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Energies
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New Insights into Intelligent Microgrids and Distributed Energy Systems
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New Insights into Intelligent Microgrids and Distributed Energy Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 2261

Special Issue Editors

Dr. Guodong Liu

E-Mail Website
Guest Editor
Electrification and Energy Infrastures Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830-6479, USA
Interests: microgrids; grid reliability and resilience
Dr. Yang Chen

E-Mail Website
Guest Editor
Department of Industrial and Systems Engineering, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA
Interests: local energy market design; power system restoration and resilience; machine learning
Dr. Lei Zhang

E-Mail Website
Guest Editor
The College of Electrical Engineering and New Energy, China Three Gorges University, Yichang, NC 43000, China
Interests: power system frequency control; active distribution network; multi-energy microgrid; integrated energy systems

Special Issue Information

Dear Colleagues,

Microgrids are low-voltage distribution systems comprising various distributed energy resources (DERs) and energy storage systems (ESSs) that are colocated with loads, and have the ability to automatically transform from grid-connected mode into islanded mode. Microgrids introduce many unique opportunities, including enhancing grid resiliency, improving the reliability of power supply, integrating renewable energy resources, reducing carbon emissions, improving energy efficiency, delaying investment in power system expansion, participating in voltage and frequency regulation, and encouraging customer interactions.

Microgrids provide an option for achieving resilient electricity supply in a clean and sustainable way. Nevertheless, large-scale deployments of microgrids have various barriers, such as affordability, control of DERs, coordination of internal DERs with neighborhood microgrids and utility grids, and policy and regulatory issues, etc. To solve these issues and facilitate the deployment of microgrids and distributed energy systems, this Special Issue focuses on the design, operation, control, implementation, and interconnection of future intelligent microgrids and distributed energy systems. Submissions are invited from researchers and practitioners working in related areas to promote a venue for cutting-edge fundamental and applied research related to future intelligent microgrids and distributed energy systems.

Dr. Guodong Liu
Dr. Yang Chen
Dr. Lei Zhang
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

  • microgrids and sustainable communities
  • distributed energy resources (DERs)
  • local energy systems
  • distributed control and operation
  • artificial intelligence (AI)-supported decision making for microgrids
  • grid resilience technologies
  • automation, control, and optimisation for microgrids and smart grids
  • modelling and simulation techniques for microgrids
  • energy management systems and advanced control
  • networked microgrid interactions and controllers
  • regulatory and technoeconomic aspects of microgrids

Published Papers (2 papers)

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Research

25 pages, 3816 KiB  
Article
A Machine Learning-Based Electricity Consumption Forecast and Management System for Renewable Energy Communities
by Miguel Matos, João Almeida, Pedro Gonçalves, Fabiano Baldo, Fernando José Braz and Paulo C. Bartolomeu
Energies 2024, 17(3), 630; https://doi.org/10.3390/en17030630 - 28 Jan 2024
Viewed by 979
Abstract
The energy sector is currently undergoing a significant shift, driven by the growing integration of renewable energy sources and the decentralization of electricity markets, which are now extending into local communities. This transformation highlights the pivotal role of prosumers within these markets, and [...] Read more.
The energy sector is currently undergoing a significant shift, driven by the growing integration of renewable energy sources and the decentralization of electricity markets, which are now extending into local communities. This transformation highlights the pivotal role of prosumers within these markets, and as a result, the concept of Renewable Energy Communities is gaining traction, empowering their members to curtail reliance on non-renewable energy sources by facilitating local energy generation, storage, and exchange. Also in a community, management efficiency depends on being able to predict future consumption to make decisions regarding the purchase, sale and storage of electricity, which is why forecasting the consumption of community members is extremely important. This study presents an innovative approach to manage community energy balance, relying on Machine Learning (ML) techniques, namely eXtreme Gradient Boosting (XGBoost), to forecast electricity consumption. Subsequently, a decision algorithm is employed for energy trading with the public grid, based on solar production and energy consumption forecasts, storage levels and market electricity prices. The outcomes of the simulated model demonstrate the efficacy of incorporating these techniques, since the system showcases the potential to reduce both the community electricity expenses and its dependence on energy from the centralized distribution grid. ML-based techniques allowed better results specially for bi-hourly tariffs and high storage capacity scenarios with community bill reductions of 9.8%, 2.8% and 5.4% for high, low, and average photovoltaic (PV) generation levels, respectively. Full article
(This article belongs to the Special Issue New Insights into Intelligent Microgrids and Distributed Energy Systems)
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12 pages, 2894 KiB  
Article
A Carbon Reduction and Waste Heat Utilization Strategy for Generators in Scalable PV—Diesel Generator Campus Microgrids
by Stephanus Erasmus and Jacques Maritz
Energies 2023, 16(18), 6749; https://doi.org/10.3390/en16186749 - 21 Sep 2023
Cited by 1 | Viewed by 832
Abstract
The increased unavailability of electricity from the National Utility in South Africa, coupled with the extreme conditions of rural areas and general lack of infrastructure, leads to the setup of unique microgrids to utilize the conditions available. One such unique microgrid, a scalable [...] Read more.
The increased unavailability of electricity from the National Utility in South Africa, coupled with the extreme conditions of rural areas and general lack of infrastructure, leads to the setup of unique microgrids to utilize the conditions available. One such unique microgrid, a scalable photovoltaic (PV)-Diesel generator microgrid, is situated in the Phuthaditjhaba district on the University of the Free State (UFS) Qwaqwa campus in South Africa. Waste heat and greenhouse gas (GHG) emissions are considered inherent by-products of campus hybrid PV—Diesel generator microgrids with high utilization opportunities for both heat exchange and carbon offsets. This paper presents confirmation that available waste heat from a typical rural campus microgrid can be stored through the use of a rock bed thermal energy storage (TES) system. It was identified that, through the temperature profile of the stored waste heat, thermal energy can be utilized through deferable (time-independent) and non-deferable (time-dependent) strategies. Both utilization strategies are dependent on the type of application or applications chosen through demand-side management. Carbon emission reduction takes place through the reduction of diesel consumption due to the utilization of waste heat for applications previously served by diesel generators. Design novelties are presented using the concept of rock bed TES within a microgrid setup. Full article
(This article belongs to the Special Issue New Insights into Intelligent Microgrids and Distributed Energy Systems)
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