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Mathematics
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Intelligent Optimization and Control Modeling in Power and Energy System
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Intelligent Optimization and Control Modeling in Power and Energy System

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".

Deadline for manuscript submissions: 20 July 2025 | Viewed by 4520

Special Issue Editors

Dr. Yuan Cao

E-Mail Website
Guest Editor
School of Automation, Central South University, 932 Lushan South Road, Changsha 410083, China
Interests: energy storage; battery management systems; battery system estimation algorithm; intelligent control algorithm; process control
Dr. Chunsheng Wang

E-Mail Website
Guest Editor
School of Information Science and Engineering, Central South University, 932 Lushan South Road, Changsha Hunan 410083 P.R. China
Interests: Power system modeling and control, electric traction and transmission control, complex system modeling, advanced control theory, and application research
Prof. Dr. Liqing Liao

E-Mail Website
Guest Editor
School of Automation, Central South University, Changsha 410083, China
Interests: multiple system modeling; control; power engineering

Special Issue Information

Dear Colleagues,

As the world economy accelerates, there is a swift surge in energy requirements. The efficiency of energy and power transmission is critical. The deployment of intelligent control and optimization methods stands out as an effective strategy for augmenting the capabilities of power generation systems, offering a blend of affordability and high performance.

This Special Issue gathers comprehensive reviews and research papers on Advanced Control, Intelligent Optimization, and Control Modeling in power and energy systems. We aim to showcase the latest technological advancements and their integration into power and energy systems.

We will place a strong emphasis on exploring areas including but not limited to artificial intelligence-based control mechanisms, neural networks, advanced intelligent optimization strategies, multi-objective optimization approaches, machine learning techniques, and the integration of power and energy technologies.

The scope of this SI includes the following:

  • Optimization and scheduling of microgrids;
  • Distributed renewable energy generation and grid optimization control;
  • Energy system health state estimation;
  • Energy scheduling optimization;
  • Power system modeling and control;
  • Load Forecasting and Demand Response;
  • Electric Vehicle (EV) charging optimization;
  • Optimization of hybrid renewable energy systems;
  • Distributed optimization techniques for power networks;
  • Data-driven modeling for dynamic power system simulations;
  • Utilization of Artificial Intelligence in the simulation, enhancement, and regulation of Energy Systems.

Dr. Yuan Cao
Dr. Chunsheng Wang
Prof. Dr. Liqing Liao
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. Mathematics 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 and energy system
  • battery System
  • intelligent optimization
  • control algorithm
  • data-driven modeling
  • modeling and simulation technologies

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

Published Papers (3 papers)

