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Novel Developments in Distribution Systems and Microgrids
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Novel Developments in Distribution Systems and Microgrids

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 (19 April 2023) | Viewed by 6602

Special Issue Editors

Prof. Dr. Lingling Li

E-Mail Website
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
Dr. Zhifeng Liu

E-Mail Website
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:

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

Prof. Dr. Lingling Li
Dr. Liu Zhifeng
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. 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
  • Multi-objective optimization
  • Dynamic economic emission dispatch
  • Sustainable development
  • Combined cooling, heating, and power system
  • Operation strategy performance assessment
  • Microgrid system operation optimization.

Related Special Issue

Published Papers (5 papers)

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Research

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15 pages, 1878 KiB  
Article
Realizing the Improvement of the Reliability and Efficiency of Intelligent Electricity Inspection: IAOA-BP Algorithm for Anomaly Detection
by Yuping Zou, Rui Wu, Xuesong Tian and Hua Li
Energies 2023, 16(7), 3021; https://doi.org/10.3390/en16073021 - 25 Mar 2023
Cited by 3 | Viewed by 1261
Abstract
Anomaly detection can improve the service level of the grid, effectively save human resources and reduce the operating cost of a power company. In this study, an improved arithmetic optimization-backpropagation (IAOA-BP) neural algorithm for an anomaly detection model was proposed for electricity inspection. [...] Read more.
Anomaly detection can improve the service level of the grid, effectively save human resources and reduce the operating cost of a power company. In this study, an improved arithmetic optimization-backpropagation (IAOA-BP) neural algorithm for an anomaly detection model was proposed for electricity inspection. The dynamic boundary strategy of the cosine control factor and the differential evolution operator are introduced into the arithmetic optimization algorithm (AOA) to obtain the improved arithmetic optimization algorithm (IAOA). The algorithm performance test proves that the IAOA has better solving ability and stability compared with the AOA, WOA, SCA, SOA and SSA. The IAOA was subsequently used to obtain the optimal weights and thresholds for BP. In the experimental phase, the proposed model is validated with electricity data provided by a power company. The results reveal that the overall determination accuracy using the IAOA-BP algorithm remains above 96%, and compared with other algorithms, the IAOA-BP has a higher accuracy and can meet the requirements grid supervision. The power load data anomaly detection model proposed in this study has some implications that might suggest how power companies can promote grid business model transformation, improve economic efficiency, enhance management and improve service quality. Full article
(This article belongs to the Special Issue Novel Developments in Distribution Systems and Microgrids)
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17 pages, 2846 KiB  
Article
Electric Power Load Forecasting Method Based on a Support Vector Machine Optimized by the Improved Seagull Optimization Algorithm
by Suqi Zhang, Ningjing Zhang, Ziqi Zhang and Ying Chen
Energies 2022, 15(23), 9197; https://doi.org/10.3390/en15239197 - 04 Dec 2022
Cited by 7 | Viewed by 1252
Abstract
Accurate load forecasting is conducive to the formulation of the power generation plan, lays the foundation for the formulation of quotation, and provides the basis for the power management system and distribution management system. This study aims to propose a high precision load [...] Read more.
Accurate load forecasting is conducive to the formulation of the power generation plan, lays the foundation for the formulation of quotation, and provides the basis for the power management system and distribution management system. This study aims to propose a high precision load forecasting method. The power load forecasting model, based on the Improved Seagull Optimization Algorithm, which optimizes SVM (ISOA-SVM), is constructed. First, aiming at the problem that the random selection of internal parameters of SVM will affect its performance, the Improved Seagull Optimization Algorithm (ISOA) is used to optimize its parameters. Second, to solve the slow convergence speed of the Seagull Optimization Algorithm (SOA), three strategies are proposed to improve the optimization performance and convergence accuracy of SOA, and an ISOA algorithm with better optimization performance and higher convergence accuracy is proposed. Finally, the load forecasting model based on ISOA-SVM is established by using the Mean Square Error (MSE) as the objective function. Through the example analysis, the prediction performance of the ISOA-SVM is better than the comparison models and has good prediction accuracy and effectiveness. The more accurate load forecasting can provide guidance for power generation and power consumption planning of the power system. Full article
(This article belongs to the Special Issue Novel Developments in Distribution Systems and Microgrids)
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21 pages, 1982 KiB  
Article
Research on Multi-Step Prediction of Short-Term Wind Power Based on Combination Model and Error Correction
by Hua Li, Zhen Wang, Binbin Shan and Lingling Li
Energies 2022, 15(22), 8417; https://doi.org/10.3390/en15228417 - 10 Nov 2022
Cited by 1 | Viewed by 1026
Abstract
The instability of wind power poses a great threat to the security of the power system, and accurate wind power prediction is beneficial to the large-scale entry of wind power into the grid. To improve the accuracy of wind power prediction, a short-term [...] Read more.
The instability of wind power poses a great threat to the security of the power system, and accurate wind power prediction is beneficial to the large-scale entry of wind power into the grid. To improve the accuracy of wind power prediction, a short-term multi-step wind power prediction model with error correction is proposed, which includes complete ensemble empirical mode decomposition adaptive noise (CEEMDAN), sample entropy (SE), improved beetle antennae search (IBAS) and kernel extreme learning machine (KELM). First, CEEMDAN decomposes the original wind power sequences into a set of stationary sequence components. Then, a set of new sequence components is reconstructed according to the SE value of each sequence component to reduce the workload of subsequent prediction. The new sequence components are respectively sent to the IBAS-KELM model for prediction, and the wind power prediction value and error prediction value of each component are obtained, and the predicted values of each component are obtained by adding the two. Finally, the predicted values of each component are added to obtain the final predicted value. The prediction results of the actual wind farm data show that the model has outstanding advantages in high-precision wind power prediction, and the error evaluation indexes of the combined model constructed in this paper are at least 34.29% lower in MAE, 34.53% lower in RMSE, and 36.36% lower in MAPE compared with other models. prediction decreased by 30.43%, RMSE decreased by 29.67%, and MAPE decreased by 28.57%, and the error-corrected three-step prediction decreased by 55.60%, RMSE decreased by 50.00%, and MAPE decreased by 54.17% compared with the uncorrected three-step prediction, and the method significantly improved the prediction accuracy. Full article
(This article belongs to the Special Issue Novel Developments in Distribution Systems and Microgrids)
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19 pages, 4539 KiB  
Article
Wind Power Prediction Method: Support Vector Regression Optimized by Improved Jellyfish Search Algorithm
by Dong-Dong Yuan, Ming Li, Heng-Yi Li, Cheng-Jian Lin and Bing-Xiang Ji
Energies 2022, 15(17), 6404; https://doi.org/10.3390/en15176404 - 01 Sep 2022
Cited by 11 | Viewed by 1290
Abstract
To address the problems of grid connection and power dispatching caused by non-stationary wind power output, an improved Jellyfish Search algorithm optimization support vector regression (IJS-SVR) model was proposed in this study to achieve high-precision wind power prediction. The random selection of internal [...] Read more.
To address the problems of grid connection and power dispatching caused by non-stationary wind power output, an improved Jellyfish Search algorithm optimization support vector regression (IJS-SVR) model was proposed in this study to achieve high-precision wind power prediction. The random selection of internal parameters of SVR model will affect its performance. In this study, the Jellyfish Search (JS) algorithm was selected and improved to propose an Improved Jellyfish Search (IJS) algorithm. Compared with the comparative algorithms, the optimized values of IJS algorithm are closer to 0. It exhibits good convergence ability, search stability, and optimization-seeking ability, as well as being more suitable for solving optimization problems. Therefore, IJS was used to optimize SVR, and the prediction model of IJS-SVR was established. Different weather and seasons affect wind power and model prediction accuracy. The wind power in spring and winter was selected for model prediction verification in this study. Compared with other methods, the IJS-SVR model proposed in this study could achieve better prediction results than other models in both seasons, and its prediction performance was better, which could improve the prediction accuracy of wind power. This study provides a more economical and effective method of wind power to solve its uncertainties and can be used as a reference for grid power generation planning and power system economic dispatch. Full article
(This article belongs to the Special Issue Novel Developments in Distribution Systems and Microgrids)
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Review

