Submit to Special Issue Submit Abstract to Special Issue Review for Processes Propose a Special Issue

Journal Menu

Journal Browser

AI-Driven Optimization in Intelligent Process Control for Power and Energy Systems

Print Special Issue Flyer
Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Process Control, Modeling and Optimization".

Deadline for manuscript submissions: 10 October 2026 | Viewed by 14367

Share This Special Issue

Editors

Dr. Lefeng Cheng

E-Mail Website
Guest Editor

School of Mechanical and Electric Engineering, Guangzhou University, Guangzhou 510006, China
Interests: artificial intelligence; evolutionary game theory; power markets; smart grids; decision-making optimization; integrated energy systems
Special Issues, Collections and Topics in MDPI journals

Dr. Xiaoshun Zhang

E-Mail Website
Guest Editor

Foshan Graduate School of Innovation, Northeastern University, Foshan 528311, China
Interests: AI optimization; power system operation; control strategies; new energy control and optimiza-tion; low-carbon energy management
Special Issues, Collections and Topics in MDPI journals

Dr. Huaizhi Wang

E-Mail Website
Guest Editor

College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China
Interests: multi-objective optimization; power generation control; reinforcement learning; control balancing; system performance; sustainability

Special Issue Information

Dear Colleagues,

Aims and Scope

The increasing complexity, decentralization, and dynamism of modern power and energy systems have presented formidable challenges for traditional process control and optimization frameworks. Rigid, model-based control systems are often inadequate to respond to the nonlinear, time-varying, and data-intensive nature of energy processes today. This Special Issue aims to explore advanced AI-driven optimization and intelligent control approaches that can enable higher adaptability, efficiency, and autonomy in the management of energy systems.

We seek high-quality contributions that investigate how artificial intelligence (AI)—including reinforcement learning, neural networks, swarm intelligence, and hybrid symbolic-neural architectures—can be harnessed to address real-time decision-making, predictive control, and process-level optimization in power and energy systems. This Special Issue provides an interdisciplinary platform for innovations that push the boundaries of energy automation, digitalization, and intelligent system design.

Background and Significance

The global transition to low-carbon, decentralized energy infrastructures has led to the proliferation of smart grids, renewable generation, microgrids, and cyber-physical energy systems. As a result, the operational landscape of power and energy systems is increasingly characterized by nonlinearity, stochasticity, high dimensionality, and interconnectivity. Traditional control strategies, which rely on deterministic models and fixed heuristics, often fall short in such complex settings.

Artificial intelligence offers a transformative pathway forward. Techniques such as deep reinforcement learning, neural-symbolic control systems, metaheuristic optimization, and federated learning enable power and energy systems to adapt, learn, and self-optimize in real-time, even under uncertainty and incomplete information. By embedding AI capabilities into the process control loop, systems can autonomously adjust to disturbances, optimize performance metrics, and coordinate distributed energy resources with minimal human intervention.

This Special Issue invites contributions that bridge theory and practice, offering novel control and optimization paradigms grounded in AI, with applications across power generation, transmission, distribution, energy storage, and load management. Emphasis is placed on the engineering implementation, scalability, and robustness of intelligent process control architectures.

Topics of Interest

Topics include, but are not limited to:

Deep reinforcement learning for real-time control of power and energy processes;
AI-based modeling and predictive control in nonlinear and uncertain environments;
Swarm intelligence and metaheuristics for distributed energy resource coordination;
AI-enhanced stability control in microgrids and autonomous power subsystems;
Federated and privacy-preserving learning in distributed control frameworks;
Intelligent fault detection, diagnosis, and reconfiguration of power systems;
Hybrid models combining symbolic AI with process dynamics for interpretable control;
Digital twins for real-time optimization and simulation of energy processes;
Adaptive process automation and self-tuning control strategies using AI;
Multi-objective optimization in energy systems using evolutionary algorithms;
Cyber-physical security enhancement using AI-driven anomaly detection;
Data-driven system identification and process learning for smart grids;
Intelligent control of energy storage systems and renewable integration;
Edge and cloud-based AI architectures for scalable energy control.

