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Electronics
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Integration of Distributed Intelligent Energy Grid
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Integration of Distributed Intelligent Energy Grid

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

Deadline for manuscript submissions: closed (15 May 2020) | Viewed by 25061

Special Issue Editor

Dr. David (Bong Jun) Choi

E-Mail Website
Guest Editor
School of Computer Science and Engineering & School of Electronic Engineering, Soongsil University, Seoul, Korea
Interests: Internet of Things; distributed intelligent energy grid; distributed algorithms; communication networks; network security

Special Issue Information

Dear Colleagues,

The smart grid is revolutionizing the way we produce, transmit, and consume energy via the two-way flow of electricity and information to realize clean, affordable, and reliable energy. All sectors of society are fueled by electrical energy and becoming more tightly integrated with the IoT. Moreover, with the rise of distributed generation, increase in the use of information and communication technology, increase in the amount of data, and increase in consumer involvement, a complex relationship among various producers, markets, and consumers of the energy grid is inevitable. Therefore, the future energy grid should not only be efficient and resilient but also support large-scale integration and orchestration of fully automated distributed devices.

The main aim of this Special Issue is to solicit high-quality research articles proposing novel and practical state-of-the-art solutions that integrate various components and consider the interests of different actors of the system to build more intelligent, responsive, and secure energy grid. Topics of interest include but are not limited to the following:

  • Data science techniques
  • Distributed artificial intelligence techniques
  • Multiagent optimization techniques
  • Game theory and competition strategies
  • Integration with IoT
  • Integration with edge computing
  • Integration with distributed energy storages/sources
  • Integration with distributed data centers
  • Integration with electric vehicles

Dr. David (Bong Jun) Choi
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. Electronics 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

  • IoT
  • data science
  • distributed artificial intelligence
  • edge computing
  • distributed algorithm
  • distributed optimization

Published Papers (3 papers)

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Research

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19 pages, 5194 KiB  
Article
Combining Machine Learning Analysis and Incentive-Based Genetic Algorithms to Optimise Energy District Renewable Self-Consumption in Demand-Response Programs
by Vincenzo Croce, Giuseppe Raveduto, Matteo Verber and Denisa Ziu
Electronics 2020, 9(6), 945; https://doi.org/10.3390/electronics9060945 - 06 Jun 2020
Cited by 6 | Viewed by 3287
Abstract
The recent rise of renewable energy sources connected to the distribution networks and the high peak consumptions requested by electric vehicle-charging bring new challenges for network operators. To operate smart electricity grids, cooperation between grid-owned and third-party assets becomes crucial. In this paper, [...] Read more.
The recent rise of renewable energy sources connected to the distribution networks and the high peak consumptions requested by electric vehicle-charging bring new challenges for network operators. To operate smart electricity grids, cooperation between grid-owned and third-party assets becomes crucial. In this paper, we propose a methodology that combines machine learning with multi-objective optimization to accurately plan the exploitation of the energy district’s flexibility with the objective of reducing peak consumption and avoiding reverse power flow. Using historical data, acquired by the smart meters deployed on the pilot district, the district’s power profile can be predicted daily and analyzed to identify potentially critical issues on the network. District’s resources, such as electric vehicles, charging stations, photovoltaic panels, buildings energy management systems, and energy storage systems, have been modeled by taking into account their operational constraints and the multi-objective optimization has been adopted to identify the usage pattern that better suits the distribution operator’s (DSO) needs. The district is subject to incentives and penalties based on its ability to respond to the DSO request. Analysis of the results shows that this methodology can lead to a substantial reduction of both the reverse power flow and peak consumption. Full article
(This article belongs to the Special Issue Integration of Distributed Intelligent Energy Grid)
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Review

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25 pages, 723 KiB  
Review
State-of-the-Art Artificial Intelligence Techniques for Distributed Smart Grids: A Review
by Syed Saqib Ali and Bong Jun Choi
Electronics 2020, 9(6), 1030; https://doi.org/10.3390/electronics9061030 - 22 Jun 2020
Cited by 139 | Viewed by 13532
Abstract
The power system worldwide is going through a revolutionary transformation due to the integration with various distributed components, including advanced metering infrastructure, communication infrastructure, distributed energy resources, and electric vehicles, to improve the reliability, energy efficiency, management, and security of the future power [...] Read more.
The power system worldwide is going through a revolutionary transformation due to the integration with various distributed components, including advanced metering infrastructure, communication infrastructure, distributed energy resources, and electric vehicles, to improve the reliability, energy efficiency, management, and security of the future power system. These components are becoming more tightly integrated with IoT. They are expected to generate a vast amount of data to support various applications in the smart grid, such as distributed energy management, generation forecasting, grid health monitoring, fault detection, home energy management, etc. With these new components and information, artificial intelligence techniques can be applied to automate and further improve the performance of the smart grid. In this paper, we provide a comprehensive review of the state-of-the-art artificial intelligence techniques to support various applications in a distributed smart grid. In particular, we discuss how artificial techniques are applied to support the integration of renewable energy resources, the integration of energy storage systems, demand response, management of the grid and home energy, and security. As the smart grid involves various actors, such as energy produces, markets, and consumers, we also discuss how artificial intelligence and market liberalization can potentially help to increase the overall social welfare of the grid. Finally, we provide further research challenges for large-scale integration and orchestration of automated distributed devices to realize a truly smart grid. Full article
(This article belongs to the Special Issue Integration of Distributed Intelligent Energy Grid)
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16 pages, 615 KiB  
Review
A Survey on the Micro-Phasor Measurement Unit in Distribution Networks
by Emile Dusabimana and Sung-Guk Yoon
Electronics 2020, 9(2), 305; https://doi.org/10.3390/electronics9020305 - 10 Feb 2020
Cited by 61 | Viewed by 7672
Abstract
The Micro-Phasor Measurement Unit ( μ PMU) or distribution-level PMU (D-PMU) is a measurement device that measures the synchronized voltage and current values of electric power distribution networks. The synchronized data obtained by μ PMUs can be used for monitoring, diagnostic, and control [...] Read more.
The Micro-Phasor Measurement Unit ( μ PMU) or distribution-level PMU (D-PMU) is a measurement device that measures the synchronized voltage and current values of electric power distribution networks. The synchronized data obtained by μ PMUs can be used for monitoring, diagnostic, and control distribution network applications, so that operators can understand the dynamic states of the distribution network in real-time. In this paper, we review the state-of-the-art μ PMU research which includes a list of μ PMU applications, monitoring and diagnostic functions, control applications, and optimal placement of the μ PMU. In addition, we analyze the benefits of μ PMUs in distribution networks; in particular, their reliability and resiliency, cost savings, and environmental and policy benefits. Full article
(This article belongs to the Special Issue Integration of Distributed Intelligent Energy Grid)
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