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

Deadline for manuscript submissions: 31 March 2027 | Viewed by 5528

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Editors

Dr. Xiaoping Xiong

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Guest Editor

School of Electrical Engineering, Guangxi University, Nanning 530004, China
Interests: optimal power system operation; cyber–physics system; distribution network communication systems; electricity market

Dr. Fanrong Wei

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Guest Editor

School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: power system security; power system operation and planning; power system analysis and computation

Dr. Lingling Xie

E-Mail Website
Guest Editor

School of Electrical Engineering, Guangxi University, Nanning 530004, China
Interests: nonlinear characteristic analysis and control of power electronic systems; construction and design of high-performance power electronic converters for renewable energy generation systems
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Special Issue Information

Dear Colleagues,

The increasing penetration of renewable energy sources and the growing complexity of modern power grids have created unprecedented challenges for ensuring stability, reliability, and efficiency. Fluctuating demand, variable renewable generation, and the integration of distributed energy resources necessitate advanced control and optimization strategies that can adapt in real-time. Adaptive control methods, combined with optimization frameworks, are becoming essential tools to guarantee secure, efficient, and sustainable grid operation under uncertainty.

This Special Issue, titled “Adaptive Control and Optimization in Power Grids,” aims to highlight novel advances in methodologies, algorithms, and applications that enhance the resilience, sustainability, and intelligence of power systems. We welcome contributions that address theoretical developments, computational techniques, simulation studies, and real-world implementations.

Topics of interest include, but are not limited to:

Adaptive and predictive control (e.g., Model Predictive Control) for frequency and voltage regulation;
Robust and stochastic optimization methods for power system operations;
Data-driven and machine learning approaches for adaptive grid control;
Short-term and probabilistic forecasting of load and renewable generation for control applications;
Coordination and optimization of demand response, energy storage, and distributed energy resources (DERs);
Digital twin frameworks, hardware-in-the-loop (HIL), and cyber-physical testbeds for validation;
Cybersecurity and resilience strategies for adaptive control and optimization in smart grids.

Dr. Xiaoping Xiong
Dr. Fanrong Wei
Dr. Lingling Xie
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

adaptive control
power system optimization
robust control
forecasting of load and renewable generation
renewable energy integration
grid resilience

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Published Papers (10 papers)

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Research

19 pages, 2997 KB

Open AccessArticle

Research on Microgrid Optimal Scheduling Based on the Salp Swarm Algorithm

by Qi Chen, Tao Ma, Li Song and Ai-Qing Tian

Processes 2026, 14(19), 3147; https://doi.org/10.3390/pr14193147 - 30 Sep 2026

Abstract

To address uneven population distribution and premature convergence of the standard Salp Swarm Algorithm (SSA) in grid-connected microgrid scheduling, this study develops an Improved Salp Swarm Algorithm (ISSA). Sine chaotic mapping is used for population initialization. A distance-adaptive follower update is then employed, in which the weight is determined by the normalized distance between an individual and the current best solution, and an acceptance-based Lévy perturbation is applied to the best solution to enhance escape from local optima. The resulting ISSA is applied to a microgrid scheduling model that minimizes economic operation and environmental costs. Benchmark function and microgrid case studies are used to evaluate optimization accuracy, convergence behavior and scheduling feasibility. Full article

(This article belongs to the Special Issue Adaptive Control and Optimization in Power Grids)

27 pages, 10085 KB

Open AccessArticle

Hierarchical Sensitivity Analysis of PV Converter Operating Profiles Under Climatic and Grid Uncertainty

by Ivelina Hinova, Silvia Baeva and Mirjana Kocaleva Vitanova

Processes 2026, 14(16), 2677; https://doi.org/10.3390/pr14162677 - 21 Aug 2026

Viewed by 427

Abstract

Photovoltaic converters operate under varying climatic conditions and non-ideal grid regimes, but factor importance is often assessed either through isolated local metrics or through pooled operating data that hide regime shifts and interaction effects. This study develops a hierarchical framework for sensitivity analysis of operating profiles of grid-connected PV converters under climatic and grid uncertainty. A compact operating-profile formulation is introduced that relates solar radiation, cell and ambient temperature, grid voltage, load, and selected design/control parameters to active power, efficiency, power factor, harmonic distortion, DC bus ripple, clipping behavior, and thermal headroom. The proposed workflow combines local normalized sensitivities for fast ranking around nominal conditions, Morris screening for factor reduction, and Sobol/Saltelli variance-based indices for global prioritization under uncertainty. The framework is demonstrated on a 100 kW synthetic reduced-order benchmark representing a three-phase two-level grid-connected PV inverter with an LCL filter. To clarify the scope of validity, the reduced-order model is cross-checked against switching-level simulations for representative nominal, clipping-prone, high-temperature and grid-stress operating windows. The results show that factor importance is not universal, but depends on the selected KPI, operating regime and uncertainty scenario. In the considered benchmark, grid voltage, cell temperature and equivalent thermal resistance are the dominant total-effect contributors, while the strongest second-order contribution appears between grid voltage and filter inductance under grid-stress conditions. The proposed framework is therefore intended as a reproducible, regime-aware sensitivity workflow rather than as a universal ranking of PV converter parameters. Full article

