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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: closed (9 October 2024) | Viewed by 17870

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Special Issue Editors

Dr. Lipeng Zhu

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Guest Editor

College of Electrical and Information Engineering, Hunan University, Changsha 410012, China
Interests: power system stability; optimal power flow; active distribution network; voltage control
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Dr. Yue Song

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Guest Editor

Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China
Interests: stability; control; optimization; microgrids; power systems
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Dr. Xinran Zhang

E-Mail Website
Guest Editor

School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
Interests: power system stability; load modeling
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Special Issue Information

Dear Colleagues,

With the increasing deployment of various advanced sensing and measurement devices, e.g., synchronous phasor measurement units (PMUs) and smart meters, modern electric power systems are experiencing a much-needed upgrade, becoming smart grids with better observability and visibility under various complicated operating conditions. In this circumstance, huge volumes of operational data are available, which means that we are able to carry out data-driven system analysis and facilitate the control of systems for diverse forms of power grids, including bulk power systems, distribution networks, microgrids, etc. In fact, with the help of various advanced machine learning and computational intelligence techniques, a substantial number of promising data-driven applications can be developed to intelligently monitor and control practical power grids, e.g., wide-area state estimation, system dynamics identification, fault diagnosis and localization, dynamic stability/security assessment, and online stability control. Compared with conventional model-based approaches, data-driven intelligent solutions have no limitation on system scale or complexity, and they are, as such, useful for analysing practical large-scale grids with extremely high complexity, especially in the context of increasing uncertainty introduced by today’s high levels of renewable energy.

Despite the significant progress in recent years, data-driven solutions still face many challenges in practice. Typically, for data-driven analysis and the control of practical large-scale power systems, the challenging issues include: insufficient/improper fusion of model-/data-based schemes; a lack of interpretability of data-driven solutions; the scarcity of risky or insecure scenarios for learning data preparation; the vulnerability to practical defective data acquisition conditions; insufficient adaptability to online time-varying conditions; data privacy and security issues during data analytics. If these challenges are well tackled and the potential of data-driven solutions is fully explored, modern power systems may be anatomized and controlled more intelligently and reliably, which could result in much stronger real-time situational awareness and better decision making.

This Special Issue aims to solicit innovative research efforts that address recent advances and new trends in the data-driven intelligent analysis and control of modern power systems. Topics of interest include, but are not limited to:

Data management and analytics in modern power grids;
Data-driven wide-area state estimation;
Data-driven energy management and operational optimization;
Data-driven renewable energy/load forecasting;
Data-driven power system dynamics identification and modelling;
Data-driven power system dynamic stability/security assessment;
Data-driven power system wide-area control and protection;
Power system fault detection and localization based on data analytics;
Power system cybersecurity enhancement based on advanced data analytics;
Data-driven situational awareness and the visualization of power systems;
Machine learning and reinforcement learning for power system online decision making;
Data-enabled intelligent applications in transmission/distribution networks and microgrids;
Implementation and pilot projects of data analytics in practical power grids.

Dr. Lipeng Zhu
Dr. Yue Song
Dr. Xinran Zhang
Guest Editors

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Keywords

data analytics
computational intelligence
machine learning
power system dynamics
power system analysis and control
smart grid
situational awareness
decision making

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Related Special Issue

Intelligent Analysis and Control of Modern Power Systems: 2nd Edition in Energies

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Research

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16 pages, 736 KiB

Open AccessFeature PaperArticle

Evaluating Deep Learning Networks Versus Hybrid Network for Smart Monitoring of Hydropower Plants

by Fatemeh Hajimohammadali, Emanuele Crisostomi, Mauro Tucci and Nunzia Fontana

Energies 2024, 17(22), 5670; https://doi.org/10.3390/en17225670 - 13 Nov 2024

Cited by 1 | Viewed by 815

Abstract

One of the main goals of the International Energy Agency (IEA) is to manage and utilize clean energy to achieve net zero emissions by 2050. Hydropower plants can significantly contribute to this goal as they are vital components of the global energy infrastructure, providing a clean, safe, and sustainable power source. Accordingly, there is great interest in developing methods to prevent errors and anomalies and ensure full operational availability. With modern hydropower plants equipped with sensors that capture extensive data, machine learning algorithms utilizing these data to detect and predict anomalies have gained research attention. This paper demonstrates that deep learning algorithms are particularly powerful in predicting time series. Three well-known deep learning networks are examined and compared to previous approaches, followed by the introduction of a new, innovative hybrid network. Using real-world data from two hydropower plants, the hybrid model outperforms individual deep learning models by achieving more accurate fault detection, reducing false positives, offering early fault prediction, and identifying faults several weeks before occurrence. These results showcase the hybrid network’s potential to enhance maintenance planning, reduce downtime, and improve operational efficiency in energy systems. Full article