Dr. Lefeng Cheng
Dr. Xiaoshun Zhang
Dr. Huaizhi Wang
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

intelligent process control
AI in energy systems
deep reinforcement learning
energy system automation
neural-symbolic control
adaptive optimization
swarm intelligence
distributed control systems
predictive control in smart grids
cyber-physical energy systems
digital twin in power engineering
AI for microgrids
process-level energy optimization
data-driven control
self-learning energy systems

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (13 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Review

21 pages, 2495 KB

Open AccessArticle

Data-Driven Risk-Aware Approximate Dynamic Programming Algorithm for Resilient Power System Operation Under High Renewable Uncertainty

by Zike Guo, Peng Yang, Xue Du, Wanmei Zhao, Jiehua Lu, Siliang Liu and Yingqi Yi

Processes 2026, 14(13), 2191; https://doi.org/10.3390/pr14132191 - 5 Jul 2026

Viewed by 302

Abstract

The accelerating integration of renewable energy sources into modern power grids has created unprecedented operational challenges, with significant system cost volatility under extreme uncertainty events. To address this challenge, this paper presents a risk-aware stochastic approximate dynamic programming (SADP) algorithm based on machine learning and parallel computing architectures. The algorithm learns optimal coordination strategies for source-grid-load-storage resources while explicitly quantifying and mitigating tail risk events that conventional approaches overlook. First, a risk-averse stochastic optimization model is constructed, which captures the complex interdependencies between renewable generation uncertainty, demand variability, and flexible resource coordination through second-order cone programming formulations. This model integrates the GlueVaR (Glued Value-at-Risk) metric, enabling simultaneous optimization across multiple risk horizons with adjustable conservatism parameters. Second, to solve the established model efficiently, an SADP algorithm based on risk-averse approximate value functions (RAVFs) is proposed, in which the training process of the RAVFs employs machine learning principles to directly encode risk preferences into operational decisions. By integrating GlueVaR into offline training across 5000 probabilistically weighted scenarios, the algorithm discovers emergent coordination patterns between distributed resources, which are rarely identified by human operators. Third, a large-scale parallel computing architecture is implemented for the SADP algorithm. This architecture decomposes the multi-period optimization problem into single-period coordinated sub-problems. During offline training, parallel computing of a series of single-period sub-problems can be performed across all probabilistic scenarios, significantly reducing training time. Extensive validation on both the modified IEEE 33-bus and 69-bus systems with integrated wind turbines, photovoltaic plants, energy storage systems, and demand response capabilities demonstrates remarkable performance improvements. Convergence analysis reveals that the AVFs stabilize within 30 training iterations, achieving sub-160 s solution times in online application even for complex networks with heterogeneous resources. By enabling real-time risk-aware decision-making under severe uncertainty, the proposed method provides grid operators with actionable strategies that balance economic efficiency and operational resilience. Full article

(This article belongs to the Special Issue AI-Driven Optimization in Intelligent Process Control for Power and Energy Systems)

► Show Figures

Figure 1

23 pages, 5517 KB

Open AccessArticle

A Lightweight Infrared Thermal Image Recognition Network with Thermal Noise Suppression and Dynamic Feature Fusion for Transformer Oil Leakage Detection

by Wenbi Tan, Tzer Hwai Gilbert Thio, Fei Lu Siaw, Xinzhi Li, Fuyu Shen, Jiazai Yang, Tongyong Zou and Youdong Jia

Processes 2026, 14(13), 2175; https://doi.org/10.3390/pr14132175 - 3 Jul 2026

Viewed by 371

Abstract

Detecting transformer oil leakage from infrared thermal images is challenging due to complex thermal backgrounds, low contrast, and sensor noise. To address these issues, this study proposes IR-OilNet, a lightweight CNN–Transformer fusion network designed for real-time infrared leakage detection in inspection robots. This research contributes to SDG 6 by preventing industrial water pollution resulting from transformer oil runoff, thereby protecting vital water sources in remote environments. The proposed framework integrates three key components: (1) a parameter-free Thermal Noise Suppression (TNS) module that reduces high-frequency thermal noise while preserving structural information; (2) a compact CNN backbone for local thermal texture extraction; and (3) a lightweight Transformer branch for global contextual modeling. To further enhance feature integration, a Dynamic Weight Fusion (DWF) module is introduced to adaptively balance local and global representations. Experiments on an expanded dataset of 2700 infrared thermal images demonstrate that IR-OilNet achieves an accuracy of 0.9992, F1-score of 0.9992, and AUC of 0.9993, while maintaining only 0.20 M parameters and 0.19 GFLOPs with real-time inference at 433 FPS. Compared with representative CNN and Transformer models, the proposed method achieves a superior trade-off between accuracy and computational efficiency, making it suitable for deployment in resource-constrained inspection systems. Full article

