Data-Driven Affinely Adjustable Robust Volt/VAr Control

TL;DR

This paper introduces a data-driven, robust Volt/VAr control scheme using neural networks to efficiently estimate voltage sensitivities, enabling fast and distributed reactive power adjustments in power systems.

Contribution

It develops a novel data-driven approach with neural networks and a rule-based bus selection for efficient voltage sensitivity estimation in Volt/VAr control.

Findings

Effective voltage regulation demonstrated on IEEE-123 bus system

Reduced information exchange in distributed control

Improved training efficiency and accuracy of sensitivity estimation

Abstract

This paper proposes a data-driven affinely adjustable robust Volt/VAr control (AARVVC) scheme, which modulates the smart inverter reactive power in an affine function of its active power, based on the voltage sensitivities with respect to real/reactive power injections. To achieve a fast and accurate estimation of voltage sensitivities, we propose a data-driven method based on deep neural network (DNN), together with a rule-based bus-selection process using the bidirectional search method. Our method only uses the operating statuses of selected buses as inputs to DNN, thus significantly improving the training efficiency and reducing information redundancy. Finally, a distributed consensus-based solution, based on the alternating direction method of multipliers (ADMM), for the AARVVC is applied to decide the inverter reactive power adjustment rule with respect to its active power. Only limited information exchange is required between each local agent and the central agent to obtain the slope of the reactive power adjustment rule, and there is no need for the central agent to solve any (sub)optimization problems. Numerical results on the modified IEEE-123 bus system validate the effectiveness and superiority of the proposed data-driven AARVVC method.