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Intelligent Optimization and Control Modeling in Power and Energy System

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

Deadline for manuscript submissions: closed (30 November 2025) | Viewed by 13407

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Special Issue Editors

Dr. Yuan Cao

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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
Special Issues, Collections and Topics in MDPI journals

Dr. Chunsheng Wang

E-Mail Website
Guest Editor

School of Information Science and Engineering, Central South University, 932 Lushan South Road, Changsha 410083, China
Interests: power system modeling and control; electric traction and transmission control; complex system modeling; advanced control theory; application research
Special Issues, Collections and Topics in MDPI journals

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

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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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Published Papers (10 papers)

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Research

23 pages, 4330 KB

Open AccessArticle

Surrogate Model-Based Optimization of a Dual-Shield Total Temperature Probe for Aero-Engine Applications

by Xuetao Zhang, Yufang Wang, Qi Lei, Jian Zhao and Yudi Ai

Mathematics 2025, 13(23), 3870; https://doi.org/10.3390/math13233870 - 3 Dec 2025

Viewed by 154

Abstract

The design of high-precision total temperature probes for aero-engines is constrained by the massive computational cost of high-fidelity simulations. This paper overcomes this barrier by introducing a surrogate model-based optimization framework for a dual-shield probe. A computationally efficient data-driven framework is established, merging conjugate-heat-transfer Computational Fluid Dynamics (CFDs), a Support Vector Regression (SVR) model, and a Genetic Algorithm (GA), which collectively replace the traditional costly design loop. The surrogate model’s exceptional predictive fidelity is confirmed, and this approach obtains improvement in measurement accuracy, successfully reducing the temperature deviation and meeting the stringent requirement. Finally, the demonstrated framework is geometry-agnostic, establishing a generalizable and cost-effective strategy for the rapid design of high-performance thermometric components in gas turbine systems. Full article

(This article belongs to the Special Issue Intelligent Optimization and Control Modeling in Power and Energy System)

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25 pages, 9469 KB

Open AccessArticle

GCML: A Short-Term Load Forecasting Framework for Distributed User Groups Based on Clustering and Multi-Task Learning

by Junling Wan, Yusen Sun, Jianguo Fan, Yu Zhou, Rui Ye and Peisen Yuan

Mathematics 2025, 13(23), 3820; https://doi.org/10.3390/math13233820 - 28 Nov 2025

Viewed by 187

Abstract

Short-term load forecasting of distributed user groups is crucial for the efficient operation of electricity markets, but existing methods mainly rely on intra-group consistency while neglecting inter-group correlations, which limits the utilization of cross-group information and reduces forecasting accuracy. To overcome these limitations, this study introduces a clustering and multi-task learning-based framework for short-term load forecasting of distributed user groups. First, historical load data are clustered to form representative consumption groups. Next, a Transformer encoder is used as a hard parameter shared network for multi-task learning. Within the multi-task framework, we apply dynamic task weighting and task-specific prediction heads, which balance multi-task losses while optimizing the forecasting performance of each group. Moreover, a filter-attention mechanism and an Inception convolution module are introduced into the encoder to improve local pattern extraction and multi-scale feature fusion. Experiments conducted on two publicly available datasets show that, for the London smart meter dataset, the MAE values of the clusters are 0.2858 and 0.4312, and the RMSE values are 0.5042 and 0.5266. On different clusters of the UCI electricity load dataset, the MAE values are 0.1617, 0.1554, and 0.2608, and the RMSE values are 0.2299, 0.2130, and 0.3678, respectively. These results demonstrate that our method outperforms baseline models and significantly improves the accuracy of distributed user short-term load forecasting in electricity markets. Full article

(This article belongs to the Special Issue Intelligent Optimization and Control Modeling in Power and Energy System)

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22 pages, 6858 KB

Open AccessArticle

Stochastic Optimization and Adaptive Control for Dynamic Bus Lane Management Under Heterogeneous Connected Traffic

by Bo Yang, Chunsheng Wang, Junxi Yang and Zhangyi Wang

Mathematics 2025, 13(22), 3666; https://doi.org/10.3390/math13223666 - 15 Nov 2025

