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Direct hydrogen production from inexhaustible seawater using abundant offshore wind power offers a promising pathway for achieving a sustainable energy industry and fuel economy. Various direct seawater electrolysis methods have been demonstrated to be effective at the laboratory scale. However, larger-scale in situ demonstrations that are completely free of corrosion and side reactions in fluctuating oceans are lacking. Here, fluctuating conditions of the ocean were considered for the first time, and seawater electrolysis in wave motion environment was achieved. We present the successful scaling of a floating seawater electrolysis system that employed wind power in Xinghua Bay and the integration of a 1.2 Nm 3  h −1 -scale pilot system. Stable electrolysis operation was achieved for over 240 h with an electrolytic energy consumption of 5 kWh Nm −3 H 2 and a high purity (>99.9%) of hydrogen under fluctuating ocean conditions (0~0.9 m wave height, 0~15 m s −1 wind speed), which is comparable to that during onshore water electrolysis. The concentration of impurity ions in the electrolyte was low and stable over a long period of time under complex and changing scenarios. We identified the technological challenges and performances of the key system components and examined the future outlook for this emerging technology. Seawater electrolysis shows promising potential toward sustainable energy generation, but large-scale in-situ demonstrations are still lacking. Here, authors report a floating platform integrating a 1.2 Nm 3 h −1 seawater direct electrolysis system with wind power for energy input in the Xinghua Bay.

Offshore wind power, with accelerated declining levelized costs, is emerging as a critical building-block to fully decarbonize the world’s largest CO 2 emitter, China. However, system integration barriers as well as system balancing costs have not been quantified yet. Here we develop a bottom-up model to test the grid accommodation capabilities and design the optimal investment plans for offshore wind power considering resource distributions, hourly power system simulations, and transmission/storage/hydrogen investments. Results indicate that grid integration barriers exist currently at the provincial level. For 2030, optimized offshore wind investment levels should be doubled compared with current government plans, and provincial allocations should be significantly improved considering both resource quality and grid conditions. For 2050, offshore wind capacity in China could reach as high as 1500 GW, prompting a paradigm shift in national transmission structure, favoring long-term storage in the energy portfolio, enabling green hydrogen production in coastal demand centers, resulting in the world’s largest wind power market. Offshore wind power may play a key role in decarbonising energy supplies. Here the authors evaluates current grid integration capabilities for wind power in China and find that investment levels should be doubled for 2030, and that long-term storage and transmissions are key to accelerated developments of offshore wind in 2050.

Wind energy has experienced accelerated cost reduction over the past five years—far greater than predicted in a 2015 expert elicitation. Here we report results from a new survey on wind costs, compare those with previous results and discuss the accuracy of the earlier predictions. We show that experts in 2020 expect future onshore and offshore wind costs to decline 37–49% by 2050, resulting in costs 50% lower than predicted in 2015. This is due to cost reductions witnessed over the past five years and expected continued advancements. If realized, these costs might allow wind to play a larger role in energy supply than previously anticipated. Considering both surveys, we also conclude that there is considerable uncertainty about future costs. Our results illustrate the importance of considering cost uncertainty, highlight the value and limits of using experts to reveal those uncertainties, and yield possible lessons for energy modellers and expert elicitation. Costs of renewable energy generation have fallen rapidly in recent years, often faster than predicted. Wiser et al. undertake an expert elicitation survey to project wind power costs to 2050, finding substantial continued cost reductions, and compare back to a previous survey to understand what has changed.

Vertical-axis wind turbines are great candidates to enable wind power extraction in urban and off-shore applications. Currently, concerns around turbine efficiency and structural integrity limit their industrial deployment. Flow control can mitigate these concerns. Here, we experimentally demonstrate the potential of individual blade pitching as a control strategy and explain the flow physics that yields the performance enhancement. We perform automated experiments using a scaled-down turbine model coupled to a genetic algorithm optimiser to identify optimal pitching kinematics at on- and off-design operating conditions. We obtain two sets of optimal pitch profiles that achieve a three-fold increase in power coefficient at both operating conditions compared to the non-actuated turbine and a 77% reduction in structure-threatening load fluctuations at off-design conditions. Based on flow field measurements, we uncover how blade pitching manipulates the flow structures to enhance performance. Our results can aid vertical-axis wind turbines increase their much-needed contribution to our energy needs. Vertical-axis wind turbines offer untapped opportunities for energy generation but suffer from dynamic stall in strong winds. Here, authors implement individual blade pitch control to benefit from stall vortices instead of suppressing them, tripling the power coefficient and reducing load transients by 70%.

Climate induced extreme weather events and weather variations will affect both the demand of energy and the resilience of energy supply systems. The specific potential impact of extreme events on energy systems has been difficult to quantify due to the unpredictability of future weather events. Here we develop a stochastic-robust optimization method to consider both low impact variations and extreme events. Applications of the method to 30 cities in Sweden, by considering 13 climate change scenarios, reveal that uncertainties in renewable energy potential and demand can lead to a significant performance gap (up to 34% for grid integration) brought by future climate variations and a drop in power supply reliability (up to 16%) due to extreme weather events. Appropriate quantification of the climate change impacts will ensure robust operation of the energy systems and enable renewable energy penetration above 30% for a majority of the cities. Climate change will induce not just a change in average temperature but higher frequency of extreme weather events, whose impacts are hard to quantify. Perera et al. quantify the impacts of climate induced extreme and regular weather variations on energy systems determining requirements for system reliability.

