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Situational Awareness and Protection Technologies for Low-Carbon Economic Operation of...
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Situational Awareness and Protection Technologies for Low-Carbon Economic Operation of New Power Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Power Electronics".

Deadline for manuscript submissions: 15 April 2025 | Viewed by 8121

Special Issue Editors

Dr. Yi Wang

E-Mail Website
Guest Editor
School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
Interests: power system estimation; parameters identification; power system dynamics; signal processing; cyber security
Prof. Dr. Yonghui Sun

E-Mail Website
Guest Editor
College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
Interests: power systems; optimal planning and operation of integrated energy system; optimization algorithms; data analysis
Dr. Yaoqiang Wang

E-Mail Website
Guest Editor
School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
Interests: power electronics and electrical drives; renewable power generation; new energy power system; power system operation and control
Dr. Yanxun Guo

E-Mail Website
Guest Editor
School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
Interests: fault analysis and protection of power system; DC circuit breaker

Special Issue Information

Dear Colleagues,

With the challenges associated with global climate change, the "low-carbon economy" based on low energy consumption, low pollution and low emissions has become a frequently debated topic at a global scale, which places a further emphasis on the need to develop higher requirements for the safe operation of power system.

State estimation, or situational awareness, and protection technologies are the key technology that can acquire, understand, display and predict the future development trend of factors that can cause changes in the system’s situation in a large-scale system environment. It has been applied in many aspects such as power energy, aerospace, etc. The new power system has diverse application scenarios, complex operating conditions and strong safety constraints. Therefore, how to apply situational awareness and protection technologies to adapt to different scenarios and ensure the reliable, safe, high-quality, low-carbon and economic operation of the new power system has become an important topic of research within the field. Especially considering the continuous improvement in real-time scheduling, reliability and economic requirements, the new power system situational awareness technology for low-carbon economic operation of electric power energy has become a major demand.

This Special Issue focuses on the development of state estimation, situational awareness and protection technologies of power systems. It aims to lay a foundation for the low-carbon economic and safe operation of new power systems. The topics of the presentations and research papers include, but are not limited to, the following:

(1) Modeling of new power system for static or dynamic state estimation.

(2) Implementation effect evaluation of new power system situational awareness technology.

(3) Prediction of future development trend of new power system situational awareness technology.

(4) Standardization of new power system situational awareness technology.

(5) Practical application scenarios of the new power system situational awareness technology.

(6) Optimization method of new power system situational awareness technology.

(7) Fault analysis and fault characteristic extraction methods of new power system.

(8) New protection schemes suitable for various fault conditions of new power system.

(9) Coordination of protection and converter control for improving security and resilience of new power system.

Dr. Yi Wang
Prof. Dr. Yonghui Sun
Dr. Yaoqiang Wang
Dr. Yanxun Guo
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. Electronics 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 2400 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

  • ower systems
  • state estimation
  • modeling and control
  • situational awareness technology
  • fault analysis
  • relay protection
  • low-carbon economic operation

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Published Papers (8 papers)