(This article belongs to the Special Issue Intelligent Analysis and Control of Modern Power Systems)

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14 pages, 1234 KiB

Open AccessArticle

Comprehensive Power Quality Assessment Based on a Data-Driven Determinant-Valued Extension Hierarchical Analysis Approach

by Jingyi Zhang, Tongtian Sheng, Pan Gu, Miao Yu, Honghao Wu, Jianqun Sun and Jinming Bao

Energies 2024, 17(13), 3141; https://doi.org/10.3390/en17133141 - 26 Jun 2024

Viewed by 1289

Abstract

The increasing demand for power quality in modern power supply facilities and the deepening changes in the power market have led to frequent power quality events, making the assessment of power quality a necessity. In view of the complexity of the model and the sensitivity of the parameters of the existing power quality assessment system, as well as the shortcomings of the traditional hierarchical analysis method, this paper proposes a data-driven power quality assessment system based on the improved determinant-valued extension hierarchical analysis, which makes the factors affecting power quality hierarchical, and enhances the conservatism of the matrix while reducing the human subjective factors, so as to analyze the main power quality problems in a clearer and more intuitive way. The evaluation system is validated and analyzed, and the corresponding evaluation result is “excellent”, which proves that the system effectively evaluates the power quality in real scenarios, and has a good prospect in power quality evaluation. Full article

(This article belongs to the Special Issue Intelligent Analysis and Control of Modern Power Systems)

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18 pages, 3419 KiB

Open AccessArticle

Transient Stability Assessment of Power Systems Based on CLV-GAN and I-ECOC

by Nan Li, Jiafei Wu, Lili Shan and Luan Yi

Energies 2024, 17(10), 2278; https://doi.org/10.3390/en17102278 - 9 May 2024

Cited by 2 | Viewed by 1050

Abstract

In order to improve the multi-class assessment performance of transient stability in power systems, a multi-class assessment model that combines the CLV-GAN algorithm with an improved error-correcting output coding technique is proposed in the paper. To address the issue of the small number of unstable samples in power systems, a sample generation model is constructed by combining a dual-encoder VAE with a GAN network. The model generates effective artificial samples to balance the sample ratio between categories by learning the latent distribution of aperiodic and oscillatory unstable samples from the distribution. The decomposition method based on an improved error-correcting output coding algorithm is applied to convert the multi-class problem into a decision fusion issue for binary models. This method improves the overall performance of the multi-class model, particularly significantly increasing the recognition accuracy of discrimination against oscillatory unstable samples and reducing the safety hazards in the operation of power systems. The simulation validation was conducted on the IEEE 39-bus and IEEE 140-bus systems to confirm the effectiveness of the proposed model. Full article

(This article belongs to the Special Issue Intelligent Analysis and Control of Modern Power Systems)

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14 pages, 2068 KiB

Open AccessArticle

Dynamics Power Quality Cost Assessment Based on a Gradient Descent Method

by Jingyi Zhang, Tongtian Sheng, Pan Gu, Miao Yu, Jiaxin Yan, Jianqun Sun and Shanhe Liu

Energies 2024, 17(9), 2104; https://doi.org/10.3390/en17092104 - 28 Apr 2024

Cited by 2 | Viewed by 995

Abstract

The escalating demand for power load is increasingly prone to triggering power quality (PQ) issues, leading to severe economic losses. Aiming at reducing the economic losses, this paper focuses on the coordinated relationship between PQ and economic costs. Firstly, a multilayer multiple linear stepwise regression method is employed to screen PQ indicators, identifying harmonic and voltage deviation as the primary influencing factors of PQ. Secondly, a gradient descent optimization algorithm based on the Least Absolute Shrinkage and Selection Operator (LASSO) is proposed, enabling rapid computation of the minimum PQ cost. Finally, through validations of two case studies, the results confirm that the proposed method can rapidly calculate the minimum PQ cost based on real-time load demands, enabling the dynamic adjustment of PQ cost to meet the evolving needs of power system development. Full article

