Reliable prediction of near surface wind over complex terrain is limited by the mismatch between the spatial resolution of operational forecasts and terrain controlled local wind variability. Here, we develop KiloGen, a diffusion posterior sampling framework for kilometre scale wind forecast enhancement. KiloGen learns a high resolution vector wind prior from Weather Research and Forecasting (WRF) model simulations and constrains posterior sampling with 25 km forecasts from the European Centre for Medium Range Weather Forecasts (ECMWF) at inference time. This formulation avoids paired ECMWF and WRF training samples and an explicitly learned mapping from coarse to fine resolution. Applied over Shanxi, China, a region with complex mountainous terrain, KiloGen reconstructs terrain organized wind structures and restores high wavenumber variability while retaining the large scale evolution of the operational forecast. Station verification shows that KiloGen achieves the lowest overall wind speed root mean square error (RMSE) among the evaluated products, with larger benefits at elevated and topographically complex sites. The improvement is strongest under strong wind conditions, reducing RMSE by approximately 10

With the increasing integration of renewable energy sources, the power distribution terminals and data volumes in smart distribution networks continue to expand, which results in cloud architecture, fully distributed algorithms and some traditional communication methods failing to meet the operational requirements with the smart distribution power networks. In comparison, 5G communication exhibits significant potential performance due to its enhanced capabilities, particularly its network slicing feature, which facilitates the adoption of multi-access edge computing (MEC) by classifying terminal services and information types, while MEC could enhance computational capabilities at distribution terminals. This paper validates the feasibility of edge computing deployment in 5G networks based on the OMNET++ platform and proposes a hybrid architecture designed for smart distribution power networks, combining the respective advantages of both cloud and edge computing, whose performance further analyzed through comparative studies with other algorithms. The results demonstrated that the proposed cloud-edge collaborative architecture in this paper was effective and showed the promising performance for the smart distribution power networks.

This study investigates the impacts of different physics schemes on hurricane forecasting across different applications of the unified forecast system (UFS) limited area model (LAM), including the UFS short-range weather (SRW) application, the hurricane analysis and forecast system (HAFS), and the UFS single-column model (SCM). The physics schemes compared here encompass cloud microphysics schemes, planetary boundary layer (PBL) parameterizations, surface layer schemes, gravity wave schemes, and land surface models. All these schemes are implemented through the common community physics package (CCPP) framework. Six experiments, each employing different combinations of physics schemes, were designed to explore their influences on hurricane forecasting. The simulated results from the UFS LAM applications are compared with the observed results from stations, radar, and satellite data. The impacts of various physics schemes on hurricane intensity, track, timing, location, and precipitation are investigated through case studies of different hurricanes, such as Barry, Lorenzo, and Ian. All experiments exhibit a right-of-track bias for hurricanes Barry and Lorenzo. In contrast, the forecasts for hurricane Ian have a left-of-track bias. The results show that the moist turbulence kinetic energy (TKE)-based eddy-diffusivity mass-flux (EDMF) PBL scheme can enhance the hurricane intensity through the moist process. The aerosol-aware (AA) Thompson microphysics scheme appears to provide better precipitation forecasts compared to those from the GFDL microphysics scheme. The combination of these updated physics schemes gives a better track comparing with the result from other old physics settings for hurricane Barry, and it also has an improved intensity in the forecast of hurricane Ian. Sensitivity tests conducted using the UFS SRW application indicate that increasing horizontal resolution and adding vertical levels can enhance model performance. Experiments also revealed that the PBL scheme significantly affects hurricane tracks, a finding further supported by SCM results. Additionally, sensitivity tests demonstrate that improved initial conditions through data assimilation can lead to better simulations of both the hurricane track and intensity, as well as more accurate predictions of hurricane landfall timing and location.

This study implements and improves a planning model for large-scale renewable energy bases that systematically exploits the spatiotemporal complementarity among wind, solar, and battery storage. First, the complementary characteristics are quantified. Then, the Complementary Characteristics-based Optimization Model is formulated to co-optimize the siting and sizing of wind farms, photovoltaic plants, and battery energy storage systems. The model aims to minimize total costs while maximizing power output during peak-demand periods. The Nelder-Mead simplex algorithm is employed to solve this optimization problem. A case study in Qinghai, China, demonstrates that compared to a conventional resource maximization approach, the CCOM increases firm capacity by 60- 80% (reaching 5,255 – 5,333 MW versus a baseline of 2,910 - 3,566 MW), albeit at a 7.2% higher capital cost. These results offer a more balanced and cost-effective solution for large-scale renewable energy integration.