(This article belongs to the Special Issue Adaptive Control and Optimization in Power Grids)

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18 pages, 1905 KB

Open AccessArticle

Physics-Constrained Bayesian-LSTM Adaptive Impedance Modeling and Resonance Source Identification for Grid-Connected PV-Storage Systems

by Jun Lin and Jiyong Li

Processes 2026, 14(15), 2459; https://doi.org/10.3390/pr14152459 - 30 Jul 2026

Viewed by 505

Abstract

With the increasing integration of photovoltaic (PV) and energy storage systems into power grids, stability issues caused by converter dynamics, grid impedance interactions, and AC/DC coupling have become increasingly significant. Conventional uniform frequency-scanning methods often fail to provide sufficient resolution in stability-critical frequency regions, limiting the accurate characterization of resonance phenomena. This paper proposes an adaptive two-port admittance modeling and stability assessment method for grid-connected PV-storage systems considering AC/DC coupling characteristics. A two-port frequency-domain model incorporating AC-side admittance, DC-side admittance, and transfer admittance is first established to describe dynamic interactions between the AC and DC subsystems. An adaptive frequency-domain modeling framework with physical constraints is then developed to improve modeling accuracy in critical frequency bands. Furthermore, an improved generalized impedance-ratio criterion considering AC/DC transfer effects and DC-side participation factors is proposed for resonance risk assessment and source identification. Simulation studies and hardware-in-the-loop experiments based on MATLAB 2024b and RT-LAB 2024.1.1 are conducted to validate the proposed method. Results demonstrate that the proposed approach effectively enhances frequency-domain resolution, improves the characterization of AC/DC-coupled resonance behavior, and accurately identifies dominant resonance sources under different operating conditions. Full article

(This article belongs to the Special Issue Adaptive Control and Optimization in Power Grids)

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17 pages, 2191 KB

Open AccessArticle

A Two-Stage Distribution Network Planning Study on Coordinating the Optimization of Economic Efficiency and Reliability

by Huazhi Liu, Liang Zhang, Fan Yang, Lihu Jia and Lemeng Liang

Processes 2026, 14(13), 2087; https://doi.org/10.3390/pr14132087 - 26 Jun 2026

Viewed by 325

Abstract

As the core platform supporting diverse user-side loads, the distribution network plays a critical role in the development of new power systems. To address the significant variations in terminal users’ power supply reliability requirements, this article proposes a two-stage distribution network planning method that optimally coordinates economic efficiency and reliability. First, a multi-type load-specific reliability evaluation index system is established. Using the K-means clustering algorithm, combined with geographic coordinates and load attribute feature matrices, a precision power supply zoning scheme is implemented. Second, considering the diverse demands of different zones, a two-stage distribution network planning model is developed. Finally, the model is solved using a binary particle swarm optimization algorithm (BPSO), and simulation verification is conducted using a case study of an actual distribution network in a certain area of Tianjin. The results indicate that, compared to traditional single-objective planning schemes, the proposed method achieves an effective balance between economic efficiency and reliability. While ensuring the power supply level for critical users, it enhances the flexibility and science of distribution network planning, thereby providing a decision-making reference for the grid-based planning and construction of a smart distribution network. Full article

(This article belongs to the Special Issue Adaptive Control and Optimization in Power Grids)

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40 pages, 21714 KB

Open AccessArticle

Mechanism and Simulation Analysis of Resonance De-Icing for 100 m High-Voltage Transmission Line