(This article belongs to the Special Issue AI-Driven Optimization in Intelligent Process Control for Power and Energy Systems)

► Show Figures

Figure 1

15 pages, 947 KB

Open AccessArticle

Emergency Power Supply Restoration Strategy of Distribution Network Considering Operational Risk of Islanded Microgrid

by Juan Zuo, Chongxin Xu, Wenbo Wang, Qian Ai and Yihui Luo

Processes 2026, 14(3), 480; https://doi.org/10.3390/pr14030480 - 29 Jan 2026

Viewed by 480

Abstract

This paper proposes an emergency power supply restoration strategy for a distribution network that considers the operational risk of an islanded microgrid in response to the issues of voltage exceeding limits and power imbalance faced during their operation. Firstly, a distribution network emergency power supply restoration model supported by a generalized dynamic islanded microgrid is constructed. By equating the alternate tie line with a virtual distributed generator (DG), the integrated power supply restoration problem of distribution network is transformed into a generalized island power distribution network division problem based on DGs. Then, the risk of islanded microgrid operation is considered and restricted by chance constraints. Finally, simulation results based on the improved IEEE-33 node distribution network show that, compared to the generalized island partitioning strategy which ignores operational risks, the proposed strategy increases the power supply restoration rate from 83.4% to 97.8% while successfully ensuring the stability of all islanded microgrids under the specified confidence level for operational risk. Full article

(This article belongs to the Special Issue AI-Driven Optimization in Intelligent Process Control for Power and Energy Systems)

► Show Figures

Figure 1

33 pages, 2919 KB

Open AccessArticle

Life-Cycle Co-Optimization of User-Side Energy Storage Systems with Multi-Service Stacking and Degradation-Aware Dispatch

by Lixiang Lin, Yuanliang Zhang, Chenxi Zhang, Xin Li, Zixuan Guo, Haotian Cai and Xiangang Peng

Processes 2026, 14(3), 477; https://doi.org/10.3390/pr14030477 - 29 Jan 2026

Viewed by 682

Abstract

The integration of a user-side energy storage system (ESS) faces notable economic challenges, including high upfront investment, uncertainty in quantifying battery degradation, and fragmented ancillary service revenue streams, which hinder large-scale deployment. Conventional configuration studies often handle capacity planning and operational scheduling at different stages, complicating consistent life-cycle valuation under degradation and multi-service participation. This paper proposes a life-cycle multi-service co-optimization model (LC-MSCOM) to jointly determine ESS power–energy ratings and operating strategies. A unified revenue framework quantifies stacked revenues from time-of-use arbitrage, demand charge management, demand response, and renewable energy accommodation, while depth of discharge (DoD)-related lifetime loss is converted into an equivalent degradation cost and embedded in the optimization. The model is validated on a modified IEEE benchmark system using real generation and load data. Results show that LC-MSCOM increases net present value (NPV) by 26.8% and reduces discounted payback period (DPP) by 12.7% relative to conventional benchmarks, and sensitivity analyses confirm robustness under discount-rate, inflation-rate, and tariff uncertainties. By coordinating ESS dispatch with distribution network operating limits (nodal power balance, voltage bounds, and branch ampacity constraints), the framework provides practical, investment-oriented decision support for user-side ESS deployment. Full article

(This article belongs to the Special Issue AI-Driven Optimization in Intelligent Process Control for Power and Energy Systems)

