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Smart Cities
Special Issues
Next Generation of Smart Grid Technologies
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Next Generation of Smart Grid Technologies

A special issue of Smart Cities (ISSN 2624-6511). This special issue belongs to the section "Smart Grids".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 2641

Special Issue Editors

Prof. Dr. Luis M. Fernández-Ramírez

E-Mail Website
Guest Editor
Research Group in Sustainable and Renewable Electrical Technologies (PAIDI-TEP023), Department of Electrical Engineering, Higher Technical School of Engineering of Algeciras, University of Cadiz, Algeciras, Spain
Interests: smart cities; smart grids; microgrids; renewable energy; wind energy; photovoltaic solar energy; energy storage systems; hydrogen and fuel cells; hybrid electric systems; electric vehicles; electric power systems; power converters and energy management/control systems
Special Issues, Collections and Topics in MDPI journals
Dr. Chun Sing Lai

E-Mail Website
Guest Editor
Department of Electronic and Electrical Engineering, Brunel University, London UB8 3PH, UK
Interests: smart cities; electric vehicles; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Payman Dehghanian

E-Mail Website
Guest Editor
The George Washington University, Department of Electrical and Computer Engineering, Washington, DC, USA
Interests: smart grid data analytics; smart grid resilience and reliability modeling and assessment; data analytics and big data science in smart grids; planning and operation of power grids with proliferation of renewable energy sources; applications of synchrophasor technology in electric power systems; asset management and maintenance scheduling in energy systems

Special Issue Information

Dear Colleagues,

The rise of smart cities necessitates a paradigm shift in energy management, placing smart grid technologies at the forefront of urban development. By integrating different energy devices (renewable energy sources, energy storage systems, green hydrogen, electric vehicles, etc.), grid-edge power electronics, and advanced control systems, information and communication technologies with traditional power grids, smart grids enable efficient, reliable, and sustainable energy utilization within urban environments. This Special Issue explores the transformative potential of smart grid technologies in shaping the future of smart cities.

This Special Issue is devoted to publishing new papers exploring innovative applications and advancements in smart grid technologies specifically tailored to smart city environments. It encompasses a diverse range of topics, including:

  • Microgrid integration and distributed energy resources—examining the seamless integration of renewable energy sources and distributed energy resources like rooftop solar, energy storage systems, and electric vehicles into the urban grid, maximizing local energy generation and reducing reliance on centralized power plants, or integration of microgrid clusters, energy communities, and multi-energy microgrids (combining different energy vectors like electricity, hydrogen, heating/cooling, etc.) into smart cities.
  • Demand-side management (DSM) and energy efficiency—exploring strategies to optimize energy consumption in buildings, industries, and transportation systems based on real-time data and dynamic pricing mechanisms.
  • Cybersecurity and data privacy—addressing the growing challenge of cyberattacks on smart grids and implementing robust security measures to protect critical infrastructure and user data.
  • Data analytics and artificial intelligence (AI) —utilizing data analytics and AI algorithms to optimize grid operations, predict demand fluctuations, and enable real-time decision-making for efficient energy management.

In this Special Issue, we encourage researchers involved in smart grid technologies for smart cities to discuss key topics in the field and submit innovative papers that make significant contributions over existing literature in the field of smart grid technologies for smart cities. We expect these papers to be widely read and highly influential within the field.

The subject areas may include, but are not limited to, the following:

  • smart microgrids for smart cities;
  • smart grids in buildings (residential, commercial, and industrial) and transport;
  • design, planification and operation of smart grids for smart cities;
  • intelligent energy storage for smart cities;
  • demand forecasting and weather forecasting;
  • efficient demand-side management;
  • intelligent control and energy management systems of smart grids;
  • digital twins of smart cities for smart cities;
  • grid stability, reliability, and resilience of smart cities;
  • market operations in smart microgrids for smart cities;
  • microgrid clusters;
  • multi-energy microgrids;
  • energy communities;
  • power converters;
  • control and communication devices;
  • information and communication technologies;
  • cybersecurity and data privacy;
  • data analytics and artificial intelligence.

