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Energies
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Operation, Control, and Planning of New Power Systems
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Operation, Control, and Planning of New Power Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F1: Electrical Power System".

Deadline for manuscript submissions: 30 April 2026 | Viewed by 156

Special Issue Editors

Dr. Peng Wang

E-Mail Website
Guest Editor
School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China
Interests: dynamic equivalent modeling of power systems; power system analysis and operation; smart grid; energy internet
Dr. Tengfei Ma

E-Mail Website
Guest Editor
Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, China
Interests: new distribution network; renewable energy; power market
Dr. Shaohua Yang

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Macau, Macau, China
Interests: integrated energy system; demand response; cyber-physical security

Special Issue Information

Dear Colleagues,

To realize the strategic goals of "Carbon Peak and Carbon Neutrality," constructing a new power system with renewable energy as its main component has become the core direction of China's energy transition. The system is undergoing a dual transformation characterized by a **high penetration of renewable energy** and a **high proportion of power electronic devices**, leading to profound changes in its dynamic characteristics, operational patterns, and stability mechanisms. Concurrently, as a critical link for resource allocation, the design of electricity market mechanisms also needs to be deeply aligned with the evolution of this physical system. To explore new theories, methods, and technologies to solve the core challenges in operation control, coordinated planning, and market mechanism design, this journal is launching a Special Issue on "Operation, Control, and Planning of New Power Systems." This Issue aims to gather cutting-edge research findings and promote academic progress and engineering practice in the field.

Scope of Submission

The Special Issue welcomes submissions within, but not limited to, the following research areas:

  1. System Modeling and Stability Mechanisms
  • Dynamic characteristics, stability mechanisms, and modeling methods for systems with a high proportion of power electronic equipment;
  • Fine-grained modeling and stochastic-deterministic hybrid analysis of renewable energy generation.
  1. Market Mechanism Design and Multi-Market Coordination
  • Electricity market mechanisms and evolution pathways adapted to high-penetration renewable energy;
  • Participation models and mechanisms for emerging market entities such as distributed energy resources, energy storage, virtual power plants (VPPs), and load aggregators;
  • Coordination mechanisms and coupling models for green electricity trading, green certificate markets, and carbon markets.
  1. Intelligent Operation and Control
  • Grid frequency and voltage stability control under a high penetration of renewable energy;
  • Coordinated optimal operation of generation–grid–load–storage interacting with market signals;
  • Application of artificial intelligence and big data in grid situation awareness, fault diagnosis, and intelligent control/decision-making.
  1. Coordinated Planning and Reliability Assessment
  • Medium- and long-term grid planning considering investment signals and revenue risks in market environments;
  • Coordinated transmission and distribution grid planning considering flexibility resources and cyber-security constraints;
  • Multi-timescale energy storage system planning and configuration methods.

Dr. Peng Wang
Dr. Tengfei Ma
Dr. Shaohua Yang
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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies 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

  • new power systems
  • power system operation
  • power system planning
  • electricity market mechanisms
  • renewable energy
  • energy storage

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Published Papers (2 papers)

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Research

24 pages, 3856 KB  
Article
A Data-Driven Approach for Distribution System State Estimation Considering Data and Topology Uncertainties
by Dezhi He, Shuchen Kang, Kaiji Liao, Chenyao Pang, Bin Tang, Chengzhong Zheng, Zhenyuan Zhang and Yiping Yuan
Energies 2026, 19(1), 128; https://doi.org/10.3390/en19010128 (registering DOI) - 26 Dec 2025
Abstract
With the increasing integration of distributed energy resources and the growing variability of multiple loads, distribution networks face significant uncertainties in measurement data, line parameters, and topology. Traditional state estimation methods, such as weighted least squares, rely on accurate network parameters and are [...] Read more.
With the increasing integration of distributed energy resources and the growing variability of multiple loads, distribution networks face significant uncertainties in measurement data, line parameters, and topology. Traditional state estimation methods, such as weighted least squares, rely on accurate network parameters and are therefore highly sensitive to measurement noise and topology variations. To address these challenges, this work proposes a comprehensive data-driven framework for ADN state estimation that features a novel integration of an improved deep residual network (i-ResNet) and transfer learning. An improved deep residual network (i-ResNet) is developed to enable fast and robust state estimation without dependence on online parameters, even under uncertain data conditions. Furthermore, a transfer learning–based model is introduced to accommodate topology changes by leveraging historical data from multiple network configurations. Experimental studies on the IEEE 33-bus and 118-bus test systems are conducted to evaluate the performance of the proposed approach. The results demonstrate that the proposed method achieves higher accuracy and faster convergence than conventional techniques, with voltage magnitude errors consistently maintained below 1%. Full article
(This article belongs to the Special Issue Operation, Control, and Planning of New Power Systems)
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23 pages, 3769 KB  
Article
Partial Discharge Pattern Recognition of GIS with Time–Frequency Energy Grayscale Maps and an Improved Variational Bayesian Autoencoder
by Yuhang He, Yuan Fang, Zongxi Zhang, Dianbo Zhou, Shaoqing Chen and Shi Jing
Energies 2026, 19(1), 127; https://doi.org/10.3390/en19010127 - 25 Dec 2025
Abstract
Partial discharge pattern recognition is a crucial task for assessing the insulation condition of Gas-Insulated Switchgear (GIS). However, the on-site environment presents challenges such as strong electromagnetic interference, leading to acquired signals with a low signal-to-noise ratio (SNR). Furthermore, traditional pattern recognition methods [...] Read more.
Partial discharge pattern recognition is a crucial task for assessing the insulation condition of Gas-Insulated Switchgear (GIS). However, the on-site environment presents challenges such as strong electromagnetic interference, leading to acquired signals with a low signal-to-noise ratio (SNR). Furthermore, traditional pattern recognition methods based on statistical parameters suffer from redundant and inefficient features that compromise classification accuracy, while existing artificial-intelligence-based classification methods lack the ability to quantify the uncertainty in defect classification. To address these issues, this paper proposes a novel GIS partial discharge pattern recognition method based on time–frequency energy grayscale maps and an improved variational Bayesian autoencoder. Firstly, a denoising-based approximate message passing algorithm is employed to sample and denoise the discharge signals, which enhances the SNR while simultaneously reducing the number of sampling points. Subsequently, a two-dimensional time–instantaneous frequency energy grayscale map of the discharge signal is constructed based on the Hilbert–Huang Transform and energy grayscale mapping, effectively extracting key time–frequency features. Finally, an improved variational Bayesian autoencoder is utilized for the unsupervised learning of the image features, establishing a GIS defect classification method with an associated confidence level by integrating probabilistic features. Validation based on measured data demonstrates the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Operation, Control, and Planning of New Power Systems)
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