Viewed by 1028

Abstract

The efficiency of intelligent urban mobility increasingly depends on adaptive mathematical models that can optimize multimodal transportation resources under stochastic and heterogeneous conditions. This study proposes a Markovian stochastic modeling and metaheuristic optimization framework for the adaptive management of bus lane capacity in mixed connected traffic environments. The heterogeneous vehicle arrivals are modeled using a Markov Arrival Process (MAP) to capture correlated and busty flow characteristics, while the system-level optimization aims to minimize total fuel consumption through discrete lane capacity allocation. To support real-time adaptation, a Hidden Markov Model (HMM) is integrated for queue-length estimation under partial observability. The resulting nonlinear and nonconvex optimization problem is solved using Genetic Algorithm (GA), Differential Evolution (DE), and Particle Swarm Optimization (PSO), ensuring robustness and convergence across diverse traffic scenarios. Numerical experiments demonstrate that the proposed stochastic–adaptive framework can reduce fuel consumption and vehicle delay by up to 68% and 65%, respectively, under high saturation and connected-vehicle penetration. The findings verify the effectiveness of coupling stochastic modeling with adaptive control, providing a transferable methodology for energy-efficient and data-driven lane management in smart and sustainable cities. 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, 1177 KB

Open AccessArticle

Emission-Constrained Dispatch Optimization Using Adaptive Grouped Fish Migration Algorithm in Carbon-Taxed Power Systems

by Kai-Hung Lu, Xinyi Jiang and Sang-Jyh Lin

Mathematics 2025, 13(17), 2722; https://doi.org/10.3390/math13172722 - 24 Aug 2025

Viewed by 687

Abstract

With increasing global pressure to decarbonize electricity systems, particularly in regions outside international carbon trading frameworks, it is essential to develop adaptive optimization tools that account for regulatory policies and system-level uncertainty. An emission-constrained power dispatch strategy based on an Adaptive Grouped Fish Migration Optimization (AGFMO) algorithm is proposed. The algorithm incorporates dynamic population grouping, a perturbation-assisted escape strategy from local optima, and a performance-feedback-driven position update rule. These enhancements improve the algorithm’s convergence reliability and global search capacity in complex constrained environments. The proposed method is implemented in Taiwan’s 345 kV transmission system, covering a decadal planning horizon (2023–2033) with scenarios involving varying load demands, wind power integration levels, and carbon tax schemes. Simulation results show that the AGFMO approach achieves greater reductions in total dispatch cost and CO₂ emissions compared with conventional swarm-based techniques, including PSO, GACO, and FMO. Embedding policy parameters directly into the optimization framework enables robustness in real-world grid settings and flexibility for future carbon taxation regimes. The model serves as decision-support tool for emission-sensitive operational planning in power markets with limited access to global carbon trading, contributing to the advanced modeling of control and optimization processes in low-carbon energy systems. Full article

(This article belongs to the Special Issue Intelligent Optimization and Control Modeling in Power and Energy System)

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20 pages, 7784 KB

Open AccessArticle

Combined Framework for State of Charge Estimation of Lithium-Ion Batteries: Optimized LSTM Network Integrated with IAOA and AUKF

by Jing Han, Yaolin Dong and Wei Wang

Mathematics 2025, 13(16), 2590; https://doi.org/10.3390/math13162590 - 13 Aug 2025

Viewed by 624

Abstract

The State of Charge (SOC) is vital for battery system management. Enhancing SOC estimation boosts system performance. This paper presents a combined framework that improves SOC estimation’s accuracy and stability for electric vehicles. The framework combines a Long Short-Term Memory (LSTM) network with an Adaptive Unscented Kalman Filter (AUKF). An Improved Arithmetic Optimization Algorithm (IAOA) fine-tunes the LSTM’s hyperparameters. Its novelty lies in its adaptive iteration algorithm, which adjusts iterations based on a threshold, optimizing computational efficiency. It also integrates a genetic mutation strategy into the AOA to overcome local optima by mutating iterations. Additionally, the AUKF’s adaptive noise algorithm updates noise covariance in real-time, enhancing SOC estimation precision. The inputs of the proposed method include battery current, voltage, and temperature, then producing an accurate SOC output. The predictions of LSTM are refined through AUKF to obtain reliable SOC estimation. The proposed framework is firstly evaluated utilizing a public dataset and then applied to battery packs on actual engineering vehicles. Results indicate that the Root Mean Square Errors (RMSEs) of the SOC estimations in practical applications are below 0.6%, and the Maximum Errors (MAX) are under 3.3%, demonstrating the accuracy and robustness of the proposed combined framework. Full article

