Control and Operation of DC/AC/Hybrid Microgrids Based on Artificial Intelligence

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Power Electronics".

Deadline for manuscript submissions: closed (15 August 2023) | Viewed by 6557

Special Issue Editors


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Guest Editor
Department of Energy, Faculty of Engineering and Science, Aalborg University, 9220 Aalborg, Denmark
Interests: artificial intelligence; cybersecurity; microgrids; quantum computing; energy storage systems
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Faculty of Engineering and Science, Aalborg University, 9220 Aalborg, Denmark
Interests: power electronics; microgrids; stability of power converters

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Guest Editor
Center for Research on Microgrids, AAU Energy, 9220 Aalborg, Denmark
Interests: microgrids; space power systems; psychobiology; brain networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) can be implemented in various applications for different goals, e.g., optimization, estimation, prediction, and control. For an important instant, AI-based approaches can be used just with data, and as a result, the studied system can be considered as a black box. Therefore, in the case of using the AI-based approaches, the minimum knowledge about the system can be expected. So, AI-based methods have the mentioned and important advantage, which can make them more interesting to be deployed in different systems. Due to the advantage of the AI-based methods and their wide applicability, they can be implemented in power and energy applications to introduce new solutions and improve their performances.

One of the most important power and energy applications can be a microgrid. Microgrids provide an opportunity to implement renewable energy resources and, as a result, green energy, locally. Therefore, by the deployment of green energy, environmental benefits can be suggested. In addition, the local implementation of energy resources makes the control and operation of the system simpler. Furthermore, microgrids can be designed in a way to reduce costs. Briefly, microgrids can offer environmentally friendly, economical, and less complex power and energy systems.

Due to the importance of microgrids and the capabilities of AI, the positive collaboration between AI and microgrids can be made to improve the performance of microgrids. The topics of this Special Issue include, but are not limited to, the following:

  • AI-based design of microgrids, e.g., optimal sizing of energy resources and energy storage systems;
  • AI-based energy and power management in microgrids;
  • Hierarchal control of microgrids using AI;
  • Control of power converters in microgrids using AI;
  • Cyber-attack detection in microgrids based on AI;
  • Cyber-attack mitigation in microgrids based on AI;
  • Stability enhancement of power converters and microgrids using AI-based methods;
  • Protection of microgrids using AI;
  • Residential microgrids operation using AI;
  • AI-based flexibility in microgrids;
  • AI-based reliability in microgrids.

Dr. Mohammad Reza Habibi
Dr. Ali Akhavan
Prof. Dr. Josep M. Guerrero
Prof. Dr. Juan C. Vasquez
Guest Editors

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Keywords

  • artificial intelligence
  • microgrids
  • cybersecurity
  • stability
  • power electronics
  • hierarchal control

Published Papers (3 papers)