Prof. Dr. Luis M. Fernández-Ramírez
Dr. Chun Sing Lai
Dr. Payman Dehghanian
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. Smart Cities 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 2000 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 energies
  • energy storage systems
  • energy communities
  • demand forecast
  • weather forecast
  • demand-side management
  • intelligent control and energy management systems
  • digital twins
  • grid stability and reliability
  • market operation
  • power converters
  • information and communication technologies
  • cybersecurity
  • data analytics
  • artificial intelligence

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

26 pages, 2755 KiB  
Article
A Retrieval-Augmented Generation Approach for Data-Driven Energy Infrastructure Digital Twins
by Saverio Ieva, Davide Loconte, Giuseppe Loseto, Michele Ruta, Floriano Scioscia, Davide Marche and Marianna Notarnicola
Smart Cities 2024, 7(6), 3095-3120; https://doi.org/10.3390/smartcities7060121 - 24 Oct 2024
Cited by 1 | Viewed by 808
Abstract
Digital-twin platforms are increasingly adopted in energy infrastructure management for smart grids. Novel opportunities arise from emerging artificial intelligence technologies to increase user trust by enhancing predictive and prescriptive analytics capabilities and by improving user interaction paradigms. This paper presents a novel data-driven [...] Read more.
Digital-twin platforms are increasingly adopted in energy infrastructure management for smart grids. Novel opportunities arise from emerging artificial intelligence technologies to increase user trust by enhancing predictive and prescriptive analytics capabilities and by improving user interaction paradigms. This paper presents a novel data-driven and knowledge-based energy digital-twin framework and architecture. Data integration and mining based on machine learning are integrated into a knowledge graph annotating asset status data, prediction outcomes, and background domain knowledge in order to support a retrieval-augmented generation approach, which enhances a conversational virtual assistant based on a large language model to provide user decision support in asset management and maintenance. Components of the proposed architecture have been mapped to commercial-off-the-shelf tools to implement a prototype framework, exploited in a case study on the management of a section of the high-voltage energy infrastructure in central Italy. Full article
(This article belongs to the Special Issue Next Generation of Smart Grid Technologies)
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29 pages, 6639 KiB  
Article
Advancing Electric Load Forecasting: Leveraging Federated Learning for Distributed, Non-Stationary, and Discontinuous Time Series
by Lucas Richter, Steve Lenk and Peter Bretschneider
Smart Cities 2024, 7(4), 2065-2093; https://doi.org/10.3390/smartcities7040082 - 28 Jul 2024
Viewed by 1034
Abstract
In line with several European directives, residents are strongly encouraged to invest in renewable power plants and flexible consumption systems, enabling them to share energy within their Renewable Energy Community at lower procurement costs. This, along with the ability for residents to switch [...] Read more.
In line with several European directives, residents are strongly encouraged to invest in renewable power plants and flexible consumption systems, enabling them to share energy within their Renewable Energy Community at lower procurement costs. This, along with the ability for residents to switch between such communities on a daily basis, leads to dynamic portfolios, resulting in non-stationary and discontinuous electrical load time series. Given poor predictability as well as insufficient examination of such characteristics, and the critical importance of electrical load forecasting in energy management systems, we propose a novel forecasting framework using Federated Learning to leverage information from multiple distributed communities, enabling the learning of domain-invariant features. To achieve this, we initially utilize synthetic electrical load time series at district level and aggregate them to profiles of Renewable Energy Communities with dynamic portfolios. Subsequently, we develop a forecasting model that accounts for the composition of residents of a Renewable Energy Community, adapt data pre-processing in accordance with the time series process, and detail a federated learning algorithm that incorporates weight averaging and data sharing. Following the training of various experimental setups, we evaluate their effectiveness by applying different tests for white noise in the forecast error signal. The findings suggest that our proposed framework is capable of effectively forecast non-stationary as well as discontinuous time series, extract domain-invariant features, and is applicable to new, unseen data through the integration of knowledge from multiple sources. Full article
(This article belongs to the Special Issue Next Generation of Smart Grid Technologies)
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