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Applied Sciences
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Advanced Technologies and Applications of Microgrids
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Advanced Technologies and Applications of Microgrids

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 July 2024 | Viewed by 1758

Special Issue Editor

Prof. Dr. Tao Zhang

E-Mail Website
Guest Editor
College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
Interests: systems engineering; microgrids; renewable energy; optimization methods

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the latest advancements and applications in the field of microgrids. Microgrids are localized grids that can operate independently or in conjunction with the traditional grid. This issue aims to explore the technological innovations that enhance the efficiency, reliability, and sustainability of microgrids. It will cover a range of topics, including but not limited to the integration of renewable energy sources, advanced control strategies, energy storage solutions, and the role of microgrids in smart cities. Additionally, the issue will delve into the challenges and opportunities in the deployment and management of microgrids in various sectors, such as residential, commercial, and industrial. Contributions are sought from researchers, engineers, and practitioners presenting new research, innovations, and case studies.

This Special Issue will publish high-quality, original research papers in the overlapping fields of:

  • Innovative design and optimization;
  • Renewable energy integration;
  • Energy storage and management;
  • Smart grid technologies;
  • Regulatory and policy frameworks;
  • Case studies and practical implementations;
  • Resilience and reliability;
  • Economic and environmental impacts.

Prof. Dr. Tao Zhang
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

  • renewable energy
  • smart grid
  • energy storage
  • microgrid control systems
  • grid resilience and reliability
  • distributed energy resources
  • sustainable energy management

Published Papers (4 papers)

