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Novel Developments in Distribution Systems and Microgrids—2nd Edition

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: closed (27 January 2025) | Viewed by 1254

Special Issue Editors


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Guest Editor
Province-Ministry Joint Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability, School of Electronic Engineering, Hebei University of Technology, Tianjin 300130, China
Interests: renewable energy; multi-objective optimization; dynamic economic emission dispatch; sustainable development; combined cooling, heating, and power system; operation strategy performance assessment; microgrid system operation optimization
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
College of Electronic Information and Automation, Tianjin University of Science and Technology, Tianjin 300222, China
Interests: computing intelligent algorithms and their applications in the field of new energy power generation; power system optimal dispatch; new energy power generation prediction technology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear colleagues,

Distributed power generation has many characteristics, such as being clean, green, sustainable, and flexible, and has become one of the most important methods of new energy power generation; however, the integration of high-density distributed generation into the grid brings challenges to optimal configuration, operation, control, and scheduling decisions of the power system. Distribution systems and microgrid techniques can effectively improve the controllability and flexibility of high-density distributed power-grid-connected operations, as well as improve power quality and power supply stability. Therefore, the development of microgrid and distribution system techniques has become a hot research direction in the energy field.

This Special Issue aims to present and disseminate the most recent advances related to the theory, design, modeling, application, control, and condition monitoring of microgrid and distribution system techniques.

Topics of interest for publication include, but are not limited to, the following:

  • Microgrid systems with new energy;
  • Dynamic economic and emission dispatch;
  • Combined cooling, heating, and power systems;
  • Microgrid system operation optimization;
  • Operation strategy performance assessment;
  • Multiobjective optimization of distribution systems and microgrids;
  • Prediction of new energy power generation.

Prof. Dr. Lingling Li
Dr. Zhifeng Liu
Guest Editors

Manuscript Submission Information

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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

  • renewable energy
  • multiobjective optimization
  • dynamic economic and emission dispatch
  • sustainable development
  • combined cooling, heating, and power systems
  • operation strategy performance assessment
  • microgrid system operation optimization.

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Published Papers (1 paper)

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Research

16 pages, 2933 KiB  
Article
Optimizing Models and Data Denoising Algorithms for Power Load Forecasting
by Yanxia Li, Ilyosbek Numonov Rakhimjon Ugli, Yuldashev Izzatillo Hakimjon Ugli, Taeo Lee and Tae-Kook Kim
Energies 2024, 17(21), 5513; https://doi.org/10.3390/en17215513 - 4 Nov 2024
Viewed by 844
Abstract
To handle the data imbalance and inaccurate prediction in power load forecasting, an integrated data denoising power load forecasting method is designed. This method divides data into administrative regions, industries, and load characteristics using a four-step method, extracts periodic features using Fourier transform, [...] Read more.
To handle the data imbalance and inaccurate prediction in power load forecasting, an integrated data denoising power load forecasting method is designed. This method divides data into administrative regions, industries, and load characteristics using a four-step method, extracts periodic features using Fourier transform, and uses Kmeans++ for clustering processing. On this basis, a Transformer model based on an adversarial adaptive mechanism is designed, which aligns the data distribution of the source domain and target domain through a domain discriminator and feature extractor, thereby reducing the impact of domain offset on prediction accuracy. The mean square error of the Fourier transform clustering method used in this study was 0.154, which was lower than other methods and had a better data denoising effect. In load forecasting, the mean square errors of the model in predicting long-term load, short-term load, and real-time load were 0.026, 0.107, and 0.107, respectively, all lower than the values of other comparative models. Therefore, the load forecasting model designed for research has accuracy and stability, and it can provide a foundation for the precise control of urban power systems. The contributions of this study include improving the accuracy and stability of the load forecasting model, which provides the basis for the precise control of urban power systems. The model tracks periodicity, short-term load stochasticity, and high-frequency fluctuations in long-term loads well, and possesses high accuracy in short-term, long-term, and real-time load forecasting. Full article
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