Grid Innovation in the Era of Smart Grids, Volume Ⅱ

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: closed (20 November 2023) | Viewed by 5687

Special Issue Editor


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Guest Editor
Dipartimento di Ingegneria Elettrica ed Elettronica, Università degli Studi di Cagliari, 09123 Cagliari, Italy
Interests: distribution network planning and operation; smart grids; microgrids; renewable energy sources; energy storage systems; demand-side integration policies
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Special Issue Information

Dear Colleagues,

Today, the conditions are particularly suitable and affordable for an industry transition to distributed energy, and smart grids are the enablers for a new era in sustainable energy. Electric utilities are undergoing a significant transformation and face new challenges and expectations to foster the energy transition and decarbonization with innovative network structures, control schemes, and methodologies. Grid innovation has become crucial, and new technologies will be the key to a smooth transition. In the smart grid era, innovative solutions and technologies for creating intelligent, flexible, and efficient power networks are needed. Thus, distribution networks need to become more flexible and intelligent so distributed energy resources (DERs) and network elements can be adequately managed. These challenges require new tools, strategies, and technologies to manage the transition to the distributed grids of tomorrow and to improve grid reliability and resiliency.

You are invited to submit original contributions that promote exploratory research and development of electricity grid innovation while addressing the challenge of a reliable and sustainable distribution system.

This Special Issue focuses on recent research and technology improvements including, but not limited to:

  • Innovation in the electricity grid
  • New planning tools for distribution networks
  • Utility perspective
  • Innovation to manage grid stability with DERs and electric vehicles
  • Role of energy storage in smart grids
  • Communication infrastructure for smart grids
  • Maintenance and management of smart grids
  • Technologies for grid maintenance
  • Next generation enablers of smart grids
  • Increased reliability of smart grids
  • Blockchain to enable customer engagement

Dr. Susanna Mocci
Guest Editor

Manuscript Submission Information

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Published Papers (2 papers)

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Research

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21 pages, 10226 KiB  
Article
Passive Power Line Communication Filter Design and Benchmarking Using Scattering Parameters
by Sebastian Avram and Radu Vasiu
Appl. Sci. 2023, 13(11), 6821; https://doi.org/10.3390/app13116821 - 04 Jun 2023
Cited by 2 | Viewed by 1849
Abstract
NB-PLC (narrowband power line communication) is a method of data communication that involves superimposing a relatively high-frequency signal (9 kHz to 500 kHz), which contains data, onto the power grid’s low frequency (50 to 60 Hz) signal. While using the existing power grid [...] Read more.
NB-PLC (narrowband power line communication) is a method of data communication that involves superimposing a relatively high-frequency signal (9 kHz to 500 kHz), which contains data, onto the power grid’s low frequency (50 to 60 Hz) signal. While using the existing power grid as a transmission medium is convenient, the power grid was not designed for this purpose, leading to challenges such as conducted emissions and infrastructure limitations. To overcome these technical challenges, passive filters are necessary. This article presents the design, simulation (using scattering parameters), and evaluation of an NB-PLC filter by comparing it to commercially available filters. Our proposed design and benchmarking methods enable the accurate prediction of the filter’s behavior in field conditions. After comparing our filter with commercially available filters, we observed that it exhibits superior characteristics. Specifically, our filter has the best insertion loss versus frequency, achieved three times higher attenuation at 50 kHz (−130 dB) compared to the best commercially available filter (−40 dB), and has a power consumption of 0.6 W, which is comparable to the most power-efficient commercial filter (0.5 W). Additionally, our filter has the second best input and output impedance of 3.6 Ω within the frequency range of 35–95 kHz. Full article
(This article belongs to the Special Issue Grid Innovation in the Era of Smart Grids, Volume Ⅱ)
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Review

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24 pages, 11455 KiB  
Review
A Review of Knowledge Graph-Based Reasoning Technology in the Operation of Power Systems
by Rui Liu, Rong Fu, Kang Xu, Xuanzhe Shi and Xiaoning Ren
Appl. Sci. 2023, 13(7), 4357; https://doi.org/10.3390/app13074357 - 29 Mar 2023
Cited by 4 | Viewed by 3118
Abstract
Knowledge graph (KG) technology is a newly emerged knowledge representation method in the field of artificial intelligence. Knowledge graphs can form logical mappings from cluttered data and establish triadic relationships between entities. Accurate derivation and reasoning of knowledge graphs play an important role [...] Read more.
Knowledge graph (KG) technology is a newly emerged knowledge representation method in the field of artificial intelligence. Knowledge graphs can form logical mappings from cluttered data and establish triadic relationships between entities. Accurate derivation and reasoning of knowledge graphs play an important role in guiding power equipment operation and decision-making. Due to the complex and weak relations from multi-source heterogeneous data, the use of KGs has become popular in research to represent potential information in power knowledge reasoning. In this review, we first summarize the key technologies of knowledge graph representation and learning. Then, based on the complexity and real-time changes of power system operation and maintenance, we present multiple data processing, knowledge representation learning, and the graph construction process. In three typical power operation and fault decision application scenarios, we investigate current algorithms in power KG acquisition, representation embedding, and knowledge completion to illustrate accurate and exhaustive recommendations. Thus, using KGs to provide reference solutions and decision guidance has a significant role in improving the efficiency of power system operations. Finally, we summarize the achievements and difficulties of current research and give an outlook for future, promising roles of KG in power systems. Full article
(This article belongs to the Special Issue Grid Innovation in the Era of Smart Grids, Volume Ⅱ)
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