► Show Figures

Figure 1

26 pages, 3788 KB

Open AccessArticle

Adaptive Modified Active Disturbance Rejection Control for the Superheated Steam Temperature System Under Wide Load Conditions

by Huiyu Wang, Zihao Tong, Zhenlong Wu, Hongtao Zheng, Bing Li and Yanfeng Jia

Processes 2026, 14(2), 308; https://doi.org/10.3390/pr14020308 - 15 Jan 2026

Cited by 3 | Viewed by 664

Abstract

The operation of the superheated steam temperature system significantly impacts the safety and economy of thermal power units. To ensure its stable operation under large-scale variable load conditions, a modified active disturbance rejection control strategy based on parameter adaptation is proposed. Firstly, a typical superheated steam temperature system model is introduced, and the cascade control structure is applied to the model. Then, on this basis, a modified active disturbance rejection control strategy based on parameter adaptation is proposed, and the parameter tuning method of the modified active disturbance rejection control is introduced. Finally, the control performance of the proposed control strategy under a wide range of variable loads is verified through comparative simulations under nominal working conditions and uncertain working conditions. To further illustrate the effectiveness of the proposed strategy, the method is applied to a certain 660 MW unit in the field. After implementing the method, the fluctuation range of superheated steam temperature on the A and B sides decreased to only 34.0% and 53.0% of the original, respectively, and the fluctuation variance on the A and B sides decreased to only 28.5% and 43.3% of the original, respectively. The above field application results fully demonstrate that the control strategy proposed does not merely remain at the theoretical simulation level, but is a key technical means that can be effectively implemented and effectively solve the problem of superheated steam temperature control in thermal power units. Full article

(This article belongs to the Special Issue AI-Driven Optimization in Intelligent Process Control for Power and Energy Systems)

► Show Figures

Figure 1

28 pages, 4421 KB

Open AccessArticle

Using Artificial Intelligence to Classify IEDs’ Control Scope from SCL Files

by Arthur Kniphoff da Cruz, Ana Clara Hackenhaar Kellermann, João Vitor Meinhardt Swarowsky, Ingridy Caroliny da Silva, Marcia Elena Jochims Kniphoff da Cruz and Lorenz Däubler

Processes 2026, 14(2), 206; https://doi.org/10.3390/pr14020206 - 7 Jan 2026

Viewed by 976

Abstract

IEC 61850 is one of the most accepted standards worldwide for the automation of electrical substations. This standard uses Substation Configuration Language (SCL) for describing the data model and services from electrical substation components, and SCL files are used for the integration of these components throughout the substation. In this context, the integration of bay level Intelligent Electronic Devices (IEDs) into the station level demands a detailed analysis of the IED’s control scope in SCL files and advanced know-how in IEC 61850, increasing the complexity in the engineering process. Hence, this work presents a method to automate the analysis of the control scope of IEDs using SCL files, generating their respective control system object. This is achieved via Machine-Learning (ML) concepts, such as supervised learning and classification algorithms. IEDs used for control and protection of feeder and transformer systems were analyzed, and control system objects were generated for them. The results indicate that the developed method makes it possible to classify the control scope of IEDs using the SCL files from the bay level. This method is a unique development for application in the engineering process of digital substations, reducing the complexity of a critical step towards substation automation. Full article

(This article belongs to the Special Issue AI-Driven Optimization in Intelligent Process Control for Power and Energy Systems)

► Show Figures

Figure 1

19 pages, 3993 KB

Open AccessArticle

Coordinated Planning Method for Distribution Network Lines Considering Geographical Constraints and Load Distribution

by Linhuan Luo, Qilin Zhou, Wei Pan, Zhian He, Minghao Liu, Longfa Yang and Xiangang Peng

Processes 2026, 14(1), 47; https://doi.org/10.3390/pr14010047 - 22 Dec 2025

Viewed by 763

Abstract

This paper proposes a coordinated planning method for distribution network lines considering geographical constraints and load distribution, aiming to improve the economy and engineering feasibility of distribution network planning. First, a hierarchical system of geographical constraints based on the Interval Analytic Hierarchy Process (IAHP) is established to systematically quantify the influence weights of spatial factors such as terrain undulation, ecological protection zones, and construction obstacles. Second, the density peak clustering algorithm and load complementarity coefficient are introduced to generate equivalent load nodes, and a spatially continuous load density grid model is constructed to accurately characterize the distribution and complementary characteristics of the load. Third, an improved A-star algorithm is adopted, which integrates a heuristic function guided by geographical weights and load density to dynamically avoid high-cost areas and approach high-load areas. Additionally, Bézier curves are used to optimize the path, reducing crossings and obstacle interference, thus enhancing the implementability of line layout. Verification via a real distribution network case study in a certain area of Guangdong Province shows that the proposed method outperforms traditional planning strategies. It significantly improves the economy, safety, and engineering feasibility of the path, providing effective decision support for distribution network line planning in complex environments. Full article