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26 pages, 1044 KiB  
Article
PredXGBR: A Machine Learning Framework for Short-Term Electrical Load Prediction
by Rifat Zabin, Khandaker Foysal Haque and Ahmed Abdelgawad
Electronics 2024, 13(22), 4521; https://doi.org/10.3390/electronics13224521 - 18 Nov 2024
Viewed by 384
Abstract
The growing demand for consumer-end electrical load is driving the need for smarter management of power sector utilities. In today’s technologically advanced society, efficient energy usage is critical, leaving no room for waste. To prevent both electricity shortage and wastage, electrical load forecasting [...] Read more.
The growing demand for consumer-end electrical load is driving the need for smarter management of power sector utilities. In today’s technologically advanced society, efficient energy usage is critical, leaving no room for waste. To prevent both electricity shortage and wastage, electrical load forecasting becomes the most convenient way out. However, the conventional and probabilistic methods are less adaptive to the acute, micro, and unusual changes in the demand trend. With the recent development of artificial intelligence (AI), machine learning (ML) has become the most popular choice due to its higher accuracy based on time-, demand-, and trend-based feature extractions. Thus, we propose an Extreme Gradient Boosting (XGBoost) regression-based model—PredXGBR-1, which employs short-term lag features to predict hourly load demand. The novelty of PredXGBR-1 lies in its focus on short-term lag autocorrelations to enhance adaptability to micro-trends and demand fluctuations. Validation across five datasets, representing electrical load in the eastern and western USA over a 20-year period, shows that PredXGBR-1 outperforms a long-term feature-based XGBoost model, PredXGBR-2, and state-of-the-art recurrent neural network (RNN) and long short-term memory (LSTM) models. Specifically, PredXGBR-1 achieves an mean absolute percentage error (MAPE) between 0.98 and 1.2% and an R2 value of 0.99, significantly surpassing PredXGBR-2’s R2 of 0.61 and delivering up to 86.8% improvement in MAPE compared to LSTM models. These results confirm the superior performance of PredXGBR-1 in accurately forecasting short-term load demand. Full article
(This article belongs to the Special Issue Situational Awareness and Protection Technologies for Low-Carbon Economic Operation of New Power Systems)
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22 pages, 4061 KiB  
Article
Optimization Decomposition of Monthly Contracts for Integrated Energy Service Provider Considering Spot Market Bidding Equilibria
by Chen Wu, Zhinong Wei, Xiangchen Jiang, Yizhen Huang and Donglou Fan
Electronics 2024, 13(10), 1945; https://doi.org/10.3390/electronics13101945 - 15 May 2024
Viewed by 969
Abstract
Under the current power trading model, especially in the context of the large-scale penetration of renewable energy and the rapid integration of renewable energy into the power system, reasonable medium- and long-term decomposition can reduce the fluctuation in the energy price when the [...] Read more.
Under the current power trading model, especially in the context of the large-scale penetration of renewable energy and the rapid integration of renewable energy into the power system, reasonable medium- and long-term decomposition can reduce the fluctuation in the energy price when the integrated energy service provider (IESP) participates in the spot market. It helps to avoid the price risk of the spot market. Additionally, it promotes the optimization of the operation of the regional energy day-ahead scheduling. At the present stage, most of the medium- and long-term contract decomposition methods focus on the decomposition of a single power and take less consideration of the bidding space in the spot market. This limitation makes it challenging to achieve efficient interaction and interconnection among multi-energy resources and smooth integration between the medium- and long-term market and the spot market. To address these issues, this paper proposes an optimal monthly contract decomposition method for IESPs that takes into account the equilibrium of spot bidding. First, the linking process and rolling framework of multi-energy transactions between the medium- and long-term market and the spot market are designed. Second, an optimal decomposition model for monthly contracts is constructed, and a daily decomposition method for monthly medium- and long-term contracts that accounts for the spot bidding equilibrium is proposed. Then, the daily preliminary decomposition result of medium- and long-term multi-energy contracts is used as the boundary condition of the day-ahead scheduling model, and the coupling characteristics of the multi-energy networks of electricity, gas, and heat are taken into account, as well as the operational characteristics. Then, considering the coupling characteristics and operating characteristics of electricity, gas, and heat networks, the optimal scheduling model of a multi-energy network is constructed to minimize the sum of cumulative daily operating costs, and the monthly final contract decomposition value and daily spot bidding space are derived. Finally, examples are calculated to verify the validity of the decomposition model, and the examples show that the proposed method can reduce the variance in spot energy purchase by about 4.64%, and, at the same time, reduce the cost of contract decomposition by about USD 0.33 million. Full article
(This article belongs to the Special Issue Situational Awareness and Protection Technologies for Low-Carbon Economic Operation of New Power Systems)
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17 pages, 8562 KiB  
Article
A Fusion Adaptive Cubature Kalman Filter Approach for False Data Injection Attack Detection of DC Microgrids
by Po Wu, Jiangnan Zhang, Shengyao Luo, Yanlou Song, Jiawei Zhang and Yi Wang
Electronics 2024, 13(9), 1612; https://doi.org/10.3390/electronics13091612 - 23 Apr 2024
Cited by 1 | Viewed by 713
Abstract
With the widespread application of information technology in microgrids, microgrids are evolving into a class of power cyber–physical systems (CPSs) that are deeply integrated with physical and information systems. Due to the high dependence of microgrids’ distributed cooperative control on real-time communication and [...] Read more.
With the widespread application of information technology in microgrids, microgrids are evolving into a class of power cyber–physical systems (CPSs) that are deeply integrated with physical and information systems. Due to the high dependence of microgrids’ distributed cooperative control on real-time communication and system state information, they are increasingly susceptible to false data injection attacks (FDIAs). To deal with this issue, in this paper, a novel false data injection attack detection method for direct-current microgrids (DC MGs) was proposed, based on fusion adaptive cubature Kalman filter (FACKF) approach. Firstly, a DC MG model with false data injection attack is established, and the system under attack is analyzed. Subsequently, an FACKF approach is proposed to detect attacks, capable of accurately identifying the attacks on the DC MG and determining the measurement units injected with false data. Finally, simulation validations were conducted under various DC MG model conditions. The extensive simulation results demonstrate that the proposed method surpasses traditional CKF detection methods in accuracy and effectiveness across different conditions. Full article
