energies-logo

Journal Browser

Journal Browser

Machine Learning Applications to Operation, Control and Protection of Microgrids

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 (30 September 2021) | Viewed by 7931

Special Issue Editors


E-Mail Website
Guest Editor
Electrical Engineering Section, Department of Mechanical and Electrical Engineering, University of Southern Denmark, Campusvej 55, DK-5230 Odense M, Denmark
Interests: microgrids; distributed generation; power quality; harmonics; protection; photovoltaics; wind turbines; UPS systems
Special Issues, Collections and Topics in MDPI journals

grade E-Mail Website
Guest Editor
Department of Electronic Engineering, Technical University of Catalonia, Barcelona, Spain
Interests: microgrids; renewable energy systems; neuroscience-based artificial intelligence; digital twins; cybersecurity
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Technology and Innovations, University of Vaasa, PB 700, 65101 Vaasa, Finland
Interests: smart grids; flexible energy systems; microgrids; protection and control of electricity; market concepts for smart grids; peer-to-peer energy trading
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Machine learning and artificial intelligence techniques provide unprecedented opportunities for developing solutions for the challenges in control, operation and protection of smart electrical grids and microgrids. These techniques have been applied to load and generation forecasts, energy management implementation, decision-making in electricity markets, contingency and resilience analysis, developing advanced control and protection schemes, etc.   

Prospective authors are invited to submit their original contributions or survey papers for publication in Energies. Topics of interest for this special issue include but are not limited to the following topics in the area of machine learning application to microgrids and distributed generation systems:

  • Protection, reliability and resilience;
  • Proactive protection in AC and DC microgrids;
  • Adaptive, robust and fault-tolerant control strategies;
  • Power quality and harmonics;
  • Lifetime modelling, condition monitoring, and failure prediction;
  • Design and expansion planning;
  • Integration of renewable energy sources;
  • Control and operation of energy storage devices;
  • Operation and energy management systems;
  • Optimized and coordinated management of flexible energy resources;
  • Electricity markets;
  • Industrial, experimental, and hardware-in-the-loop tests and validation.

Dr. Mehdi Savaghebi
Prof. Dr. Josep M. Guerrero
Prof. Dr. Hannu Laaksonen
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

  • Microgrids
  • Distributed Generation
  • Renewable Energy
  • Machine Learning
  • Artificial Intelligence
  • Control of Power electronic converters
  • Energy Management System
  • Protection and Faults

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

12 pages, 3165 KiB  
Article
Home-Microgrid Energy Management Strategy Considering EV’s Participation in DR
by Mohammad Hossein Fouladfar, Nagham Saeed, Mousa Marzband and Giuseppe Franchini
Energies 2021, 14(18), 5971; https://doi.org/10.3390/en14185971 - 20 Sep 2021
Cited by 5 | Viewed by 2093
Abstract
Electric vehicles (EVs) have a lot of potential to play an essential role in the smart power grid. EVs not only can reduce the amount of emission yielded from fossil fuels but also can be considered as an energy storage system (ES) and [...] Read more.
Electric vehicles (EVs) have a lot of potential to play an essential role in the smart power grid. EVs not only can reduce the amount of emission yielded from fossil fuels but also can be considered as an energy storage system (ES) and a backup system. EVs could support the demand response (DR) strategy that is considered as utmost importance to shift electricity demand in peak hours. This article aims to assess the impact of the presence of EV on DR strategy in a home-microgrid (H-MG). In order to reach the optimal set point, our energy management system (EMS) has been merged with differential evolution (DE) method. The results were auspicious and showed that the proposed method could decrease market clearing price (MCP) by 26% and increase the performance of DR by 17%. Full article
Show Figures

Figure 1

12 pages, 1678 KiB  
Article
Model-Free Neural Network-Based Predictive Control for Robust Operation of Power Converters
by Sanaz Sabzevari, Rasool Heydari, Maryam Mohiti, Mehdi Savaghebi and Jose Rodriguez
Energies 2021, 14(8), 2325; https://doi.org/10.3390/en14082325 - 20 Apr 2021
Cited by 19 | Viewed by 4271
Abstract
An accurate definition of a system model significantly affects the performance of model-based control strategies, for example, model predictive control (MPC). In this paper, a model-free predictive control strategy is presented to mitigate all ramifications of the model’s uncertainties and parameter mismatch between [...] Read more.
An accurate definition of a system model significantly affects the performance of model-based control strategies, for example, model predictive control (MPC). In this paper, a model-free predictive control strategy is presented to mitigate all ramifications of the model’s uncertainties and parameter mismatch between the plant and controller for the control of power electronic converters in applications such as microgrids. A specific recurrent neural network structure called state-space neural network (ssNN) is proposed as a model-free current predictive control for a three-phase power converter. In this approach, NN weights are updated through particle swarm optimization (PSO) for faster convergence. After the training process, the proposed ssNN-PSO combined with the predictive controller using a performance criterion overcomes parameter variations in the physical system. A comparison has been carried out between the conventional MPC and the proposed model-free predictive control in different scenarios. The simulation results of the proposed control scheme exhibit more robustness compared to the conventional finite-control-set MPC. Full article
Show Figures

Figure 1

Back to TopTop