The European Union has set ambitious CO 2 reduction targets, stimulating renewable energy production and accelerating deployment of offshore wind energy in northern European waters, mainly the North Sea. With increasing size and clustering, offshore wind farms (OWFs) wake effects, which alter wind conditions and decrease the power generation efficiency of wind farms downwind become more important. We use a high-resolution regional climate model with implemented wind farm parameterizations to explore offshore wind energy production limits in the North Sea. We simulate near future wind farm scenarios considering existing and planned OWFs in the North Sea and assess power generation losses and wind variations due to wind farm wake. The annual mean wind speed deficit within a wind farm can reach 2–2.5 ms −1 depending on the wind farm geometry. The mean deficit, which decreases with distance, can extend 35–40 km downwind during prevailing southwesterly winds. Wind speed deficits are highest during spring (mainly March–April) and lowest during November–December. The large-size of wind farms and their proximity affect not only the performance of its downwind turbines but also that of neighboring downwind farms, reducing the capacity factor by 20% or more, which increases energy production costs and economic losses. We conclude that wind energy can be a limited resource in the North Sea. The limits and potentials for optimization need to be considered in climate mitigation strategies and cross-national optimization of offshore energy production plans are inevitable.

More than 12 GW of offshore wind turbines are currently in operation in European waters. To optimise the use of the marine areas, wind farms are typically clustered in units of several hundred turbines. Understanding wakes of wind farms, which is the region of momentum and energy deficit downwind, is important for optimising the wind farm layouts and operation to minimize costs. While in most weather situations (unstable atmospheric stratification), the wakes of wind turbines are only a local effect within the wind farm, satellite imagery reveals wind-farm wakes to be several tens of kilometres in length under certain conditions (stable atmospheric stratification), which is also predicted by numerical models. The first direct in situ measurements of the existence and shape of large wind farm wakes by a specially equipped research aircraft in 2016 and 2017 confirm wake lengths of more than tens of kilometres under stable atmospheric conditions, with maximum wind speed deficits of 40%, and enhanced turbulence. These measurements were the first step in a large research project to describe and understand the physics of large offshore wakes using direct measurements, together with the assessment of satellite imagery and models.

Based on wind speed, direction and power data, an assessment method of wind energy potential using finite mixture statistical distributions is proposed. Considering the correlation existing and the effect between wind speed and direction, the angular-linear modeling approach is adopted to construct the joint probability density function of wind speed and direction. For modeling the distribution of wind power density and estimating model parameters of null or low wind speed and multimodal wind speed data, based on expectation–maximization algorithm, a two-component three-parameter Weibull mixture distribution is chosen as wind speed model, and a von Mises mixture distribution with nine components and six components are selected as the models of wind direction and the correlation circular variable between wind speed and direction, respectively. A comprehensive technique of model selection, which includes Akaike information criterion, Bayesian information criterion, the coefficient of determination R 2 and root mean squared error, is used to select the optimal model in all candidate models. The proposed method is applied to averaged 10-min field monitoring wind data and compared with the other estimation methods and judged by the values of R 2 and root mean squared error, histogram plot and wind rose diagram. The results show that the proposed method is effective and the area under study is not suitable for wide wind turbine applications, and the estimated wind energy potential would be inaccuracy without considering the influence of wind direction.

The impact of variable renewable energy (VRE) sources on an electricity system depends on technological characteristics, demand, regulatory practices and renewable resources. The costs of integrating wind or solar power into electricity networks have been debated for decades yet remain controversial and often misunderstood. Here we undertake a systematic review of the international evidence on the cost and impact of integrating wind and solar to provide policymakers with evidence to inform strategic choices about which technologies to support. We find a wide range of costs across the literature that depend largely on the price and availability of flexible system operation. Costs are small at low penetrations of VRE and can even be negative. Data are scarce at high penetrations, but show that the range widens. Nonetheless, VRE sources can be a key part of a least-cost route to decarbonization. As the cost of variable renewable energy generation has fallen and its proportion in power mixes has increased, discussion of its integration costs has intensified. Heptonstall and Gross systematically review the literature on these costs and asses the range of impacts it is shown to have.

Accurate solar and wind generation forecasting along with high renewable energy penetration in power grids throughout the world are crucial to the days-ahead power scheduling of energy systems. It is difficult to precisely forecast on-site power generation due to the intermittency and fluctuation characteristics of solar and wind energy. Solar and wind generation data from on-site sources are beneficial for the development of data-driven forecasting models. In this paper, an open dataset consisting of data collected from on-site renewable energy stations, including six wind farms and eight solar stations in China, is provided. Over two years (2019–2020), power generation and weather-related data were collected at 15-minute intervals. The dataset was used in the Renewable Energy Generation Forecasting Competition hosted by the Chinese State Grid in 2021. The process of data collection, data processing, and potential applications are described. The use of this dataset is promising for the development of data-driven forecasting models for renewable energy generation and the optimization of electricity demand response (DR) programs for the power grid.Measurement(s)renewable energy generationTechnology Type(s)supervisory control and data acquisition systemSample Characteristic - LocationChina