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

Abstract

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.

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

“…The aim of assessing the DG hosting capacity is to determine the maximum DG capacity that can be installed in the distribution network without violating operation constraints. Thus, the objective function (7) sums up all of the DG capacities at any candidate bus:…”

“…Since the objective function (7) only contains the capacity of each DG, we only consider the Benders feasibility cut in the master problem. Unlike the conventional BD method, which simply generates Benders cuts from the dual variable in the subproblem, we follow the method in [34] and modify our Benders cut into a bilinear form.…”

“…Step 3: construct an MILP to derive the LB. This problem is composed of the objective function in (7) and the constraints in ( 8)-( 13), (17), and ( 20) with fixed ŵs from the master problem. Then, solve this problem and obtain its optimal objective V ̂(k) L .…”

“…As is well-known, the outputs of neighboring DG outputs usually have strong spatial correlations owing to similar meteorological conditions in the distribution system area; these correlations amplify the uncertainty in the distribution network [6]. Reference [7] demonstrates that such such correlation has a strong impact on PV hosting capacity. Furthermore, the complex geographical environment of the distribution system area leads to various abnormal marginal distributions and structures of the nonlinear dependencies among different DG outputs [8], [9].…”

Robust dispatching model of active distribution network considering PV time-varying spatial correlation

“…Currently, common methods to establish a PV-output spatial-correlation model mainly include correlation coefficient matrices, covariance matrix, Copula function, and deep neural network. In (Wu et al, 2021), the Pearson correlation coefficient matrix was used to calculate the spatial correlation of the two PVs, from which empirical distributions of spatial correlation coefficients and distances were obtained. In (Luo et al, 2020), the Kendall rank correlation coefficient, which can measure the correlation of nonlinear variables, was used as the parameter of the Frank-Copula function.…”