Probabilistic Voltage Sensitivity Analysis (PVSA) to Quantify Impact of High PV Penetration on Unbalanced Distribution System

TL;DR

This paper introduces a computationally efficient analytical framework for probabilistic voltage sensitivity analysis in unbalanced distribution systems, enabling stochastic impact assessment of high photovoltaic penetration.

Contribution

It develops an analytical approximation for voltage change due to PV variability and derives its probability distribution, reducing computational complexity compared to scenario-based methods.

Findings

Accurately approximates voltage change at network nodes due to PV variability.

Provides bounds on approximation error to ensure reliability.

Demonstrates effectiveness on IEEE 37 bus system.

Abstract

From an operational and planning perspective, it is important to quantify the impact of increasing penetration of photovoltaics on the distribution system. Most existing impact assessment studies are scenario-based where derived results are scenario specific and not generalizable. Moreover, stochasticity in the temporal behavior of spatially distributed PVs requires a large number of scenarios that increase with the size of the network and the level of penetration. Therefore, we propose a new computationally efficient analytical framework of voltage sensitivity analysis that allows for stochastic analysis of voltage change due to random changes in PV generation. We first derive an analytical approximation for voltage change at any node of the network due to change in power at other nodes in an unbalanced distribution network. The quality of this approximation is reinforced via bounds on the approximation error. Then, we derive the probability distribution of voltage change at a certain node due to random changes in power injections/consumptions at multiple locations of the network. The accuracy of the proposed PVSA is illustrated using a modified version of the IEEE 37 bus test system. The proposed PVSA can serve as a powerful tool for proactive monitoring/control and ease the computational burden associated with perturbation based cybersecurity mechanisms.