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Modeling, Optimization, and Control in Smart Grids
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Modeling, Optimization, and Control in Smart Grids

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

Deadline for manuscript submissions: 31 May 2024 | Viewed by 4531

Special Issue Editors

Prof. Dr. Paulo Coelho

E-Mail Website
Guest Editor
Smart Cities Research Center (Ci2-IPT), Polytechnic Institute of Tomar, 2300-313 Tomar, Portugal
Interests: control theory; intelligent control systems; renewable energies; smart grids/cities; mobile robotics; aerial robotics; electrical vehicles/intelligent vehicles
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Mario Gomes

E-Mail Website
Guest Editor
Smart Cities Research Center (Ci2-IPT), Polytechnic Institute of Tomar, 2300-313 Tomar, Portugal
Interests: power systems; electrical installations; power markets; distributed energy resources; microgrids; smart grids
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, there has been a significant deployment of smart grids and a consequent rise in renewable power generation, resulting in a complex ecosystem with new entering actors. Therefore, the increased use of renewable energy and the emergence of distributed generation and storage systems necessitate new decision, optimization and control schemes for the management of energy resources, mainly due to the variability and intermittency of the renewable sources. Thus, modeling, optimization and control strategies need to be developed to manage the variability and randomness of the resources while ensuring the stability of the grid connected to renewable energy resources (RERs). In addition, advances in key technologies such as energy storage, communication, control, artificial intelligence, machine learning, IoT, smart electric vehicles, security and privacy issues have paved the way to new research directions and problem solving in the operation of smart grids.

This Special Issue aims to present and disseminate the most recent advances related to the theory, design, modeling, application, optimization, communication, control, and also planning and management of smart grids.

Topics of interest for publication include, but are not limited to:

  • Modeling of control strategies for a robust smart grid;
  • Smart grids, optimization, and artificial intelligence;
  • Communication and control based on machine learning methodologies;
  • Algorithms for modeling, optimization, and control;
  • Wide area for monitoring, control, and protection;
  • Advanced modeling approaches;
  • Integration of renewable generation, distribution, and energy storage;
  • Microgrids, distributed energy supply, and electricity markets;
  • Energy management systems, demand response, efficiency, and challenges;
  • Integration of electrical vehicles in smart grids;
  • Smart grid protection/security;
  • Distributed communications and sensing/metering in a smart grid.

We look forward to receiving your contributions.

Prof. Dr. Paulo Coelho
Prof. Dr. Mario Gomes
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
  • modeling
  • optimization
  • control systems
  • renewable energy
  • power systems
  • microgrids
  • artificial intelligence
  • machine learning
  • intelligent control
  • energy-efficient
  • security
  • demand response
  • smart systems

Published Papers (5 papers)