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19 pages, 7670 KiB  
Article
Research on the Control of Multi-Agent Microgrid with Dual Neural Network Based on Priority Experience Storage Policy
by Fengxia Xu, Shulin Tong, Chengye Li and Xinyang Du
Electronics 2023, 12(3), 565; https://doi.org/10.3390/electronics12030565 - 22 Jan 2023
Cited by 1 | Viewed by 1278
Abstract
In this paper, an improved dual neural network control method based on multi-agent system is proposed to solve the problem of rating the frequency deviation and voltage deviation of the microgrid system due to the uneven impedance distribution of the circuit. The microgrid [...] Read more.
In this paper, an improved dual neural network control method based on multi-agent system is proposed to solve the problem of rating the frequency deviation and voltage deviation of the microgrid system due to the uneven impedance distribution of the circuit. The microgrid multi-agent system control model is constructed; the microgrid operation problem is transformed into Markov decision-making process, and the frequency error model of distributed secondary control adjusting system is established. In the course of training, the priority experience replay mechanism is introduced to accelerate the training reward return by using the experience of high feedback reward, and the frequency and voltage bias of the microgrid system are reduced. The model of isolated island microgrid of distributed power supply communication topology is established, and the control strategy of double neural network is simulated. Compared with the traditional sagging control method, the double neural network algorithm proposed in this paper stabilizes the frequency of the grid at rated frequency and improves the convergence speed. Simulation results show that the proposed method is helpful to provide stable and high-quality power resources for enterprises. Full article
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22 pages, 5326 KiB  
Article
Stochastic Optimal Strategy for Power Management in Interconnected Multi-Microgrid Systems
by Mahshid Javidsharifi, Hamoun Pourroshanfekr Arabani, Tamas Kerekes, Dezso Sera and Josep M. Guerrero
Electronics 2022, 11(9), 1424; https://doi.org/10.3390/electronics11091424 - 28 Apr 2022
Cited by 11 | Viewed by 1888
Abstract
A novel stochastic strategy for solving the problem of optimal power management of multi-microgrid (MMG) systems is suggested in this paper. The considered objectives are minimizing the total cost and emission of the system. The suggested algorithm is applied on a MMG consisting [...] Read more.
A novel stochastic strategy for solving the problem of optimal power management of multi-microgrid (MMG) systems is suggested in this paper. The considered objectives are minimizing the total cost and emission of the system. The suggested algorithm is applied on a MMG consisting of four microgrids (MG), each including fossil fuel-based generator units, wind turbine (WT), photovoltaic (PV) panel, battery, and local loads. The unscented transformation (UT) method is applied to deal with the inherent uncertainties of the renewable energy sources (RES) and forecasted values of the load demand and electricity price. The proposed algorithm is applied to solve the power management of a sample MMG system in both deterministic and probabilistic scenarios. It is justified through simulation results that the suggested algorithm is an efficient approach in satisfying the minimization of the cost and the environmental objective functions. When considering uncertainties, it is observed that the maximum achievable profit is about 23% less than that of the deterministic condition, while the minimum emission level increases 22%. It can be concluded that considering uncertainties has a significant effect on the economic index. Therefore, to present more accurate and realistic results it is essential to consider uncertainties in solving the optimal power management of MMG system. Full article
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Review

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20 pages, 731 KiB  
Review
Deep Learning for Forecasting-Based Applications in Cyber–Physical Microgrids: Recent Advances and Future Directions
by Mohammad Reza Habibi, Saeed Golestan, Josep M. Guerrero and Juan C. Vasquez
Electronics 2023, 12(7), 1685; https://doi.org/10.3390/electronics12071685 - 3 Apr 2023
Cited by 2 | Viewed by 1812
Abstract
Renewable energy resources can be deployed locally and efficiently using the concept of microgrids. Due to the natural uncertainty of the output power of renewable energy resources, the planning for a proper operation of microgrids can be a challenging task. In addition, the [...] Read more.
Renewable energy resources can be deployed locally and efficiently using the concept of microgrids. Due to the natural uncertainty of the output power of renewable energy resources, the planning for a proper operation of microgrids can be a challenging task. In addition, the information about the loads and the power consumption of them can create benefits to increase the efficiency of the microgrids. However, electrical loads can have uncertainty due to reasons such as unpredictable behavior of the consumers. To exploit a microgrid, energy management is required at the upper level of operation and control in order to reduce the costs. One of the most important tasks of the energy management system is to satisfy the loads and, in other words, develop a plan to maintain equilibrium between the power generation and power consumption. To obtain information about the output power of renewable energy resources and power consumption, deep learning can be implemented as a powerful tool, which is able to predict the desired values. In addition, weather conditions can affect the output power of renewable energy-based resources and the behavior of the consumers and, as a result, the power consumption. So, deep learning can be deployed for the anticipation of the weather conditions. This paper will study the recent works related to deep learning, which has been implemented for the prediction of the output power of renewable energy resources (i.e., PVs and wind turbines), electrical loads, and weather conditions (i.e., solar irradiance and wind speed). In addition, for possible future directions some strategies are suggested, the most important of which is the implementation of quantum computing in cyber–physical microgrids. Full article
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