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Advances in Resilient Operation, Optimization, and Control of Smart Grids and Microgrids

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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 (3 May 2024) | Viewed by 8248

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Special Issue Editors

Dr. Omar Ali Beg

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Guest Editor

College of Engineering, The University of Texas Permian Basin, Odessa, TX 79762-0001, USA
Interests: resilient microgrids against cyber-attacks using techniques based on formal verification, cooperative control, and artificial intelligence

Dr. Shan Zuo

E-Mail Website
Guest Editor

Electrical & Computer Engineering, University of Connecticut, Storrs, CT 06269, USA
Interests: cyber-physical systems; heterogeneous multi-agent systems; distributed decision making; attack-resilient control; deep reinforcement learning

Dr. Zongjie Wang

E-Mail Website
Guest Editor

School of Engineering, University of Connecticut, Storrs, CT 06269, USA
Interests: grid resilience; power system optimization; renewable energy integration; transmission and distribution coordination

Special Issue Information

Dear Colleagues,

Recently, smart grids and microgrids have gained much importance with advancements in distributed energy resources (DERs), renewable energy, computing, communication, and artificial intelligence technologies. Microgrids are ﬁnite-inertia power systems that integrate distributed generation to include renewable energy resources, energy storage, and loads. Smart grids are prone to cyber-attacks due to extensive reliance on computing and communication technologies. This has given rise to research in the resilient operation, optimization, and control of microgrids and smart grids.

In this context, this Special Issue aims to present and disseminate the most recent advances related to techniques for resilient operations, optimization, and control of microgrids and smart girds.

The topics of interest for publication include but are not limited to:

All aspects of resilient operations for microgrids and smart grids under cyber-attacks and faults;
Power systems optimization;
Distributed cooperative control of microgrids and smart grids;
Artificial Intelligence-based resilient microgrids.

Dr. Omar Ali Beg
Dr. Shan Zuo
Dr. Zongjie Wang
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

resilient microgrids
optimization
cooperative control
cyber-attacks
renewable energy integration

Published Papers (7 papers)

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Research

Jump to: Review

18 pages, 10276 KiB

Open AccessArticle

Structure Optimization of Ultra-Light Power Generation System

by Pingping Wen, Zhibao Yuan, Haiping Xu and Zengquan Yuan

Energies 2024, 17(5), 976; https://doi.org/10.3390/en17050976 - 20 Feb 2024

Viewed by 554

Abstract

A wide-speed, ultra-light power generation system is a critical power generation unit structure, often because of its high efficiency and power density. Lightness and reliability are two key design indicators within the system, albeit they could lead to contradictory problems, particularly in systems containing prime movers, batteries, generators, rectifiers, and inverters. Ultra-light generator sets are facing more severe problems and contradictions in designing in terms of matching, coordinating, and stabilizing the components in the systems. This paper describes the system design of a low-cost and high-reliability microgenerator set: a gasoline engine, three-phase permanent magnet synchronous generator, rectifier, and inverter. Moreover, the matching relationship between the four parts and the design effect of each part of the power generation system was analyzed, simulated, and tested to verify the effectiveness and feasibility of the so-designed system. Full article

(This article belongs to the Special Issue Advances in Resilient Operation, Optimization, and Control of Smart Grids and Microgrids)

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22 pages, 3233 KiB

Open AccessArticle

A Dynamic Incentive Mechanism for Smart Grid Data Sharing Based on Evolutionary Game Theory

by Lihua Zhang, Qingyu Lu, Rui Huang, Shihong Chen, Qianqian Yang and Jinguang Gu

