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Energies
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Power Management for Distributed Generators Integrated System
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Power Management for Distributed Generators Integrated System

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F2: Distributed Energy System".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 7408

Special Issue Editors

Dr. Md. Shafiul Alam

E-Mail Website1 Website2
Guest Editor
Applied Research Center for Environment & Marine Studies, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi Arabia
Interests: energy and environment; greenhouse gas emission management, integrated electric power and gas system; renewable energy grid integration; AC/DC microgrid; energy storage devices; optimization and machine learning for energy system
Special Issues, Collections and Topics in MDPI journals
Dr. Md Alamgir Hossain

E-Mail Website
Guest Editor
Research Fellow at Queensland Micro-and Nano-technology Centre, Griffith University, Brisbane, QLD, Australia
Interests: AC/DC microgrids; machine learning; optimization, converter interfaced DG; energy storage devices
Special Issues, Collections and Topics in MDPI journals
Dr. Aasim Ullah

E-Mail Website
Guest Editor
Assistant Professor, Department of EEE, International Islamic University Chittagong (IIUC), Chittagong, Bangladesh
Interests: high voltage; micro grid; grid integration; machine learning; machine design

Special Issue Information

Dear Colleagues,

Nowadays, a paradigm shift in power systems is observed due to the high-level integration of electronic power converter-based distributed generators (DGs). Due to the scarcity and continuous depletion of conventional fossil fuels, several renewable energy sources (RESs), such as solar photovoltaic (PV), wind, concentrated solar power (CSP), among others, are integrated as DGs in the power system. Intermittent power generation by such DGs mandates the integration of sufficient energy storage devices to balance the power generation and load. Energy storage devices also play an important role in demand-side management. Such DG-integrated small systems are sometimes termed microgrids, which could operate in both grid-connected or islanded modes.

Many challenges arise due to the high-level integration of DGs with the system, such as reduced inertia, energy imbalance, and voltage fluctuations. Therefore, to deal with these challenges, new control, modelling, and energy management algorithms are needed to develop with cutting-edge technologies. A large number of energy sources, loads, and energy storage devices are properly controlled and managed to minimize the negative impacts on the main grids.

This Special Issue invites submission of high-quality novel research papers and review articles covering a wide range of topics related to power management in high-level distributed generators integrated systems, such as efficient energy management, novel voltage control approach, energy storage devices lifetime improvement, state-of-charge management of energy storage system, optimal operation of AC/DC microgrids, demand-side management, electric vehicle impacts on energy management, cybersecurity for microgrid energy management, advanced optimization to facilitate renewable energy integration, and novel inertia emulation techniques.

The main topics of interest in this Special Issue include:

  • Control of power electronic converters for DG integration;
  • Energy storage devices integration with AC/DC microgrids;
  • Power management of RESs (such as PV, wind, and so on) integrated system;
  • Inertia response improvement for RESs integrated system;
  • Control of AC/DC microgrid;
  • Optimization and advanced algorithm for energy management;
  • Modeling and control of DG;
  • Machine learning for DG integration.

Dr. Md. Shafiul Alam
Dr. Md Alamgir Hossain
Dr. Aasim Ullah
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

  • distributed generator
  • power management
  • voltage control
  • renewable energy
  • energy storage devices
  • power electronic converters

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Published Papers (4 papers)

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Editorial

Jump to: Research

3 pages, 158 KiB  
Editorial
Power Management for Distributed Generators Integrated System
by Md Shafiul Alam
Energies 2022, 15(16), 5813; https://doi.org/10.3390/en15165813 - 10 Aug 2022
Cited by 2 | Viewed by 1342
Abstract
The integration of distributed generation systems, including intermittent solar photovoltaic (PV) and wind, has a significant impact on the power system [...] Full article
(This article belongs to the Special Issue Power Management for Distributed Generators Integrated System)

