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Advancements in Optimization and Control Algorithms for Intelligent Electric Power...
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Advancements in Optimization and Control Algorithms for Intelligent Electric Power Systems

A Special Issue of Processes (ISSN 2227-9717) belonging to the section "Energy Systems".

Deadline for manuscript submissions: 31 January 2027 | Viewed by 4774

Editors

Dr. Omar Avalos Alvarez

E-Mail Website
Guest Editor
Department of Electro-Photonics, Universidad de Guadalajara, Guadalajara 44430, México
Interests: machine learning; eetaheuristics; power systems application; image processing
Prof. Dr. Jorge Gálvez

E-Mail Website
Guest Editor
Department of Innovation Based on Information and Knowledge, Universidad de Guadalajara, Guadalajara 44430, Mexico
Interests: neural networks; machine learning; evolutionary computing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Primitivo Diaz

E-Mail Website
Guest Editor
Department of Electro-Photonics, Universidad de Guadalajara, Guadalajara 44430, México
Interests: energy management systems; optimal power flow; machine learning; power system control; distribution network reconfiguration
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Rolando Menchaca-Méndez

E-Mail Website
Guest Editor
Centro de Investigación en Computación, Instituto Politécnico Nacional, Ciudad de México 07700, Mexico
Interests: wireless networks: VANETs; sensor networks; cyber-physical systems; intelligent transportation systems; network architectures; Internet of Things (IoT) and Artificial Intelligence (AI) applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The growing demand for electricity poses significant challenges in ensuring a reliable and efficient energy supply for electric power systems, which is a complex task to address today. Optimization and control algorithms have emerged as an advanced problem-solving methodology across various scientific and engineering fields, providing sophisticated strategies for improving system performance, resource allocation, decision-making processes, and traditional as well as intelligent control approaches. The integration of optimization and control methodologies has further broadened the horizons of computational intelligence. This Special Issue aims to explore cutting-edge advancements in optimization and control, with a particular emphasis on their application within intelligent electric power systems, and their synergy with emerging technologies such as the Internet of Things (IoT) and advanced communication networks. These integrated approaches enable the systematic exploration of solution spaces, identification of optimal configurations, and dynamic control of systems that can adapt to changing conditions.

This Special Issue therefore welcomes original research and review articles covering the following topics:

  •  Optimization and control of power systems;
  • Advanced algorithms for smart grid optimization and control;
  • Intelligent optimization and control for modern power grids;
  • Dynamic control and optimization of renewable energy;
  • Robust control for power systems;
  • Photovoltaic system optimization;
  • Optimization and control of battery energy storage;
  • Optimization of energy management of power systems;
  • Dynamic control for electric power systems;
  • Algorithms for fault diagnosis and design of fault-tolerant control;
  • Energy optimization and its applications;
  • Control and Integration of emerging technologies in power systems;
  • IoT for sensing, monitoring, and control;
  • Networked control systems for distributed energy resources;
  • Data analytics and machine learning for predictive control and optimization in smart grids;
  • Real-time optimization and control using distributed intelligence.

Dr. Omar Avalos Alvarez
Prof. Dr. Jorge Gálvez
Prof. Dr. Primitivo Diaz
Prof. Dr. Rolando Menchaca-Méndez
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-anonymized 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

  • power systems
  • smart grid
  • renewable energy
  • dynamic control
  • fault tolerance
  • energy optimization
  • algorithms

