to generate stochastic scenarios, and iii) a multi-objective optimization method used to find the optimal decisions in the presence of conflict between multiple objectives. The uncertainties considered in the model include the stochastic attributes of time to failure, deviation between real and predicted failure times of components, and uncertain maintenance consequences. The proposed framework was applied in a generic 150MW-offshore wind farm located in the North sea. Results demonstrate that the deterministic scenario underestimates the maintenance costs and production losses, leading to the consequence that the developed maintenance strategy becomes unsatisfactory. A new series of solutions including priority solutions and trade-offs is provided for decision-makers to satisfy different goals while involving uncertainty. In addition, the influence of different uncertainties on the maintenance performance is quantified to assess the significance. The proposed optimization framework constitutes a useful decision-making tool to instruct the long-term maintenance strategy for offshore wind farms in a practical environment involving a high degree of uncertainty.
... In the last decade, electricity production from wind energy has grown exponentially worldwide in the last decade, benefiting from technological advances 4 , declining production costs, and strong subsidies from states and investors 5,6 . In terms of the Levelized Cost of Energy, an almost 55% drop is anticipated from 2018 to 2030 7 , and 37% to 49% declines in production costs by 2050 8 , making the offshore wind sector increasingly competitive with fossil fuels 7 . ...
Offshore wind energy is widely regarded as one of the most credible sources for
increasing renewable energy production towards a resilient and decarbonised energy supply. However, current expectations for the expansion of energy production from offshore wind may lead to significant environmental impacts. Assessing ecological risks to marine ecosystems from electricity production from wind is both timely and vital. It will support the adoption of management measures that minimize impacts and the environmental sustainability of the offshore wind energy sector.
... The deployment of low-carbon technologies is a key measure to tackle climate change. As the global energy system transformation progresses, low-cost wind energy has become a mainstream electricity source [1] with further cost reductions expected until 2050 [2][3][4]. According to the European Commission's own 2050 scenarios, onshore wind is expected to remain the leading renewable energy source in Europe in terms of installed capacity and should grow from about 200 GW today to 750 GW (about 1900 TWh) [5,6]. ...
The required acceleration of onshore wind deployment requires the consideration of both economic and social criteria. With a spatially explicit analysis of the validated European turbine stock, we show that historical siting focused on cost-effectiveness of turbines and minimization of local disamenities, resulting in substantial regional inequalities. A multi-criteria turbine allocation approach demonstrates in 180 different scenarios that strong trade-offs have to be made in the future expansion by 2050. The sites of additional onshore wind turbines can be associated with up to 43% lower costs on average, up to 42% higher regional equality, or up to 93% less affected population than at existing turbine locations. Depending on the capacity generation target, repowering decisions and spatial scale for siting, the mean costs increase by at least 18% if the affected population is minimized – even more so if regional equality is maximized. Meaningful regulations that compensate the affected regions for neglecting one of the criteria are urgently needed.
... Offshore wind is a rapidly maturing large-scale low-carbon energy technology and becoming a pivotal component in the future energy mix of countries around the North Sea and Baltic Sea region, i.e., in decarbonizing the energy systems. Targeted subsidies, progress in marine spatial planning (European MSP Platform, 2020), incentives in the form of grid connections & site development has increased the deployments of fixed-bottom offshore wind (FBOW 1 ) in the EU and UK (Fig. 1) and unlocked cost reductions (Jansen et al., 2020). FBOW refers to the variant of offshore wind where substructures are embedded in to the seabed and are rigid in its motion. ...
... After that, a sharp decline was observed (Voormolen et al., 2016). Subsidies awarded and strike prices/tariffs 2 achieved in offshore wind auctions showed a similar development trend, with recent auction outcomes indicating offshore wind technology as soon-to-be fully subsidy-free (Jansen et al., 2020); refer to Appendix A. Below, we review the past literature that has forecasted the cost developments of offshore wind and their drivers to identify the research gaps. Then, we present the objective of this study. ...
Offshore wind is a rapidly maturing low-carbon energy technology, for which the technology cost has increased before starting to decline. In literature, the cost development trends of offshore wind and factors responsible were poorly studied. Understanding the factors contributing to the cost developments and their individual impacts are vital for long-term energy policy actions and investment decisions. Therefore, this study combined three different but highly complementary quantitative methodologies to analyze the technological progress observed for fixed-bottom offshore wind in the EU and UK. The technology diffusion curve was first applied to identify the individual development phases of offshore wind technology. Then, the cost developments observed across the identified phases were quantified using experience curve and bottom-up cost modeling methodologies. In the formative phase of the development process, the offshore wind farm's specific capital expenditure had increased from 2 M€/MW in 2000 to 5 M€/MW in 2010, thereby resulting in negative LR. The increase in specific capital expenditure increased the Levelized Cost of Energy (LCoE) from ~110 €/MWh to above 150 €/MWh. After that, during the upscaling and growth phase, the specific capital expenditure declined from 5.4 M€/MW in 2011 to 3.3 M€/MW in 2020. LR of 8–11 % was observed for specific capital expenditure in this phase. In the same phase, the LCoE declined more rapidly than the specific capital expenditure, i.e., from roughly 150 €/MWh in 2011 to 69 €/MWh in 2020, a 54 % decline. This rapid decline observed in recent years was due to the favorable financing conditions, increased capacity factor, and decreased technology costs, including investment and operational costs. Based on the technological progress assessed for offshore wind and its contributing factors in this study, we also estimated the near-term offshore wind LCoE, 55 €/MWh in 2021–2023 and 48 €/MWh in 2024–2026, which aligns well with recent auction outcomes.