(This article belongs to the Special Issue Intelligent Analysis and Control of Modern Power Systems)

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22 pages, 3891 KiB

Open AccessFeature PaperArticle

Power System Transient Stability Assessment Using Convolutional Neural Network and Saliency Map

by Heungseok Lee, Jongju Kim, June Ho Park and Sang-Hwa Chung

Energies 2023, 16(23), 7743; https://doi.org/10.3390/en16237743 - 24 Nov 2023

Cited by 4 | Viewed by 1735

Abstract

This study proposes a model for transient stability assessment, which is a convolutional neural network model combined with a saliency map (S–CNN model). The convolutional neural network model is trained on dynamic data acquired through the data measurement devices of a power system. Applying the saliency map to the acquired dynamic data visually highlights the critical aspects of transient stability assessment. This reduces data training time by eliminating unnecessary aspects during the convolutional neural network model training, thus improving training efficiency. As a result, the proposed model can achieve high performance in transient stability assessment. The dynamic data are acquired by configuring benchmark models, IEEE 39 and 118 bus systems, through MATLAB/Simulink and performing time-domain simulations. Based on the acquired dynamic data, the performance of the proposed model is verified through a confusion matrix. Furthermore, an analysis of the effects of noise interference on the performance is conducted. Full article

(This article belongs to the Special Issue Intelligent Analysis and Control of Modern Power Systems)

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18 pages, 4167 KiB

Open AccessArticle

Optimal Scheduling of Power Systems with High Proportions of Renewable Energy Accounting for Operational Flexibility

by Yi Lin, Wei Lin, Wei Wu and Zhenshan Zhu

Energies 2023, 16(14), 5537; https://doi.org/10.3390/en16145537 - 21 Jul 2023

Cited by 4 | Viewed by 1797

Abstract

The volatility and uncertainty of high-penetration renewable energy pose significant challenges to the stability of the power system. Current research often fails to consider the insufficient system flexibility during real-time scheduling. To address this issue, this paper proposes a flexibility scheduling method for high-penetration renewable energy power systems that considers flexibility index constraints. Firstly, a quantification method for flexibility resources and demands is introduced. Then, considering the constraint of the flexibility margin index, optimization scheduling strategies for different time scales, including day-ahead scheduling and intra-day scheduling, are developed with the objective of minimizing total operational costs. The intra-day optimization is divided into 15 min and 1 min time scales, to meet the flexibility requirements of different time scales in the power system. Finally, through simulation studies, the proposed strategy is validated to enhance the system’s flexibility and economic performance. The daily operating costs are reduced by 3.1%, and the wind curtailment rate is reduced by 4.7%. The proposed strategy not only considers the economic efficiency of day-ahead scheduling but also ensures a sufficient margin to cope with the uncertainty of intra-day renewable energy fluctuations. Full article

(This article belongs to the Special Issue Intelligent Analysis and Control of Modern Power Systems)

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15 pages, 1330 KiB

Open AccessArticle

Data-Driven Dynamic Stability Assessment in Large-Scale Power Grid Based on Deep Transfer Learning

by Weijia Wen, Xiao Ling, Jianxin Sui and Junjie Lin

Energies 2023, 16(3), 1142; https://doi.org/10.3390/en16031142 - 20 Jan 2023

Cited by 6 | Viewed by 2252

Abstract

For data-driven dynamic stability assessment (DSA) in modern power grids, DSA models generally have to be learned from scratch when faced with new grids, resulting in high offline computational costs. To tackle this undesirable yet often overlooked problem, this work develops a light-weight framework for DSA-oriented stability knowledge transfer from off-the-shelf test systems to practical power grids. A scale-free system feature learner is proposed to characterize system-wide features of various systems in a unified manner. Given a real-world power grid for DSA, selective stability knowledge transfer is intelligently carried out by comparing system similarities between it and the available test systems. Afterward, DSA model fine-tuning is performed to make the transferred knowledge adapt well to practical DSA contexts. Numerical test results on a realistic system, i.e., the provincial GD Power Grid in China, verify the effectiveness of the proposed framework. Full article

(This article belongs to the Special Issue Intelligent Analysis and Control of Modern Power Systems)

