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Advances in Simulations and Analysis of Electrical Power Systems: Enhancing Efficiency, Reliability and Sustainability

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F1: Electrical Power System".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 4508

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Special Issue Editors

Dr. Qin Wang

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Guest Editor

Department of Electrical and Electronic Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China
Interests: power systems; grid integration of renewable energy resources; smart distribution systems; transactive energy; electricity markets

Prof. Dr. Mingjian Cui

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Guest Editor

School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Interests: renewable energy; smart grid; power system operation; machine learning; power system cybersecurity; optimization modeling
Special Issues, Collections and Topics in MDPI journals

Dr. Ershun Du

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Guest Editor

Laboratory of Low Carbon Energy, Tsinghua University, Beijing 100084, China
Interests: power system planning; renewable energy integration; energy policy; electrical power engineering; power engineering; power systems modelling; renewable energy; electrical engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The field of electrical power systems has undergone significant advancements in recent years, driven by the need for efficient and sustainable energy solutions. Simulation and analysis techniques play a crucial role in understanding, optimizing, and enhancing the performance of electrical power systems. This Special Issue aims to explore the latest advances in simulation and analysis methodologies and their applications in enhancing the efficiency, reliability, and sustainability of power systems.

The primary aim of this Special Issue is to provide a platform for researchers, practitioners, and experts in the field of electrical power systems to share their insights, innovations, and findings related to simulation and analysis. By assembling diverse perspectives, the Special Issue seeks to foster interdisciplinary collaborations, promote knowledge exchange, and contribute to the development of more efficient and sustainable electrical power systems.

The Special Issue invites contributions that address various aspects of simulation and analysis in electrical power systems. Topics of interest include, but are not limited to, the following:

Advanced simulation techniques for power system modeling and analysis
Optimization algorithms and tools for power system operation and planning
Integration of renewable energy sources in power system simulations
Simulation-based studies on grid stability, reliability, and resilience
Analysis of power system dynamics and control strategies
Simulation and analysis of smart grid technologies and architectures
Cybersecurity analysis and simulation for power system protection
Simulation-based studies on demand response and energy management systems
Impact analysis of electric vehicles and energy storage systems on power grids
Simulation and analysis of microgrids and distributed energy resources

Authors are encouraged to present original research, case studies, and review articles that contribute to the understanding and advancement of simulation and analysis techniques in electrical power systems. This Special Issue welcomes both theoretical contributions and practical applications, aiming to bridge the gap between academia and industry.

By exploring these topics, this Special Issue seeks to inspire innovative solutions, foster sustainable practices, and facilitate the transition towards a more reliable, efficient, and environmentally friendly electrical power system.

Dr. Qin Wang
Prof. Dr. Mingjian Cui
Dr. Ershun Du
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

simulation
analysis
electrical power systems
efficiency
reliability
sustainability

Published Papers (5 papers)

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Research

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16 pages, 7659 KiB

Open AccessArticle

The Effect of the Vertical Layout on Underground Cable Current Carrying Capacity

by Ahmet Ozyesil, Burak Altun, Yunus Berat Demirol and Bora Alboyaci

Energies 2024, 17(3), 674; https://doi.org/10.3390/en17030674 - 31 Jan 2024

Viewed by 847

Abstract

Underground cable installation in historical areas, natural protected areas, narrow streets, or residential areas with high traffic flows is very difficult due to both legal permits and the conditions of the work sites. The trefoil layout requires a smaller channel than the flat layout. However, the trefoil layout carries some risks, such as damage to the cables together in the event of short circuit faults and reduced ampacity in single-side-bonded systems. This study’s scope examines the current carrying capacities and thermal effects of directly buried underground cables in trefoil and vertical layouts using CYMCAP power cable analysis software. A field investigation was also carried out to verify the analysis results. The performance of the recommended method was evaluated by considering current and temperature measurements from the fieldwork and analysis. According to the studied cable design, the current carrying capacities of the cables in flat and vertical layouts are similar and higher than in the trefoil layout. However, it should be taken into consideration that these results will vary depending on a cable system’s design parameters. As a result, this article emphasizes that a vertical layout can be considered as a layout option in certain areas. Full article

(This article belongs to the Special Issue Advances in Simulations and Analysis of Electrical Power Systems: Enhancing Efficiency, Reliability and Sustainability)

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23 pages, 8995 KiB

Open AccessArticle

Evaluation Method for Hosting Capacity of Rooftop Photovoltaic Considering Photovoltaic Potential in Distribution System

by Yilin Xu, Jie He, Yang Liu, Zilu Li, Weicong Cai and Xiangang Peng

Energies 2023, 16(22), 7677; https://doi.org/10.3390/en16227677 - 20 Nov 2023

Viewed by 723

Abstract

Regarding the existing evaluation methods for photovoltaic (PV) hosting capacity in the distribution system that do not consider the spatial distribution of rooftop photovoltaic potential and are difficult to apply on the actual large-scale distribution systems, this paper proposes a PV hosting capacity evaluation method based on the improved PSPNet, grid multi-source data, and the CRITIC method. Firstly, an improved PSPNet is used to efficiently abstract the rooftop in satellite map images and then estimate the rooftop PV potential of each distribution substation supply area. Considering the safety, economy, and flexibility of distribution system operation, we establish a multi-level PV hosting capacity evaluation system. Finally, based on the rooftop PV potential estimation of each distribution substation supply area, we combine the multi-source data of the grid digitalization system to carry out security verification and indicator calculation and convert the indicator calculation results of each scenario into a comprehensive score through the CRITIC method. We estimate the rooftop photovoltaic potential and evaluate the PV hosting capacity of an actual 10 kV distribution system in Shantou, China. The results show that the improved PSPNet solves the hole problem of the original model and obtains a close-to-realistic rooftop photovoltaic potential estimation value. In addition, the proposed method considering the photovoltaic potential in this paper can more accurately evaluate the rooftop PV hosting capacity of the distribution system compared with the traditional method, which provides data support for the power grid corporation to formulate a reasonable PV development and hosting capacity enhancement program. Full article

