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Applied Sciences
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Design, Optimization and Control Strategy of Smart Grids
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Design, Optimization and Control Strategy of Smart Grids

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 5767

Special Issue Editors

Dr. Santiago Bogarra

E-Mail Website
Guest Editor
Department of Electrical Engineering, ESEIAAT, Universitat Politècnica de Catalunya, 08222 Terrassa, Spain
Interests: electric aircraft; smart grid; smart sensors; electric; power system quality
Dr. Juan Antonio Ortega-Redondo

E-Mail Website
Guest Editor
Department of Electronic Engineering, Technical University of Catalonia, 08034 Barcelona, Spain
Interests: industrial diagnosis; energy storage systems; smart sensors; intelligent monitoring; signal processing

Special Issue Information

Dear Colleagues,

We are delighted to welcome you to the forefront of innovation in smart grids with our Special Issue, entitled "Design, Optimization and Control Strategy of Smart Grids".

At the core of cutting-edge smart grid advancements are the principles of design, optimization and the strategy of control, which play a pivotal role in the continued evolution of modern power systems. These innovations are closely aligned with global efforts to achieve sustainable development goals and implement energy transition policies, fostering the integration of intelligent technologies and strategies that enhance the grid's reliability, efficiency and adaptability. Through optimized control mechanisms and smart design, the smart grid emerges as a key enabler of a more resilient, sustainable and responsive energy infrastructure.

In the rapidly advancing field of smart grid technologies, the design of infrastructure is key to modernizing energy systems by integrating renewable energy, storage systems and electric vehicles. Tools like digital twins, automated grid management and smart charging infrastructure help to ensure efficient operation, while cybersecurity measures protect grid resilience. Optimization techniques, such as demand-side management, advanced forecasting and virtual power plants, enhance performance by balancing energy use and improving grid flexibility. Control strategies, including the real-time management of distributed resources and AI-driven fault detection, further stabilize the grid and maintain reliability in the face of increasing renewable integration.

Recommended topics include, but are not limited to, the following:

  • Design of smart grid infrastructure:
    • Integration of renewable energy sources, energy storage systems and electric vehicles;
    • Smart charging infrastructure and vehicle-to-grid technologies;
    • Digital twins for smart grid modeling and simulation;
    • Digital substations and automated grid management;
    • Optimal sizing, placement and management of energy storage systems;
    • Cybersecurity measures and protocols;
    • Overvoltage protection systems in photovoltaics installations.
  • Optimization techniques for smart grids:
    • Demand-side management and demand response optimization;
    • Optimization of generation, distribution and hybrid energy systems;
    • Advanced load forecasting, predictive control and data-driven decision-making;
    • Storage management for peak shaving, load leveling and curtailment management;
    • Power quality and grid reliability with high renewable penetration;
    • Virtual power plants and grid flexibility optimization;
    • Aging modeling of energy store systems;
    • Digital battery passport applications.
  • Control strategies for smart grid operations:
    • Real-time control of distributed energy resources;
    • Model predictive control applications;
    • Frequency and voltage control techniques;
    • Grid stabilization for variable renewable sources;
    • Coordinated control of energy storage and renewable sources;
    • Machine learning and AI for fault detection;
    • Fault detection, isolation and restoration;
    • Real-time simulation and hardware-in-the-loop testing.

Dr. Santiago Bogarra
Dr. Juan Antonio Ortega-Redondo
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. Applied Sciences 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 2400 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

  • renewable energy integration
  • energy storage systems integration
  • grid optimization techniques
  • real-time control strategies
  • cybersecurity for smart grids
  • digital twins in smart grids
  • virtual power plants
  • fault detection and restoration

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Published Papers (6 papers)