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17 pages, 1638 KiB

Open AccessArticle

Comprehensive Analysis of Transient Overvoltage Phenomena for Metal-Oxide Varistor Surge Arrester in LCC-HVDC Transmission System with Special Protection Scheme

by Jaesik Kang

Energies 2022, 15(19), 7034; https://doi.org/10.3390/en15197034 - 25 Sep 2022

Cited by 1 | Viewed by 2425

Abstract

This paper proposes a systematic and deterministic method for metal-oxide varistor (MOV) surge arrester selection based on the comprehensive analysis in line-commutated converter (LCC)-based high-voltage direct current (HVDC) transmission systems. For the MOV surge arrester, this paper investigates several significant impacts on the transient overvoltage (TOV) phenomena, which is affected by practical factors such as an operating point of the LCC-HVDC system, synchronous machine operating status of the power system, AC passive filter trip, and communication delay in a special protection system (SPS). In order to determine an appropriate rating of surge arrester, especially for TOV, this paper considers a pattern, magnitude, and duration of TOV based on various fault scenarios in an electrical power system with an LCC-HVDC system. A screening study method with 60 Hz and RMS-based balance system is conducted for examining a wide range of fault scenarios, and then for the specific test cases that need a detailed analysis, electro-magnetic transient (EMT)-based analysis models are developed with an approvable boundary setting method through the equivalent network translation tool. A detailed EMT study is subsequent based on the distinguished cases; as a result, the exact number of metal-oxide resistor stacks could be obtained through the detailed TOV study according to this procedure. The efficacy of the selection method from the proposed procedure based on the comprehensive analysis are verified on a specific power system with a 1.5 GW DC ± 500 kV symmetric monopole LCC-HVDC transmission system. Full article

(This article belongs to the Special Issue Intelligent Analysis and Control of Modern Power Systems)

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15 pages, 3921 KiB

Open AccessArticle

A Multistage Current Charging Method for Energy Storage Device of Microgrid Considering Energy Consumption and Capacity of Lithium Battery

by Chuanping Wu, Yu Liu, Tiannian Zhou and Shiran Cao

Energies 2022, 15(13), 4526; https://doi.org/10.3390/en15134526 - 21 Jun 2022

Cited by 4 | Viewed by 1801

Abstract

Modular multilevel converter battery energy storage systems (MMC-BESSs) have become an important device for the energy storage of grid-connected microgrids. The efficiency of the power transmission of MMC-BESSs has become a new research hotspot. This paper outlines a multi-stage charging method to minimize energy consumption and maximize the capacity of MMC-BESSs. Firstly, based on condition monitoring and data collection, the functional relationship between the internal resistance/capacity and other states of lithium batteries is established. Since the energy consumption of the battery is related to internal resistance, current, and time, the energy consumption calculation expression of the battery pack is established, and the objective function is designed to optimize energy consumption and capacity in order to determine the charging current curve of each stage. Compared with the constant current charging method, the proposed multistage current charging method for an MMC-BESS decreases energy consumption by 4.3% and increases the capacity of 5 SOC intervals by 1.56%. Full article

(This article belongs to the Special Issue Intelligent Analysis and Control of Modern Power Systems)

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Review

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38 pages, 4629 KiB

Open AccessReview

A Survey of Quantitative Techniques in Electricity Consumption—A Global Perspective

by Atif Maqbool Khan and Artur Wyrwa

Energies 2024, 17(19), 4910; https://doi.org/10.3390/en17194910 - 30 Sep 2024

Cited by 1 | Viewed by 1695

Abstract

This study uses the Scopus and Web of Science databases to review quantitative methods to forecast electricity consumption from 2015 to 2024. Using the PRISMA approach, 175 relevant publications were identified from an initial set of 821 documents and subsequently subjected to bibliometric analysis. This analysis examined publication trends, citation metrics, and collaboration patterns across various countries and institutions. Over the period analyzed, the number of articles has steadily increased, with a more rapid rise observed after 2020. Although China dominates this research field, strong bibliographic coupling worldwide indicates significant international collaboration. The study suggests that no single method consistently outperforms others across all contexts and that forecasting methods should be adapted to regional contexts, considering specific economic, social, and environmental factors. Furthermore, we emphasize that review papers should compare methods and results regarding both time horizon and temporal resolution, as these aspects are crucial for the accuracy and applicability of the forecasts. Full article

(This article belongs to the Special Issue Intelligent Analysis and Control of Modern Power Systems)

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