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Processes
Special Issues
Power System Operation, Energy Management, and Control
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Power System Operation, Energy Management, and Control

A Special Issue of Processes (ISSN 2227-9717) belonging to the section "AI-Enabled Process Engineering".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 2389

Editors

Dr. Mingxu Xiang

E-Mail Website
Guest Editor
School of Electrical Engineering, Chongqing University, Chongqing 400030, China
Interests: electricity market；power system operation
Dr. Yi Wang

E-Mail Website
Guest Editor
School of Electrical Engineering, Chongqing University, Chongqing 400030, China
Interests: electricity pricing theory; mechanism design in electricity market
Dr. Pei Yong

E-Mail Website
Guest Editor
School of Electrical Engineering, Chongqing University, Chongqing 400030, China
Interests: planning and operation of power systems with high share of renewables; energy storage and flexibility resources integration
Dr. Qian Gao

E-Mail Website
Guest Editor Assistant
School of Electrical Engineering, Chongqing University, Chongqing 400030, China
Interests: power system optimization; mixed-integer linear programming; machine learning

Special Issue Information

Dear Colleagues,

The ongoing transition toward low-carbon power systems is accelerating the large-scale integration of renewable energy, power-electronics-interfaced generation, distributed energy resources, and flexible demand. However, these developments introduce pronounced uncertainty, variability, and reduced system inertia, thereby complicating real-time balancing and secure operation of power systems. As a result, power system operators face growing challenges in coordinating decisions across time horizons to ensure reliability, economic efficiency, and resilience of power systems, including day-ahead scheduling, intra-day rescheduling, and real-time control. Enhancing the power system operation quality through innovative tools, such as the electricity market mechanism, the demand-side resource control, the coordinated dispatch of multiple resources, etc., has become crucial.

This Special Issue, "Power System Operation, Energy Management, and Control", aims to collect high-quality contributions related to power scheduling, market design, and power control for power systems with high-penetration renewables.

Topics include, but are not limited to, the following:

  • Day-ahead and intraday scheduling modeling with high renewable penetration.
  • Frequency control in power systems with high renewable penetration.
  • Market clearing and pricing mechanisms considering participation of renewables.
  • AI technologies for power system operation.
  • Demand-side resources management.

We hope you will consider contributing to this Special Issue.

Sincerely,

Dr. Mingxu Xiang
Dr. Yi Wang
Dr. Pei Yong
Guest Editors

Dr. Qian Gao
Guest Editor Assistant

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

  • power system operation
  • power scheduling
  • power control
  • electricity market
  • artificial intelligence

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

Published Papers (5 papers)