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18 pages, 3296 KiB  
Article
Data-Driven Voltage Control Method of Active Distribution Networks Based on Koopman Operator Theory
by Zhaobin Du, Xiaoke Lin, Guoduan Zhong, Hao Liu and Wenxian Zhao
Mathematics 2024, 12(24), 3944; https://doi.org/10.3390/math12243944 - 15 Dec 2024
Viewed by 856
Abstract
The advent of large-scale distributed generation (DG) has introduced several challenges to the voltage control of active distribution networks (ADNs). These challenges include the heterogeneity of control devices, the complexity of models, and their inherent fluctuations. To maintain ADN voltage stability more economically [...] Read more.
The advent of large-scale distributed generation (DG) has introduced several challenges to the voltage control of active distribution networks (ADNs). These challenges include the heterogeneity of control devices, the complexity of models, and their inherent fluctuations. To maintain ADN voltage stability more economically and quickly, a data-driven ADN voltage control scheme is proposed in this paper. Firstly, based on the multi-run state sensitivity matrix, buses with similar voltage responses are clustered, and critical buses are selected to downsize the scale of the model. Secondly, a linear voltage-to-power dynamics model in high-dimensional state space is trained based on the offline data of critical bus voltages, DGs, and energy storage system (ESS) outputs, utilizing the Koopman theory and the Extended Dynamic Mode Decomposition (EDMD) method. A linear model predictive voltage controller, which takes ADN stability and control cost into account, is also proposed. Finally, the effectiveness and applicability of the method are verified by applying it to an improved 33-bus ADN system. The proposed control method can respond more quickly and accurately to the voltage fluctuation problems caused by source-load disturbances and short-circuit faults. Full article
(This article belongs to the Special Issue Intelligent Optimization and Control Modeling in Power and Energy System)
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24 pages, 1829 KiB  
Article
Economic Load Dispatch Problem Analysis Based on Modified Moth Flame Optimizer (MMFO) Considering Emission and Wind Power
by Hani Albalawi, Abdul Wadood and Herie Park
Mathematics 2024, 12(21), 3326; https://doi.org/10.3390/math12213326 - 23 Oct 2024
Viewed by 1393
Abstract
In electrical power system engineering, the economic load dispatch (ELD) problem is a critical issue for fuel cost minimization. This ELD problem is often characterized by non-convexity and subject to multiple constraints. These constraints include valve-point loading effects (VPLEs), generator limits, emissions, and [...] Read more.
In electrical power system engineering, the economic load dispatch (ELD) problem is a critical issue for fuel cost minimization. This ELD problem is often characterized by non-convexity and subject to multiple constraints. These constraints include valve-point loading effects (VPLEs), generator limits, emissions, and wind power integration. In this study, both emission constraints and wind power are incorporated into the ELD problem formulation, with the influence of wind power quantified using the incomplete gamma function (IGF). This study proposes a novel metaheuristic algorithm, the modified moth flame optimization (MMFO), which improves the traditional moth flame optimization (MFO) algorithm through an innovative flame selection process and adaptive adjustment of the spiral length. MMFO is a population-based technique that leverages the intelligent behavior of flames to effectively search for the global optimum, making it particularly suited for solving the ELD problem. To demonstrate the efficacy of MMFO in addressing the ELD problem, the algorithm is applied to four well-known test systems. Results show that MMFO outperforms other methods in terms of solution quality, speed, minimum fuel cost, and convergence rate. Furthermore, statistical analysis validates the reliability, robustness, and consistency of the proposed optimizer, as evidenced by the consistently low fitness values across iterations. Full article
(This article belongs to the Special Issue Intelligent Optimization and Control Modeling in Power and Energy System)
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16 pages, 3102 KiB  
Article
Machine Learning Prediction of Fuel Cell Remaining Life Enhanced by Variational Mode Decomposition and Improved Whale Optimization Algorithm
by Zerong Huang, Daxing Zhang, Xiangdong Wang, Xiaolong Huang, Chunsheng Wang, Liqing Liao, Yaolin Dong, Xiaoshuang Hou, Yuan Cao and Xinyao Zhou
Mathematics 2024, 12(19), 2959; https://doi.org/10.3390/math12192959 - 24 Sep 2024
Cited by 2 | Viewed by 1451
Abstract
In predicting the remaining lifespan of Proton Exchange Membrane Fuel Cells (PEMFC), it is crucial to accurately capture the multi-scale variations in cell performance. This study employs Variational Mode Decomposition (VMD) to decompose performance data into intrinsic modes, elucidating critical multi-scale dynamics vital [...] Read more.
In predicting the remaining lifespan of Proton Exchange Membrane Fuel Cells (PEMFC), it is crucial to accurately capture the multi-scale variations in cell performance. This study employs Variational Mode Decomposition (VMD) to decompose performance data into intrinsic modes, elucidating critical multi-scale dynamics vital for understanding the complex degradation processes in fuel cells. In addition to VMD, this research utilizes an Improved Whale Optimization Algorithm (IWOA) to optimize a Back Propagation (BP) Neural Network. The IWOA focuses on precise adjustments of weights and biases, enabling the BP network to effectively interpret complex nonlinear relationships within the dataset. This optimization enhances the predictive model’s reliability and stability. Extensive experimental evaluations demonstrate that the integration of VMD, and the learning capabilities of the IWOA-optimized BP network significantly improves the model’s accuracy and stability across multiple predictions, thereby increasing the reliability of lifespan predictions for PEMFCs. This methodology offers a robust framework for extending the operational life and efficiency of fuel cells. Full article
(This article belongs to the Special Issue Intelligent Optimization and Control Modeling in Power and Energy System)
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