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Recent Advances in Applications of Smart Grid Technologies

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 1389

Special Issue Editors


E-Mail Website
Guest Editor
Department of Engineering, University of Palermo, 90128 Palermo, Italy
Interests: smart grids; power cables fault detection; transactive energy; energy blockchain; vehicle-to-grid; optimization in smart grids
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Engineering, University of Palermo, Building 9, 90128 Palermo, Italy
Interests: smart grids; energy blockchain; vehicle-to-grid; demand response; energy services; renewables integration in power system; energy communities
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Engineering, University of Palermo, Building 9, 90128 Palermo, Italy
Interests: smart grids; vehicle-to-grid; demand response; energy services; renewables integration in power system; energy communities; grounding systems; islanded systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart grids are electricity systems that integrate advanced communication, control, and monitoring technologies to optimize the efficiency, safety, and reliability of electricity distribution. Smart grids incorporate digital devices and sensors along the power grid that enable two-way communication between users and energy providers. Nowadays, due to the increasing implementation of renewable sources as well as massive digitalization, these devices and technologies are becoming more widespread and expanding in use and capability, thus allowing for improved control of energy resources and the active participation of end-users.

For this reason, we invite you to submit your contributions to this Special Issue titled ‘Recent Advances in and Applications of Smart Grid Technologies’.

Areas of interest include (but are not limited to) the following topics:

  • Advanced Metering Infrastructure (AMI): for two-way communication between utilities and consumers, enabling real-time monitoring and management of energy use.
  • Distributed Energy Resources (DERs): the management and control of electricity systems in relation to the variability and discontinuity of these renewable energy sources, with the aim of optimizing their use.
  • Demand Side Management Programs and User Experience: the development of new energy demand automation programs to incentivize consumers to change their electricity use in response to supply conditions or grid constraints; contributions developed together with SSH experts are also welcome.
  • Electric Vehicle (EV) Integration: the integration of electric vehicle charging infrastructure by managing charging patterns, optimizing load distribution, and coordinating charging sessions to avoid network overload.
  • Data Analytics and Artificial Intelligence (AI): new technologies that help utilities optimize network operations, predict equipment failures, detect anomalies, and improve overall system efficiency.
  • Cybersecurity: with the increasing digitalization and connectivity of network infrastructure, cyber threat protection measures are needed to ensure the integrity and reliability of network operations

We are looking forward to receiving your contributions.

Prof. Dr. Eleonora Riva Sanseverino
Dr. Gaetano Zizzo
Dr. Giuseppe Sciumè
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

  • smart grids
  • AMI
  • DERs
  • demand side management
  • electric vehicle integration
  • cybersecurity

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Published Papers (1 paper)

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Research

22 pages, 4692 KiB  
Article
Refining Long Short-Term Memory Neural Network Input Parameters for Enhanced Solar Power Forecasting
by Linh Bui Duy, Ninh Nguyen Quang, Binh Doan Van, Eleonora Riva Sanseverino, Quynh Tran Thi Tu, Hang Le Thi Thuy, Sang Le Quang, Thinh Le Cong and Huyen Cu Thi Thanh
Energies 2024, 17(16), 4174; https://doi.org/10.3390/en17164174 - 22 Aug 2024
Viewed by 849
Abstract
This article presents a research approach to enhancing the quality of short-term power output forecasting models for photovoltaic plants using a Long Short-Term Memory (LSTM) recurrent neural network. Typically, time-related indicators are used as inputs for forecasting models of PV generators. However, this [...] Read more.
This article presents a research approach to enhancing the quality of short-term power output forecasting models for photovoltaic plants using a Long Short-Term Memory (LSTM) recurrent neural network. Typically, time-related indicators are used as inputs for forecasting models of PV generators. However, this study proposes replacing the time-related inputs with clear sky solar irradiance at the specific location of the power plant. This feature represents the maximum potential solar radiation that can be received at that particular location on Earth. The Ineichen/Perez model is then employed to calculate the solar irradiance. To evaluate the effectiveness of this approach, the forecasting model incorporating this new input was trained and the results were compared with those obtained from previously published models. The results show a reduction in the Mean Absolute Percentage Error (MAPE) from 3.491% to 2.766%, indicating a 24% improvement. Additionally, the Root Mean Square Error (RMSE) decreased by approximately 0.991 MW, resulting in a 45% improvement. These results demonstrate that this approach is an effective solution for enhancing the accuracy of solar power output forecasting while reducing the number of input variables. Full article
(This article belongs to the Special Issue Recent Advances in Applications of Smart Grid Technologies)
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