Research

Jump to: Editorial

22 pages, 7389 KiB  
Article
Energy Management Strategies of Grid-Connected Microgrids under Different Reliability Conditions
by Mohammed Abdullah H. Alshehri, Youguang Guo and Gang Lei
Energies 2023, 16(9), 3951; https://doi.org/10.3390/en16093951 - 8 May 2023
Cited by 2 | Viewed by 1547
Abstract
The demand for a reliable, cheap and environmentally friendly source of energy makes the integration of renewable energy into power networks a global challenge. Furthermore, reliability, as one of the core elements of efficient and cost-effective energy management options, is still among the [...] Read more.
The demand for a reliable, cheap and environmentally friendly source of energy makes the integration of renewable energy into power networks a global challenge. Furthermore, reliability, as one of the core elements of efficient and cost-effective energy management options, is still among the dominant factors/techniques that receive more attention for realistic penetrations of renewable energy into the electricity grid. This paper proposes an efficient way of energy management for a grid-connected microgrid. The grid-connected microgrid used in the analysis consists of solar photovoltaic (P.V.) and battery. In this microgrid configuration, oftentimes, the output power might not be equal to the system demand; in this regard, it is expected that the mismatch between these output powers is not zero. However, to reduce the mismatch between demand and supply to be close to zero, this paper proposes strategies of increasing the rated power of solar, battery and grid separately and combining them with a view of finding the cheapest option among these strategies. The results have shown that the cost increment for different options is USD 280.792, 84.48 and 48.204 for storage, P.V. and grid, respectively. These have shown that the storage option is the most expansive option for improving P.V. grid-connected microgrids. This is followed immediately by the P.V. option, which is weather dependent. On the other hand, the grid option is the cheapest option for system reliability improvement. This paper is expected to be useful to both new researchers and experts who are working in energy management with an emphasis on the reliability aspect. Full article
(This article belongs to the Special Issue Power Management for Distributed Generators Integrated System)
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16 pages, 1407 KiB  
Article
Distributed Weight Adaptive Control for Frequency Regulation of Islanded Microgrid
by Guoxing Yu, Huihui Song, Meng Liu, Zongxun Song and Yanbin Qu
Energies 2022, 15(11), 4136; https://doi.org/10.3390/en15114136 - 4 Jun 2022
Viewed by 1647
Abstract
The consensus control method based on a multi-agent system has been widely applied in the distributed control and optimization of microgrids. However, the following drawbacks are still common in current research: (1) ignoring the influence of consensus control commands on the synchronization stability [...] Read more.
The consensus control method based on a multi-agent system has been widely applied in the distributed control and optimization of microgrids. However, the following drawbacks are still common in current research: (1) ignoring the influence of consensus control commands on the synchronization stability of the physical grid under primary control; (2) only focusing on improving one property ofcontrol performance, lacking comprehensive considerations of multiple properties. With the aim of solving these problems, in this paper we propose a weight-adaptive robust control strategy for implementing distributed frequency regulation of islanded microgrids. Firstly, the frequency synchronization stability of the physical layer is analyzed by means of a coupled oscillator theory and the design objectives of the controllable parameters for the information layer are formed. Subsequently, the relationship between the weight coefficients and the two important control performances of convergence speed and delay robustness is strictly analyzed. Based on this, an adaptive coefficient that can be autonomously adjusted according to the frequency deviation is designed to achieve a trade-off between convergence speed and delay robustness. Finally, three simulation studies are presented to verify the effectiveness of the proposed control strategy. Full article
(This article belongs to the Special Issue Power Management for Distributed Generators Integrated System)
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12 pages, 2380 KiB  
Article
Prediction of Node Importance of Power System Based on ConvLSTM
by Xu Wu, Junqi Geng, Meng Liu, Zongxun Song and Huihui Song
Energies 2022, 15(10), 3678; https://doi.org/10.3390/en15103678 - 17 May 2022
Cited by 1 | Viewed by 1736
Abstract
In power systems, the destruction of some important nodes may cause cascading faults. If the most important node in the power system can be found, the important node can be protected in advance, thereby avoiding a blackout accident. At present, the evaluation algorithm [...] Read more.
In power systems, the destruction of some important nodes may cause cascading faults. If the most important node in the power system can be found, the important node can be protected in advance, thereby avoiding a blackout accident. At present, the evaluation algorithm of node importance is calculated based on the power flow of the power grid, so the calculation results must be lagging behind, and it does not have the ability to provide early warning for the power grid to provide protection signals. Therefore, it is necessary to predict the importance of nodes in the power system. After using a reasonable prediction model to predict the importance of nodes, we can simulate the future state of power system operation and avoid accidents for the dispatching agency of the power grid company according to the prediction results. This paper proposes a prediction model based on convolutional long short-term memory (ConvLSTM) to predict the importance of nodes. This method has obvious advantages over the long short-term memory (LSTM) network. The convolution operation is used to replace the original full connection operation of the LSTM network, which not only utilizes the advantages of convolution to extract spatial features but also retains the ability of LSTM to process time-series features. The simulation results show that the prediction of node importance using the ConvLSTM network is much more accurate than LSTM. Using statistical indicators to compare and analyze the prediction results, it can be seen that ConvLSTM has higher prediction accuracy. Therefore, using the ConvLSTM model to predict node importance has certain significance for grid dispatching agencies to accurately simulate the future state of the power system and avoid risks in advance. Full article
(This article belongs to the Special Issue Power Management for Distributed Generators Integrated System)
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