This study provides a national-scale projection of China’s photovoltaic (PV) potential by combining CMIP6 model accuracy assessment with long-term turning-point detection. Based on rate-of-change evaluation against historical observations, four models (ACCESS-CM2, ACCESS-ESM1.5, IPSL-CM6A-LR, and KIOST-ESM) are identified as most reliable, and their ensemble mean (MM4) is used to examine PV potential from 1984 to 2100. Under low- and medium-emission scenarios, PV potential shows sustained growth with turning points around 2034 and 2028. In contrast, under the high-emission SSP585 scenario, MM4 reveals a critical turning point in 2035 followed by a multi-decadal decline until 2094, driven by a dual temperature-induced constraint: direct reductions in PV module efficiency and indirect suppression of surface irradiance through enhanced water vapor and cloud optical effects. These radiative feedbacks ultimately dominate the long-term trajectory of PV potential under SSP585. Compared with MM4, the remaining models (MM11) yield more optimistic projections but likely overestimate future PV potential. Overall, the identified turning points reflect a shift from aerosol-driven brightening to warming-driven declines, underscoring the need for high-accuracy models and adaptive strategies under high-emission pathways.

Establishing power systems with a high share of renewable energy sources is a pivotal step toward achieving a globally sustainable transition to green and low-carbon energy. This study focuses on low-output wind power that affects the generation capacity of power systems with a high share of renewable energy sources. Utilizing the Coupled Model Intercomparison Project Phase 6 datasets, a predictive model for low-output wind power was employed to investigate regional trends worldwide. The frequency and duration of low-output wind-power events exhibited increasing trends globally, particularly in East Asia and South America, but not in North America. By 2060, the annual total days with low-output wind power in East Asia and South America could rise to 13 and 5 d, and the maximum continuous duration of low-output wind power could reach 5 and 2 d, respectively. As wind power becomes a primary electricity source, such low output could lead to shortages in energy supply within the power system, triggering large-scale power outages. This issue calls for critical attention when establishing power systems with a high share of renewable energy sources. The conclusions provide a basis for analyzing power supply risks and configuring flexible power sources for scenarios with a high share of renewable energy.

The development of wind energy is indispensable in the pursuit of global carbon neutrality. This article's analysis of observational data across China reveals the annual average wind speed declined at a rate of −0.167 m · s−1 decade−1 between 1981 and 2014. This rate is 33 times faster than projections from the Coupled Model Intercomparison Project (CMIP) of the World Climate Research Programme. We propose a novel wind power scale estimation method based on annual average wind speed, suitable for assessing climate change impacts. Considering China's planned wind power generation in 2030, climate change may increase the required wind installed capacity by over 25% under the observed trend scenario. In contrast, historical average and CMIP scenarios could substantially overestimate wind potential while underestimating the necessary future wind power development scale. Climate change poses potential adverse impacts on China's carbon peak goals, necessitating targeted measures to mitigate these effects.

This article proposes a novel method for analytically assessing the value of renewable forecasting accuracy in power system operation. Different from previous assessment methods where renewable forecasting accuracy is perturbed numerically, the proposed method directly derives sensitivity formulae of the operation model with respect to (w.r.t.) renewable forecasting error distribution change. Compared with conventional analytical sensitivity methods dealing with only deterministic parameters, deriving such sensitivity formulae is challenging and necessitates the closed-form expression of forecasting error distribution parameters in the operation model. To tackle this challenge, we propose to model and reformulate the two-stage stochastic unit commitment for wind-penetrated power systems based on the Point Estimate Method (PEM). Then, leveraging the explicitly expressed wind power statistical moments in the PEM-based model, the sensitivity formulae of expected operation costs w.r.t. both global and nodal wind power forecasting error distribution change are derived. The proposed method provides to assess the value of renewable forecasting accuracy and identify critical nodes with uncertain injections that are most influential on system operation without exhausting numerical simulations. Case studies on modified IEEE systems verify the effectiveness of the proposed method.

The global experience on wind farm development reveals that the wind power uncertainty is affected by large-scale wind farm sizes due to the spatial smoothing effect. The dependence on wind farm sizes features the wind power uncertainty as decision-dependent uncertainty (DDU) during the wind farm expansion process. This paper proposes a stochastic expansion planning model for large-scale wind farms considering the DDU and addresses the coupling relationship between expansion decisions and DDU. Specifically, to explore how the structural features of the expansion model change with DDU in different forms, the proposed expansion model with DDU is established based on Point Estimate Method (PEM). Via the explicit expression between decisions and decision-dependent statistical moments of uncertain parameters in the PEM-based model, an in-depth discussion of model features is implemented. An iterative solution method with convergence analysis is proposed based on the explored model features to tackle the DDU. Besides, a modified Benders decomposition method is adopted in each iteration for the PEM-based two-stage optimization model potentially containing both min-min and min-max terms. The effects of DDU on wind farm expansion schemes are analyzed. Case studies verify the proposed expansion model and the solution method.

