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Computers
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Artificial Intelligence-Driven Innovations in Resilient Energy Systems
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Artificial Intelligence-Driven Innovations in Resilient Energy Systems

A special issue of Computers (ISSN 2073-431X).

Deadline for manuscript submissions: 22 October 2025 | Viewed by 2108

Special Issue Editors

Dr. Morteza Nazari Heris

E-Mail Website
Guest Editor
Department of Engineering, East Carolina University, Greenville, NC, USA
Interests: smart energy systems; resilience; EV charging infrastructure; machine learning; sustainable energy
Dr. Mostafa Mohammadpourfard

E-Mail Website
Guest Editor
National Wind Institute, Texas Tech University, Lubbock, TX, USA
Interests: smart grid cybersecurity; computer security; machine learning; renewable energy cybersecurity
Dr. Qiushi Cui

E-Mail Website
Guest Editor
School of Electrical Engineering, Chongqing University, Chongqing 40044, China
Interests: machine learning and big data applications in power systems; power system protection; smart cities; microgrid; EV integration; real-time simulation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Energy systems that are flexible and resilient to changing conditions and demands are essential for the shift to smart communities. This Special Issue will examine the most recent Artificial Intelligence (AI)-driven developments that improve the sustainability and resilience of energy systems, emphasizing practical applications in microgrids, smart grids, infrastructure for electric vehicles, and the integration of renewable energy sources. We are looking for novel studies, reviews, and case studies that highlight how machine learning, advanced algorithms, and data analytics can be used to optimize energy systems in the future.

Contributions should focus on the main obstacles and possibilities presented by building robust energy networks that assist in the growth of smart communities. We welcome submissions that showcase multidisciplinary strategies and practical applications, offering perspectives on the changing field of energy management and its effects on society.

We look forward to your valuable contributions.

Dr. Morteza Nazari Heris
Dr. Mostafa Mohammadpourfard
Dr. Qiushi Cui
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. Computers is an international peer-reviewed open access monthly 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 1800 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

  • machine learning
  • deep learning
  • data analytics
  • smart grid
  • renewable energy
  • energy management
  • cyber-physical systems
  • resilience analysis
  • energy storage
  • demand response
  • microgrids
  • energy efficiency
  • grid integration

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Published Papers (3 papers)

