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Transforming Power Systems and Smart Grids with Deep Learning
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Transforming Power Systems and Smart Grids with Deep Learning

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".

Deadline for manuscript submissions: 25 July 2026 | Viewed by 4869

Special Issue Editor

Dr. Gajendra Singh Chawda

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Tuskegee University, Tuskegee, AL 36088, USA
Interests: power quality; RE penetration; adaptive algorithms; rural/weak AC grids; EV and big data analysis

Special Issue Information

Dear Colleagues,

The rapid evolution of modern society has placed unprecedented demands on energy systems, pushing traditional power infrastructures to their limits. In response, the integration of intelligent, data-driven technologies, including artificial intelligence (AI) and machine learning (ML), into power systems and smart grids is revolutionizing how electricity is generated, transmitted, distributed, and consumed. These advancements are enabling energy networks to become more adaptive, autonomous, resilient, and sustainable.

The application of intelligent technologies spans numerous facets of modern power systems, including dynamic load forecasting, real-time grid optimization, predictive asset maintenance, enhanced fault detection, renewable energy integration, and cyber–physical security. These innovations not only address the challenges posed by the increasing penetration of renewable energy sources and distributed generation but also unlock new opportunities for improved efficiency, higher reliability, and greater sustainability.

This Special Issue aims to present and disseminate the latest research, methodologies, and technological advancements related to the intelligent transformation of power systems and smart grids. Contributions exploring theoretical developments, practical implementations, emerging applications, and interdisciplinary approaches are highly encouraged.

Topics of interest for this Special Issue include, but are not limited to, the following:

  • AI- and ML-based load forecasting and demand prediction;
  • Renewable energy integration and grid optimization with intelligent algorithms;
  • Predictive maintenance and asset management using intelligent systems;
  • Intelligent fault diagnosis and grid resilience enhancement;
  • Machine-intelligence-based control for AC and DC converters enabling high renewable energy (RE) penetration;
  • Cybersecurity solutions and anomaly detection in intelligent grids;
  • Edge computing and real-time decision-making in smart grid environments;
  • Digital twins and simulation platforms for advanced energy networks;
  • Data-driven energy management and optimization systems;
  • Intelligent microgrid control and distributed energy resource (DER) management;
  • Explainable AI (XAI) and trustworthy intelligent systems for energy applications;
  • Federated learning and decentralized AI solutions for smart grids;
  • Advanced optimization algorithms for smart grid planning and operation.

We invite researchers and practitioners from academia, industry, and government sectors to contribute their original research articles, reviews, and case studies to this Special Issue.

Dr. Gajendra Singh Chawda
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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

  • power systems
  • smart grids
  • intelligent technologies
  • artificial intelligence (AI)
  • machine learning (ML)
  • data-driven technologies
  • renewable energy integration
  • grid optimization
  • predictive maintenance
  • fault detection
  • cyber–physical security
  • dynamic load forecasting
  • real-time grid optimization
  • microgrid control
  • distributed energy resources (DERs)
  • edge computing
  • digital twins
  • federated learning
  • explainable AI (XAI)
  • autonomous energy systems
  • optimization algorithms

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Published Papers (3 papers)

