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Applied Sciences
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Advances in Microgrids and Smartgrids Control Systems
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Advances in Microgrids and Smartgrids Control Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: 20 June 2024 | Viewed by 3471

Special Issue Editor

Dr. Xueqian Fu

E-Mail Website
Guest Editor
College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Interests: rural microgrid; agricultural energy internet; statistical machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The modern power system is undergoing tremendous changes, especially in the low-carbon and intelligent fields. The large-scale grid connection of photovoltaic power generation and wind power generation has changed the operation and control mode of the power grid. In the field of information science, 6G communication theory and artificial intelligence have developed rapidly. The applications of statistical machine learning and non-orthogonal multiple access communication in power systems may make them more intelligent than before. The intelligent control of microgrids and distribution networks can solve the problems caused by the large-scale grid integration of clean energy.

In this Issue, we will bring together research that discusses and highlights the advances in microgrids and smart grid control systems. Microgrid control theory research involves multiple application scenarios in cities, industries and rural areas. In addition, we believe that artificial intelligence technology, carbon emission reduction technology and wireless communication technology are of great significance for the improvement of smart grid control. This Special Issue aims to publish high-quality, original research papers in the overlapping fields of:

  • Smart grid control and operation;
  • Smart grid planning under uncertainty;
  • NOMA in smart grid applications;
  • Net zero-carbon emission microgrid;
  • Photovoltaic power generation forecast;
  • Weather-based interruption prediction;
  • Microgrid resilience to extreme weather events;
  • Integration of bifacial photovoltaics in agrivoltaic systems.

Dr. Xueqian Fu
Guest Editor

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. Applied Sciences 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

  • uncertainty
  • photovoltaic
  • agrivoltaic system
  • rural microgrid
  • zero-carbon emission
  • extreme weather event
  • agricultural energy internet
  • statistical machine learning
  • non-orthogonal multiple access

Published Papers (3 papers)