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Research

20 pages, 2897 KB  
Article
Parameter Estimation of First-Order Frequency Response Model for AC Distribution Networks with Hydro-Energy Storage
by Song Gao, Wei Wang, Yuwei Xiang, Shuyu Zhou, Pengfei Li and Mingtong Yang
Processes 2026, 14(17), 2862; https://doi.org/10.3390/pr14172862 - 7 Sep 2026
Viewed by 319
Abstract
To address the frequency response modeling problem of AC distribution networks with hydro-energy storage, a parameter estimation method for a first-order frequency response model based on the nonlinear least-squares technique is proposed in this paper. First, by analogy with the swing equation of [...] Read more.
To address the frequency response modeling problem of AC distribution networks with hydro-energy storage, a parameter estimation method for a first-order frequency response model based on the nonlinear least-squares technique is proposed in this paper. First, by analogy with the swing equation of synchronous generators, a first-order frequency response model of the AC distribution network is established, which is capable of reflecting the overall external characteristics of the hydro-energy storage system. Then, based on the post-disturbance frequency data, the dominant decay coefficient and the steady-state frequency deviation are estimated using the nonlinear least-squares method. Subsequently, the inertia time constant is further obtained through the inverse proportionality relationship between the dominant decay coefficient and the inertia time constant. Finally, an electromagnetic transient simulation model of the AC distribution network with hydro-energy storage is built in PSCAD/EMTDC, and the effectiveness of the proposed frequency response model and its parameter estimation method is verified by multiple sets of simulation results. Full article
(This article belongs to the Special Issue Power System Operation, Energy Management, and Control)
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20 pages, 3319 KB  
Article
Optimized Control of Power Self-Balancing in Distribution Networks Based on Virtual Power Plant Aggregation
by Zhenlan Dou, Chunyan Zhang, Xichao Zhou, Rui Wang and Chuanliang Xiao
Processes 2026, 14(17), 2819; https://doi.org/10.3390/pr14172819 - 1 Sep 2026
Viewed by 345
Abstract
To address the voltage violation problem in distribution networks with large-scale distributed generators (DGs), this paper proposes an optimized control strategy for power self-balancing in distribution networks based on virtual power plant (VPP) aggregation. An optimized control architecture for power self-balancing is constructed, [...] Read more.
To address the voltage violation problem in distribution networks with large-scale distributed generators (DGs), this paper proposes an optimized control strategy for power self-balancing in distribution networks based on virtual power plant (VPP) aggregation. An optimized control architecture for power self-balancing is constructed, comprising a VPP aggregation layer, an independent optimization control layer for individual VPPs, a coordinated optimization control layer for multiple VPPs, and an emerging benefits allocation layer. Then, at the VPP aggregation layer, a VPP aggregation method is proposed considering VPP benefit coupling degree, resource adequacy, and coordination interaction degree. At the independent optimization control layer, an independent optimization control model incorporating active and reactive power regulation of DGs is established for each VPP to achieve power self-balancing for voltage control within the VPP. At the coordinated optimization control layer, a multi-VPP coordination optimization model is constructed based on a linking matrix to achieve coordination among multiple VPPs during the power self-balancing control process and obtain emerging benefits. At the emerging benefits allocation layer, an allocation model based on the contribution degree of each VPP is established to ensure fair distribution of the emerging benefits. Finally, the effectiveness of the proposed method is validated using an actual 10 kV feeder system in Zhejiang Province, China. Full article
(This article belongs to the Special Issue Power System Operation, Energy Management, and Control)
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37 pages, 2601 KB  
Article
Research on an Intelligent Diagnosis and Decision Support System for Pumped Storage Units Based on Multi-Source Data Fusion and Hybrid Intelligent Algorithms
by Xuan Liu, Jie Bai, Bingjie Dou, Tianyu Liu, Xiaohui Yang and Jie Zhao
Processes 2026, 14(16), 2618; https://doi.org/10.3390/pr14162618 - 17 Aug 2026
Viewed by 407
Abstract
Pumped storage hydropower (PSH) is a key regulating resource for renewable energy integration and power system stability. Due to frequent start-stop operations, deep peak-load regulation, and bidirectional operating conditions, stator winding insulation degradation, rotor inter-turn short circuits, and end-winding vibration have become the [...] Read more.
Pumped storage hydropower (PSH) is a key regulating resource for renewable energy integration and power system stability. Due to frequent start-stop operations, deep peak-load regulation, and bidirectional operating conditions, stator winding insulation degradation, rotor inter-turn short circuits, and end-winding vibration have become the dominant failure modes of pumped storage units. Conventional monitoring systems are limited by single-source sensing, asynchronous data acquisition, high misdiagnosis rates, and maintenance decisions that rely heavily on expert experience, making traditional periodic maintenance increasingly inadequate. To address these challenges, this study proposes an intelligent diagnosis and decision support system based on multi-source data fusion and hybrid intelligent algorithms. An Intelligent Electronic Device (IED)-based condition monitoring platform is developed by integrating multiple sensing technologies. Complete Variational Mode Decomposition (CVMD) and Kernel Principal Component Analysis (KPCA) are employed to extract representative features from multi-physical-field data, while an attention-enhanced Long Short-Term Memory (LSTM) network is introduced for accurate fault identification. In addition, adaptive time-alignment and joint denoising algorithms are developed to improve data quality and diagnostic robustness. A predictive maintenance framework incorporating health assessment and remaining useful life prediction is further established to optimize maintenance scheduling. Results demonstrate that the proposed system achieves a fault prediction accuracy of over 90% and reduces annual maintenance costs by approximately 15–20%. The proposed framework provides an effective solution for intelligent operation and maintenance of modern pumped storage units. Full article
