Assessment Model for Distributed Wind Generation Hosting Capacity Considering Complex Spatial Correlations

To facilitate the large-scale integration of distributed wind generation(DWG), the uncertainty of DWG outputs needs to be quantified, and the maximum DWG hosting capacity(DWGHC) of distribution systems must be assessed. However, the structure of the high-dimensional nonlinear dependencies and the abnormal marginal distributions observed in geographically dispersed DWG outputs lead to the increase of the complexity of the uncertainty analysis. To address this issue,this paper proposes a novel assessment model for DWGHC that considers the spatial correlations between distributed generation(DG) outputs. In our method, an advanced dependence modeling approach called vine copula is applied to capture the high-dimensional correlation between geographically dispersed DWG outputs and generate a sufficient number of correlated scenarios. To avoid an overly conservative hosting capacity in some extreme scenarios, a novel chance-constrained assessment model for DWGHC is developed to determine the optimal sizes and locations of DWG for a given DWG curtailment probability. To handle the computational challenges associated with large-scale scenarios, a bilinear variant of Benders decomposition(BD) is employed to solve the chance-constrained problem.The effectiveness of the proposed method is demonstrated using a typical 38-bus distribution system in eastern China.

Potential Assessment of Spatial Correlation to Improve Maximum Distributed PV Hosting Capacity of Distribution Networks

Successful distributed photovoltaic (PV) planning now requires a hosting capacity assessment process that accounts for an appropriate model of PV output and its uncertainty. This paper explores how the PV hosting capacity of distribution networks can be increased by means of spatial correlation among distributed PV outputs. To achieve this, a novel PV hosting capacity assessment method is proposed to account for arbitrary geographically dispersed distributed PVs. In this method, the empirical relation between the spatial correlation coefficient and distance is fitted by historical data in one place and then applied to model the joint probability distribution of PV outputs at a neighboring location. To derive the PV hosting capacity at candidate locations, a stochastic PV hosting capacity assessment model that aims to maximize the PV hosting capacity under thermal and voltage constraints is proposed. Benders decomposition algorithm is also employed to reduce the computational cost associated with the numerous sampling scenarios. Finally, a rural 59-bus distribution network in Suzhou, China, is used to demonstrate the effectiveness of the proposed PV hosting capacity assessment methodology and the significant benefits obtained by increasing geographical distance.