DG Hosting Capacity Assessment Considering Dependence Among Wind Speed, Solar Radiation, and Load Demands

Abstract

Dependence of distributed generation (DG) outputs and load plays an essential role in renewable energy accommodation. This paper presents a novel DG hosting capacity (DGHC) evaluation method for distribution networks considering high-dimensional dependence relations among solar radiation, wind speed, and various load types (i.e., commercial, residential, and industrial). First, an advanced dependence modeling method called regular vine (R-vine) is applied to capture the complex dependence structure of solar radiation, wind speed, commercial loads, industrial loads, and residential loads. Then, a chance-constrained DGHC evaluation model is employed to figure out maximum hosting capacity of each DG and its optimal allocation plan with different operational risks. Finally, a Benders decomposition algorithm is also employed to reduce computational burden. The proposed approaches are validated using a set of historical data from China. Results show dependence among different DGs and loads has significant impact on hosting capacity. Results also suggest using the R-vine model to capture dependence among distributed energy resources (DERs) and load. This finding provides useful advice for distribution networks in installing renewable energy generations.

Keywords

• Benders decomposition
• correlation
• DG hosting capacity
• regular vine