Solar and wind resources are vital for the sustainable energy transition. Although renewable potentials have been widely assessed in existing literature, few studies have examined the statistical characteristics of the inherent renewable uncertainties arising from natural randomness, which is inevitable in stochastic-aware research and applications. To realize China's carbon neutrality goal proposed in 20201, the installed capacity of renewable energy resources should be significantly increased. As China mentioned in the 2020 Climate Ambition Summit, the installation of wind and solar energy should reach no less than 1.2 Terawatt (TW) in 2030, almost 3 times more than that in 20192, becoming the dominant electricity generation resource. However, due to the salient intermittency and volatility, wind and solar energy operation and modeling face the critical challenges of a high degree of uncertainty, which must be considered in energy research3,4,5.Various studies have investigated the generalized spatial and temporal characteristics of renewable energy resources in regional areas and compiled standardized test datasets, including statistical analysis studies of current wind and solar resources6,7,8,9,10 and important impact factors of renewable energy generation11, current wind and solar energy resource estimation studies using meteorological data and prediction methods12,13,14, and future wind and solar energy resource assessment studies based on wind speed and solar irradiation data15,16,17,18,19. However, renewable energy resources rely on weather conditions and thus are highly unstable, posing great challenges to accurate and reliable prediction. Some studies have examined the uncertainty of solar and wind power equipped with energy storage to assess their potential to mee. Nationwide analysis of the uncertainty of wind and solar generationWe obtain an error-analysis benchmark for the forecasting of hourly wind and solar output potential in 30 provinces of China in 2016 using the autoregressive integrated moving average (ARIMA) model based on installation and hourly generation data retrieved from our previous study11. The spatial distributions of the wind and solar uncertainty across China are analyzed through the prediction error, as shown in Fig. 1a, b, respectively, excluding Taiwan, Hong Kong, and Macau, as well as wind energy in Tibet and solar energy in Chongqing (unsuitable for wind/solar energy construction10 or data limitations). The prediction error is calculated as the predicted value minus the actual value (please refer to Methods). The wind prediction error ranges from 2.1 to 13.6%, with the largest error in Tianjin (TJ) and the smallest error in Yunnan (YN). The overall prediction error of solar energy is smaller than that of wind energy, ranging from 3.9 to 10.0%, and the largest provincial prediction error is observed in Shanghai (SH), while the smallest provincial prediction error comes from Xinjiang (XJ). Detailed error analysis of wind and solar power for each province is shown in Supplementary Figs. 1–3, respectively. We divide the 30 provinces into four groups according to the wind prediction error: (i) >9%, (ii) 7–9%, (iii) 5–7%, and (iv) <5%. Four groups can also be distinguished in term. We provide an error-analysis benchmark for hourly wind and solar generation in 30 provinces of China with significance for research, industry, and policy decision-making. The proposed benchmark reveals statistical characteristics of wind and solar uncertainty, which is indispensable for academic research. First, it can help to build the PDF of wind and solar generation, providing scenario basis for stochastic economic dispatch43. Energy scheduling may also use renewable generation and consider their prediction errors as a probability distribution44. Second, the benchmark is applicable for robust optimization, because the best and worst-case operating conditions can be obtained through prediction results. It can also replace the assumed prediction errors to generate reasonable probability distribution and be used as expected forms in optimization formulations45,46. Third, risk assessment can also benefit from the benchmark, as the security region of power systems can be depicted based on the prediction results and errors47. Without our work, most of these research use assumed renewable generation and prediction error. In industry, the benchmark plays a critical role as a guiding reference for intuitive analysis of resource distributions and fluctuations, which could help to evaluate investment revenue and the risk of renewable projects. If prediction errors are large and renewable generation is unstable, renewable projects will take more risks, and the investment should be reduced. In addition, policy-makers and system plan. Wind and solar output dataHourly wind and solar output data for 2016 pertaining to 30 provinces of China are retrieved from previous work11, except for Tibet wind, Chongqing solar, Taiwan, Hong Kong, and Macao. The dataset contains 8760 h of wind and solar output data, and wind and solar installed capacity data for these 30 provinces are included. We denote the hourly wind output as ({W}_{i,t+{{{{mathrm{1,0}}}}}}) and the hourly solar output as ({S}_{i,t+{{{{mathrm{1,0}}}}}}), where i and t are province and time slot indices, respectively, for (iin [1,N],tin [1,T]), (N=30), and (T=8760). As previously mentioned, daily wind and solar output data are also required for the analysis, which can be calculated as Eqs. (1)-(2):$${W}_{{{{{{rm{Day}}}}}},{{{{{rm{i}}}}}},{{{{{rm{c}}}}}},0}={{max }}({W}_{i,t,0},{W}_{i,t+1,0}, cdots {W}_{i,t+23,0}),t=24 cdot (c-1)$$(1) $${S}_{{{{{{rm{Day}}}}}},{{{{{rm{i}}}}}},{{{{{rm{c}}}}}},0}={{max }}({S}_{i,t,0},{S}_{i,t+1,0}, cdots {S}_{i,t+23,0}),t=24 cdot (c-1)$$(2) where ({S}_{{{{mbox{Day}}}},i,c,0}) and ({W}_{{{{mbox{Day}}}},i,c,0}) are the daily solar and wind output, respectively, of province i in time slot t, and c is a day index, for (cin left[1,{C}right],{{{{{rm{and}}}}}},C=365).Benchmark prediction model.