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Research

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22 pages, 5097 KiB  
Article
Strategy for Visual Measurement of Power Quality Based on Higher-Order Statistics and Exploratory Big Data Analysis
by Juan-José González-de-la-Rosa, Olivia Florencias-Oliveros and Paula Remigio-Carmona
Appl. Sci. 2025, 15(12), 6422; https://doi.org/10.3390/app15126422 - 7 Jun 2025
Viewed by 310
Abstract
This article proposes a strategy for the visual characterization of power quality in big data analysis contexts, culminating in the development of a visualization tool based on higher-order statistics, which exhibits an efficiency between 83.33% and 100% in detecting 50 Hz synthetic and [...] Read more.
This article proposes a strategy for the visual characterization of power quality in big data analysis contexts, culminating in the development of a visualization tool based on higher-order statistics, which exhibits an efficiency between 83.33% and 100% in detecting 50 Hz synthetic and real-life simple and hybrid events, showing its significant potential for real-world applications marked by non-linear loads and non-Gaussian behaviors and surpassing the detection of traditional tools such as boxplot by up to 50%. Efficient energy management is closely accompanied by an optimum energy data management (EDM). It implies the acquisition, analysis, and interpretation of data to make decisions regarding the best energy usage with subsequent cost reductions. Through a study of indicators, including higher-order statistics, crest factor, SNR and THD, the article establishes nominal values and behavioral patterns, expanding the previous knowledge of these parameters. The indicators are presented as vertices in a radar-type charting tool, providing a multidimensional spatial visualization from individual indices that allows the behavioral pattern associated with each type of disturbance to be characterized combined with a decision tree. In addition, boxplots reflecting data processing are included, which facilitates the comparison and discussion of both visualization instruments: radar chart and boxplot. Full article
(This article belongs to the Special Issue Design, Optimization and Control Strategy of Smart Grids)
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23 pages, 4010 KiB  
Article
Optimizing Power Consumption in Aquaculture Cooling Systems: A Bayesian Optimization and XGBoost Approach Under Limited Data
by Sina Ghaemi, Hessam Gholmohamadi, Amjad Anvari-Moghaddam and Birgitte Bak-Jensen
Appl. Sci. 2025, 15(11), 6273; https://doi.org/10.3390/app15116273 - 3 Jun 2025
Viewed by 324
Abstract
Driven by increased integration of renewable energy sources, the widespread decarbonization of power systems has led to energy price fluctuations that require greater adaptability and flexibility from grid users in order to maximize profits. Industrial loads equipped with flexible resources can optimize energy [...] Read more.
Driven by increased integration of renewable energy sources, the widespread decarbonization of power systems has led to energy price fluctuations that require greater adaptability and flexibility from grid users in order to maximize profits. Industrial loads equipped with flexible resources can optimize energy consumption rather than merely reacting to immediate events, thereby capitalizing on volatile energy prices. However, the absence of sufficient measured data in industrial processes limits the ability to fully harness this flexibility. To address this challenge, we presents a black-box optimization model for optimizing the energy consumption of cooling systems in the aquaculture industry using Extreme Gradient Boosting (XGBoost) and Bayesian Optimization (BO). XGBoost is employed to establish a nonlinear relationship between cooling system power consumption and available measured data. Based on this model, Bayesian Optimization with the Lower Confidence Bound (LCB) acquisition function is used to determine the optimal discharge temperature of water into breeding pools, minimizing day-ahead electricity costs. The proposed approach is validated using real-world data from a case study at the Port of Hirtshals, Denmark based on measurements from 2023. Our findings illustrate that leveraging the inherent flexibility of industrial processes can yield financial benefits while providing valuable signals for grid operators to adjust consumption behaviors through appropriate price mechanisms. Furthermore, machine learning techniques prove effective in optimizing energy consumption for industries with limited measured data, delivering accurate and practical estimations. Full article
(This article belongs to the Special Issue Design, Optimization and Control Strategy of Smart Grids)
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16 pages, 6519 KiB  
Article
Empowering Optimal Operations with Renewable Energy Solutions for Grid Connected Merredin WA Mining Sector
by Md Ohirul Qays, Ravi Kumar, Minhaz Ahmed, Stefan Lachowicz and Uzma Amin
Appl. Sci. 2025, 15(10), 5516; https://doi.org/10.3390/app15105516 - 14 May 2025
Viewed by 465
Abstract
Mining sectors require a continuous and reliable power supply; however, reliance on traditional grid utilities results in high costs and disruptions and increases extreme carbon emission. The Merredin WA sector seeks to resolve critical energy challenges affecting mining operations in Western Australia. Thus, [...] Read more.
Mining sectors require a continuous and reliable power supply; however, reliance on traditional grid utilities results in high costs and disruptions and increases extreme carbon emission. The Merredin WA sector seeks to resolve critical energy challenges affecting mining operations in Western Australia. Thus, this research proposes an optimal solar PV system with battery storage and backup generation for the mining sector to ensure a stable and cost-effective power supply that reduces harmful environmental effect. A hybrid data-driven long short-term memory (LSTM)-classical optimization framework is designed here, thereby optimizing PV-battery storage operational cost savings and energy usage. The optimization results indicate that approximately 57% of load demand can be fulfilled by the proposed optimal PV system with future cost savings of USD $8627.53 per annum. The optimization method also resulted in the lowest computation time of 1.153 s and highest accuracy 99.247% when compared with other existing algorithms. Furthermore, the integration of renewable energy (RE) technologies within mining operations substantially reduces carbon emissions by 67%, thus contributing to broader global sustainability purposes. The study presents a sustainable and economically viable energy solution for mining operations, setting a precedent for RE adoption in remote and energy-intensive industries. Full article
(This article belongs to the Special Issue Design, Optimization and Control Strategy of Smart Grids)
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32 pages, 9871 KiB  
Article
Energy Trading Strategy for Virtual Power Plants with Incomplete Resource Aggregation Based on Hybrid Game Theory
by Jing Wan, Jinrui Tang, Rui Chen, Leiming Suo, Honghui Yang, Yubo Song and Haibo Zhang
Appl. Sci. 2025, 15(4), 2100; https://doi.org/10.3390/app15042100 - 17 Feb 2025
Cited by 1 | Viewed by 698
Abstract
Shared energy storage (SES) and some photovoltaic prosumers (PVPs) are difficult to aggregate by the virtual power plant (VPP) in the short term. In order to realize the optimal operation of the VPP in the incomplete resource aggregation environment and to promote the [...] Read more.
Shared energy storage (SES) and some photovoltaic prosumers (PVPs) are difficult to aggregate by the virtual power plant (VPP) in the short term. In order to realize the optimal operation of the VPP in the incomplete resource aggregation environment and to promote the mutual benefit of multiple market entities, the energy trading strategy based on the hybrid game of SES–VPP–PVP is proposed. Firstly, the whole system configuration with incomplete resource aggregation is proposed, as well as the preconfigured market rules and the general problem for the optimal energy trading strategy of VPP. Secondly, the novel hybrid game theory-based optimization for the energy trading strategy of VPP is proposed based on the multi-level game theory model. And, the corresponding solving process using Karush–Kuhn–Tucker (KKT), dichotomy, and alternating direction method of multipliers (ADMM) algorithms are also constructed to solve nonconvex nonlinear models. The effectiveness of the proposed strategy is verified through the comparison of a large number of simulation results. The results show that our proposed energy trading strategy can be used for optimal low-carbon operation of VPPs with large-scale renewable energy and some unaggregated electricity consumers and distributed photovoltaic stations, while SES participates as an independent market entity. Full article
(This article belongs to the Special Issue Design, Optimization and Control Strategy of Smart Grids)
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20 pages, 2317 KiB  
Article
Dynamic Thermal Rating and Development of Renewable Energy Zones in Sicily
by Fabio Massaro, Nicola Collura, Salvatore Paradiso, Paolo Di Gloria and Chiara Vergine
Appl. Sci. 2025, 15(4), 1987; https://doi.org/10.3390/app15041987 - 14 Feb 2025
Cited by 1 | Viewed by 742
Abstract
Sicily, an Italian region in the south of Italy, is a candidate for becoming an energy “hub” in the Mediterranean in the coming years. Its geographical location, between the African continent and Europe, makes it a key hub for the distribution of electricity [...] Read more.
Sicily, an Italian region in the south of Italy, is a candidate for becoming an energy “hub” in the Mediterranean in the coming years. Its geographical location, between the African continent and Europe, makes it a key hub for the distribution of electricity flows. The region has great potential for the development of renewable energy sources. Inspired by the Australian “Renewable Energy Zones” model, the identification of existing Renewable Energy Zones (REZs) and the proper design of future REZs on the territory will simplify and improve the operation of the network. A very effective tool in managing REZs is Dynamic Thermal Rating (DTR) technology. This technology allows for dynamic rating of the high voltage transmission system, ensuring that the maximum allowable current is transported while preventing the annealing of the electrical conductor and complying with all necessary safety parameters. The objective of the work carried out in collaboration with Terna S.p.A. (Italian Transmission System Operator) was to evaluate the application of this technology on the Sicilian electricity network in order to reduce potential network congestion and system inefficiencies. The results obtained demonstrate the significant potential of Dynamic Thermal Rating in Sicily. These devices would allow a significant increase in the current flow of electrical lines without violating safety limits. Full article
(This article belongs to the Special Issue Design, Optimization and Control Strategy of Smart Grids)
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Review

