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Application of Artificial Intelligence and Optimization Algorithms in Power Systems...
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Application of Artificial Intelligence and Optimization Algorithms in Power Systems and Energy Storage

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Energy Systems".

Deadline for manuscript submissions: 31 July 2026 | Viewed by 990

Special Issue Editors

Dr. Muhammad Usama

E-Mail Website
Guest Editor
ESIGELEC, IRSEEM, Université de Rouen Normandie, 76000 Rouen, France
Interests: data-driven control; predictive control; optimization strategies; sensorless control; motor drives; microgrids
Dr. Nicolas Langlois

E-Mail Website
Guest Editor
ESIGELEC, IRSEEM, Université de Rouen Normandie, 76000 Rouen, France
Interests: mechatronics; systems dynamics; control theory; automotive engineering; nonlinear control; instrumentation; MATLAB simulation; diesel engines; advanced control theory; model predictive control

Special Issue Information

Dear Colleagues,

This Special Issue explores recent advances in the use of artificial intelligence (AI) and optimization algorithms to solve complex challenges in power systems and energy storage. As modern energy systems become increasingly integrated with renewable energy sources and smart grid technologies, intelligent algorithms play a crucial role in enhancing efficiency, reliability, and sustainability. In addition, with the increasing complexity of power grids, driven by the integration of renewable energy, decentralized generation, and real-time demand fluctuations, there is a pressing need for intelligent, adaptive, and efficient solutions.

We invite original research and review articles focused on AI-based control strategies that address power system stability, load forecasting, grid resilience, fault detection, control strategies, and optimization techniques in energy storage systems. Contributions addressing practical implementations, hybrid AI models, and multi-objective optimization in power grid operations are also encouraged.

This Special Issue will provide a platform for innovative solutions that can drive the future of intelligent energy systems.

Dr. Muhammad Usama
Dr. Nicolas Langlois
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 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. Processes 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

  • AI-driven control
  • power systems
  • optimization strategies
  • smart grids
  • renewable energy storage
  • grid stability
  • metaheuristic techniques

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Published Papers (2 papers)

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Research

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21 pages, 3646 KB  
Article
Short-Term Load Forecasting in Price-Volatile Markets: A Pattern-Clustering and Adaptive Modeling Approach
by Xiangluan Dong, Yan Yu, Hongyang Jin, Zhanshuo Hu and Jieqiu Bao
Processes 2026, 14(1), 5; https://doi.org/10.3390/pr14010005 - 19 Dec 2025
Viewed by 387
Abstract
Under the ongoing electricity market reforms, short-term load forecasting (STLF) is increasingly challenged by pronounced non-stationarity driven by price fluctuations. This study proposes an adaptive STLF framework tailored to price-induced non-stationarity. Firstly, a market state identification method based on load–price joint clustering is [...] Read more.
Under the ongoing electricity market reforms, short-term load forecasting (STLF) is increasingly challenged by pronounced non-stationarity driven by price fluctuations. This study proposes an adaptive STLF framework tailored to price-induced non-stationarity. Firstly, a market state identification method based on load–price joint clustering is developed to structurally model the temporal interactions between price and load. It allows the automatic extraction of typical market patterns and helps uncover how price fluctuations drive load variations. Secondly, a gated mixture forecasting network is proposed to dynamically adapt to the inertia of historical price fluctuations. By integrating parallel branches with an adaptive weighting mechanism, the model dynamically captures historical price features and achieves both rapid response and steady correction under market volatility. Finally, a Transformer-based expert model with multi-scale dependency learning is introduced to capture sequential dependencies and state transitions across different load regimes through self-attention, thereby enhancing model generalization and stability. Case studies using real market data confirm that the proposed approach delivers substantial performance improvements, offering reliable support for system dispatch and market operations. Relative to mainstream baseline models, it reduces MAPE by 1.08–2.62 percentage points. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence and Optimization Algorithms in Power Systems and Energy Storage)
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Review

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70 pages, 1137 KB  
Review
A Review of Artificial Intelligence Techniques for Low-Carbon Energy Integration and Optimization in Smart Grids and Smart Homes
by Omosalewa O. Olagundoye, Olusola Bamisile, Chukwuebuka Joseph Ejiyi, Oluwatoyosi Bamisile, Ting Ni and Vincent Onyango
Processes 2026, 14(3), 464; https://doi.org/10.3390/pr14030464 - 28 Jan 2026
Viewed by 219
Abstract
The growing demand for electricity in residential sectors and the global need to decarbonize power systems are accelerating the transformation toward smart and sustainable energy networks. Smart homes and smart grids, integrating renewable generation, energy storage, and intelligent control systems, represent a crucial [...] Read more.
The growing demand for electricity in residential sectors and the global need to decarbonize power systems are accelerating the transformation toward smart and sustainable energy networks. Smart homes and smart grids, integrating renewable generation, energy storage, and intelligent control systems, represent a crucial step toward achieving energy efficiency and carbon neutrality. However, ensuring real-time optimization, interoperability, and sustainability across these distributed energy resources (DERs) remains a key challenge. This paper presents a comprehensive review of artificial intelligence (AI) applications for sustainable energy management and low-carbon technology integration in smart grids and smart homes. The review explores how AI-driven techniques include machine learning, deep learning, and bio-inspired optimization algorithms such as particle swarm optimization (PSO), whale optimization algorithm (WOA), and cuckoo optimization algorithm (COA) enhance forecasting, adaptive scheduling, and real-time energy optimization. These techniques have shown significant potential in improving demand-side management, dynamic load balancing, and renewable energy utilization efficiency. Moreover, AI-based home energy management systems (HEMSs) enable predictive control and seamless coordination between grid operations and distributed generation. This review also discusses current barriers, including data heterogeneity, computational overhead, and the lack of standardized integration frameworks. Future directions highlight the need for lightweight, scalable, and explainable AI models that support decentralized decision-making in cyber-physical energy systems. Overall, this paper emphasizes the transformative role of AI in enabling sustainable, flexible, and intelligent power management across smart residential and grid-level systems, supporting global energy transition goals and contributing to the realization of carbon-neutral communities. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence and Optimization Algorithms in Power Systems and Energy Storage)
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