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Summary Electric heating is perceived as an effective way to tackle heavy wind curtailment and severe smog in northern China, wherein distributed electric heating (DEH) accounts for a significant proportion. However, more research is required with regard to its thermal characteristic. This paper presents the multiperiod heating storage control for a DEH load to reduce wind curtailment. To cope with intermittent wind power, a novel multi‐objective and layered optimization method is proposed by decomposing the integrated electricity and heating optimization problem into the electricity optimization subproblem and the heating optimization subproblem. Additionally, the influence of solar radiation on the thermal characteristic of the DEH load is considered in the building thermal process model when creating the day‐ahead schedule of the DEH load control, and this is also regarded as the basis for grouping DEH users. Last, the simulation and economic feasibility analysis based on the data measured by the DEH system of Jilin Province, China, are carried out. The results show that this proposal can reduce the electricity cost for DEH users by 25%, and the critical feasible price for curtailed wind power is varying between 86.1 and 115.3 yuan/MWh according to different subsidy methods. [ABSTRACT FROM AUTHOR]

According to the requirement of energy sustainable development strategy in Jilin province, this paper evaluates the performance of wind power coupling compressed air energy storage projects for a wind farm in Jilin from the perspective of sustainability. Firstly, a sustainable criteria system is established from economic, social and ecological dimensions, and 10 secondary criteria are given. Secondly, to fully express the hesitancy and fuzziness of experts, linguistic fuzzy hesitant sets (LFHSs) are used to describe the subjective evaluation information. And then, the cloud model is developed to deal with the randomness and fuzziness of LFHS. Thirdly, a multi criteria decision-making model based on improved Choquet fuzzy integral and deviation degree is proposed for a multi criteria evaluation problem with initial data in the form of crisp values, interval numbers and linguistic fuzzy hesitant sets. The deviation degree can deal with multi-source heterogeneous data and Choquet fuzzy integral improved with Mobius transformation can eliminate the overlap information between criteria. Finally, the case study in Jilin is given to illustrate the effective of the proposed evaluation criteria and technique. From the results, the project 4: wind power coupling compressed air energy storage system with meshing switch is the most suitable alternative for the wand farm, followed by the project of wind power coupling compressed air energy storage system with variable configuration. Moreover, the sensitivity analysis reveals that the project 4 always occupies its top ranking when its weight is floating within a certain range. At last, some policy implications to promote wind power industry are given for the government. • The sustainability evaluation criteria system for wind power project is presented. • A MCDM method based on Choquet fuzzy integral and Mobius transformation is proposed. • Linguistic fuzzy hesitant sets are used to present subjective evaluation information. • The proposed model can deal with multi-source heterogeneous data. [ABSTRACT FROM AUTHOR]

• A meso-microscale coupling model is applied to a real onshore wind farm. • The atmospheric stability is introduced and refined in the multi-scale coupling process. • The wind flow data is simulated throughout the year with a resolution of 1 h. • Downscaling simulation is performed using mesoscale data from three different heights. The abundance level of wind resources is the first and most important factor to consider in the wind farm design. The accuracy of wind energy resource assessment directly determines the operating income of a wind farm after its construction is completed. Therefore, aiming to meet the requirements of accurate wind resource assessment in areas with a complex terrain or those lacking the observation information, this paper proposes a multi-scale coupling numerical simulation method based on atmospheric stability. First, the atmospheric flow field simulation results at the heights of 200 m, 300 m, and 400 m are extracted from the mesoscale Weather Research and Forecasting (WRF) model and used as inlet boundary conditions of a microscale Computational fluid dynamics (CFD) model. Then, to obtain more accurate coupling data at the height of a wind turbine hub, the atmospheric stability parameter is introduced into the multi-scale coupling process, data correction is conducted at the three heights, and a comparison with the observation data of a wind tower is made. Finally, a numerical analysis is conducted using real data of a wind farm in eastern Inner Mongolia and Jilin. The analysis results show that the proposed method can improve the assessing accuracy of wind resources of wind farms using mesoscale data, which is valuable to a certain extent in real-world applications. [ABSTRACT FROM AUTHOR]

• A novel short-term wind speed forecasting approach is proposed. • The hyper-parameters of the SVM model is optimized with Jaya Algorithm. • The effectiveness of the proposed approach is validated with real datasets. Wind energy is an emerging environmentally friendly energy source. However, due to the uncertainty and volatility of wind speed, wind energy cannot be effectively exploited, and it is essential to build an accurate wind speed forecasting model. In this paper, a Jaya algorithm-based support vector machine (Jaya-SVM) model is proposed for short-term wind speed forecasting. Different from the typical SVM regression, the most representative features of input data are selected and the hyper-parameters of SVM are optimized by using the Jaya optimization algorithm. To examine the performance of the Jaya-SVM model, seven other wind speed forecasting models, Least Absolute Shrinkage and Selection Operator, Extreme Gradient Boosting model, Multi-Layer Perceptron Regression model, Deep Belief Network, Gaussian Process Regression, Stacked Sparse Autoencoder and Granular Computing method are employed for comparison. The wind speed data collected in Jilin, China is fully utilized. The computational results demonstrate that the Jaya-SVM model generates the best results among all eight models in terms of MAE, MSE, MAPE, R 2 and reliability, having the capacity of accurate wind speed forecasting. [ABSTRACT FROM AUTHOR]