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Research

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31 pages, 5128 KiB  
Article
Enhancing Smart Home Efficiency with Heuristic-Based Energy Optimization
by Yasir Abbas Khan, Faris Kateb, Ateeq Ur Rehman, Atif Sardar Khan, Fazal Qudus Khan, Sadeeq Jan and Ali Naser Alkhathlan
Computers 2025, 14(4), 149; https://doi.org/10.3390/computers14040149 - 16 Apr 2025
Viewed by 529
Abstract
In smart homes, heavy reliance on appliance automation has increased, along with the energy demand in developing urban areas, making efficient energy management an important factor. To address the scheduling of appliances under Demand-Side Management, this article explores the use of heuristic-based optimization [...] Read more.
In smart homes, heavy reliance on appliance automation has increased, along with the energy demand in developing urban areas, making efficient energy management an important factor. To address the scheduling of appliances under Demand-Side Management, this article explores the use of heuristic-based optimization techniques (HOTs) in smart homes (SHs) equipped with renewable and sustainable energy resources (RSERs) and energy storage systems (ESSs). The optimal model for minimization of the peak-to-average ratio (PAR), considering user comfort constraints, is validated by using different techniques, such as the Genetic Algorithm (GA), Binary Particle Swarm Optimization (BPSO), Wind-Driven Optimization (WDO), Bacterial Foraging Optimization (BFO) and the Genetic Modified Particle Swarm Optimization (GmPSO) algorithm, to minimize electricity costs, the PAR, carbon emissions and delay discomfort. This research investigates the energy optimization results of three real-world scenarios. The three scenarios demonstrate the benefits of gradually assembling RSERs and ESSs and integrating them into SHs employing HOTs. The simulation results show substantial outcomes, as in the scenario of Condition 1, GmPSO decreased carbon emissions from 300 kg to 69.23 kg, reducing emissions by 76.9%; bill prices were also cut from an unplanned value of 400.00 cents to 150 cents, a 62.5% reduction. The PAR was decreased from an unscheduled value of 4.5 to 2.2 with the GmPSO algorithm, which reduced the value by 51.1%. The scenario of Condition 2 showed that GmPSO reduced the PAR from 0.5 (unscheduled) to 0.2, a 60% reduction; the costs were reduced from 500.00 cents to 200.00 cents, a 60% reduction; and carbon emissions were reduced from 250.00 kg to 150 kg, a 60% reduction by GmPSO. In the scenario of Condition 3, where batteries and RSERs were integrated, the GmPSO algorithm reduced the carbon emission value to 158.3 kg from an unscheduled value of 208.3 kg, a reduction of 24%. The energy cost was decreased from an unplanned value of 500 cents to 300 cents with GmPSO, decreasing the overall cost by 40%. The GmPSO algorithm achieved a 57.1% reduction in the PAR value from an unscheduled value of 2.8 to 1.2. Full article
(This article belongs to the Special Issue Artificial Intelligence-Driven Innovations in Resilient Energy Systems)
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14 pages, 3430 KiB  
Article
Optimal Selection of Sampling Rates and Mother Wavelet for an Algorithm to Classify Power Quality Disturbances
by Jonatan A. Medina-Molina, Enrique Reyes-Archundia, José A. Gutiérrez-Gnecchi, Javier A. Rodríguez-Herrejón, Marco V. Chávez-Báez, Juan C. Olivares-Rojas and Néstor F. Guerrero-Rodríguez
Computers 2025, 14(4), 138; https://doi.org/10.3390/computers14040138 - 6 Apr 2025
Viewed by 301
Abstract
The introduction of renewable energy sources, distributed energy systems, and power electronics equipment has led to the emergence of the Smart Grid. However, these developments have also caused the worsening of power quality. Selecting the correct sampling frequency and feature extraction techniques are [...] Read more.
The introduction of renewable energy sources, distributed energy systems, and power electronics equipment has led to the emergence of the Smart Grid. However, these developments have also caused the worsening of power quality. Selecting the correct sampling frequency and feature extraction techniques are essential for appropriately analyzing power quality disturbances. This work compares the performance of an algorithm based on a Support Vector Machine and Discrete Wavelet Transform for the classification of power quality disturbances using eight sampling rates and five different mother wavelets. The algorithm was tested in noisy and noiseless scenarios to show the methodology. The results indicate that a success rate of 99.9% is obtained for the noiseless signals using a sampling rate of 9.6 kHz and 95.2% for signals with a signal-to-noise ratio of 30 dB with a sampling rate of 30 kHz. Full article
(This article belongs to the Special Issue Artificial Intelligence-Driven Innovations in Resilient Energy Systems)
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Review

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27 pages, 560 KiB  
Review
Distributed Peer-to-Peer Optimization Based on Robust Reinforcement Learning with Demand Response: A Review
by Andrés Martínez and Paul Arévalo
Computers 2025, 14(2), 65; https://doi.org/10.3390/computers14020065 - 13 Feb 2025
Viewed by 817
Abstract
The increasing adoption of renewable energy resources and the growing need for efficient and adaptable energy management have emphasized the importance of innovative solutions for energy sharing and storage. This study aims to analyze the application of advanced optimization techniques in decentralized energy [...] Read more.
The increasing adoption of renewable energy resources and the growing need for efficient and adaptable energy management have emphasized the importance of innovative solutions for energy sharing and storage. This study aims to analyze the application of advanced optimization techniques in decentralized energy systems, focusing on strategies that improve energy distribution, adaptability, and reliability. This research employs a comprehensive review methodology, examining reinforcement learning approaches, demand response mechanisms, and the integration of battery energy storage systems to enhance the flexibility and scalability of P2P energy markets. The main findings highlight significant advancements in robust decision-making frameworks, the management of energy storage systems, and real-time optimization for decentralized trading. Additionally, this study identifies key technical and regulatory challenges, such as computational complexity, market uncertainty, and the lack of standardized legal frameworks, while proposing pathways to address them through intelligent energy management and collaborative solutions. The originality of this work lies in its structured analysis of emerging energy trading models, providing valuable insights into the future design of decentralized energy systems that are efficient, sustainable, and resilient. Full article
(This article belongs to the Special Issue Artificial Intelligence-Driven Innovations in Resilient Energy Systems)
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