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Research

16 pages, 3522 KiB  
Article
A Deep Reinforcement Learning Approach for Microgrid Energy Transmission Dispatching
by Shuai Chen, Jian Liu, Zhenwei Cui, Zhiyu Chen, Hua Wang and Wendong Xiao
Appl. Sci. 2024, 14(9), 3682; https://doi.org/10.3390/app14093682 - 26 Apr 2024
Viewed by 291
Abstract
Optimal energy transmission dispatching of microgrid systems involves complicated transmission energy allocation and battery charging/discharging management and remains a difficult and challenging research problem subject to complex operation conditions and action constraints due to the randomness and volatility of new energy. Traditional microgrid [...] Read more.
Optimal energy transmission dispatching of microgrid systems involves complicated transmission energy allocation and battery charging/discharging management and remains a difficult and challenging research problem subject to complex operation conditions and action constraints due to the randomness and volatility of new energy. Traditional microgrid transmission dispatching mainly considers the matching of the demand side and the supply side from a macro perspective, without considering the impact of line loss. Therefore, a Hierarchical Deep Q-network (HDQN) approach for microgrid energy dispatching is proposed to address this issue. The approach takes the power flow of each line and the battery charging/discharging behavior as decision variables to minimize the system operation cost. The proposed approach employs a two-layer agent optimization architecture for simultaneously processing the discrete and continuous variables, with one agent making upper layer decisions on the charging and discharging behavior of the batteries, and the other agent making lower layer decisions on the transmission energy allocation for the line. The experimental results indicate that the proposed approach achieves better performance than existing approaches. Full article
(This article belongs to the Special Issue Advanced Technologies and Applications of Microgrids)
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24 pages, 12979 KiB  
Article
A Multi-Stage Constraint-Handling Multi-Objective Optimization Method for Resilient Microgrid Energy Management
by Yongjing Lv, Kaiwen Li, Hong Zhao and Hongtao Lei
Appl. Sci. 2024, 14(8), 3253; https://doi.org/10.3390/app14083253 - 12 Apr 2024
Viewed by 307
Abstract
In recent years, renewable energy has seen widespread application. However, due to its intermittent nature, there is a need to develop energy management systems for its scheduling and control. This paper introduces a multi-stage constraint-handling multi-objective optimization method tailored for resilient microgrid energy [...] Read more.
In recent years, renewable energy has seen widespread application. However, due to its intermittent nature, there is a need to develop energy management systems for its scheduling and control. This paper introduces a multi-stage constraint-handling multi-objective optimization method tailored for resilient microgrid energy management. The microgrid encompasses diesel generators, energy storage systems, renewable energy sources, and various load types. The intelligent management of generators, batteries, switchable loads, and controllable loads ensures a reliable power supply for the critical loads. Beyond operational costs, our model also considers grid dependency as a key objective, making it particularly suited for energy management in extreme environments such as islands, border regions, and military bases. Managing complex controls of generators, batteries, switchable loads, and controllable loads presents challenging constraints that the management strategy must meet. To tackle this challenge, we propose an multi-objective optimization algorithm with multi-stage constraint-handling strategy to handle the high-dimensional complex constraints of the resilient energy management problem. Our proposed approach demonstrates superior performance compared to nine leading constrained multi-objective optimization algorithms across various test scenarios. Furthermore, the benefits of our method become increasingly evident as the complexity of the problem increases. Compared to the classical NSGA-II, the proposed NSGA-II-MC method achieved a 49.7% improvement in the Hypervolume metric on large-scale problems. Full article
(This article belongs to the Special Issue Advanced Technologies and Applications of Microgrids)
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17 pages, 2014 KiB  
Article
Configuration Optimization of Mobile Photovoltaic-Diesel-Storage Microgrid System Based on CPS-MOEA
by Tianlong Li, Tao Zhang and Wenhua Li
Appl. Sci. 2024, 14(7), 3015; https://doi.org/10.3390/app14073015 - 03 Apr 2024
Viewed by 470
Abstract
This paper presents a two-step approach for optimizing the configuration of a mobile photovoltaic-diesel-storage microgrid system. Initially, we developed a planning configuration model to ensure a balance between the mobility of components and a sustainable power supply. Then, we introduced a method that [...] Read more.
This paper presents a two-step approach for optimizing the configuration of a mobile photovoltaic-diesel-storage microgrid system. Initially, we developed a planning configuration model to ensure a balance between the mobility of components and a sustainable power supply. Then, we introduced a method that merges optimization and decision-making. The first phase identifies Pareto optimal solutions (POSs) with a favorable distribution by using a multi-objective evolutionary algorithm with classification-based preselection (CPS-MOEA). In the second phase, we utilize the fuzzy C-means algorithm (FCM) and the grey relational projection (GRP) method for comprehensive decision-making. This aims to select the most suitable and compromise solution from the POSs, closely aligning with the decision-maker’s preferences. Beyond addressing the optimal planning and configuration issue, the experimental results show that the method surpasses other widely used multi-objective optimization algorithms, including the Preference Inspired Co-evolution Algorithm (PICEA-g), the Multi-Objective Particle Swarm Optimization Algorithm (MOPSO), and the third stage of Generalized Differential Evolution (GDE3). Full article
(This article belongs to the Special Issue Advanced Technologies and Applications of Microgrids)
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20 pages, 12920 KiB  
Article
Enhancing Renewable Energy Accommodation by Coupling Integration Optimization with Flexible Retrofitted Thermal Power Plant
by Yao Zou, Qinqin Xia, Yuan Chi and Qianggang Wang
Appl. Sci. 2024, 14(7), 2907; https://doi.org/10.3390/app14072907 - 29 Mar 2024
Viewed by 297
Abstract
Thermal power units (TPUs) play a crucial role in accommodating the high penetration of renewable energy sources (RESs) like wind turbines (WTs) and photovoltaics (PVs). This paper proposes an evaluation framework to quantitatively analyze the flexibility potential of retrofitted TPUs in enhancing the [...] Read more.
Thermal power units (TPUs) play a crucial role in accommodating the high penetration of renewable energy sources (RESs) like wind turbines (WTs) and photovoltaics (PVs). This paper proposes an evaluation framework to quantitatively analyze the flexibility potential of retrofitted TPUs in enhancing the accommodate capability of RESs through coupling integration and optimal scheduling. Firstly, the coordination framework for coupling TPUs with RESs is outlined, including a comprehensive analysis of benefits and implementation strategies. Secondly, an annual optimal scheduling model for TPUs and RESs is developed, incorporating deep peak regulation services, ladder-type constraints for retrofitted TPUs, and their operational characteristics before and after the coupling integration. Thirdly, indices to evaluate RES accommodation levels and TPU regulation capacities are proposed to quantify the performance of power sources. Finally, a real-world case study is conducted to demonstrate that integrating retrofitted TPUs with RESs through coupling significantly enhances RES utilization by 3.6% and boosts TPUs’ downward regulation capabilities by 32%. Full article
(This article belongs to the Special Issue Advanced Technologies and Applications of Microgrids)
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