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

21 pages, 3483 KB  
Article
Fast Generation of Feasible Unit Commitment Schedules Based on Standardized Net-Load Trajectory Similarity and Historical Schedule Transfer
by Bo Zhou, Yunyang Xu, Xinwei Sun, Congkai Huang and Yikui Liu
Processes 2026, 14(17), 2760; https://doi.org/10.3390/pr14172760 - 28 Aug 2026
Viewed by 372
Abstract
High penetration of wind and photovoltaic generation reshapes power system net-load profiles and increases the computational burden of repeatedly solving security-constrained unit commitment problems under multiple renewable scenarios. This paper proposes a fast schedule generation method based on historical schedule transfer. A library [...] Read more.
High penetration of wind and photovoltaic generation reshapes power system net-load profiles and increases the computational burden of repeatedly solving security-constrained unit commitment problems under multiple renewable scenarios. This paper proposes a fast schedule generation method based on historical schedule transfer. A library is constructed by pairing feasible unit commitment schedules with their corresponding 24 h net-load trajectories. For a target day, hour-wise, standardized net-load trajectories are compared using Euclidean distance, and the Top-K most similar historical schedules are selected as candidates. Each candidate is verified under the target-day operating conditions. When no candidate satisfies the feasibility or economic requirements, a limited-perturbation model is applied to repair the retrieved schedule with minimal changes in unit statuses. Case studies show that the proposed method effectively transfers historical commitment patterns, maintains operational feasibility and near-optimal economic performance, and reduces the computational effort required for unit commitment schedule generation. Full article
(This article belongs to the Special Issue Advancements in Optimization and Control Algorithms for Intelligent Electric Power Systems)
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15 pages, 1886 KB  
Article
A Dynamic Threshold Adjustment-Based Low-Switching-Frequency Voltage Equalization Strategy for MMC
by Xinxin Chen, Yanjun Ma, Duanjiao Li, Wenxing Sun, Junjun Zhang, Dejun Ba, Lijun Hang and Xiaofeng Lyu
Processes 2026, 14(11), 1792; https://doi.org/10.3390/pr14111792 - 30 May 2026
Cited by 1 | Viewed by 442
Abstract
This paper addresses the capacitor voltage balancing issue of submodules (SMs) in Modular Multilevel Converters (MMCs) operating under low switching frequencies by proposing a voltage balancing control strategy based on dynamic threshold adjustment. First, a dynamic model of SM capacitor voltage in MMCs [...] Read more.
This paper addresses the capacitor voltage balancing issue of submodules (SMs) in Modular Multilevel Converters (MMCs) operating under low switching frequencies by proposing a voltage balancing control strategy based on dynamic threshold adjustment. First, a dynamic model of SM capacitor voltage in MMCs is established, and the causes of capacitor voltage imbalance are analyzed. Then, based on the coupling relationship between switching frequency and voltage balancing, and the imbalance model under dynamic operating conditions, a dynamic threshold adjustment strategy is designed. A Fuzzy Logic Controller (FLC) is employed to dynamically adjust the voltage imbalance threshold in real time, ensuring capacitor voltage balance while optimizing the switching frequency and reducing system losses. Simulation results show that the proposed strategy can effectively maintain SM capacitor voltage balance under low-switching-frequency conditions, thereby improving system stability. Full article
(This article belongs to the Special Issue Advancements in Optimization and Control Algorithms for Intelligent Electric Power Systems)
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24 pages, 2759 KB  
Article
Resilience Enhancement Strategy for Power Systems: A Novel Active Response Model
by Yanjing Zhang, Gang Chen, Liang Guo, Kunhua Liu and Shufang Zhou
Processes 2026, 14(10), 1585; https://doi.org/10.3390/pr14101585 - 14 May 2026
Viewed by 499
Abstract
With the continuously increasing proportion of renewable energy integration, the structure of power grid networks has become increasingly complex. Under extreme weather conditions such as typhoons and hail, faults like line breaks or information disruptions can occur in the power grid, imposing significant [...] Read more.
With the continuously increasing proportion of renewable energy integration, the structure of power grid networks has become increasingly complex. Under extreme weather conditions such as typhoons and hail, faults like line breaks or information disruptions can occur in the power grid, imposing significant burdens and risks on the economic and reliable operation of the power system. However, existing methods still focus on the allocation of human repair teams, with insufficient utilization of flexible resources within the system, resulting in low efficiency in restoring power supply to the power system. To address this challenge, this paper proposes a resilience enhancement strategy for the power system under typhoon scenarios. It leverages active resources on the grid side and fully exploits the flexibility of both the supply and demand sides to enhance the resilience of the power system. Firstly, this