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Energy Efficiency in Power Lines

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 8648

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Special Issue Editors

Dr. Pablo Castro

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Guest Editor

Mechanical Engineering, Energy intensification, Electric and Energy Engineering Department, University of Cantabria, 39005 Santander, Spain
Interests: high temperature measurements; energy efficiency in power lines and numerical heat transfer

Dr. Raquel Martínez

E-Mail Website
Guest Editor

Electric and Energy Engineering Department, University of Cantabria, Santander, 39005, Spain
Interests: dynamic line rating; power quality; energy efficiency; transformers ferroresonance; transformers maintenance

Dr. Alberto Laso

E-Mail Website
Guest Editor

Electric and Energy Engineering Department, University of Cantabria, Santander, 39005, Spain.
Interests: smart grids; renewable energy integration; power lines

Special Issue Information

Dear Colleagues,

Increasing the energy efficiency in power lines has become a critical issue to reach the goal of energy sustainability in upcoming decades. The addition of new renewable capacity and distributed generation and storage are challenging the electric grid with new and more complex requirements.

Power lines should increase the intelligence of their systems in order to become real smart grids. This implies new sensors and equipment, more data acquisition combined with big data analysis and new ways of grid management.

Furthermore, the electrification path to achieve a better sustainability in heat, transport and green energy production will make the efficiency of the main carrier, the electricity through the power lines, to become a very important issue in the next years.

All these challenges will bring a lot of interest from researchers, academics and industry. This Special Issue includes, but is not limited, to the following topics:

Renewable energy integration;
Dynamic line rating;
New sensors, equipment and data acquisition of electric grid parameters;
Good practices in power lines management;
Energy backup systems;
Electric vehicle integration;
Smart grids;
Distributed energy production;
Energy storage systems;
Equipment digitization;
Electric grid management;
Big data analysis;
State of the art reviews of these topics are also welcomed;

Dr. Pablo Castro
Dr. Raquel Martinez
Dr. Alberto Laso
Guest Editors

Manuscript Submission Information

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Keywords

renewable energy integration
dynamic line rating
energy backup systems
electric vehicle
smart grids
distributed energy production
energy storage systems
electric grid management
big data analysis

Published Papers (4 papers)

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Research

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14 pages, 5010 KiB

Open AccessArticle

Power Factor Prediction in Three Phase Electrical Power Systems Using Machine Learning

by José Manuel Gámez Medina, Jorge de la Torre y Ramos, Francisco Eneldo López Monteagudo, Leticia del Carmen Ríos Rodríguez, Diego Esparza, Jesús Manuel Rivas, Leonel Ruvalcaba Arredondo and Alejandra Ariadna Romero Moyano

Sustainability 2022, 14(15), 9113; https://doi.org/10.3390/su14159113 - 25 Jul 2022

Cited by 2 | Viewed by 2671

Abstract

The power factor in electrical power systems is of paramount importance because of the influence on the economic cost of energy consumption as well as the power quality requested by the grid. Low power factor affects both electrical consumers and suppliers due to an increase in current requirements for the installation, bigger sizing of industrial equipment, bigger conductor wiring that can sustain higher currents, and additional voltage regulators for the equipment. In this work, we present a technique for predicting power factor variations in three phase electrical power systems, using machine learning algorithms. The proposed model was developed and tested in medium voltage installations and was found to be fairly accurate thus representing a cost reduced approach for power quality monitoring. The model can be modified to predict the variation of the power factor, taking into account removable energy sources connected to the grid. This new way of analyzing the behavior of the power factor through prediction has the potential to facilitate decision-making by customers, reduce maintenance costs, reduce the probability of injecting disturbances into the network, and above all affords a reliable model of behavior without the need for real-time monitoring, which represents a potential cost reduction for the consumer. Full article

(This article belongs to the Special Issue Energy Efficiency in Power Lines)

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15 pages, 719 KiB

Open AccessArticle

Hybrid Predictive Modeling for Charging Demand Prediction of Electric Vehicles

by Young-Eun Jeon, Suk-Bok Kang and Jung-In Seo

Sustainability 2022, 14(9), 5426; https://doi.org/10.3390/su14095426 - 30 Apr 2022

