Model-less Robust Voltage Control in Active Distribution Networks using Sensitivity Coefficients Estimated from Measurements

TL;DR

This paper introduces a model-less, measurement-based voltage control method for active distribution networks that accounts for measurement uncertainties using online sensitivity coefficient estimation, improving control robustness.

Contribution

It proposes a recursive least squares-based online estimation of voltage sensitivity coefficients and their uncertainties for robust, model-less voltage control in distribution networks.

Findings

Effective voltage regulation in a simulated low-voltage system

Robust control performance despite measurement errors

Improved voltage constraint adherence

Abstract

Measurement-rich power distribution networks may enable distribution system operators (DSOs) to adopt model-less and measurement-based monitoring and control of distributed energy resources (DERs) for mitigating grid issues such as over/under voltages and lines congestions. However, measurement-based monitoring and control applications may lead to inaccurate control decisions due to measurement errors. In particular, estimation models relying on regression-based schemes result in significant errors in the estimates (e.g., nodal voltages) especially for measurement devices with high Instrument Transformer (IT) classes. The consequences are detrimental to control performance since this may lead to infeasible decisions. This work proposes a model-less robust voltage control accounting for the uncertainties of measurement-based estimated voltage sensitivity coefficients. The coefficients and their uncertainties are obtained using a recursive least squares (RLS)-based online estimation, updated whenever new measurements are available. This formulation is applied to control distributed controllable photovoltaic (PV) generation in a distribution network to restrict the voltage within prescribed limits. The proposed scheme is validated by simulating a CIGRE low-voltage system interfacing multiple controllable PV plants.