(This article belongs to the Special Issue Advances in Simulations and Analysis of Electrical Power Systems: Enhancing Efficiency, Reliability and Sustainability)

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17 pages, 5390 KiB

Open AccessArticle

Evaluation of Rooftop Photovoltaic Power Generation Potential Based on Deep Learning and High-Definition Map Image

by Wenbo Cui, Xiangang Peng, Jinhao Yang, Haoliang Yuan and Loi Lei Lai

Energies 2023, 16(18), 6563; https://doi.org/10.3390/en16186563 - 12 Sep 2023

Viewed by 961

Abstract

Photovoltaic (PV) power generation is booming in rural areas, not only to meet the energy needs of local farmers but also to provide additional power to urban areas. Existing methods for estimating the spatial distribution of PV power generation potential either have low accuracy and rely on manual experience or are too costly to be applied in rural areas. In this paper, we discuss three aspects, namely, geographic potential, physical potential, and technical potential, and propose a large-scale and efficient PV potential estimation system applicable to rural rooftops in China. Combined with high-definition map images, we proposed an improved SegNeXt deep learning network to extract roof images. Using the national standard Design Code for Photovoltaic Power Plants (GB50797-2012) and the Bass model, computational results were derived. The average pixel accuracy of the improved SegNeXt was about 96%, which well solved the original problems of insufficient finely extracted edges, poor adhesion, and poor generalization ability and can cope with different types of buildings. Leizhou City has a geographic potential of 1500 kWh/m², a physical potential of 25,186,181.7 m², and a technological potential of 442.4 MW. For this paper, we innovatively used the Bass Demand Diffusion Model to estimate the installed capacity over the next 35 years and combined the Commodity Diffusion Model with the installed capacity, which achieved a good result and conformed to the dual-carbon “3060” plan for the future of China. Full article

(This article belongs to the Special Issue Advances in Simulations and Analysis of Electrical Power Systems: Enhancing Efficiency, Reliability and Sustainability)

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Review

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29 pages, 1607 KiB

Open AccessReview

A Survey on the Use of Synthetic Data for Enhancing Key Aspects of Trustworthy AI in the Energy Domain: Challenges and Opportunities

by Michael Meiser and Ingo Zinnikus

Energies 2024, 17(9), 1992; https://doi.org/10.3390/en17091992 - 23 Apr 2024

Viewed by 392

Abstract

To achieve the energy transition, energy and energy efficiency are becoming more and more important in society. New methods, such as Artificial Intelligence (AI) and Machine Learning (ML) models, are needed to coordinate supply and demand and address the challenges of the energy transition. AI and ML are already being applied to a growing number of energy infrastructure applications, ranging from energy generation to energy forecasting and human activity recognition services. Given the rapid development of AI and ML, the importance of Trustworthy AI is growing as it takes on increasingly responsible tasks. Particularly in the energy domain, Trustworthy AI plays a decisive role in designing and implementing efficient and reliable solutions. Trustworthy AI can be considered from two perspectives, the Model-Centric AI (MCAI) and the Data-Centric AI (DCAI) approach. We focus on the DCAI approach, which relies on large amounts of data of sufficient quality. These data are becoming more and more synthetically generated. To address this trend, we introduce the concept of Synthetic Data-Centric AI (SDCAI). In this survey, we examine Trustworthy AI within a Synthetic Data-Centric AI context, focusing specifically on the role of simulation and synthetic data in enhancing the level of Trustworthy AI in the energy domain. Full article

(This article belongs to the Special Issue Advances in Simulations and Analysis of Electrical Power Systems: Enhancing Efficiency, Reliability and Sustainability)

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17 pages, 572 KiB

Open AccessReview

Advancements and Future Directions in the Application of Machine Learning to AC Optimal Power Flow: A Critical Review

by Bozhen Jiang, Qin Wang, Shengyu Wu, Yidi Wang and Gang Lu

Energies 2024, 17(6), 1381; https://doi.org/10.3390/en17061381 - 13 Mar 2024

Viewed by 668

Abstract

Optimal power flow (OPF) is a crucial tool in the operation and planning of modern power systems. However, as power system optimization shifts towards larger-scale frameworks, and with the growing integration of distributed generations, the computational time and memory requirements of solving the alternating current (AC) OPF problems can increase exponentially with system size, posing computational challenges. In recent years, machine learning (ML) has demonstrated notable advantages in efficient computation and has been extensively applied to tackle OPF challenges. This paper presents five commonly employed OPF transformation techniques that leverage ML, offering a critical overview of the latest applications of advanced ML in solving OPF problems. The future directions in the application of machine learning to AC OPF are also discussed. Full article

(This article belongs to the Special Issue Advances in Simulations and Analysis of Electrical Power Systems: Enhancing Efficiency, Reliability and Sustainability)

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