paper aims at the economic operation of the power system, taking into account the physical and operational constraints of both the supply and demand sides, including power flow constraints, mobile energy storage system (MESS) transfer constraints, and phase-shifting transformer (PST) regulation constraints. Meanwhile, an improved grasshopper optimization algorithm is introduced to achieve efficient and rapid problem-solving. Finally, the effectiveness and feasibility of the proposed method are demonstrated through validation using an improved IEEE-33 bus test system. Through analysis, the total system load loss was reduced by 75.6%, with the maximum load loss during the typhoon decreasing by 72.4%. The approach enables real-time response to the dynamic impacts of typhoons, swiftly stabilizes load fluctuations, and significantly enhances the resilience of the power system. Full article
(This article belongs to the Special Issue Advancements in Optimization and Control Algorithms for Intelligent Electric Power Systems)
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22 pages, 1499 KB  
Article
Intelligent Decision Formulation and Composite Neural Network-Driven Optimization Scheduling for Multi-Agent Collaboration in the Electricity Market for Unit Commitment
by Xingyou Zhang, Congcong Liu, Pengfei Li, Xiaolu Chen, Nan Yang, Zhenhua Li and Juncong Hao
Processes 2026, 14(4), 661; https://doi.org/10.3390/pr14040661 - 14 Feb 2026
Viewed by 698
Abstract
With the relentless expansion of installed capacity in renewable energy (RE) sources, including wind and photovoltaic power, the operational landscape of the power system has witnessed a substantial surge in uncertainty and complexity. The conventional unit commitment (UC) model falls short when it [...] Read more.
With the relentless expansion of installed capacity in renewable energy (RE) sources, including wind and photovoltaic power, the operational landscape of the power system has witnessed a substantial surge in uncertainty and complexity. The conventional unit commitment (UC) model falls short when it comes to addressing the challenges posed by a high proportion of RE integration and the collaborative involvement of multiple entities, especially within the electricity market framework. UC now faces a dual challenge: it must not only grapple with the pronounced uncertainty on the source–load side but also harmonize the operational characteristics of emerging entities, such as independent energy storage (ES) systems and virtual power plants. All of this must be achieved while strictly adhering to market regulations and grid safety constraints. To tackle these issues, this paper proposes an intelligent scheduling model built upon a composite neural network. This model enables real-time optimization of scheduling for thermal power, renewable energy, and ES systems through multi-agent collaboration. By doing so, it effectively mitigates the uncertainty associated with RE sources and enhances the safety, economic efficiency, and flexibility of the power system. Full article
(This article belongs to the Special Issue Advancements in Optimization and Control Algorithms for Intelligent Electric Power Systems)
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13 pages, 876 KB  
Article
A Verification Method for a Bay Configuration File of Intelligent Substation Renovation and Expansion in Power Systems
by Di Lu, Shi Ru, Peiyong Yu, Minhao Hu, Wei Yang, Hao Wang and Hongbo Zou
Processes 2025, 13(10), 3273; https://doi.org/10.3390/pr13103273 - 14 Oct 2025
Cited by 3 | Viewed by 871
Abstract
New renovation and expansion projects of intelligent substations may cause frequent changes in the internal configuration of intelligent electronic devices, making it difficult for on-site inspection personnel to verify their configuration files. Therefore, this paper proposes an intelligent substation renovation and expansion project [...] Read more.
New renovation and expansion projects of intelligent substations may cause frequent changes in the internal configuration of intelligent electronic devices, making it difficult for on-site inspection personnel to verify their configuration files. Therefore, this paper proposes an intelligent substation renovation and expansion project bay configuration file verification method based on cyclic redundancy check dynamic verification. Firstly, the logical relationship of configuration description files involved in the renovation and expansion project was introduced in detail, and bay decoupling and secondary circuit identification techniques were used to assign secondary equipment to bays. Then, the cyclic redundancy check dynamic verification method is introduced for the online diagnosis of configuration files and to manage and locate the secondary circuits associated with the bay of the renovation and expansion project. Finally, the semantic recognition method was used to verify the correctness of the virtual circuit configuration information. The effectiveness of the method was verified using a 220 kV substation in the Heilongjiang power grid as an example, which can effectively meet the application needs of intelligent substation renovation and expansion projects. Full article
(This article belongs to the Special Issue Advancements in Optimization and Control Algorithms for Intelligent Electric Power Systems)
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