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Advances in Sustainable Development of Power Systems with Artificial Intelligence
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Advances in Sustainable Development of Power Systems with Artificial Intelligence

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

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 5079

Special Issue Editors

Prof. Dr. Pei Zhang

E-Mail Website
Guest Editor
International School of Electrical Engineering, Beijing Jiaotong University, Beijing, China
Interests: power system operation and economical dispatch; power system planning; application of information technologies in power systems; renewable energy
Dr. Zhao Liu

E-Mail Website
Guest Editor
chool of Electrical Engineering, Beijing Jiaotong University, Beijing, China
Interests: power system transient stability; power system resilience; multi-energy systems; applications of artificial intelligence in power systems
Dr. Zhenhuan Ding

E-Mail Website
Guest Editor
School of Artificial Intelligence, Anhui University, Hefei, China
Interests: power distribution systems; lithium-ion battery technologies; applications of reinforcement learning in power systems

Special Issue Information

Dear Colleagues,

With the increasing penetrations of renewable energy generations, the sustainable development of power systems around the globe is facing new challenges in terms of planning and operations. Wind and solar energy are the major driving forces for the development, but they also introduced uncertainties, changed the system dynamics, and revised the operation principles. These impacts are on all voltage levels in power systems and at multiple fronts, including the large-scale renewable generations in the bulk grid and distributed generations in distribution systems before and behind the meters. In the foreseeable future, the sustainable development of power systems will fundamentally change how power engineering approaches the systems and how people use electricity.

In recent years, the fascinating progress in artificial intelligence research has attracted wide attention. This also provides a potential solution direction for power systems development. Numerous studies have been published in scientific journals in terms of the applications of artificial intelligence in power systems planning and operations considering renewable energy penetrations. However, there is still a gap between the theoretical research in academia and the challenges faced by engineers in the power industry. The majority of current machine learning algorithms are originally designed for tasks in computer science and computer engineering field. As a result, the performance, efficiency, reliability, and interpretability of the machine learning algorithms are not optimized for power system applications. In this Special Issue, we are aiming to bridge this gap with new advances in the applications of artificial intelligence in power systems planning and operations. The topics include, but not limited to, the following:

  • Knowledge–data complementary framework for power system planning and operations;
  • Model–data combined driven technology in power system planning and operations;
  • Physics-informed neural networks in power system planning and operations;
  • Human-in-the-loop artificial intelligence framework for power system planning and operations;
  • Deep learning-based renewable energy generation estimations and available power analysis;
  • Deep reinforcement learning-based power system operations;
  • Meta learning and federated learning in power system planning and operations;
  • Artificial intelligence-based power system market and multi-agent games;
  • Artificial intelligence-based power system stability analysis and control;
  • Explainable machine learning in power systems planning and operations;
  • Other advances in the sustainable development of power systems with artificial intelligence.

Prof. Dr. Pei Zhang
Dr. Zhao Liu
Dr. Zhenhuan Ding
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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • power systems sustainable development
  • renewable energy
  • artificial intelligence
  • power systems planning
  • power systems operations

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Published Papers (2 papers)

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Research

19 pages, 3146 KiB  
Article
Long-Term Solar Power Time-Series Data Generation Method Based on Generative Adversarial Networks and Sunrise–Sunset Time Correction
by Haobo Shi, Yanping Xu, Baodi Ding, Jinsong Zhou and Pei Zhang
Sustainability 2023, 15(20), 14920; https://doi.org/10.3390/su152014920 - 16 Oct 2023
Cited by 3 | Viewed by 1804
Abstract
Constructing long-term solar power time-series data is a challenging task for power system planners. This paper proposes a novel approach to generate long-term solar power time-series data through leveraging Time-series Generative Adversarial Networks (TimeGANs) in conjunction with adjustments based on sunrise–sunset times. A [...] Read more.
Constructing long-term solar power time-series data is a challenging task for power system planners. This paper proposes a novel approach to generate long-term solar power time-series data through leveraging Time-series Generative Adversarial Networks (TimeGANs) in conjunction with adjustments based on sunrise–sunset times. A TimeGAN model including three key components, an autoencoder network, an adversarial network, and a supervised network, is proposed for data generation. In order to effectively capture autocorrelation and enhance the fidelity of the generated data, a Recurrent Neural Network (RNN) is proposed to construct each component of TimeGAN. The sunrise and sunset time calculated based on astronomical theory is proposed for adjusting the start and end time of solar power time-series, which are generated by the TimeGAN model. This case study, using real datasets of solar power stations at two different geographic locations, indicates that the proposed method is superior to previous methods in terms of four aspects: annual power generation, probability distribution, fluctuation, and periodicity features. A comparison of production cost simulation results between using the solar power data generated via the proposed method and using the actual data affirms the feasibility of the proposed method. Full article
(This article belongs to the Special Issue Advances in Sustainable Development of Power Systems with Artificial Intelligence)
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17 pages, 5796 KiB  
Article
Robust Wavelet Transform Neural-Network-Based Short-Term Load Forecasting for Power Distribution Networks
by Yijun Wang, Peiqian Guo, Nan Ma and Guowei Liu
Sustainability 2023, 15(1), 296; https://doi.org/10.3390/su15010296 - 24 Dec 2022
Cited by 17 | Viewed by 2359
Abstract
A precise short-term load-forecasting model is vital for energy companies to create accurate supply plans to reduce carbon dioxide production, causing our lives to be more environmentally friendly. A variety of high-voltage-level load-forecasting approaches, such as linear regression (LR), autoregressive integrated moving average [...] Read more.
A precise short-term load-forecasting model is vital for energy companies to create accurate supply plans to reduce carbon dioxide production, causing our lives to be more environmentally friendly. A variety of high-voltage-level load-forecasting approaches, such as linear regression (LR), autoregressive integrated moving average (ARIMA), and artificial neural network (ANN) models, have been proposed in recent decades. However, unlike load forecasting in high-voltage transmission systems, load forecasting at the distribution network level is more challenging since distribution networks are more variable and nonstationary. Moreover, existing load-forecasting models only consider the features of the time domain, while the demand load is highly correlated to the frequency-domain information. This paper introduces a robust wavelet transform neural network load-forecasting model. The proposed model utilizes both time- and frequency-domain information to improve the model’s prediction accuracy. Firstly, three wavelet transform methods, variational mode decomposition (VMD), empirical mode decomposition (EMD), and empirical wavelet transformation (EWT), were introduced to transform the time-domain demand load data into frequency-domain data. Then, neural network models were trained to predict all components simultaneously. Finally, all the predicted data were aggregated to form the predicted demand load. Three cases were simulated in the case study stage to evaluate the prediction accuracy under different layer numbers, weather information, and neural network types. The simulation results showed that the proposed robust time–frequency load-forecasting model performed better than the traditional time-domain forecasting models based on the comparison of the performance metrics, including the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE). Full article
(This article belongs to the Special Issue Advances in Sustainable Development of Power Systems with Artificial Intelligence)
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