Energies 2023, 16(24), 8125; https://doi.org/10.3390/en16248125 - 18 Dec 2023

Viewed by 853

Abstract

With the increasing popularization and application of the smart grid, the harm of the data silo issue in the smart grid is more and more prominent. Therefore, it is especially critical to promote data interoperability and sharing in the smart grid. Existing data-sharing schemes generally lack effective incentive mechanisms, and data holders are reluctant to share data due to privacy and security issues. Because of the above issues, a dynamic incentive mechanism for smart grid data sharing based on evolutionary game theory is proposed. Firstly, several basic assumptions about the evolutionary game model are given, and the evolutionary game payoff matrix is established. Then, we analyze the stabilization strategy of the evolutionary game based on the payoff matrix, and propose a dynamic incentive mechanism for smart grid data sharing based on evolutionary game theory according to the analysis results, aiming to encourage user participation in data sharing. We further write the above evolutionary game model into a smart contract that can be invoked by the two parties involved in data sharing. Finally, several factors affecting the sharing of data between two users are simulated, and the impact of different factors on the evolutionary stabilization strategy is discussed. The simulation results verify the positive or negative incentives of these parameters in the data-sharing game process, and several factors influencing the users’ data sharing are specifically analyzed. This dynamic incentive mechanism scheme for smart grid data sharing based on evolutionary game theory provides new insights into effective incentives for current smart grid data sharing. Full article

(This article belongs to the Special Issue Advances in Resilient Operation, Optimization, and Control of Smart Grids and Microgrids)

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19 pages, 7893 KiB

Open AccessArticle

Smart Switching in Single-Phase Grid-Connected Photovoltaic Power Systems for Inrush Current Elimination

by Gerardo de J. Martínez-Figueroa, Santiago Bogarra and Felipe Córcoles

Energies 2023, 16(20), 7211; https://doi.org/10.3390/en16207211 - 23 Oct 2023

Cited by 1 | Viewed by 732

Abstract

Grid-connected photovoltaic (PV) power systems are one of the most promising technologies to address growing energy demand and ecological challenges. This paper proposes smart switching to mitigate inrush currents during the connection of single-phase transformers used in PV systems. An effective inrush current mitigation contributes to the reliability of PV systems. The inrush current severity is influenced by the pseudorandom residual flux at the transformer core and the energization point-on-wave. The most common approach to avoid inrush currents is controlled connection, which requires prior knowledge of the residual flux. However, the residual flux can differ in each case, and its measurement or estimation can be impractical. The proposed smart switching is based on a comprehensive analysis of the residual flux and the de-energization trajectories, and only requires two pieces of data (ϕ_RM and ϕ₀, flux values of the static and dynamic loops when the respective currents are null), calculated from two simple no-load tests. It has a clear advantage over common approaches: no need to estimate or measure the residual flux before each connection, avoiding the need for expensive equipment or complex setups. Smart switching can be easily implemented in practical settings, as it considers different circuit breakers with distinctive aperture features, making it cost-effective for PV systems. Full article

(This article belongs to the Special Issue Advances in Resilient Operation, Optimization, and Control of Smart Grids and Microgrids)

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23 pages, 1900 KiB

Open AccessArticle

An Incentive-Based Mechanism to Enhance Energy Trading among Microgrids, EVs, and Grid

by Muhammad Ahsan Khan, Akhtar Hussain, Woon-Gyu Lee and Hak-Man Kim

Energies 2023, 16(17), 6359; https://doi.org/10.3390/en16176359 - 1 Sep 2023

Cited by 1 | Viewed by 1362

Abstract

The growing penetration of electric vehicles (EVs) introduces both opportunities and challenges for power grid operators. Incentivization is considered a viable option to tempt EV owners to participate in supporting the grid during peak load intervals while receiving compensation for their services. Therefore, this study proposes a two-step incentive mechanism to reduce the peak load of the grid by enabling power trading among the microgrid, EVs and the utility grid. In the first step, an incentive price is determined for EVs considering the grid-loading conditions during different hours of the day. In the second step, a multi-objective optimization problem is formulated to optimize trading among different entities, such as EVs, the microgrid and the utility grid. The two objectives considered in this study are the operation cost of the microgrid and the revenue of EVs. Monte Carlo simulations are used to deal with uncertainties associated with EVs. Simulations are conducted to analyze the impact of different weight parameters on the energy-trading amount and operation cost of EVs and MG. In addition, a sensitivity analysis is conducted to analyze the impact of changes in the EV fleet size on the energy-trading amount and operation cost. Full article

(This article belongs to the Special Issue Advances in Resilient Operation, Optimization, and Control of Smart Grids and Microgrids)

