Special Issue Information

Dear Colleagues,

Smart grids, still being the future perspective of electric distribution grids, are also the real and actual implementation of the evolution of these grids. There are many factors that foster this realization and the enabling technologies play a fundamental role in this process. The fields in which these technologies can be applied are widespread and the ways in which they are deployed define the actual instance of the smart grid concept. The targeted objectives are directly related to the improvement of the energy usage (e.g., sustainability, efficiency, reliability, and quality of service) or to a smarter management of the grids by distribution system operators (e.g., maintenance, reconfiguration, and workforce management). Some of the examples of these technologies are the smart meters, energy storage devices, including electric vehicles, together with the algorithm used to manage them, the communication systems and their usage to control the players in the grids, IoT and artificial intelligence applications. In this context, regulatory and economic issues should be considered, and suitably addressed.

Our aim is to bring together scientists and researchers to publish their experimental and advanced research results on the application on smart grid technologies.

Prof. Dr. Samuele Grillo
Dr. Francesco Conte
Guest Editors

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Keywords

smart grid
IoT
artificial intelligence
energy storage
electric vehicles
optimal control
predictive control
predictive maintenance
optimal planning
smart meter

Published Papers (1 paper)

Research

14 pages, 788 KiB

Open AccessArticle

Three-Phase State Estimation for Distribution-Grid Analytics

by Karthikeyan Nainar and Florin Iov

Clean Technol. 2021, 3(2), 395-408; https://0-doi-org.brum.beds.ac.uk/10.3390/cleantechnol3020022 - 01 May 2021

Cited by 5 | Viewed by 2615

Abstract

Power-distribution grids consist of assets such as transformers, cables, and switches, of which the proper utilization is essential for the provision of a secure and reliable power supply to end customers. Distribution-system operators (DSOs) are responsible for the operation and maintenance of these assets. Due to the increased use of renewable sources such as wind and solar, grid assets are prone to operation conditions outside safe boundaries, such as overloading, large voltage unbalance, and a rise in voltage. At present, distribution grids are poorly monitored by DSOs, and the above-mentioned problems may thereby go unnoticed until the failure of a critical asset occurs. The deployment of smart meters in distribution grids has enabled measurements of grid variables such as power, current, and voltage. However, their measurements are used only for billing purposes, and not for monitoring and improving the operating condition of distribution grids. In this paper, a state-estimation algorithm is proposed that utilizes smart-meter data for offline analysis, and estimates the loading of grid assets and power losses. Single- and three-phase state-estimation algorithms are compared through simulation studies on a real-life low-voltage distribution grid using measured smart-meter data. The three-phase state-estimation algorithm based on the nonlinear weighted least-squares method was found to be more accurate in estimating cable loading and line power losses. The proposed method is useful for DSOs to analyze power flows in their distribution grids and take necessary actions such as grid upgrades or the rerouting of power flows. Full article

(This article belongs to the Special Issue Smart Grid Technologies)

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