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Advances in Control Theory and Applications in Energy Systems
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Advances in Control Theory and Applications in Energy Systems

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E2: Control Theory and Mechanics".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 1861

Editors

Prof. Dr. Shuaihu Li

E-Mail Website
Guest Editor
School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha, China
Interests: power system stability analysis and control; self-healing regulation of smart grid
Dr. Ning Li

E-Mail Website
Guest Editor
School of Electrical Engineering, Xi’an University of Technology, Xi'an, China
Interests: stability control BS and support of GFCS; predictive control for power-electronicized systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Guangming Lang

E-Mail Website
Guest Editor
School of Mathematics and Statistics, Changsha University of Science and Technology, Changsha, China
Interests: fuzzy sets; granular computing; three-way decisions
Dr. Yanjian Peng

E-Mail Website
Guest Editor
School of Electronic and Information Engineering, Changsha University of Science and Technology, Changsha, China
Interests: power system stability analysis and control

Special Issue Information

Dear Colleagues,

This Special Issue focuses on recent advancements in control theory and their transformative applications in energy systems, addressing critical challenges such as renewable energy integration, grid stability, and demand-side management. The contributions highlight innovative control methodologies, including model predictive control (MPC), adaptive control, and machine learning-based techniques, tailored for diverse energy domains like power generation, storage, and smart grids. Key themes include enhancing efficiency through dynamic optimization, mitigating uncertainties in distributed energy resources (DERs), and enabling real-time decision-making for resilient energy infrastructure.

By bridging theoretical insights with practical implementations, this Issue showcases how cutting-edge control strategies can drive sustainability, reliability, and scalability in modern energy networks. The articles provide comprehensive reviews, mathematical modeling, case studies, and experimental validations, offering valuable guidance for researchers and practitioners aiming to optimize energy system performance in an evolving technological landscape. 

Prof. Dr. Shuaihu Li
Dr. Ning Li
Prof. Dr. Guangming Lang
Dr. Yanjian Peng
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. 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

  • energy systems
  • grid stability
  • advanced control
  • adaptive control
  • machine learning-based techniques
  • dynamic optimization

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

Published Papers (3 papers)

