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Smart and Predictive Control for Power Distribution Grids with Prolific Distributed Generation: A Step toward the Smart Grid Paradigm

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: closed (10 February 2022) | Viewed by 6746

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Special Issue Editors

Prof. Dr. Julien Eynard

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Guest Editor

PROMES-CNRS laboratory, University of Perpignan Via Domitia, Perpignan, France
Interests: control engineering; machine learning; energy-related systems and technologies

Prof. Dr. Stéphane Grieu

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Guest Editor

PROMES-CNRS Laboratory, University of Perpignan Via Domitia, Perpignan, France
Interests: solar resource assessment and forecasting; distributed generation management; smart buildings; smart grids; thermal/electrical microgrids; machine/deep learning; reinforcement learning; model-based predictive control; non-linear optimization
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Special Issue Information

Dear Colleagues,

In recent years, large-scale deployment across the world of renewable-energy-based power generation sources, referred to as distributed generation, has been well under way. This is thanks to their potential as a promising alternative to fossil fuels for sustainable and eco-friendly power grids. Because power grids were originally designed for centralized generation with unidirectional power flow, large-scale deployment of distributed generation brings with it numerous operational issues. There is no shortage of works in the scientific literature tackling the different aspects of the power grid with prolific distributed generation transition into a “smart grid”.

Thus, the topic of this Special Issue covers all aspects of converting the power distribution grid into a smart grid using smart and predictive control tools. This includes the development of predictive controllers but also smart controllers which are able to improve the capabilities of the power distribution grid, using flexible assets such as electrical or non-electrical storage systems, to balance production and consumption, to suppress voltage or current constraints, and to improve robustness. The works presented in this issue should take into consideration the complexity of the control tools developed to ensure their ability to be implemented in real time. Works based on real data sets and experiments will be greatly appreciated. Because the prediction of stochastic quantities such as grid load and power generation, for example, using machine learning tools, is required for the implementation of predictive strategies, works on this topic are also welcome.

Submit your paper and select the Journal “Energies” and the Special Issue “Smart and Predictive Control for Power Distribution Grids with Prolific Distributed Generation: A Step toward the Smart Grid Paradigm” via: MDPI submission system. Please contact the special issue editor ([email protected]) for any queries. Our papers will be published on a rolling basis and we will be pleased to receive your submission once you have finished it.

Prof. Dr. Julien Eynard
Prof. Dr. Stéphane Grieu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Smart-grid paradigm
Microgrids
Model-based predictive control
Smart control
Power distribution grids
Distributed generation
Time-series forecasting
Machine learning
Complexity analysis for real-time implementation

Published Papers (2 papers)

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Research

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28 pages, 644 KiB

Open AccessArticle

Innovative Application of Model-Based Predictive Control for Low-Voltage Power Distribution Grids with Significant Distributed Generation

by Nouha Dkhili, David Salas, Julien Eynard, Stéphane Thil and Stéphane Grieu

Energies 2021, 14(6), 1773; https://doi.org/10.3390/en14061773 - 23 Mar 2021

Cited by 2 | Viewed by 1567

Abstract

In past decades, the deployment of renewable-energy-based power generators, namely solar photovoltaic (PV) power generators, has been projected to cause a number of new difficulties in planning, monitoring, and control of power distribution grids. In this paper, a control scheme for flexible asset management is proposed with the aim of closing the gap between power supply and demand in a suburban low-voltage power distribution grid with significant penetration of solar PV power generation while respecting the different systems’ operational constraints, in addition to the voltage constraints prescribed by the French distribution grid operator (ENEDIS). The premise of the proposed strategy is the use of a model-based predictive control (MPC) scheme. The flexible assets used in the case study are a biogas plant and a water tower. The mixed-integer nonlinear programming (MINLP) setting due to the water tower ON/OFF controller greatly increases the computational complexity of the optimisation problem. Thus, one of the contributions of the paper is a new formulation that solves the MINLP problem as a smooth continuous one without having recourse to relaxation. To determine the most adequate size for the proposed scheme’s sliding window, a sensitivity analysis is carried out. Then, results given by the scheme using the previously determined window size are analysed and compared to two reference strategies based on a relaxed problem formulation: a single optimisation yielding a weekly operation planning and a MPC scheme. The proposed problem formulation proves effective in terms of performance and maintenance of acceptable computational complexity. For the chosen sliding window, the control scheme drives the power supply/demand gap down from the initial one up to 38%. Full article

(This article belongs to the Special Issue Smart and Predictive Control for Power Distribution Grids with Prolific Distributed Generation: A Step toward the Smart Grid Paradigm)

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Review

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27 pages, 2533 KiB

Open AccessReview

The Current State of the Art in Research on Predictive Maintenance in Smart Grid Distribution Network: Fault’s Types, Causes, and Prediction Methods—A Systematic Review

by Moamin A. Mahmoud, Naziffa Raha Md Nasir, Mathuri Gurunathan, Preveena Raj and Salama A. Mostafa

Energies 2021, 14(16), 5078; https://doi.org/10.3390/en14165078 - 18 Aug 2021

Cited by 25 | Viewed by 4403

Abstract

With the exponential growth of science, Internet of Things (IoT) innovation, and expanding significance in renewable energy, Smart Grid has become an advanced innovative thought universally as a solution for the power demand increase around the world. The smart grid is the most practical trend of effective transmission of present-day power assets. The paper aims to survey the present literature concerning predictive maintenance and different types of faults that could be detected within the smart grid. Four databases (Scopus, ScienceDirect, IEEE Xplore, and Web of Science) were searched between 2012 and 2020. Sixty-five (n = 65) were chosen based on specified exclusion and inclusion criteria. Fifty-seven percent (n = 37/65) of the studies analyzed the issues from predictive maintenance perspectives, while about 18% (n = 12/65) focused on factors-related review studies on the smart grid and about 15% (n = 10/65) focused on factors related to the experimental study. The remaining 9% (n = 6/65) concentrated on fields related to the challenges and benefits of the study. The significance of predictive maintenance has been developing over time in connection with Industry 4.0 revolution. The paper’s fundamental commitment is the outline and overview of faults in the smart grid such as fault location and detection. Therefore, advanced methods of applying Artificial Intelligence (AI) techniques can enhance and improve the reliability and resilience of smart grid systems. For future direction, we aim to supply a deep understanding of Smart meters to detect or monitor faults in the smart grid as it is the primary IoT sensor in an AMI. Full article

(This article belongs to the Special Issue Smart and Predictive Control for Power Distribution Grids with Prolific Distributed Generation: A Step toward the Smart Grid Paradigm)

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