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Research

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17 pages, 1088 KiB  
Article
Controllable Meshing of Distribution Grids through a Multi-Leg Smart Charging Infrastructure (MLSCI)
by Fabio Bignucolo and Luca Mantese
Energies 2024, 17(8), 1960; https://0-doi-org.brum.beds.ac.uk/10.3390/en17081960 - 20 Apr 2024
Viewed by 363
Abstract
The paper provides a novel approach for controllably meshing traditional medium-voltage networks by means of a fast-charging parking station with multiple points of delivery connected to different radial feeders. Regulating power flows at each point of delivery while the charging service is being [...] Read more.
The paper provides a novel approach for controllably meshing traditional medium-voltage networks by means of a fast-charging parking station with multiple points of delivery connected to different radial feeders. Regulating power flows at each point of delivery while the charging service is being provided, which means actively controlling power exchanges between radial distribution feeders can significantly increase the hosting capacity of the power system. Remarkable benefits are expected when the distribution networks to which the charging infrastructure is connected differ in terms of main characteristics, e.g., rated voltage level, end-user type and operating profiles, and the number and type of renewable plants. The paper focuses on technical targets, such as loss reduction and power quality in terms of admitted voltage deviation from the rated value. The power exchanges between distribution feeders are made possible by a controlled DC link, where bi-directional DC/DC converters are connected so as to charge or discharge vehicles according to the Vehicle-To-Grid approach. A multiplexer topology in which several vehicles can be alternatively connected to the same DC/DC converter is modeled. The proposed concept can contribute to network flexibility by controllably meshing distribution feeders and, jointly, by modulating charging processes according to assigned charging constraints. Full article
(This article belongs to the Special Issue Modeling, Optimization, and Control in Smart Grids)
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20 pages, 327 KiB  
Article
Load Margin Assessment of Power Systems Using Physics-Informed Neural Network with Optimized Parameters
by Murilo Eduardo Casteroba Bento
Energies 2024, 17(7), 1562; https://0-doi-org.brum.beds.ac.uk/10.3390/en17071562 - 25 Mar 2024
Viewed by 574
Abstract
Challenges in the operation of power systems arise from several factors such as the interconnection of large power systems, integration of new energy sources and the increase in electrical energy demand. These challenges have required the development of fast and reliable tools for [...] Read more.
Challenges in the operation of power systems arise from several factors such as the interconnection of large power systems, integration of new energy sources and the increase in electrical energy demand. These challenges have required the development of fast and reliable tools for evaluating the operation of power systems. The load margin (LM) is an important index in evaluating the stability of power systems, but traditional methods for determining the LM consist of solving a set of differential-algebraic equations whose information may not always be available. Data-Driven techniques such as Artificial Neural Networks were developed to calculate and monitor LM, but may present unsatisfactory performance due to difficulty in generalization. Therefore, this article proposes a design method for Physics-Informed Neural Networks whose parameters will be tuned by bio-inspired algorithms in an optimization model. Physical knowledge regarding the operation of power systems is incorporated into the PINN training process. Case studies were carried out and discussed in the IEEE 68-bus system considering the N-1 criterion for disconnection of transmission lines. The PINN load margin results obtained by the proposed method showed lower error values for the Root Mean Square Error (RMSE), Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE) indices than the traditional training Levenberg-Marquard method. Full article
(This article belongs to the Special Issue Modeling, Optimization, and Control in Smart Grids)
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42 pages, 6534 KiB  
Article
Application and Challenges of Coalitional Game Theory in Power Systems for Sustainable Energy Trading Communities
by Filipe Bandeiras, Álvaro Gomes, Mário Gomes and Paulo Coelho
Energies 2023, 16(24), 8115; https://0-doi-org.brum.beds.ac.uk/10.3390/en16248115 - 17 Dec 2023
Cited by 1 | Viewed by 1058
Abstract
The role of prosumers is changing as they become active and empowered members of the grid by exchanging energy. This introduces bidirectional power flow and other challenges into the existing power systems, which require new approaches capable of dealing with the increased decentralization [...] Read more.
The role of prosumers is changing as they become active and empowered members of the grid by exchanging energy. This introduces bidirectional power flow and other challenges into the existing power systems, which require new approaches capable of dealing with the increased decentralization and complexity. Such approaches rely on game-theoretic models and mechanisms to analyze strategic decisions in competitive settings. More specifically, a coalitional game can encourage participants to trade energy with one another and obtain fair and sustainable outcomes. Therefore, the contents of this work address the coalitional game for sustainable energy trading, as well as the challenges associated with its application in power systems. This is achieved by identifying literature works that successfully implemented coalitional games in energy trading and management applications while providing an overview of solution concepts and discussing their properties and contributions to sustainability. Moreover, this work also proposes conditions that peer-to-peer energy trading should satisfy to be considered sustainable. Finally, a case study is presented to demonstrate how a coalitional game and various solution concepts can be successfully implemented to ensure the benefits and stability of cooperation in power systems. The weighted Shapley value is proposed to allocate profits among communities according to their level of sustainability. Full article
(This article belongs to the Special Issue Modeling, Optimization, and Control in Smart Grids)
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20 pages, 6168 KiB  
Article
Voltage Stability Assessment of a Campus DC Microgrid Implemented in Korea as a Blockchain-Based Power Transaction Testbed
by Hyeonseok Hwang, Soo Hyoung Lee, Donghee Choi, Sangbong Choi and Backsub Sung
Energies 2023, 16(21), 7297; https://0-doi-org.brum.beds.ac.uk/10.3390/en16217297 - 27 Oct 2023
Cited by 1 | Viewed by 750
Abstract
Recently, the generalization of P2P (peer-to-peer) technology with enhanced security due to blockchain technology and the expansion of renewable energy-based distributed energy resources have led to blockchain technology being applied in power transactions, thus giving the potential to become a new platform for [...] Read more.
Recently, the generalization of P2P (peer-to-peer) technology with enhanced security due to blockchain technology and the expansion of renewable energy-based distributed energy resources have led to blockchain technology being applied in power transactions, thus giving the potential to become a new platform for DC microgrid operation. Meanwhile, the voltage of a DC microgrid represents the balance of energy supply and demand and also serves as a stability index. The balance is represented as a steady state; the stability is represented during and after events. This paper examines the stability of the DC microgrid built on a university campus in Korea and, in particular, the blockchain technology-based power transactions performed in the DC microgrid. The test is based on the pre-planned transaction schedule applied in the DC microgrid. The transaction schedule has used day-ahead and real-time bidding data. Although many technologies are included in the project, this paper focuses on the voltage stability of the DC microgrid. In addition, the DC protection is applied and evaluated. To consider general DC protection, the DC breaker was simplified with several IGBTs, diodes, capacitors, and arrestors and was designed to interrupt the fault current within five milliseconds. The stability was evaluated using a PSCAD/EMTDCTM. Full article
(This article belongs to the Special Issue Modeling, Optimization, and Control in Smart Grids)
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Review

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21 pages, 2602 KiB  
Review
Optimal Power Flow for Unbalanced Three-Phase Microgrids Using an Interior Point Optimizer
by Piyapath Siratarnsophon, Woosung Kim, Nicholas Barry, Debjyoti Chatterjee and Surya Santoso
Energies 2024, 17(1), 32; https://0-doi-org.brum.beds.ac.uk/10.3390/en17010032 - 20 Dec 2023
Viewed by 986
Abstract
Optimal power flow (OPF) analysis enables the in-depth study and examination of islanded microgrid design and operation. The development of the analysis framework, including modeling, formulating, and selecting effective OPF solvers, however, is a nontrivial task. As a result, this paper presents a [...] Read more.
Optimal power flow (OPF) analysis enables the in-depth study and examination of islanded microgrid design and operation. The development of the analysis framework, including modeling, formulating, and selecting effective OPF solvers, however, is a nontrivial task. As a result, this paper presents a tutorial on an OPF modeling framework, offering a mathematical model that can be readily implemented using established open-source software tools such as OpenDSS, Pyomo, and IPOPT. The framework is versatile, capable of representing single-phase and unbalanced three-phase islanded microgrids. Various inverter models, such as those of grid forming and following equipped with their operating characteristics, can be incorporated. The efficacy of the proposed framework is demonstrated in studying the OPF of single-phase and three-phase microgrids. Full article
(This article belongs to the Special Issue Modeling, Optimization, and Control in Smart Grids)
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