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Modeling, Optimization, and Control in Smart Grids: 2nd Edition
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Modeling, Optimization, and Control in Smart Grids: 2nd Edition

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

Deadline for manuscript submissions: 20 June 2025 | Viewed by 2162

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

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Related Special Issue

Published Papers (2 papers)

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15 pages, 2866 KiB  
Article
Incentive Determination for Demand Response Considering Internal Rate of Return
by Gyuhyeon Bae, Ahyun Yoon and Sungsoo Kim
Energies 2024, 17(22), 5660; https://doi.org/10.3390/en17225660 - 13 Nov 2024
Viewed by 797
Abstract
The rapid expansion of renewable energy sources has led to increased instability in the power grid of Jeju Island, leading to the implementation of the plus demand response (DR) system, which aims to boost electricity consumption during curtailment periods. However, the frequency of [...] Read more.
The rapid expansion of renewable energy sources has led to increased instability in the power grid of Jeju Island, leading to the implementation of the plus demand response (DR) system, which aims to boost electricity consumption during curtailment periods. However, the frequency of curtailment owing to the increased utilization of renewable energy is outpacing the implementation of plus DR, highlighting the need for additional resources, such as energy storage systems (ESS). High initial investment costs have been the primary hindrance to the adoption of ESS by DR-participating companies but have not been fully considered in earlier studies on DR incentive determination. Therefore, this study proposes an algorithm for calculating appropriate incentives for plus DR participation considering the investment costs required for ESS. Based on actual load data, incentives are determined using an iterative mixed-integer programming (MIP) optimization method that progressively adjusts the incentive level to address the overall nonlinearity arising from both the multiplication of variables and the nonlinear characteristics of the internal rate of return (IRR), ensuring that the target IRR is achieved. A case study on the impact of factors such as IRR, ESS costs, and fluctuations in electricity rates on incentive calculations demonstrated that plus DR incentives required to achieve IRR targets of 5%, 10%, and 15% have increased linearly from 142.2 KRW/kWh to 363.0 KRW/kWh, confirming that the appropriate incentive level can be effectively determined based on ESS investment costs and target IRR. This result could help promote ESS adoption among DR companies and plus DR participation, thereby enhancing power grid stability. Full article
(This article belongs to the Special Issue Modeling, Optimization, and Control in Smart Grids: 2nd Edition)
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22 pages, 586 KiB  
Systematic Review
Cybersecurity and Major Cyber Threats of Smart Meters: A Systematic Mapping Review
by Jones Márcio Nambundo, Otávio de Souza Martins Gomes, Adler Diniz de Souza and Raphael Carlos Santos Machado
Energies 2025, 18(6), 1445; https://doi.org/10.3390/en18061445 - 14 Mar 2025
Viewed by 838
Abstract
Smart meters are a vital part of the smart grid, enabling energy management, real-time control, and data collection. Despite advances in technology, there is still a lack of content and limited understanding of the specific cybersecurity threats facing these devices, as well as [...] Read more.
Smart meters are a vital part of the smart grid, enabling energy management, real-time control, and data collection. Despite advances in technology, there is still a lack of content and limited understanding of the specific cybersecurity threats facing these devices, as well as the effectiveness of existing mitigation strategies. This study analyzed 41 articles sourced from three academic databases (Scopus, Web of Science, and IEEE Xplore). A cutting-edge study was conducted, including a comprehensive review of relevant literature on smart meters, cybersecurity vulnerabilities, and mitigation strategies. Elements were selected based on pre-assessment and classification processes, and the data were extracted and combined to provide detailed insights into the new devices. The study identified several significant cybersecurity risks for smart meters, including data breaches, unauthorized access, data manipulation, denial-of-service (DoS) attacks, and malware introduction. The study also highlighted the vulnerabilities exploited by these threats, such as undocumented communications, weak authentication, and outdated software. Recommended mitigation strategies include strengthening access and authentication mechanisms, securing communication systems, regular software updates, code management, anomaly detection, and access control. The findings indicate that although there are good strategies and methods to mitigate these cyber threats, significant research gaps remain. These gaps include design requirements, software and firmware updates, physical security, the use of big data to detect vulnerabilities, user data privacy, and inconsistencies in machine learning algorithms. Future research should focus on these aspects to improve the stability and reliability of smart meters. Full article
(This article belongs to the Special Issue Modeling, Optimization, and Control in Smart Grids: 2nd Edition)
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