by Yu Zhang, Yinke Dou, Fujia Liu, Liangliang Zhao, Yangyang Jiao and Huajian Li

Processes 2026, 14(12), 1952; https://doi.org/10.3390/pr14121952 - 15 Jun 2026

Viewed by 423

Abstract

To address safety hazards such as line damage and operational instability caused by icing on high-voltage overhead transmission lines, this study conducts numerical simulation research on wire vibration de-icing based on the ANSYS finite element platform. Using a 100 m span transmission line as the research model, 49.8 m ice-covered sections are set on both sides of the line, and the 0.4 m range in the middle is designated as the concentrated excitation force area of the vibration motor. By applying intermittent harmonic loads in the excitation stage, the process of mechanical vibration de-icing is accurately reproduced. At the same time, life and death element technology is introduced to remove ice-covered units with stress exceeding the critical failure threshold, accurately realizing the dynamic simulation of the entire process of ice-covering cracking and detachment. This study selects resonance frequency bands that are suitable for the structural characteristics of the transmission line through static analysis, modal analysis, and harmonic response analysis, and preliminarily locks in candidate excitation frequencies. Combined with transient dynamics simulation, the optimal excitation frequency for vibration de-icing of transmission lines is determined by comprehensively considering the efficiency of de-icing and the safety constraints of conductor dancing. A method for determining the optimal de-icing frequency based on multi-step finite element analysis has been developed, which can provide theoretical support and simulation reference for the structural design, frequency matching, and operational parameter optimization of mechanical vibration de-icing devices for high-voltage transmission lines and overhead cables. Full article

(This article belongs to the Special Issue Adaptive Control and Optimization in Power Grids)

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15 pages, 1459 KB

Open AccessArticle

Adaptive Distance Protection Setting Method Based on Sensitivity Constraints and Disturbance-Domain Model

by Jianbin Ci, You Yu, Tao Li, Zhenting Sun, Jingfu Tian, Ming Dong, Qiang Ma, Shiming Wang and Jingshan Mo

Processes 2026, 14(11), 1752; https://doi.org/10.3390/pr14111752 - 27 May 2026

Viewed by 392

Abstract

With the expansion of transmission networks and the increasing penetration of inverter-based resources (IBRs), fixed offline distance-protection settings face increasing difficulty in balancing selectivity, sensitivity, and operating speed. This problem is particularly evident in Zone III remote-backup protection, where conservative load-avoidance settings may create blind zones. This paper proposes an adaptive three-zone distance-protection setting method based on explicit sensitivity constraints and a disturbance-domain model. The method has two main features. First, online recalculation is restricted to the local disturbance domain affected by topology changes, thereby avoiding network-wide recomputation. Second, Zone II and Zone III settings are determined by a constrained model that incorporates real-time branch coefficients, load impedance, sensitivity requirements, and downstream coordination limits. A fallback mechanism is also included to maintain security under data loss or abnormal measurements. In a 220 kV case study, the proposed method increases the Zone II sensitivity coefficient from 1.92 to 1.95 and the Zone III remote-backup sensitivity coefficient from 0.83 to 1.35. Additional tests under high-resistance faults, measurement errors, volatile load, and inverter-based resource integration show that the method preserves selectivity while reducing backup protection blind zones. The disturbance-domain strategy also reduces the average recalculation time from 820 ms to 18 ms in the tested regional setting-calculation scenario. Full article

(This article belongs to the Special Issue Adaptive Control and Optimization in Power Grids)

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19 pages, 6417 KB

Open AccessArticle

Improved Football Team Training Algorithm Based on Modal Decomposition and BiLSTM Method for Short-Term Wind Power Forecasting

by Lingling Xie, Yanjing Luo, Chunhui Li, Long Li and Fengyuan Liu

Processes 2026, 14(6), 951; https://doi.org/10.3390/pr14060951 - 17 Mar 2026

Viewed by 551

Abstract

Reliable wind power forecasting is essential for maintaining the safe and stable operation of power systems with high renewable energy penetration. This study proposes a short-term wind power forecasting model based on decomposition–optimization–prediction, integrating complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN), the improved football team training algorithm (IFTTA), and the bidirectional long short-term memory network model (BiLSTM). CEEMDAN is employed to decompose the non-stationary wind power sequence into relatively stable intrinsic mode functions (IMFs), thereby separating multi-scale fluctuation features. The IFTTA incorporates a dynamic probability allocation strategy and an adaptive parameter adjustment mechanism, which contributes to a better balance between global exploration and local exploitation. After optimizing the hyperparameters of BiLSTM using IFTTA, the prediction performance significantly improved. Validations were conducted on three datasets from Xinjiang, Ningxia, and Inner Mongolia, China, each containing 1440 samples (1152 for training and 288 for testing). Comparisons with the benchmark forecasting model demonstrate that the pro-posed model reduces the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) by at least 25.29%, 29.62%, and 20.66%, respectively. Correspondingly, the coefficient of determination (R²) was improved by at least 0.0069. This model provides an effective solution for short-term wind power prediction in practical engineering. Full article

(This article belongs to the Special Issue Adaptive Control and Optimization in Power Grids)

