Future Electricity Network Infrastructures

Topic Information

Dear Colleagues,

This Topic aims to provide a platform for researchers and practicing engineers to share their ideas, recent developments, and successful practices in power and electrical engineering. The issue will publish high-quality papers that are strictly related to the various theories and practical applications in the area of machine learning applications based on future power system operations with high penetration of renewable resources and its related network architecture. Topics of interest include but are not limited to:

• future electricity network infrastructures
• machine learning
• DC network architecture
• smart and intelligent buildings
• smart EV charging
• smart cities

Prof. Dr. Tek-Tjing Lie
Prof. Dr. Guojie Li
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Inventions inventions	2.1	4.8	2016	18.5 Days	CHF 1800
Sensors sensors	3.4	7.3	2001	18.6 Days	CHF 2600
Electronics electronics	2.6	5.3	2012	16.4 Days	CHF 2400
Electricity electricity	-	4.8	2020	27.9 Days	CHF 1000
Energies energies	3.0	6.2	2008	16.8 Days	CHF 2600
Technologies technologies	4.2	6.7	2013	21.1 Days	CHF 1600

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Published Papers (4 papers)

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16 pages, 5218 KiB  

Open AccessArticle

A Voltage-Level Optimization Method for DC Remote Power Supply of 5G Base Station Based on Converter Behavior

by Binxin Zhu, Hao Guo, Yizhang Wang and Kaihong Wang

Electronics 2024, 13(1), 51; https://doi.org/10.3390/electronics13010051 - 21 Dec 2023

Viewed by 1317

Abstract

Unlike the concentrated load in urban area base stations, the strong dispersion of loads in suburban or highway base stations poses significant challenges to traditional power supply methods in terms of efficiency and cost. High-voltage direct current (HVDC) remote supply have better application [...] Read more.

Unlike the concentrated load in urban area base stations, the strong dispersion of loads in suburban or highway base stations poses significant challenges to traditional power supply methods in terms of efficiency and cost. High-voltage direct current (HVDC) remote supply have better application potential in this scenario due to their low transmission losses, attracting much attention. However, existing research has problems such as ambiguous optimal power supply distance under different voltage levels and a lack of behavioral models for converters. Therefore, this paper starts from the behavior of underlying converters, analyzes the loss composition of different converters in HVDC long-distance supply, and establishes a refined model for converters by determining the mathematical relationship between converter losses and operating power. Considering the economic feasibility of power supply solutions throughout the lifecycle, a modeling method is proposed that optimizes the voltage level of converters considering the behavior of converters for different supply distances. The optimal voltage level for different supply distances is discussed, and the effectiveness of the model is verified through examples, providing valuable guidance for optimizing the voltage level in HVDC long-distance supply for 5G base stations. Full article

(This article belongs to the Topic Future Electricity Network Infrastructures)

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11 pages, 864 KiB  

Open AccessArticle

Impact of Communication System Characteristics on Electric Vehicle Grid Integration: A Large-Scale Practical Assessment of the UK’s Cellular Network for the Internet of Energy

by Mehdi Zeinali, Nuh Erdogan, Islam Safak Bayram and John S. Thompson

Electricity 2023, 4(4), 309-319; https://doi.org/10.3390/electricity4040018 - 3 Nov 2023

Cited by 4 | Viewed by 1875

Abstract

The ever-increasing number of plug-in electric vehicles (PEVs) requires appropriate electric vehicle grid integration (EVGI) for charging coordination to maintain grid stability and enhance PEV user convenience. As such, the widespread adoption of electric mobility can be successful. EVGI is facilitated through charging [...] Read more.

The ever-increasing number of plug-in electric vehicles (PEVs) requires appropriate electric vehicle grid integration (EVGI) for charging coordination to maintain grid stability and enhance PEV user convenience. As such, the widespread adoption of electric mobility can be successful. EVGI is facilitated through charging stations and empowers PEV users to manage their charging demand by using smart charging solutions. This makes PEV grids assets that provide flexibility to the power grid. The Internet of Things (IoT) feature can make smooth EVGI possible through a supporting communication infrastructure. In this regard, the selection of an appropriate communication protocol is essential for the successful implementation of EVGI. This study assesses the efficacy of the UK’s 4G network with TCP and 4G UDP protocols for potential EVGI operations. For this, an EVGI emulation test bed is developed, featuring three charging parking lots with the capacity to accommodate up to 64 PEVs. The network’s performance is assessed in terms of data packet loss (e.g., the data-exchange capability between EVGI entities) and latency metrics. The findings reveal that while 4G TCP often outperforms 4G UDP, both achieve latencies of less than 1 s with confidence intervals of 90% or greater for single PEV cases. However, it is observed that the high penetration of PEVs introduces a pronounced latency due to queuing delays in the network including routers and the base station servers, highlighting the challenges associated with maintaining efficient EVGI coordination, which in turn affects the efficient use of grid assets. Full article