中尺度涡旋是引发强降水等一系列气象灾害的重要天气系统之一,中尺度涡旋识别是对其进行研究的重要基础.目前,如何客观准确地识别中尺度涡旋并对其进行较全面的评估仍是一项充满挑战的任务.本文从我国中尺度涡旋的基本特征出发,提出了一种基于风场和相对涡度场的涡旋识别标准并发展了适用于高分辨率格点数据的中尺度涡旋客观识别算法.该算法能准确识别出中尺度气旋性环流并定位涡旋中心,较现有常规中尺度涡旋识别方法而言,具有误判率低、定位精度高等特点.本文将该客观识别算法应用于长江流域频发的 3类中尺度涡旋(高原涡、西南涡、大别山涡)的识别中,结果表明对于不同时间段、不同分辨率的再分析资料(逐 6小时的 0.5°×0.5°NCEP CFSR再分析资料、逐小时的 0.25°×0.25°ERA5再分析资料),本识别算法对 3类中尺度涡旋均有较好的识别效果.本文基于1979～2020年共42年暖季(5～9月)大别山涡的数据集(共计36357时次)对新发展的中尺度涡旋客观识别算法进行了定量评估,结果表明本算法能够长期稳定地识别涡旋,42年的平均命中率为 95.5%.此外,本文提出了涡旋连续性判定和三维追踪方案,较现有常规中尺度涡旋追踪方法具有显著优势.

Abstract Variability and uncertainty in wind resources pose significant challenges to the expansion planning of wind farms and associated flexible resources. In addition, the spatial smoothing effect, indicating the impact of wind farm scale on aggregated wind power prediction errors, further aggravates the challenge. This paper proposes a chance‐constrained co‐expansion planning method considering the spatial smoothing effect, where the expansion of wind farm capacity, batter energy storage capacity, and power transmission lines are co‐optimized. Specifically, a decision‐dependent uncertainty (DDU) model is established capturing the dependency of wind power uncertainties on wind farm expansion decisions under the spatial smoothing effect. Unlike traditional optimization diagram where decisions are made under only decision‐independent uncertainty (DIU) with fixe properties, properties of decision‐dependent uncertain parameters would be inversely altered by decisions. To effectively tackle the coupling relation between decisions and DDU, DDU‐based chance constraints are formulated in an analytical manner, where the decisions and decision‐dependent uncertain parameters are expressed in a closed form. Eventually, with piecewise linearization of the DDU model and the polynomial approximation of cumulative distribution function of uncertain parameters, the proposed chance‐constrained optimization model with DDU is converted into a mixed‐integer second‐order cone program (MISOCP). Case studies verify the effectiveness of the proposed method.

A sandstorm is one of the extreme weather events causing extensive damage to overhead transmission lines (OTLs). Due to potential postsandstorm insulator flashover, OTLs face high failure probabilities, and implementing in-time maintenance on OTLs is crucial to mitigate postsandstorm losses. Aiming at determining an optimal maintenance sequence of targeted OTLs, this article proposes a decision-dependent stochastic approach for the joint operation and maintenance of OTLs. First, considering the inherent dependency of the uncertain availability of OTLs on maintenance decisions, the multiperiod maintenance process of OTLs is modeled as a stochastic process with decision-dependent uncertainty (DDU). Second, a two-stage stochastic model with DDU is formulated, where the maintenance sequence and unit commitment decisions are made in the first stage and the second stage comprises scenariowise operation. Then, to tackle the coupling relation between decisions and DDU, a unique modeling transformation technique is adopted to convert the established decision-dependent stochastic model into a computationally efficient form. Case studies verify the effectiveness of the proposed method for postsandstorm maintenance scheduling.

掌握冻雨的时空分布特征对于电力、交通、通信、农林等部门具有较高的指导意义.前期冻雨研究多基于站点观测资料,受限于该资料长度较短、分布不均与部分缺失等因素,目前对我国冻雨时空分布特征的认识可能尚存不足.新一代ERA5再分析资料中包含了其他再分析资料所未提供的冻雨资料,为进一步认识我国冻雨时空分布特征提供了可能.本文使用ERA5冻雨资料分析了 1979～2020年我国年冻雨日数和年冻雨量的时空分布特征,结果表明:我国年冻雨日数和年冻雨量集中分布在贵州、湖南等地,直接影响7条"西电东送"特高压直流输电线路,影响长度总计约4900 km;冻雨集中分布地区的年冻雨日数及年冻雨量均呈下降趋势;年冻雨日数EOF(Empirical Orthogonal Function)第一模态(方差贡献36.96％)主要分布在黑河—腾冲线以东,总体呈下降趋势,且以秦岭—淮河线为界呈南北反相分布;年冻雨日数EOF第二模态(方差贡献11.56％)反映出中国冻雨集中地区的2个局地反相分布区,位相交替周期为1～5年;年冻雨量EOF模态的时空特征同年冻雨日数类似.