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Research

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21 pages, 2353 KB  
Article
An Adaptive Bidding Strategy for Virtual Power Plants in Day-Ahead Markets Under Multiple Uncertainties
by Wei Yang and Wenjun Wang
Energies 2026, 19(8), 1878; https://doi.org/10.3390/en19081878 - 12 Apr 2026
Viewed by 623
Abstract
To address the challenges posed by multiple uncertainties in modern power systems to the market bidding of Virtual Power Plants (VPPs), this paper proposes an adaptive bidding strategy based on Deep Reinforcement Learning (DRL). First, a heterogeneous VPP aggregation model integrating dedicated energy [...] Read more.
To address the challenges posed by multiple uncertainties in modern power systems to the market bidding of Virtual Power Plants (VPPs), this paper proposes an adaptive bidding strategy based on Deep Reinforcement Learning (DRL). First, a heterogeneous VPP aggregation model integrating dedicated energy storage, Vehicle-to-Grid (V2G), and flexible loads is constructed, incorporating complex physical and operational constraints. Second, to overcome the “myopic” local optimality problem of traditional DRL in temporal arbitrage tasks, a potential-based reward shaping mechanism linked to future price trends is designed to guide the agent toward long-term optimal strategies. Finally, multi-dimensional comparative experiments and mechanism analyses are conducted in a simulated day-ahead electricity market. Simulation results demonstrate the following: (1) The proposed algorithm exhibits robust convergence stability and effectively handles stochastic noise in market prices and renewable generation. (2) Economically, the strategy significantly outperforms the rule-based strategy and remains highly competitive with the deterministic-optimization benchmark under perfect-information assumptions. (3) Mechanism analysis further reveals that the DRL agent breaks through the rigid logic of fixed thresholds, learning a non-linear dynamic game mechanism based on “Price-SOC” states, thereby achieving full-depth utilization of energy storage resources. This work provides an interpretable data-driven paradigm for intelligent VPP decision-making in uncertain environments. Full article
(This article belongs to the Special Issue Transforming Power Systems and Smart Grids with Deep Learning)
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35 pages, 1839 KB  
Article
Adversarially Robust Reinforcement Learning for Energy Management in Microgrids with Voltage Regulation Under Partial Observability
by Elida Domínguez, Xiaotian Zhou and Hao Liang
Energies 2026, 19(6), 1497; https://doi.org/10.3390/en19061497 - 17 Mar 2026
Viewed by 499
Abstract
Modern microgrids increasingly rely on learning-based energy management systems (EMSs) for real-time decision-making, yet remain vulnerable to cyber–physical disturbances, sensor tampering, and model uncertainty. Existing resilient control and robust reinforcement learning methods provide useful foundations, but rarely address adversarial measurement perturbations that distort [...] Read more.
Modern microgrids increasingly rely on learning-based energy management systems (EMSs) for real-time decision-making, yet remain vulnerable to cyber–physical disturbances, sensor tampering, and model uncertainty. Existing resilient control and robust reinforcement learning methods provide useful foundations, but rarely address adversarial measurement perturbations that distort belief evolution under partial observability. This gap is critical, as structured perturbations in sensing channels can destabilize learning-based policies and propagate into voltage-regulation violations. This paper proposes an adversarially robust reinforcement learning framework for energy management with voltage regulation under partial observability in microgrids. The EMS decision-making problem is formulated as a partially observable Markov decision process (POMDP) that accounts for adversarial measurement perturbations, belief evolution, and system-level economic and voltage constraints. To avoid excessive conservatism under worst-case uncertainty, an adversary-aware belief construction based on adversarial belief balancing (A3B) is employed to focus on policy-relevant perturbations. Building on this belief representation, an adversarially robust learning framework is developed by incorporating adversarial counterfactual error (ACoE) as a learning regularization mechanism, enabling a balance between nominal operating efficiency and robustness under adversarial measurement distortion. The case study is conducted on a medium-voltage radial distribution feeder (IEEE 123-Node Test Feeder). Case study results demonstrate that the proposed ACoE-regularized policies substantially reduce voltage-deficit events, improve policy stability, and maintain operational constraints under adversarial perturbations, consistently outperforming standard proximal policy optimization (PPO)-based controllers. These results indicate that counterfactual-aware, belief-based learning substantially enhances voltage quality and operational resilience in microgrids with high penetration of distributed energy resources. Full article
(This article belongs to the Special Issue Transforming Power Systems and Smart Grids with Deep Learning)
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Review

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27 pages, 1401 KB  
Review
Federated Learning for Decentralized Electricity Market Optimization: A Review and Research Agenda
by Tymoteusz Miller, Irmina Durlik, Ewelina Kostecka, Polina Kozlovska and Aleksander Nowak
Energies 2025, 18(17), 4682; https://doi.org/10.3390/en18174682 - 3 Sep 2025
Cited by 3 | Viewed by 3218
Abstract
Decentralized electricity markets are increasingly shaped by the proliferation of distributed energy resources, the rise of prosumers, and growing demands for privacy-aware analytics. In this context, federated learning (FL) emerges as a promising paradigm that enables collaborative model training without centralized data aggregation. [...] Read more.
Decentralized electricity markets are increasingly shaped by the proliferation of distributed energy resources, the rise of prosumers, and growing demands for privacy-aware analytics. In this context, federated learning (FL) emerges as a promising paradigm that enables collaborative model training without centralized data aggregation. This review systematically explores the application of FL in energy systems, with particular attention to architectures, heterogeneity management, optimization tasks, and real-world use cases such as load forecasting, market bidding, congestion control, and predictive maintenance. The article critically examines evaluation practices, reproducibility issues, regulatory ambiguities, ethical implications, and interoperability barriers. It highlights the limitations of current benchmarking approaches and calls for domain-specific FL simulation environments. By mapping the intersection of technical design, market dynamics, and institutional constraints, the article formulates a pluralistic research agenda for scalable, fair, and secure FL deployments in modern electricity systems. This work positions FL not merely as a technical innovation but as a socio-technical intervention, requiring co-design across engineering, policy, and human factors. Full article
(This article belongs to the Special Issue Transforming Power Systems and Smart Grids with Deep Learning)
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