(This article belongs to the Special Issue AI-Driven Optimization in Intelligent Process Control for Power and Energy Systems)

► Show Figures

Figure 1

15 pages, 1756 KB

Open AccessArticle

Well Group Scheduling Strategy for Photovoltaic Utilization Based on Improved Particle Swarm Optimization Algorithm

by Guangfeng Qi, Chenghan Zhu, Yingqiang Yan, Jiehua Feng, Dongya Zhao and Fei Li

Processes 2025, 13(12), 3951; https://doi.org/10.3390/pr13123951 - 6 Dec 2025

Viewed by 525

Abstract

Photovoltaic (PV) generation, a vital component of renewable energy, is key to supporting energy supply and reducing reliance on traditional energy sources. Given the substantial energy consumption of oilfield well groups, increasing the proportion of PV energy is imperative. Furthermore, as oilfields enter mid-to-late production stages, wells experience reduced oil production with increased energy consumption, necessitating intermittent pumping schedules. This paper addresses the optimized scheduling of pumping unit well groups within a photovoltaic-grid microgrid. The article aims to minimize the difference between the well group system’s total energy consumption and the PV power generation. A nonlinear mixed-integer programming (NMIP) model is constructed, incorporating a PV power forecasting model, a well group energy consumption model, and relevant constraints. An improved Particle Swarm Optimization (PSO) algorithm, integrating a hybrid coding scheme and multiple improvement strategies, is proposed to efficiently solve the NMIP model. The resulting optimal intermittent pumping schedule maximizes on-site PV power consumption, effectively mitigating PV energy wastage and potential grid stability issues associated with direct grid integration. The effectiveness of the proposed optimization algorithm is validated through numerical simulation case studies. Full article

(This article belongs to the Special Issue AI-Driven Optimization in Intelligent Process Control for Power and Energy Systems)

► Show Figures

Figure 1

18 pages, 9366 KB

Open AccessArticle

Multi-Objective Rolling Linear-Programming-Model-Based Predictive Control for V2G-Enabled Electric Vehicle Scheduling in Industrial Park Microgrids

by Tianlu Luo, Feipeng Huang, Houke Zhou and Guobo Xie

Processes 2025, 13(11), 3421; https://doi.org/10.3390/pr13113421 - 24 Oct 2025

Cited by 2 | Viewed by 1427

Abstract

With the rapid growth of electricity demand in industrial parks and the increasing penetration of renewable energy, vehicle-to-grid (V2G) technology has become an important enabler for mitigating grid stress while improving charging economy. This paper proposes a multi-objective rolling linear-programming-model-based predictive control (LP-MPC) method for coordinated electric vehicle (EV) scheduling in industrial park microgrids. The model explicitly considers transformer capacity limits, EV state-of-charge (SOC) dynamics, bidirectional charging/discharging constraints, and photovoltaic (PV) generation uncertainty. By solving a linear programming problem in a receding horizon framework, the approach simultaneously achieves load peak shaving, valley filling, and EV revenue maximization with real-time feasibility. A simulation study involving 300 EVs, 100 kW PV, and a 1000 kW transformer over 24 h with 5-min intervals demonstrates that the proposed LP-MPC outperforms greedy and heuristic load-leveling strategies in peak load reduction, load variance minimization, and charging cost savings while meeting all SOC terminal requirements. These results validate the effectiveness, robustness, and economic benefits of the proposed method for V2G-enabled industrial park microgrids. Full article

(This article belongs to the Special Issue AI-Driven Optimization in Intelligent Process Control for Power and Energy Systems)