The development of wind energy is indispensable in the pursuit of global carbon neutrality. Following decades of climate change, China's annual average wind speed has shown a clear decline, but the rate of this decline and its potential impacts on the need for wind power development in China have not been quantified. Here, we reveal that China's observed wind speed has declined significantly at -0.169 m/s/10 yr, 33.33 times the rate predicted by the Coupled Model Intercomparison Project (CMIP) of World Climate Research Programme, indicating a severe underestimation by those models. We attribute this underestimation to CMIP neglecting the atmospheric boundary layer height and greatly underestimating the Arctic amplification effect on wind speed. Scenario analyses demonstrate that China’s future wind power installed capacity, investment costs, and land area occupied based on the observed trend scenario will increase by approximately 53% compared to any trend of three (high, medium, and low) CMIP emission scenarios in order to meet China’s wind-generated electricity target and carbon peak emission goal in 2030. Hence, formulating its wind energy development plans based on these CMIP scenarios’ trends will prevent China from meeting its low-carbon electricity generation of carbon peak emission target by 2030 and delaying the 2060 goal of carbon neutrality, emphasizing that the CMIP models urgently need to be improved. These findings should serve as a warning to countries throughout the Northern Hemisphere to formulate wind power development plans in consideration of the climate change impacts in the pursuit of global carbon neutrality.

精准的风速预报是风电功率预测的基础,风场的预报评估是提升风电功率预测水平的有效途径之一.基于中尺度数值天气预报模式(weather research and forecast,WRF),采用风险评分、相关系数及均方根误差等定量分析指标,结合西北电网数值预报结果,开展6种大气边界层参数化方案的适用性研究.结果表明:YSU(Yonsei University)方案预报的地面纬向风平均相关系数最高为0.87、均方根误差最低仅1.0 m/s,且地面经向风、850 hPa和500hPa高度场、以及500 hPa风速预报效果均最优,是提升西北电网风场预报的最优边界层方案.本研究为西北电网风场(以及其他气象要素)预报效果的提升指明了方向.

In this paper, we discuss a class of two-stage hierarchical games with multiple leaders and followers, which is called Nash–Stackelberg–Nash (N–S–N) games. Particularly, we consider N–S–N games under decision-dependent uncertainties (DDUs). DDUs refer to the uncertainties that are affected by the strategies of decision-makers and have been rarely addressed in game equilibrium analysis. In this paper, we first formulate the N–S–N games with DDUs of complete ignorance, where the interactions between the players and DDUs are characterized by uncertainty sets that depend parametrically on the players’ strategies. Then, a rigorous definition for the equilibrium of the game is established by consolidating generalized Nash equilibrium and Pareto-Nash equilibrium. Afterward, we prove the existence of the equilibrium of N–S–N games under DDUs by applying Kakutani’s fixed-point theorem. Finally, an illustrative example is provided to show the impact of DDUs on the equilibrium of N–S–N games.

•Concentrating solar power (CSP) plays an important role in China’s carbon neutrality path.•The geographical, technical, and CO2 emission reduction potential of CSP in China was evaluated by province.•Approximately 1.02 × 106 km2 of land (11% of land area) can support CSP development.•Over 99% of China's technical potential is concentrated in five western provinces.

Virtual power plant (VPP) provides a flexible solution to distributed energy resources integration by aggregating renewable generation units, conventional power plants, energy storages, and flexible demands. This paper proposes a novel stochastic adaptive robust optimization (SARO) model for determining the optimal self-scheduling plan for VPP’s participation in the day-ahead energy-reserve market. We consider exogenous uncertainties (or called decision-independent uncertainties, DIUs) associated with market clearing prices and available wind generation, as well as endogenous uncertainties (or called decision-dependent uncertainties, DDUs) pertaining to real-time reserve deployment requests. A tractable solution methodology based on modified Benders dual decomposition is developed to effectively solve the proposed SARO model with both DIUs and DDUs. Case studies are conducted to verify the efficiency and applicability of the proposed approach. Comparative results show that the proposed method can mitigate the conservatism of robust strategy by capturing a satisfactory trade-off between profitability and real-time operation feasibility.