(This article belongs to the Topic Future Electricity Network Infrastructures)

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19 pages, 2353 KiB  

Open AccessArticle

Modeling of Efficient Control Strategies for LCC-HVDC Systems: A Case Study of Matiari–Lahore HVDC Power Transmission Line

by Adeel Ahmed, Danish Khan, Ahmed Muddassir Khan, Muhammad Umair Mustafa, Manoj Kumar Panjwani, Muhammad Hanan, Ephraim Bonah Agyekum, Solomon Eghosa Uhunamure and Joshua Nosa Edokpayi

Sensors 2022, 22(7), 2793; https://doi.org/10.3390/s22072793 - 6 Apr 2022

Cited by 4 | Viewed by 5976

Abstract

With the recent development in power electronic devices, HVDC (High Voltage Direct Current) systems have been recognized as the most prominent solution to transmit electric power economically. Today, several HVDC projects have been implemented physically. The conventional HVDC systems use grid commutation converters, [...] Read more.

With the recent development in power electronic devices, HVDC (High Voltage Direct Current) systems have been recognized as the most prominent solution to transmit electric power economically. Today, several HVDC projects have been implemented physically. The conventional HVDC systems use grid commutation converters, and its commutation relies on an AC system for the provision of voltage. Due to this reason, there are possibilities of commutation failure during fault. Furthermore, once the DC (Direct Current) system power is interrupted momentarily, the reversal of work power is likely to cause transient over-voltage, which will endanger the safety of power grid operation. Hence, it is necessary to study the commutation failure and transient over-voltage issues. To tackle the above issues, in this paper, the dynamic and transient characteristics of Pakistan’s first HVDC project, i.e., the Matiari–Lahore ±660 kV transmission line has been analyzed in an electromagnetic transient model of PSCAD/EMTDC. Based on the characteristics of the DC and the off-angle after the failure, a new control strategy has been proposed. The HVDC system along with its proposed control strategy has been tested under various operating conditions. The proposed controller increases the speed of fault detection, reduces the drop of AC voltage and DC and suppresses the commutation failure probability of LCC-HVDC (line commutated converter- high voltage direct current). Full article

(This article belongs to the Topic Future Electricity Network Infrastructures)
(This article belongs to the Section Electronic Sensors)

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18 pages, 8305 KiB  

Open AccessArticle

Atrous Convolutions and Residual GRU Based Architecture for Matching Power Demand with Supply

by Samee Ullah Khan, Ijaz Ul Haq, Zulfiqar Ahmad Khan, Noman Khan, Mi Young Lee and Sung Wook Baik

Sensors 2021, 21(21), 7191; https://doi.org/10.3390/s21217191 - 29 Oct 2021

Cited by 22 | Viewed by 3557

Abstract

Nowadays, for efficient energy management, local demand-supply matching in power grid is emerging research domain. However, energy demand is increasing day by day in many countries due to rapid growth of the population and most of their work being reliant on electronic devices. [...] Read more.

Nowadays, for efficient energy management, local demand-supply matching in power grid is emerging research domain. However, energy demand is increasing day by day in many countries due to rapid growth of the population and most of their work being reliant on electronic devices. This problem has highlighted the significance of effectively matching power demand with supply for optimal energy management. To resolve this issue, we present an intelligent deep learning framework that integrates Atrous Convolutional Layers (ACL) with Residual Gated Recurrent Units (RGRU) to establish balance between the demand and supply. Moreover, it accurately predicts short-term energy and delivers a systematic method of communication between consumers and energy distributors as well. To cope with the varying nature of electricity data, first data acquisition step is performed where data are collected from various sources such as smart meters and solar plants. In the second step a pre-processing method is applied on raw data to normalize and clean the data. Next, the refined data are passed to ACL for spatial feature extraction. Finally, a sequential learning model RGRU is used that learns from complicated patterns for the final output. The proposed model obtains the smallest values of Mean Square Error (MSE) including 0.1753, 0.0001, 0.0177 over IHEPC, KCB, and Solar datasets, respectively, which manifests better performance as compared to existing approaches. Full article

(This article belongs to the Topic Future Electricity Network Infrastructures)
(This article belongs to the Section Electronic Sensors)

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