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 January 2026 | Viewed by 419

Special Issue Editors


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Guest Editor
ESIGELEC, IRSEEM, Université de Rouen Normandie, 76000 Rouen, France
Interests: data-driven control; predictive control; optimization strategies; sensorless control; motor drives; microgrids

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

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Keywords

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

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Published Papers (1 paper)

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Research

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 197
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
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