► Show Figures

Figure 1

25 pages, 2787 KB

Open AccessArticle

Quantifying Weather’s Share in Dynamic Grid Emission Factors via SHAP: A Multi-Timescale Attribution Framework

by Zeqi Zhang, Yingjie Li, Danhui Lai, Ningrui Zhou, Qinhui Zhan and Wei Wang

Processes 2025, 13(11), 3393; https://doi.org/10.3390/pr13113393 - 23 Oct 2025

Cited by 1 | Viewed by 966

Abstract

Accurately quantifying the impact of weather on dynamic grid carbon intensity is crucial for power system decarbonization. This study proposes a novel, interpretable machine learning framework integrating tree-based models with SHapley Additive exPlanations (SHAP) to quantify this impact across multiple timescales via a standardized “Weather Share” metric. Applied to city-level hourly data from China, the analysis reveals that meteorological variables collectively explain 21.64% of the hourly variation in carbon intensity, with air temperature and solar irradiance being the dominant drivers. Significant temporal variations are observed: the weather share is higher in summer (29.8%) and winter (23.5%) than in transition seasons and increases markedly to 32.7% during extreme high-temperature events. The proposed framework provides a robust, quantitative tool for grid operators, offering actionable insights for weather-aware carbon reduction strategies and highlighting critical time windows for targeted interventions. Full article

(This article belongs to the Special Issue AI-Driven Optimization in Intelligent Process Control for Power and Energy Systems)

► Show Figures

Figure 1

17 pages, 6459 KB

Open AccessArticle

A Star-Connected STATCOM Soft Open Point for Power Flow Control and Voltage Violation Mitigation

by Tianlu Luo, Yanyang Liu, Feipeng Huang and Guobo Xie

Processes 2025, 13(10), 3030; https://doi.org/10.3390/pr13103030 - 23 Sep 2025

Cited by 1 | Viewed by 1029

Abstract

Soft open point (SOP) offers a viable alternative to traditional tie switches for optimizing power flow distribution between connected feeders, thereby improving power quality and enhancing the reliability of distribution networks (DNs). Among existing medium-voltage (MV) SOP demonstration projects, the modular multilevel converter (MMC) back-to-back voltage source converter (BTB-VSC) is the most commonly adopted configuration. However, MMC BTB-VSC suffers from high cost and significant volume, with device requirements increasing substantially as the number of feeders grows. To address these challenges, this paper proposes a novel star-connected cascaded H-bridge (CHB) STATCOM SOP (SCS-SOP). The SCS-SOP integrates the static synchronous compensator (STATCOM) and low-voltage (LV) BTB-VSC into a single device, enabling reactive power support within feeders and active power exchange between feeders, while achieving reduced component cost and volume, simplified power decoupling control, and increasing power quality management capabilities. The topology derivation, configuration, operational principles, and control strategies of the SCS-SOP are elaborated. Finally, simulation and experimental models of a two-port 3 Mvar/300 kW SCS-SOP are developed, with results validating the theoretical analysis. Full article

(This article belongs to the Special Issue AI-Driven Optimization in Intelligent Process Control for Power and Energy Systems)

► Show Figures

Figure 1

Review

Jump to: Research

101 pages, 20860 KB

Open AccessReview

AI-Enhanced Evolutionary Game Theory for Intelligent Coordination and Adaptive Optimization in Low-Carbon Energy Systems: A Multi-Scale Review from Smart Grids to Carbon Markets