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43 pages, 6738 KiB  
Review
Smart Grid Protection, Automation and Control: Challenges and Opportunities
by Sergio Rubio, Santiago Bogarra, Marco Nunes and Xavier Gomez
Appl. Sci. 2025, 15(6), 3186; https://doi.org/10.3390/app15063186 - 14 Mar 2025
Viewed by 2643
Abstract
The evolution of Protection and Control (P&C) systems has developed though analogue and digital generations, and is presently advancing towards the utilization of Virtualization of Protection, Automation and Control environments (VPAC). This article focuses on redefining the features of traditional and modern P&C [...] Read more.
The evolution of Protection and Control (P&C) systems has developed though analogue and digital generations, and is presently advancing towards the utilization of Virtualization of Protection, Automation and Control environments (VPAC). This article focuses on redefining the features of traditional and modern P&C systems, Centralized Protection Automation and Control (CPAC), and VPAC, focusing on the integration of Intelligent Electronic Devices (IEDs) with secure communication that is time-effective in the centralized distribution of power and prevention of network vulnerability. Though standards such as IEC 61850-9-2 LE have been adopted, the actualization of full interoperability between diverse IED manufacturers remains elusive. With the digitization of technologies, P&C systems are naturally transitioning to virtual environments, with timing precision, redundancy and security being imperative. Latency and resource management and allocation in VPAC systems are considerable global issues. This paper discusses the issues of maintaining low operational performance in virtual substation environments while satisfying the requirements for performance in real time. The impacts of large volumes of data and artificial intelligence on the management of the grid are studied, and AI-based analytics that predict system failures and automatically change load flows are shown, as they have the potential to increase the flexibility and stability of the grid. The use of big data enables electric power utilities to enhance their protection systems, anticipate disturbances and improve energy management methods. The paper presents a comparative analysis between traditional P&C and its virtualized counterparts, with strong emphasis placed on the flexibility and scaling of VPAC resources. Full article
(This article belongs to the Special Issue Design, Optimization and Control Strategy of Smart Grids)
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