(This article belongs to the Special Issue Intelligent Optimization and Control Modeling in Power and Energy System)

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17 pages, 5309 KB

Open AccessArticle

Energy Optimization Strategy for Wind–Solar–Storage Systems with a Storage Battery Configuration

by Yufeng Wang, Haining Ji, Runteng Luo, Bin Liu and Yongzi Wu

Mathematics 2025, 13(11), 1755; https://doi.org/10.3390/math13111755 - 25 May 2025

Cited by 2 | Viewed by 2087

Abstract

With the progressive advancement of the energy transition strategy, wind–solar energy complementary power generation has emerged as a pivotal component in the global transition towards a sustainable, low-carbon energy future. To address the inherent challenges of intermittent renewable energy generation, this paper proposes a comprehensive energy optimization strategy that integrates coordinated wind–solar power dispatch with strategic battery storage capacity allocation. Through the development of a linear programming model for the wind–solar–storage hybrid system, incorporating critical operational constraints including load demand, an optimization solution was implemented using the Artificial Fish Swarm Algorithm (AFSA). This computational approach enabled the determination of an optimal scheme for the coordinated operation of wind, solar, and storage components within the integrated energy system. Based on the case study analysis, the AFSA optimization algorithm achieves a 1.07% reduction in total power generation costs compared to the traditional Simulated Annealing (SA) approach. Comparative analysis reveals that the integrated grid-connected operation mode exhibits superior economic performance over the standalone storage microgrid system. Additionally, we conducted a further analysis of the key factors contributing to the enhancement of economic benefits. The strategy proposed in this paper significantly enhances power supply stability, reduces overall costs and promotes the large-scale application of green energy. Full article

(This article belongs to the Special Issue Intelligent Optimization and Control Modeling in Power and Energy System)

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18 pages, 10635 KB

Open AccessArticle

Stability and Performance Analysis of Single-Step FCS-MPC System Based on Regional ISS Theory

by Weiguang Hu, Long Chen and Zhangyi Wang

Mathematics 2025, 13(10), 1616; https://doi.org/10.3390/math13101616 - 14 May 2025

Viewed by 725

Abstract

In recent years, finite-control-set model predictive control (FCS-MPC) has attracted significant attention in power electronic converter control, resulting in substantial research advancements. However, no formal method currently exists to prove the stability of FCS-MPC systems. Additionally, many application studies have yet to adequately address the relationship between the selection of design parameters and system performance. To address the lack of stability and performance guarantees in FCS-MPC system design, this paper investigates a class of single-step FCS-MPC systems. The analysis is based on regional input-to-state stability (ISS) theory. Sufficient conditions for ensuring regional stability are derived, and a method for estimating the system’s domain of attraction and ultimate bounded region is developed. Simulation experiments validated the analytical results and revealed the relationships between the domain of attraction and system stability, as well as between the ultimate bounded region and steady-state performance. The results indicate that appropriate parameter design can ensure system stability. Furthermore, the proposed method elucidates how changes in design parameters affect system stability and steady-state performance, providing a theoretical foundation for designing a class of FCS-MPC systems. Full article

(This article belongs to the Special Issue Intelligent Optimization and Control Modeling in Power and Energy System)

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18 pages, 3296 KB

Open AccessArticle

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

Cited by 2 | Viewed by 1664

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 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 KB

Open AccessArticle

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

Cited by 4 | Viewed by 2169

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 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 KB

Open AccessArticle

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 5 | Viewed by 2576

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 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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