by Guorui Wang, Liang Zhong and Yixuan Zeng

Processes 2026, 14(16), 2568; https://doi.org/10.3390/pr14162568 - 11 Aug 2026

Viewed by 170

Abstract

The modern energy transition has outpaced the control and optimization frameworks built to govern it. As power and energy systems fragment into webs of renewable generators, storage operators, flexible loads, and carbon-constrained firms, the deterministic, single-optimizer models that once sufficed buckle against nonlinearity, bounded rationality, and strategic conflict among parties who learn and revise as they go. Evolutionary game theory (EGT), which traces how strategies propagate through populations by imitation and selection rather than instantaneous optimization, offers a route through this difficulty—one this review develops across three scales of low-carbon coordination central to cleaner production: enterprise-level industrial symbiosis, system-level smart energy operation, and market-level carbon governance. We synthesize three decades of theory alongside the recent fusion of EGT with artificial intelligence, where deep reinforcement learning approximates high-dimensional payoffs, federated learning lets rival firms co-train models without surrendering proprietary data, and blockchain underwrites decentralized mechanism execution. The synthesis is accompanied by two illustrative numerical case studies, constructed for this review rather than drawn from the surveyed literature, whose quantitative outputs are reported below as demonstrations of modeled behavior rather than as empirical measurements. In the first of these, cooperative emergence in industrial symbiosis hinges on critical thresholds that travel from 0.15 to 0.75 as subsidies and transaction costs vary, with anchor-enterprise targeting accelerating cooperation 2.4-fold while cutting outcome variance 3-fold. In smart energy coordination, AI-enhanced learning buys 32 to 41% faster convergence, yet pays 25 to 39% larger oscillations—a speed–stability tension whose resolution lives in a narrow learning-rate band near 0.08 to 0.12, outside which either sluggishness or instability takes hold. Carbon-market behavior turns on price thresholds: emitters switch abruptly from buying quotas toward investing in abatement once the clearing price clears firm-specific triggers, a discrete state switch that smooth equilibrium analysis misses entirely. Across all three domains, fragmented data, path dependence, and regime-switching dynamics recur as the binding constraints on modeling and on governance alike. Four mechanisms prove invariant to scale—the decisive weight of initial conditions, the catalytic leverage of well-positioned anchor agents, the equilibrium-shaping force of institutional design, and the computational reach added by AI integration—which suggests that insight earned in one domain transfers to the others. We close by mapping open problems in heterogeneity modeling, verification under deep uncertainty, and the still-unrealized coupling of digital twins with privacy-preserving learning. EGT emerges not as retrospective description but as prospective guidance for the cooperative transitions on which credible decarbonization depends. Full article

(This article belongs to the Special Issue AI-Driven Optimization in Intelligent Process Control for Power and Energy Systems)

► Show Figures

Figure 1

25 pages, 1003 KB

Open AccessReview

Power Quality Mitigation in Modern Distribution Grids: A Comprehensive Review of Emerging Technologies and Future Pathways

by Mingjun He, Yang Wang, Zihong Song, Zhukui Tan, Yongxiang Cai, Xinyu You, Guobo Xie and Xiaobing Huang

Processes 2025, 13(8), 2615; https://doi.org/10.3390/pr13082615 - 18 Aug 2025

Cited by 6 | Viewed by 4910

Abstract

The global transition toward renewable energy and the electrification of transportation is imposing unprecedented power quality (PQ) challenges on modern distribution networks, rendering traditional governance models inadequate. To bridge the existing research gap of the lack of a holistic analytical framework, this review first establishes a systematic diagnostic methodology by introducing the “Triadic Governance Objectives–Scenario Matrix (TGO-SM),” which maps core objectives—harmonic suppression, voltage regulation, and three-phase balancing—against the distinct demands of high-penetration photovoltaic (PV), electric vehicle (EV) charging, and energy storage scenarios. Building upon this problem identification framework, the paper then provides a comprehensive review of advanced mitigation technologies, analyzing the performance and application of key ‘unit operations’ such as static synchronous compensators (STATCOMs), solid-state transformers (SSTs), grid-forming (GFM) inverters, and unified power quality conditioners (UPQCs). Subsequently, the review deconstructs the multi-timescale control conflicts inherent in these systems and proposes the forward-looking paradigm of “Distributed Dynamic Collaborative Governance (DDCG).” This future architecture envisions a fully autonomous grid, integrating edge intelligence, digital twins, and blockchain to shift from reactive compensation to predictive governance. Through this structured approach, the research provides a coherent strategy and a crucial theoretical roadmap for navigating the complexities of modern distribution grids and advancing toward a resilient and autonomous future. Full article

(This article belongs to the Special Issue AI-Driven Optimization in Intelligent Process Control for Power and Energy Systems)

► Show Figures

Journal Menu

Journal Browser

AI-Driven Optimization in Intelligent Process Control for Power and Energy Systems

Share This Special Issue

Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (13 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI