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4.8
Journals
Electricity
Special Issues
Feature Papers to Celebrate the ESCI Coverage
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Feature Papers to Celebrate the ESCI Coverage

A special issue of Electricity (ISSN 2673-4826).

Deadline for manuscript submissions: 28 February 2025 | Viewed by 2891

Special Issue Editor

Prof. Dr. Andreas Sumper

E-Mail Website
Guest Editor
CITCEA-UPC, Department of Electrical Engineering, Universitat Politecnica de Catalunya, 08028 Barcelona, Spain
Interests: electrical engineering; energy efficiency; power grid; renewable energy; smart grid; digital energy; artificial intelligence; electric cars charging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce that Electricity (ISSN: 2673-4826) has received its first CiteScore of 4.8. Electricity’s CiteScore ranks as Q2 in the “Electrical and Electronic Engineering” category and Q2 in the “Energy” category. This milestone reflects the high quality of the journal's publications and the increasing attention that Electricity's papers are receiving. This success is due to the streamlined editorial process, the rigorous scientific review process and the journal's core values of transparency, fairness and professionalism.

To celebrate this achievement, we are launching a Special Issue to publish high-quality papers from Editorial Board Members and leading researchers invited by the editorial team. Submissions can include both long research papers and papers describing the state of the art in any of the areas covered by the journal. All papers will be published openly and free of charge.

Join us in this celebration by contributing your work to this Special Issue and help us continue to advance the field of electrical engineering.

Prof. Dr. Andreas Sumper
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. Electricity is an international peer-reviewed open access quarterly 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 1000 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

  • electric infrastructure and applications
  • electric power systems
  • electric transportation
  • electricity storage
  • energy conversion
  • power markets and economics
  • smart grids and smart electricity applications
  • sustainable and green electricity

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

15 pages, 2695 KiB  
Article
The Total Cost of Reliable Electricity Distribution
by Joel Seppälä, Joonas Kari and Pertti Järventausta
Electricity 2024, 5(4), 916-930; https://doi.org/10.3390/electricity5040046 - 21 Nov 2024
Viewed by 439
Abstract
Clean transition increases the demand for reliable electricity distribution, but while the capacity can be improved through investments, responding to the demand increases costs for the customers. This study presents a methodological improvement to the assessment of the reasonability of pricing, by comprehensively [...] Read more.
Clean transition increases the demand for reliable electricity distribution, but while the capacity can be improved through investments, responding to the demand increases costs for the customers. This study presents a methodological improvement to the assessment of the reasonability of pricing, by comprehensively analyzing pricing regulation data to define the total cost of electricity distribution by clustering. A novel systematic view on the volume and distribution of economic steering shows that according to the regulation data in Finland, the total annual cost of distribution for the present level of reliability varies from EUR 490/a in an urban environment to EUR 1220/a per customer in sparsely populated areas. The majority of the total costs of distribution stem from actual utility expenses. The approach and results may be used for implementing TOTEX models for future pricing regulation. Full article
(This article belongs to the Special Issue Feature Papers to Celebrate the ESCI Coverage)
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18 pages, 9214 KiB  
Article
Harnessing Deep Learning for Enhanced MPPT in Solar PV Systems: An LSTM Approach Using Real-World Data
by Bappa Roy, Shuma Adhikari, Subir Datta, Kharibam Jilenkumari Devi, Aribam Deleena Devi and Taha Selim Ustun
Electricity 2024, 5(4), 843-860; https://doi.org/10.3390/electricity5040042 - 4 Nov 2024
Viewed by 922
Abstract
Maximum Power Point Tracking (MPPT) is essential for maximizing the efficiency of solar photovoltaic (PV) systems. While numerous MPPT methods exist, practical implementations often lean towards conventional techniques due to their simplicity. However, these traditional methods can struggle with rapid fluctuations in solar [...] Read more.
Maximum Power Point Tracking (MPPT) is essential for maximizing the efficiency of solar photovoltaic (PV) systems. While numerous MPPT methods exist, practical implementations often lean towards conventional techniques due to their simplicity. However, these traditional methods can struggle with rapid fluctuations in solar irradiance and temperature. This paper introduces a novel deep learning-based MPPT algorithm that leverages a Long Short-Term Memory (LSTM) deep neural network (DNN) to effectively track maximum power from solar PV panels, utilizing real-world data. The simulations of three algorithms—Perturb and Observe (P&O), Artificial Neural Network (ANN), and the proposed LSTM-based MPPT—were conducted using MATLAB (2021b) and RT_LAB (24.3.3) with an OPAL-RT simulator for real-time analysis. The data used for this study were sourced from NASA/POWER’s Native Resolution Daily Data of solar irradiation and temperature specific to Imphal, Manipur, India. The obtained results demonstrate that the LSTM-based MPPT system achieves a superior power tracking accuracy under changing solar conditions, producing an average output of 74 W. In comparison, the ANN and P&O methods yield average outputs of 57 W and 62 W, respectively. This significant improvement, i.e., 20–30%, underscores the effectiveness of the LSTM technique in enhancing the power output of solar PV systems. By incorporating real-world data, valuable insights into solar power generation specific to the selected location are provided. Furthermore, the outputs of the model were verified through real-time simulations using the OPAL-RT simulator OP4510, showcasing the practical applicability of this approach in real-world scenarios. Full article
(This article belongs to the Special Issue Feature Papers to Celebrate the ESCI Coverage)
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16 pages, 2347 KiB  
Article
Impact of Weather Conditions on Reliability Indicators of Low-Voltage Cable Lines
by Kornelia Banasik and Andrzej Łukasz Chojnacki
Electricity 2024, 5(3), 606-621; https://doi.org/10.3390/electricity5030030 - 4 Sep 2024
Viewed by 1009
Abstract
This article examines the impact of meteorological conditions represented by ambient temperature, ambient humidity, wind speed, and daily precipitation sum on the reliability of low-voltage cable lines. Cable line reliability is crucial to the stability and safety of power systems. Failure of cable [...] Read more.
This article examines the impact of meteorological conditions represented by ambient temperature, ambient humidity, wind speed, and daily precipitation sum on the reliability of low-voltage cable lines. Cable line reliability is crucial to the stability and safety of power systems. Failure of cable lines can lead to power outages. This can cause serious economic and social consequences, as well as threaten human safety, especially in the public sector and critical infrastructure. In addition, any interruption of cable lines generates costs related to repairs, operational losses, and possible contractual penalties. This is why it is so important to investigate the causes of power equipment failures. Many power system failures are caused by weather factors. The main purpose of this article is to quantify the actual impact of weather conditions on the performance and reliability of power equipment in distribution networks. Reliability indicators (failure rate, failure duration, restoration rate, and failure coefficient) for low-voltage cable lines were calculated as a function of weather conditions. Empirical values of the indicators were determined based on many years of observations of power lines operating in the Polish power system. An analysis of the conformity of their empirical distribution with the assumed theoretical model was also conducted. By quantifying the impact of specific weather factors on the operation of power equipment, it becomes possible to identify the ranges in which failures are most likely. Full article
(This article belongs to the Special Issue Feature Papers to Celebrate the ESCI Coverage)
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