Data-driven Local Control Design using Optimization and Machine Learning Techniques

TL;DR

This paper presents a data-driven approach combining optimization and machine learning to design local controls for distribution networks, eliminating the need for communication infrastructure and outperforming standard controls.

Contribution

It introduces a novel data-driven algorithm that uses historical data and machine learning to emulate optimal control behavior without communication.

Findings

Outperforms standard local control methods.

Successfully emulates optimal power flow control.

Effective on unbalanced low-voltage distribution networks.

Abstract

The optimal control of distribution networks often requires monitoring and communication infrastructure, either centralized or distributed. However, most of the current distribution systems lack this kind of infrastructure and rely on suboptimal, fit-and-forget, local controls to ensure the security of the network. In this paper, we propose a data-driven algorithm that uses historical data, advanced optimization techniques, and machine learning methods, to design local controls that emulate the optimal behavior without the use of any communication. We demonstrate the performance of the optimized local control on a three-phase, unbalanced, low-voltage, distribution network. The results show that our data-driven methodology clearly outperforms standard industry local control and successfully imitates an optimal-power-flow-based control.