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19 pages, 8652 KiB

Open AccessArticle

Clustering Combined Multi-Objective Optimal Operation of Transmission Systems Considering Voltage Stability

by Kyeongseon Park, Dongyeong Lee and Gilsoo Jang

Energies 2023, 16(16), 5914; https://doi.org/10.3390/en16165914 - 10 Aug 2023

Viewed by 680

Abstract

In recent years, power systems have undergone major changes called energy transitions during which synchronous generators have been replaced with power electronics-based generation. Therefore, the voltage stability of power systems has become a major concern owing to the absence of synchronous generators. This study proposes multi-objective optimization using the non-dominated sorting genetic algorithm III to achieve optimal reactive power reserve procurement and improve the voltage stability of the overall system. These systematic approaches require high computational power and are unsuitable for the operational frameworks currently used for large-scale power systems. Previous works have rarely considered the local characteristics of reactive power or generation de-commitment with sufficient re-dispatch owing to greater renewable energy integration. We propose a framework for achieving systematic optimization by considering various objective functions while utilizing the regional aspect of reactive power via spectral clustering-based voltage control area (VCA) identification. The proposed method comprises systematic and regional approaches to optimizing systems for voltage stability improvement based on VCAs. The results demonstrate that the proposed method shows satisfactory performance. These results will be helpful for decision making for power system operations in harsher environments with more renewable energy. Full article

(This article belongs to the Special Issue Advances in Resilient Operation, Optimization, and Control of Smart Grids and Microgrids)

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Review

Jump to: Research

28 pages, 657 KiB

Open AccessReview

Networked Microgrids: A Review on Configuration, Operation, and Control Strategies

by Mohammad Javad Bordbari and Fuzhan Nasiri

Energies 2024, 17(3), 715; https://doi.org/10.3390/en17030715 - 2 Feb 2024

Viewed by 934

Abstract

The increasing impact of climate change and rising occurrences of natural disasters pose substantial threats to power systems. Strengthening resilience against these low-probability, high-impact events is crucial. The proposition of reconfiguring traditional power systems into advanced networked microgrids (NMGs) emerges as a promising solution. Consequently, a growing body of research has focused on NMG-based techniques to achieve a more resilient power system. This paper provides an updated, comprehensive review of the literature, particularly emphasizing two main categories: networked microgrids’ configuration and networked microgrids’ control. The study explores key facets of NMG configurations, covering formation, power distribution, and operational considerations. Additionally, it delves into NMG control features, examining their architecture, modes, and schemes. Each aspect is reviewed based on problem modeling/formulation, constraints, and objectives. The review examines findings and highlights the research gaps, focusing on key elements such as frequency and voltage stability, reliability, costs associated with remote switches and communication technologies, and the overall resilience of the network. On that basis, a unified problem-solving approach addressing both the configuration and control aspects of stable and reliable NMGs is proposed. The article concludes by outlining potential future trends, offering valuable insights for researchers in the field. Full article

(This article belongs to the Special Issue Advances in Resilient Operation, Optimization, and Control of Smart Grids and Microgrids)

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23 pages, 2617 KiB

Open AccessReview

A Review of AI-Based Cyber-Attack Detection and Mitigation in Microgrids

by Omar A. Beg, Asad Ali Khan, Waqas Ur Rehman and Ali Hassan

Energies 2023, 16(22), 7644; https://doi.org/10.3390/en16227644 - 18 Nov 2023

Cited by 3 | Viewed by 2326

Abstract

In this paper, the application and future vision of Artificial Intelligence (AI)-based techniques in microgrids are presented from a cyber-security perspective of physical devices and communication networks. The vulnerabilities of microgrids are investigated under a variety of cyber-attacks targeting sensor measurements, control signals, and information sharing. With the inclusion of communication networks and smart metering devices, the attack surface has increased in microgrids, making them vulnerable to various cyber-attacks. The negative impact of such attacks may render the microgrids out-of-service, and the attacks may propagate throughout the network due to the absence of efficient mitigation approaches. AI-based techniques are being employed to tackle such data-driven cyber-attacks due to their exceptional pattern recognition and learning capabilities. AI-based methods for cyber-attack detection and mitigation that address the cyber-attacks in microgrids are summarized. A case study is presented showing the performance of AI-based cyber-attack mitigation in a distributed cooperative control-based AC microgrid. Finally, future potential research directions are provided that include the application of transfer learning and explainable AI techniques to increase the trust of AI-based models in the microgrid domain. Full article

(This article belongs to the Special Issue Advances in Resilient Operation, Optimization, and Control of Smart Grids and Microgrids)

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