Experimental Verification of Self-Adapting Data-Driven Controllers in Active Distribution Grids

Stavros Karagiannopoulos,Athanasios Vasilakis,Panos Kotsampopoulos,Gabriela Hug,Petros Aristidou,Nikos Hatziargyriou

doi:10.3390/en14102837

Stavros Karagiannopoulos, Athanasios Vasilakis + Show 4 more

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https://doi.org/10.3390/en14102837

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Abstract

Lately, data-driven algorithms have been proposed to design local controls for Distributed Generators (DGs) that can emulate the optimal behaviour without any need for communication or centralised control. The design is based on historical data, advanced off-line optimization techniques and machine learning methods, and has shown great potential when the operating conditions are similar to the training data. However, safety issues arise when the real-time conditions start to drift away from the training set, leading to the need for online self-adapting algorithms and experimental verification of data-driven controllers. In this paper, we propose an online self-adapting algorithm that adjusts the DG controls to tackle local power quality issues. Furthermore, we provide experimental verification of the data-driven controllers through power Hardware-in-the-Loop experiments using an industrial inverter. The results presented for a low-voltage distribution network show that data-driven schemes can emulate the optimal behaviour and the online modification scheme can mitigate local power quality issues.

Highlights

IntroductionModern distribution system operators need to control Distributed Generators (DGs), such as Photovoltaic units (PV), wind turbines, and other distributed energy resources, such as battery energy storage systems and controllable loads, to guarantee safe grid operation, increase their operational flexibility or provide ancillary services to higher voltage levels
We present the balanced Low Voltage (LV) distribution networks (DN) used for the experimental verification of the proposed data-driven schemes
We first present the results under expected conditions and we investigate the suitability of data-driven controllers to emulate the optimal response

Summary

Introduction

Modern distribution system operators need to control Distributed Generators (DGs), such as Photovoltaic units (PV), wind turbines, and other distributed energy resources, such as battery energy storage systems and controllable loads, to guarantee safe grid operation, increase their operational flexibility or provide ancillary services to higher voltage levels. Centralised approaches based on optimal control of DGs usually require a communication, remote monitoring and control infrastructure, which current distribution networks (DN) lack due to high costs and complexity. Local schemes offer communication-free, robust, cheap, but sub-optimal solutions which do not fully exploit the DG capabilities. Data-driven control algorithms have been proposed, which use historical data, advanced off-line optimization techniques, and machine learning methods, to design local controls that emulate the optimal behaviour without the use of any communication [1,2,3,4]

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