A Novel Probabilistic Framework to Study the Impact of PV-battery Systems on Low-Voltage Distribution Networks

TL;DR

This paper introduces a new probabilistic framework that integrates home energy management decisions into Monte Carlo simulations to evaluate the impact of PV-battery systems on low-voltage distribution networks efficiently.

Contribution

It presents a novel approach combining Bayesian demand profile generation and policy function approximation for battery scheduling within MC analysis, reducing computational costs significantly.

Findings

Uncoordinated battery scheduling has limited impact on network performance.

The proposed framework reduces simulation time to 5% of traditional optimization methods.

PV-battery systems may not inherently mitigate network issues without coordination.

Abstract

Battery storage, particularly residential battery storage coupled with rooftop PV, is emerging as an essential component of the smart grid technology mix. However, including battery storage and other flexible resources like electric vehicles and loads with thermal inertia into a probabilistic analysis based on Monte Carlo (MC) simulation is challenging, because their operational profiles are determined by computationally intensive optimization. Additionally, MC analysis requires a large pool of statistically-representative demand profiles to sample from. As a result, the analysis of the network impact of PV-battery systems has attracted little attention in the existing literature. To fill these knowledge gaps, this paper proposes a novel probabilistic framework to study the impact of PV-battery systems on low-voltage distribution networks. Specifically, the framework incorporates home energy management(HEM) operational decisions within the MC time series power flow analysis. First, using available smart meter data, we use a Bayesian nonparametric model to generate statistically-representative synthetic demand and PV profiles. Second, a policy function approximation that emulates battery scheduling decisions is used to make the simulation of optimization-based HEM feasible within the MC framework. The efficacy of our method is demonstrated on three representative low-voltage feeders, where the computation time to execute our MC framework is 5% of that when using explicit optimization methods in each MC sample. The assessment results show that uncoordinated battery scheduling has a limited beneficial impact, which is against the conjecture that batteries will serendipitously mitigate the technical problems induced by PV generation.