(This article belongs to the Special Issue Power System Operation, Energy Management, and Control)
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18 pages, 3143 KB  
Article
Power-System Transient Stability Assessment Based on High-Level Sample Feature Extraction and Model Updating
by Shuolin Zhang, Yue Yu, Ye Tao and Lin Xue
Processes 2026, 14(15), 2468; https://doi.org/10.3390/pr14152468 - 31 Jul 2026
Viewed by 357
Abstract
To address the insufficient feature representation of conventional data-driven transient stability assessment (TSA) models and their limited adaptability to changes in power-system operating conditions, which result in inadequate assessment accuracy, this paper proposes a TSA method based on high-level sample feature extraction and [...] Read more.
To address the insufficient feature representation of conventional data-driven transient stability assessment (TSA) models and their limited adaptability to changes in power-system operating conditions, which result in inadequate assessment accuracy, this paper proposes a TSA method based on high-level sample feature extraction and model updating. First, a self-supervised contrastive random feature perturbation model for transient stability assessment, called TSA-SCRF, is developed. By introducing random perturbations into steady-state power flow features and employing contrastive learning, the proposed model extracts robust deep feature representations while preserving fault-type information. Second, a boundary-aware ensemble support vector machine (BAESVM) is constructed, which exploits multiple kernel functions to learn complementary discriminative information and dynamically assigns classifier weights according to both classifier performance and the samples’ decision distances. Finally, high-value newly acquired samples are selected based on sample uncertainty and, together with the support vectors of the original model, are utilized for model updating. Case studies conducted on a provincial power grid in China demonstrate that the proposed method improves the accuracy of data-driven transient stability assessment and enhances the model’s adaptability to changes in power-system operating conditions. Full article
(This article belongs to the Special Issue Power System Operation, Energy Management, and Control)
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26 pages, 1229 KB  
Article
Multi-Dimensional Impact Assessment of Large-Scale Flexible Load Integration into Distribution Networks Based on Fine-Grained Behavioral Models
by Xueying Zhang, Chong Gao, Shizhao Hu, Runyu Wu, Cheng Tang and Keyun Li
Processes 2026, 14(15), 2384; https://doi.org/10.3390/pr14152384 - 23 Jul 2026
Viewed by 517
Abstract
With the large-scale integration of flexible loads, including electric vehicles (EVs), distributed energy storage systems (DSTs), communication base stations (COMs), and internet data centers (IDCs), into distribution networks, the increasing diversity of their operating characteristics is producing increasingly complex impacts on capacity requirements, [...] Read more.
With the large-scale integration of flexible loads, including electric vehicles (EVs), distributed energy storage systems (DSTs), communication base stations (COMs), and internet data centers (IDCs), into distribution networks, the increasing diversity of their operating characteristics is producing increasingly complex impacts on capacity requirements, power flow conditions, and reactive power support capabilities. However, existing studies have predominantly focused on isolated analyses of individual load types and still lack a unified evaluation framework for multiple representative flexible loads, making it difficult to systematically reveal the heterogeneous impacts of their grid integration. To address this gap, this paper develops fine-grained behavioral models for four representative flexible load categories by incorporating their key operational constraints and behavioral characteristics. A multi-dimensional quantitative assessment framework is then established across three dimensions: capacity, power flow and operation, and reactive power support and disturbance. Under a unified distribution network scenario, the impacts of large-scale integration are compared across load types and graduated penetration levels. The results show that different flexible loads exert significantly heterogeneous effects on distribution network operating states: COMs and IDCs are more likely to intensify local capacity pressure, operational fluctuations, and reactive power support burdens; EVs are more prominently associated with peak-period migration and reverse power flow risks; and the overall impact of DSTs remains comparatively moderate. The proposed methodology provides a unified analytical framework for assessing the impacts of multiple flexible load types on distribution networks and offers a reference for subsequent distribution network planning and operational optimization. Full article
(This article belongs to the Special Issue Power System Operation, Energy Management, and Control)
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