Abstract

The high-penetration of Distributed Energy Resources (DER) in low voltage distribution grids, mainly photovoltaics (PV), might lead to overvoltage in the point of common coupling, thus, limiting the entrance of renewable sources to fulfill the requirements from the network operator. Volt-var is a common control function for DER power converters that is used to enhance the stability and reliability of the voltage in the distribution system. In this study, a centralized algorithm provides local volt-var control parameters to each PV inverter, which are based on the electrical grid characteristics. Because accurate information of grid characteristics is typically not available, the parametrization of the electrical grid is done using a local power meter data and a voltage sensitivity matrix. The algorithm has different optimization modes that take into account the minimization of voltage deviation and line current. To validate the effectiveness of the algorithm and its deployment in a real infrastructure, the solution has been tested in an experimental setup with PV emulators under laboratory conditions. The volt-var control algorithm successfully adapted its parameters based on grid topology and PV inverter characteristics, achieving a voltage reduction of up to 25% of the allowed voltage deviation.

Highlights

  • Distributed Energy Resources (DER), such as photovoltaic (PV) systems, are being increasingly integrated into distribution networks due to their low carbon emission when generating energy, an affordable price at small-scale level, and to the technology maturity as a strategy to face climate change [1].Several problems appear when massive deployment of DER occurs, such as harmonics distortion, reverse power flows, or power losses [2]

  • EN 50438 Standards and national grid codes allow for grid-tied PV inverters to participate actively in voltage regulation adjusting the exchange of reactive power [6]

  • Experimental validation of the volt-var algorithm with actual PV inverters is a shortcoming from previous studies that allows for testing its effective performance, with its behaviour being connected with real communication devices, and serving as a previous step for deployment at a larger-scale pilot in the same SmArt BI-directional multi eNergy gAteway (SABINA) project, as mentioned in the introduction

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Summary

Introduction

Distributed Energy Resources (DER), such as photovoltaic (PV) systems, are being increasingly integrated into distribution networks due to their low carbon emission when generating energy, an affordable price at small-scale level, and to the technology maturity as a strategy to face climate change [1]. A simplified system that counts on a grid emulator, a PV inverter, and an impedance emulating the grid’s length are used in a microgrid laboratory to study the influence of a reactive power control algorithm in a power system without loads This simplified representation of a power system in a two-bus equivalent model proved to be accurate when estimating the overvoltage impact due to PV across a distribution network [23], and it is the necessary step to certify the functionality prior its testing at a larger scale pilot site in Greece in the framework of the H2020 SABINA project [24] (SmArt Bi-directional multi eNergy gAteway). The presentation of a new algorithm that adapts the volt-var parameters to different network conditions (strong or weak grids) and PV inverter characteristics based on power meter data at the DER level to reduce overvoltage and line loading. The validation of its effectiveness in a laboratory with real equipment and with communication elements, such as RTUs, being a bridge between simulation environments (which is the most common case in the literature) and a large-scale deployment (which will be done further in the SABINA project)

Adaptive Volt-Var Control Algorithm
Two-Bus Equivalent Model
Energy Smart Laboratory
Test Design
Evaluation
Results and Discussion
Optimized Volt-Var Parameters
The Added Value of Going from Simulations to Real Testing
Additional Power Quality Considerations
Conclusions
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