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Sustainability
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Advanced Planning, Operation and Control Methods for Sustainable Power Systems
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Advanced Planning, Operation and Control Methods for Sustainable Power Systems

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 1924

Special Issue Editors

Dr. Wei Dai

E-Mail Website
Guest Editor
School of Electrical Engineering, Guangxi University, Nanning 530004, China
Interests: power systems planning and operation; multiple energy systems; renewable energy; application of artificial intelligence in power systems
Dr. Wei Lin

E-Mail Website
Guest Editor
School of Electrical Engineering, Chongqing University, Chongqing 400044, China
Interests: operations research in power systems; trustworthy AI and its applications; energy policy analysis
Dr. Lun Yang

E-Mail Website
Guest Editor
School of Automation Science and Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Interests: sustainable power and energy systems operations
Special Issues, Collections and Topics in MDPI journals
Dr. Qian Jiang

E-Mail Website
Guest Editor
School of Electrical Engineering, Guangxi University, Nanning 530004, China
Interests: power systems; smart grid; integrated energy systems; P2P

Special Issue Information

Dear Colleagues,

As global efforts toward carbon neutrality accelerate, modern power systems are undergoing a fundamental transformation. The increasing penetration of intermittent and weather-dependent renewable energy sources is reshaping the structure, dynamics, and operational mechanisms of power systems. The randomness of renewable energy, the proliferation of distributed energy resources, and the increased coupling among multi-regional power grids have significantly increased the complexity, uncertainty, and coordination challenges in the planning, operation, and control of modern sustainable power systems. These challenges demand higher levels of flexibility, reliability, economic stability, and resilience to guarantee a more secure and reliable power supply.

Thus, this Special Issue will focus on discussing various relevant topics associated with the advanced theories and technologies that enable robust, adaptive, and sustainable power system planning, operation, and control to achieve flexible adjustability of clean energy, resilient grid structure, proactive management of the demand side, and coordinated control of various flexible resources. It will emphasize interdisciplinary approaches involving electrical engineering, artificial intelligence, atmospheric science, and operations research to develop holistic solutions to sustainable power systems with a high proportion of renewable energy. For this Special Issue, we solicit original research papers that target, but are not restricted to, the following topics:

  • Multi-type flexible resources planning in power systems.
  • Coordinated planning and multi-level autonomous operation technologies for transmission and distribution systems.
  • Operation and planning of multi-dimensional security/stability-constrained power systems.
  • Climate-resilient power system planning.
  • Modeling and characterization of multi-temporal/spatial uncertainties.
  • Probabilistic modeling and prediction of renewable energy uncertainty.
  • Source–grid–load–storage interaction mechanisms for the efficient integration of high-penetration renewable energy.
  • Optimal scheduling of power systems considering source-load uncertainty.
  • Power and energy balance risk assessment, supply security evaluation, and early warning under extreme events.
  • Artificial intelligence for the planning, operation, and control of the decision-making processes of sustainable power systems.
  • Fusion of learning and optimization methods for the operation and control of power systems.
  • Control technologies and optimization algorithms for regional power systems with high penetration of renewable energy.
  • Control strategies for the grid-connected operation of wind turbines and PV generators.

Dr. Wei Dai
Dr. Wei Lin
Dr. Lun Yang
Dr. Qian Jiang
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. Sustainability 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

  • sustainable power systems
  • renewable energy sources
  • power system operation optimization and control techniques
  • grid expansion planning
  • flexibility sources
  • demand response
  • energy storage systems
  • artificial intelligence

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Published Papers (2 papers)

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Research

29 pages, 1406 KB  
Article
Physics-Informed Neural Network of Half-Inverse Gradient Method for Solving the Power Flow
by Zhencheng Liang, Zonglong Weng, Biyun Chen, Bin Li and Peijie Li
Sustainability 2026, 18(9), 4386; https://doi.org/10.3390/su18094386 - 29 Apr 2026
Viewed by 724
Abstract
Power flow (PF) analysis is fundamental for power system operation and planning, yet traditional methods like Newton–Raphson face problems in convergence and computational efficiency. While deep learning (DL) offers promising solutions, its “black-box” nature and unstable training dynamics hinder practical adoption. This paper [...] Read more.
Power flow (PF) analysis is fundamental for power system operation and planning, yet traditional methods like Newton–Raphson face problems in convergence and computational efficiency. While deep learning (DL) offers promising solutions, its “black-box” nature and unstable training dynamics hinder practical adoption. This paper proposes a physics-informed neural network (PINN) framework integrated with a novel half-inverse gradient (HIG) mechanism to address these limitations. First, a systematic study of gradient scaling in PF optimisation found that the lack of enough inverse matrix compensation was the main cause of training instability. Second, we design a residual-driven HIG method that compensates gradient matrices via inverse operations, enabling accelerated convergence while maintaining numerical stability. Third, we develop parameterized voltage variables with differentiable activation functions to enforce hard operational constraints. The HIG optimizer leverages automatic differentiation and truncated singular value decomposition to balance diagonal/non-diagonal gradient information, achieving 99% accuracy in case4gs and case30 studies. Experiments on case118 demonstrate the framework’s scalability, with 65% accuracy compared to about 38% for baseline physics-informed approaches. Full article
(This article belongs to the Special Issue Advanced Planning, Operation and Control Methods for Sustainable Power Systems)
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20 pages, 2643 KB  
Article
An Operation Mode Analysis Method for Power Systems with High-Proportion Renewable Energy Integration Based on Autoencoder Clustering
by Ying Zhao, Lianle Qin, Liangsong Zhou, Huaiyuan Zong and Xinxin Guo
Sustainability 2026, 18(3), 1698; https://doi.org/10.3390/su18031698 - 6 Feb 2026
Viewed by 437
Abstract
With the integration of high-proportion renewable energy, the operation modes of the power system are becoming increasingly complex and diverse. The typical operation modes selected with manual experience cannot comprehensively represent system operating characteristics. To more accurately analyze system operating characteristics, an analysis [...] Read more.
With the integration of high-proportion renewable energy, the operation modes of the power system are becoming increasingly complex and diverse. The typical operation modes selected with manual experience cannot comprehensively represent system operating characteristics. To more accurately analyze system operating characteristics, an analysis method for power system operation modes based on autoencoder clustering is proposed. Compared to other clustering methods, the autoencoder clustering method can adapt to data of different types and structures, extract features and perform clustering in a reduced-dimensional space, and suppress noise in the data to a certain extent. First, multi-dimensional analysis metrics for power system operation modes are proposed. The metrics are used to evaluate system characteristics such as cleanliness, security, flexibility, and adequacy. The evaluation metrics for clustering are designed based on the metrics. Second, an operation mode analysis framework is constructed. The framework uses an autoencoder to extract implicit coupling relationships between system operation variables. The encoded feature vectors are used for clustering, which helps to find the internal similarities of the operation modes. Regulation resources such as pumped hydro storage are also considered in the framework. Finally, the proposed method is tested on the IEEE 39-node system. In the test, the comparison of clustering evaluation metrics and operation mode analysis errors shows that the proposed method has the best clustering performance and operation mode analysis effect compared to other clustering methods. The results prove that the proposed method can effectively extract the inner correlations and coupling relations of high-dimensional operating vectors, form consistent operation mode clusters, select typical operation modes, and accurately assess the characteristics and risks of the power system with high-proportion renewable energy integration. This paper helps to build a stronger power system that can integrate a higher proportion of renewable energy, replace fossil fuel generation, and contribute to a higher level of sustainable development. Full article
(This article belongs to the Special Issue Advanced Planning, Operation and Control Methods for Sustainable Power Systems)
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