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23 pages, 550 KiB  
Review
A Review of Research on Dynamic and Static Economic Dispatching of Hybrid Wind–Thermal Power Microgrids
by Lingling Li, Jiarui Pei and Qiang Shen
Energies 2023, 16(10), 3985; https://doi.org/10.3390/en16103985 - 09 May 2023
Cited by 2 | Viewed by 1195
Abstract
As fossil energy is increasingly depleted, promoting the integration of renewable energy into the grid and improving its utilization rate has become an irresistible development trend in China’s power industry. However, the volatility of wind power increases the difficulty of economic dispatch in [...] Read more.
As fossil energy is increasingly depleted, promoting the integration of renewable energy into the grid and improving its utilization rate has become an irresistible development trend in China’s power industry. However, the volatility of wind power increases the difficulty of economic dispatch in power systems. With the rising participation of wind power in the system, the complexity of traditional microgrid dynamic scheduling problems has increased, transforming into a dynamic economic scheduling problem for wind power thermal power hybrid microgrids. Starting from the concept and research significance of economic dispatch, this article analyzes the current research status of microgrid economic dispatch as well as the impact and influencing factors of wind energy grid connection on it. It summarizes the research performed by scholars in two aspects: scheduling models and solving algorithms in static dispatch, as well as how to deal with wind power randomness in dynamic dispatch and how to balance environmental protection while ensuring economic maximization. Finally, the existing problems in current research were summarized and future development directions were prospected. This research has important application prospects in improving the economy of the system and protecting the ecological environment. Full article
(This article belongs to the Special Issue Novel Developments in Distribution Systems and Microgrids)
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