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31 pages, 2346 KB

Open AccessArticle

Research on MPC-Based Power Allocation Strategy and Dynamic Value Evaluation of Wind–Hydrogen Coupled Systems

by Jiyong Li, Chen Ye, Hao Huang, Zhiliang Cheng, Yide Peng and Kaiyue Wang

Processes 2026, 14(6), 924; https://doi.org/10.3390/pr14060924 - 14 Mar 2026

Viewed by 654

Abstract

With rising renewable energy penetration, wind–hydrogen coupling systems are key to large-scale green hydrogen production and wind power integration. This paper proposes a multi-timescale power allocation measure and evaluation framework that executes scheduling planning, rolling updates and real-time control sequentially. First, an intelligent power allocation strategy based on model predictive control (MPC) and State of Health (SOH) prediction is designed, which pursues short-term operational efficiency while actively avoiding electrolyzer-damaging conditions. Second, a comprehensive evaluation model integrating dynamic hydrogen value and flexibility value is built, overcoming the limitations of traditional fixed-hydrogen-value and single-system-value evaluations to quantify operational strategy viability more accurately. Simulation results show that the proposed strategy boosts the system’s lifecycle Net Present Value (NPV) by ~12.7% versus conventional strategies, verifying the framework’s effectiveness and superiority in improving wind–hydrogen coupling system performance. Full article

(This article belongs to the Special Issue Adaptive Control and Optimization in Power Grids)

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22 pages, 6763 KB

Open AccessArticle

Smart Protection Relay for Power Transformers Using Time-Domain Feature Recognition

by Hengchu Shi, Hao You, Xiaofan Chen, Ruisi Li, Shoudong Xu, Jianqiao Zhang and Ruiwen He

Processes 2026, 14(3), 449; https://doi.org/10.3390/pr14030449 - 27 Jan 2026

Viewed by 996

Abstract

Conventional transformer protection schemes are limited by the difficulty in distinguishing inrush currents from internal and external faults, which restricts operational accuracy to below 70%. Existing solutions are constrained by a trade-off: sensitivity is compromised when setting values are increased, while speed is sacrificed when time delays are introduced. To address these limitations, a novel deep learning-based method for transformer fault identification is proposed. First, a feature model is constructed utilizing the time-domain sum of voltage and current fault components alongside current polarity characteristics. Subsequently, a channel attention-based Capsule Network (SE-CapsuleNet) is employed to automatically extract deep features across normal operation, inrush currents, and fault types. Simulation results demonstrate that inrush conditions are accurately differentiated from fault states. Robustness is maintained under high fault resistance (400 Ω) and 20 dB noise interference, while immunity to current transformer (CT) saturation and core residual magnetism is exhibited. The proposed protection relay simultaneously meets the requirements of rapid response, high sensitivity, and safety stability. Full article

(This article belongs to the Special Issue Adaptive Control and Optimization in Power Grids)

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17 pages, 1888 KB

Open AccessArticle

Wind Power Prediction for Extreme Meteorological Conditions Based on SSA-TCN-GCNN and Inverse Adaptive Transfer Learning

by Jiale Liu, Weisi Deng, Weidong Gao, Haohuai Wang, Chonghao Li and Yan Chen

Processes 2026, 14(2), 353; https://doi.org/10.3390/pr14020353 - 19 Jan 2026

Cited by 1 | Viewed by 558

Abstract

Extreme weather conditions, specifically typhoons and strong gusts, create a highly transient environment for wind power data collection, leading to performance degradation that significantly impacts the safety and stability of the wind power system. To accurately predict wind power trends under these conditions, this paper proposes a prediction model integrating Singular Spectrum Analysis (SSA), Temporal Convolutional Network (TCN), Convolutional Neural Network (CNN), and a global average pooling layer, combined with inverse adaptive transfer learning. First, SSA is applied to reduce noise in the collected wind power operation data and extract key information. Subsequently, a prediction model is constructed based on TCN, CNN, and global average pooling. The model employs dilated causal convolutions to capture long-term dependencies and uses two-dimensional convolution kernels to extract local mutation features. Furthermore, a domain-adaptive transfer learning module is designed to adjust the model’s parameter weights via backward optimization based on the Maximum Mean Discrepancy (MMD) between the source and target domains. Experimental validation is conducted using real-world wind power operation data from a wind farm in Guangxi, containing 3000 samples sampled at 10 min intervals specifically during severe typhoon periods. Experimental results demonstrate that even with only 60% of the target data, the proposed method outperforms the traditional TCN neural network, reducing the Root Mean Square Error (RMSE) by 58.1% and improving the Coefficient of Determination (R²) by 32.7%, thereby verifying its effectiveness in data-scarce extreme scenarios. Full article

(This article belongs to the Special Issue Adaptive Control and Optimization in Power Grids)

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