Industrial Arrangement of Large-scale,Non-grid-connected Wind Power Industrial Zones in Coastal Areas of China

Constructing an industrial system for a large-scale,non-grid-connected wind power industry is a key step towards the diverse utilization of wind power.However,wind power exploitation is not only a technical challenge but an industrial problem as well.The objective of this study is to introduce a concept of large-scale,non-grid-connected wind power(LSNGCWP) industrial zones and establish an evaluation model to assess their industrial arrangement.The data of wind energy,industry,nature resources and socio-economy were collected in this study.Using spatial overlay analysis of geographic information system,this study proposes a spatial arrangement of the LSNGCWP indus-trial zones in the coastal areas of China,which could be summarized as the 'one line and three circles' structure,which will contribute to the optimization of the industrial structure,advance the wind power technology,coordinate the multi-industrial cooperation,and upgrade the industrial transformation of China's coastal areas.

Evaluation and Forecasting of Wind Energy Investment Risk along the Belt and Road Based on a Novel Hybrid Intelligent Model

The timely and effective investment risk assessment and forecasting are of great significance to ensure the investment safety and sustainable development of wind energy along the Belt and Road.In order to obtain the scientific and real-time forecasting result,this paper constructs a novel hybrid intelligent model based on improved cloud model combined with GRA-TOPSIS and MBA-WLSSVM.Firstly,the factors influencing investment risk of wind energy along the Belt and Road are identified fromthree dimensions:endogenous risk,exogenous risk and process risk.Through the fuzzy threshold method,the final input index system is selected.Secondly,the risk evaluation method based on improved cloud model andGRA-TOPSIS is proposed.Thirdly,a modern intelligent model based on MBA-WLSSVMis designed.In modified bat algorithm(MBA),tent chaotic map is utilized to improve the basic bat algorithm,while weighted least squares support vector machine(WLSSVM)adopts wavelet kernel function to replace the traditional radial basis function to complete the model improvement.Finally,an example is given to verify the scientificity and accuracy of themodel,which is helpful for investors tomake fast and effective investment risk forecasting of wind energy along the Belt and Road.The example analysis proves that the proposedmodel can provide reference and basis for investment corpus to formulate the investment strategy in wind energy along the Belt and Road.