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Research

18 pages, 537 KB  
Article
Adaptive Regularized Numerical Differentiation of Noisy Signals with Application to RoCoF Estimation in Power Systems
by Farshad Merrikh-Bayat
Mathematics 2026, 14(13), 2337; https://doi.org/10.3390/math14132337 - 2 Jul 2026
Viewed by 413
Abstract
The total variation regularization (TVR) is one of the methods widely used for numerical differentiation of noisy signals. In this method, an appropriate value must be assigned to the regularization parameter. Although various approaches have been proposed for this purpose, their practical application [...] Read more.
The total variation regularization (TVR) is one of the methods widely used for numerical differentiation of noisy signals. In this method, an appropriate value must be assigned to the regularization parameter. Although various approaches have been proposed for this purpose, their practical application may still depend on problem characteristics, prior knowledge, or additional tuning. Consequently, reliable automatic selection of the regularization parameter remains an important issue. The goal of this study is to develop a new adaptive method for estimating the derivative of a discrete-time noisy signal based on the TVR. In this method, at any moment of time, first the optimal value of the regularization parameter is calculated for a fixed-length sliding window over the most recent samples. Subsequently, for the obtained optimal regularization parameter, the numerical derivative of the samples in the sliding window are computed. The main contribution of the proposed method is in the adaptive strategy developed for automatic selection of the regularization parameter from the data which assigns small values to the regularization parameter when the signal changes rapidly, and vice versa. The proposed method is used for accurate and effective rate of change of frequency (RoCoF) estimation in power systems under different circumstances. Full article
(This article belongs to the Special Issue Advances in Control Theory and Applications in Energy Systems)
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25 pages, 1096 KB  
Article
Stochastic Control of Corporate Abatement Effort Under Carbon Price Uncertainty and Surplus-Allowance Monetization
by Haichao Yang
Mathematics 2026, 14(11), 1850; https://doi.org/10.3390/math14111850 - 26 May 2026
Viewed by 302
Abstract
This study formulates a corporate abatement decision problem under carbon price uncertainty as a continuous-time stochastic control model. To this end, the carbon price is modeled as a geometric Brownian motion, while abatement capacity is accumulated through costly effort and depreciates over time. [...] Read more.
This study formulates a corporate abatement decision problem under carbon price uncertainty as a continuous-time stochastic control model. To this end, the carbon price is modeled as a geometric Brownian motion, while abatement capacity is accumulated through costly effort and depreciates over time. Specifically, the firm chooses its abatement effort to maximize expected discounted profits while accounting for allowance purchasing costs, compliance-related penalties, abatement costs, and potential revenues from surplus allowances. The paper contributes by integrating stochastic carbon prices, endogenous abatement-capacity accumulation, allowance-shortage/allowance-surplus asymmetry, and surplus allowance monetization into a unified corporate abatement framework. Applying the dynamic programming principle, the associated Hamilton–Jacobi–Bellman equation is derived, and the bounded optimal abatement effort is characterized in feedback form. Since the resulting nonlinear HJB equation generally does not admit a closed-form solution, a finite-difference scheme with damped policy iteration is used for numerical analysis. The results show that optimal abatement effort is strongly state-dependent. Higher carbon prices strengthen abatement incentives in the allowance-shortage region, whereas effort declines sharply after reaching allowance neutrality if surplus allowances cannot be monetized. Moreover, partial monetization of surplus allowances significantly increases abatement effort in the surplus region and can shift firms’ behavior from passive compliance to active low-carbon investment. Overall, these findings suggest that surplus allowance monetization plays an important role in sustaining firms’ abatement incentives under carbon price uncertainty. Full article
(This article belongs to the Special Issue Advances in Control Theory and Applications in Energy Systems)
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19 pages, 4280 KB  
Article
Adaptive Recursive Model Predictive Current Control for Linear Motor Drives in CNC Machine Tools Based on Cartesian Distance Minimization
by Lin Song, Ziling Nie, Jun Sun, Yangwei Zhou, Jingxin Yuan and Huayu Li
Mathematics 2026, 14(8), 1377; https://doi.org/10.3390/math14081377 - 20 Apr 2026
Viewed by 534
Abstract
With the increasing demand for high speed and high-precision motion control in CNC machine tools, permanent magnet linear synchronous motors (PMLSMs) have been widely adopted in feed drive systems due to their excellent dynamic performance and positioning accuracy. However, existing model predictive current [...] Read more.
With the increasing demand for high speed and high-precision motion control in CNC machine tools, permanent magnet linear synchronous motors (PMLSMs) have been widely adopted in feed drive systems due to their excellent dynamic performance and positioning accuracy. However, existing model predictive current control (MPCC) variants still face challenges regarding high computational overhead and strong dependency on accurate motor parameters, which limit their industrial applicability. To address these issues, this paper proposes an adaptive recursive MPCC for PMLSM drives based on the Cartesian distance minimization principle. An adaptive recursive prediction scheme that is inspired by the feedback structure of recurrent architectures is first introduced. By cyclically utilizing the previously sampled current to predict the next period’s state, the strategy effectively decouples the control law from inductance variations. The dependence on resistance is further mitigated by analyzing the correlation between the ideal current vector and voltage vector deviations. Second, the selection of the optimal voltage vector is transformed into a geometric problem: minimizing the Cartesian distance between the reference voltage and 19 candidate deviations within a proposed virtual voltage vector hexagon. To minimize the computational burden, the vector space is partitioned into eight regions, allowing the optimal candidate to be selected from only two pre-derived deviations. The experimental results demonstrate that the proposed method significantly outperforms existing MPCC benchmarks. Specifically, the execution time is reduced by 63.6%. Under severe parameter mismatch, the current THD is reduced from 14.82% to 6.35%, and the thrust ripple is improved from 12.06 N to 5.25 N, validating its superior robustness and efficiency. Full article
(This article belongs to the Special Issue Advances in Control Theory and Applications in Energy Systems)
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