Cited by 4 | Viewed by 1756

Abstract

In recent years, the supply of electric vehicles, which are eco-friendly cars that use electric energy rather than fossil fuels, which cause air pollution, is increasing. Accordingly, it is emerging as an urgent task to predict the charging demand for the smooth supply of electric energy required to charge electric vehicle batteries. In this paper, to predict the charging demand, time series analysis is performed based on two types of frames: One is using traditional time series techniques such as dynamic harmonic regression, seasonal and trend decomposition using Loess, and Bayesian structural time series. The other is the most widely used machine learning techniques, including random forest and extreme gradient boosting. However, the tree-based machine learning approaches have the disadvantage of not being able to capture the trend, so a hybrid strategy is proposed to overcome this problem. In addition, the seasonal variation is reflected as the feature by using the Fourier transform which is useful in the case of describing the seasonality patterns of time series data with multiple seasonality. The considered time series models are compared and evaluated through various accuracy measures. The experimental results show that the machine learning approach based on the hybrid strategy generally achieves significant improvements in predicting the charging demand. Moreover, when compared with the original machine learning method, the prediction based on the proposed hybrid strategy is more accurate than that based on the original machine learning method. Based on these results, it can find out that the proposed hybrid strategy is useful for smoothly planning future power supply and demand and efficiently managing electricity grids. Full article

(This article belongs to the Special Issue Energy Efficiency in Power Lines)

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20 pages, 2315 KiB

Open AccessArticle

Statistical Modeling of Energy Harvesting in Hybrid PLC-WLC Channels

by Victor Fernandes, Thiago F. A. Nogueira, H. Vincent Poor and Moisés V. Ribeiro

Sustainability 2022, 14(1), 442; https://doi.org/10.3390/su14010442 - 31 Dec 2021

Cited by 2 | Viewed by 1342

Abstract

This work introduces statistical models for the energy harvested from the in-home hybrid power line-wireless channel in the frequency band from 0 to 100 MHz. Based on numerical analyses carried out over the data set obtained from a measurement campaign together with the use of the maximum likelihood value criterion and the adoption of five distinct power masks for power allocation, it is shown that the log-normal distribution yields the best model for the energies harvested from the free-of-noise received signal and from the additive noise in this setting. Additionally, the total harvested energy can be modeled as the sum of these two statistically independent random variables. Thus, it is shown that the energies harvested from this kind of hybrid channel is an easy-to-simulate phenomenon when carrying out research related to energy-efficient and self-sustainable networks. Full article

(This article belongs to the Special Issue Energy Efficiency in Power Lines)

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Review

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27 pages, 449 KiB

Open AccessReview

Techniques to Locate the Origin of Power Quality Disturbances in a Power System: A Review

by Raquel Martinez, Pablo Castro, Alberto Arroyo, Mario Manana, Noemi Galan, Fidel Simon Moreno, Sergio Bustamante and Alberto Laso

Sustainability 2022, 14(12), 7428; https://doi.org/10.3390/su14127428 - 17 Jun 2022

Cited by 14 | Viewed by 2000

Abstract

The complexity in the power system topology, together with the new paradigm in generation and demand, make achieving an adequate level of supply quality a complicated goal for distribution companies. The electrical system power quality is subject to different regulations. On one hand, EN-50160 establishes the characteristics of the voltage supplied by public electricity networks, therefore affecting distribution companies. On the other hand, the EN-61000 series of standards regulates the electromagnetic compatibility of devices connected to the network, therefore affecting the loads. Power companies and device manufacturers are both responsible and affected in the issue of quality of supply. Despite the regulations, there are certain aspects of the supply quality that are not solved. One of the most important is the location of the disturbance’s origin. This paper presents a review of the main techniques to locate the disturbance’s origin in the electric network through two approaches: identification of the disturbance’s cause and the location of the origin. Full article

(This article belongs to the Special Issue Energy Efficiency in Power Lines)

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