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Research

13 pages, 12693 KiB  
Article
Information Gap Decision Theory-Based Stochastic Optimization for Smart Microgrids with Multiple Transformers
by Shuang Rong, Yanlei Zhao, Yanxin Wang, Jiajia Chen, Wanlin Guan, Jiapeng Cui and Yanlong Liu
Appl. Sci. 2023, 13(16), 9305; https://doi.org/10.3390/app13169305 - 16 Aug 2023
Cited by 1 | Viewed by 671
Abstract
Multi-microgrid collaborative scheduling can promote the local consumption of renewable energy in the smart grid and reduce the operating costs of the power grid park. At the same time, the access of the distributed energy storage (ES) system provides an opportunity to further [...] Read more.
Multi-microgrid collaborative scheduling can promote the local consumption of renewable energy in the smart grid and reduce the operating costs of the power grid park. At the same time, the access of the distributed energy storage (ES) system provides an opportunity to further enhance the park’s peak shaving and valley filling capacity, thereby reducing costs. However, the uncertainty of photovoltaic (PV) power generation and load demand seriously affects the profit maximization of the microgrid in the park. To address this challenge, this paper proposes a stochastic optimal scheduling strategy for industrial park smart microgrids with multiple transformers based on the information gap decision theory (IGDT). We first introduce a revenue maximization model for industrial parks, incorporating a two-part tariff system and distributed ES. Subsequently, we employ an envelope constraint model to accurately represent the uncertainty associated with PV generation and load demand. By integrating these components, we establish the IGDT stochastic optimization scheduling model for industrial parks with multiple transformers. Finally, we simulate and analyze the performance of the proposed IGDT model under various cost deviation factors during typical spring and summer days. The simulation results demonstrate the effectiveness of the proposed control strategy in mitigating the impact of PV generation and load uncertainty on industrial parks. The IGDT-based scheduling approach provides an efficient solution for maximizing revenue and enhancing the operational stability of industrial park microgrids. Full article
(This article belongs to the Special Issue Advances in Microgrids and Smartgrids Control Systems)
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21 pages, 4503 KiB  
Article
Aggregation Dispatch and Control Strategies for Multi-Type Loads in Industrial Parks
by Qunru Zheng, Ping Yang, Yuhang Wu, Zhen Xu and Peng Zhang
Appl. Sci. 2023, 13(16), 9205; https://doi.org/10.3390/app13169205 - 13 Aug 2023
Viewed by 646
Abstract
With the continuous expansion of renewable energy construction, the power system requires a larger-scale flexible dispatchable and controllable resource for power balance. Fully tapping into the power regulation capability of multi-type loads in industrial parks, making them a low-cost flexible dispatchable and controllable [...] Read more.
With the continuous expansion of renewable energy construction, the power system requires a larger-scale flexible dispatchable and controllable resource for power balance. Fully tapping into the power regulation capability of multi-type loads in industrial parks, making them a low-cost flexible dispatchable and controllable resource, is an effective approach to establish power regulation capability at scale in the new power system. However, the control characteristics of multi-type loads in industrial parks vary greatly, and their control delay characteristics, response speed, and sustainable response time are all different. Traditional dispatch and control methods cannot achieve precise control of the massive and multi-type loads in industrial parks. Therefore, this paper establishes unified models for the control characteristics of multi-type loads in industrial parks, quantitatively characterizes their control delay characteristics, start–stop characteristics, and control response speed. Based on this, the aggregated dispatch and control model and dispatch and control strategy for multi-type loads in industrial parks are developed, which provide a predictive control rate for individual loads considering the delay and segmented response characteristics to achieve precise aggregation control of multi-type loads in industrial parks. Simulation results show that the aggregated dispatch and control model and its aggregated dispatch and control strategy achieve precise control of multi-type loads in industrial parks. Flexible dispatchable and controllable loads can provide low-cost power regulation capability for the new power system. Full article
(This article belongs to the Special Issue Advances in Microgrids and Smartgrids Control Systems)
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23 pages, 7223 KiB  
Article
Achieving an Optimal Decision for the Joint Planning of Renewable Power Supply and Energy Storage for Offshore Oil–Gas Platforms
by Changbin Hu, Jufu Deng, Chao Liu, Shanna Luo, Xuecheng Li and Heng Lu
Appl. Sci. 2023, 13(15), 8833; https://doi.org/10.3390/app13158833 - 31 Jul 2023
Cited by 1 | Viewed by 936
Abstract
To address the complexity of siting and sizing for the renewable energy and energy storage (ES) of offshore oil–gas platforms, as well as to enhance the utilization of renewable energy and to ensure the power-flow stability of offshore oil–gas platforms, this paper proposes [...] Read more.
To address the complexity of siting and sizing for the renewable energy and energy storage (ES) of offshore oil–gas platforms, as well as to enhance the utilization of renewable energy and to ensure the power-flow stability of offshore oil–gas platforms, this paper proposes a hierarchical clustering-and-planning method for wind turbine (WT)/photovoltaic (PV) ES. The proposed strategy consists of three stages. First, the WT/PV power generation is forecast by a LightGBM model. The WT/PV siting and sizing at each node of the distribution network is optimized with a particle swarm optimization (PSO) algorithm, with the objectives of economy and stability. In the second stage, the distribution network is partitioned into sub-clusters, based on a voltage and loss-sensitivity index. Finally, the ES siting and sizing is optimized with PSO to minimize the line loss and the voltage fluctuation for each sub-cluster. The relationship between the economic and stability indicators is conducted quantitatively in the joint-planning approach. Considering the 10 kV distribution network of an oil–gas platform in the Bohai Sea of China as an example, our experiments demonstrated that by adjusting the WT/PV ES capacity for different gas-turbine power outputs, line losses can be reduced by 55–66% and voltage fluctuations can be reduced by 30.4–47.5%. Full article
(This article belongs to the Special Issue Advances in Microgrids and Smartgrids Control Systems)
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