(This article belongs to the Special Issue Situational Awareness and Protection Technologies for Low-Carbon Economic Operation of New Power Systems)
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19 pages, 3988 KiB  
Article
Bad Data Repair for New Energy Stations in Power System Based on Multi-Model Parallel Integration Approach
by Chenghao Li, Mingyang Liu, Ze Gao, Yi Wang and Chunsun Tian
Electronics 2024, 13(5), 870; https://doi.org/10.3390/electronics13050870 - 23 Feb 2024
Viewed by 877
Abstract
The accurate and reliable acquisition of measurement information is very important for the stable operation of power systems, especially the operation status information of new energy stations. With the increasing proportion of new energy stations in power systems, the quality issues of data [...] Read more.
The accurate and reliable acquisition of measurement information is very important for the stable operation of power systems, especially the operation status information of new energy stations. With the increasing proportion of new energy stations in power systems, the quality issues of data from these stations, caused by communication congestion, interference, and network attacks, become more pronounced. In this paper, to deal with the issue of low accuracy and poor performance of bad data restoration in new energy stations, a novel deep learning approach by combining the modified long short-term memory (LSTM) neural network and Wasserstein generative adversarial network with gradient penalty (WGAN-GP) is proposed. The proposed method can be implemented in a parallel ensemble way. First, the normal data set acquired from multiple sections of new energy stations is utilized to train the modified LSTM and WGAN-GP model. Secondly, according to the data characteristics and rules captured by each model, the two models are systematically integrated and the bad data repair model pool is constructed. Subsequently, the results of model repair are screened and merged twice by the parallel integration framework to obtain the final repair result. Finally, the extensive experiments are carried out to verify the proposed method. The simulative results of energy stations in a real provincial power grid demonstrate that the proposed method can effectively repair bad data, thereby enhancing the data quality of new energy stations. Full article
(This article belongs to the Special Issue Situational Awareness and Protection Technologies for Low-Carbon Economic Operation of New Power Systems)
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16 pages, 3038 KiB  
Article
Optimal Capacity Configuration of Energy Storage in PV Plants Considering Multi-Stakeholders
by Fan Sun, Weiqing Wang and Dongliang Nan
Electronics 2024, 13(4), 760; https://doi.org/10.3390/electronics13040760 - 14 Feb 2024
Cited by 1 | Viewed by 883
Abstract
With the integration of large-scale renewable energy generation, some new problems and challenges are brought for the operation and planning of power systems with the aim of mitigating the adverse effects of integrating photovoltaic plants into the grid and safeguarding the interests of [...] Read more.
With the integration of large-scale renewable energy generation, some new problems and challenges are brought for the operation and planning of power systems with the aim of mitigating the adverse effects of integrating photovoltaic plants into the grid and safeguarding the interests of diverse stakeholders. In this paper, a methodology for allotting capacity is introduced, which takes into account the active involvement of multiple stakeholders in the energy storage system. The objective model for maximizing the financial proceeds of the PV plant, the system for the storage of energy, and a power grid company is studied. Then, in order to maximize the benefit of three stakeholders, a modified particle swarm optimization algorithm is devised, employing the prevailing typical allocation strategy. Finally, a case study is provided based on the modified IEEE 14-bus and the actual power grid from South Xinjiang, China. The simulation results and findings of the case study conclusively illustrate that the proposed methodology adeptly ensures the maximization of interests for the triad of stakeholders. Full article
(This article belongs to the Special Issue Situational Awareness and Protection Technologies for Low-Carbon Economic Operation of New Power Systems)
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18 pages, 2064 KiB  
Article
Dynamic Aggregation Method for Load Aggregators Considering Users’ Deviation Electricity
by Linxi Li, Xun Dou, Hanyu Yang, Yadie Fu, Jiancheng Yu, Xianxu Huo and Chao Pang
Electronics 2024, 13(2), 278; https://doi.org/10.3390/electronics13020278 - 8 Jan 2024
Cited by 1 | Viewed by 1169
Abstract
Constructing a new energy-based power system is not only an important direction for the transformation and upgrade of China’s power system, but also a key to achieving peak carbon and carbon neutrality. How to fully utilize situation awareness technology to adapt to diverse [...] Read more.
Constructing a new energy-based power system is not only an important direction for the transformation and upgrade of China’s power system, but also a key to achieving peak carbon and carbon neutrality. How to fully utilize situation awareness technology to adapt to diverse and differentiated scenarios has become a crucial breakthrough point for ensuring the reliable, safe, high-quality, low-carbon, and economical operation of the new power system. Starting from the distribution network demand resources, this paper proposes a dynamic aggregation method for load aggregators considering the user deviation quantity, to deal with the current situation that the adjustable load-side resource points are multi-faceted and wide, and the operating subjects are complex and difficult to participate directly in the grid dispatch. First, considering there is subjectivity in the electricity behavior of users under the jurisdiction of the load aggregator, a deviation amount may be generated during the actual aggregation process, which reduces the profit of the load aggregator. Therefore, a load aggregator-level user deviation dynamic volume forecasting method based on the Markov chain is proposed, which is used to predict the deviation quantity of users during the dispatch cycle and achieve a dynamic status estimate on the load side of the new power system. On this basis, a dynamic aggregation model for load aggregators based on the deviation volume was constructed with the objective of maximizing the revenue of load aggregators. The examples, by comparing the aggregation results of users under three scenarios, show the proposed method can effectively guarantee the income of load aggregators, verify the effectiveness of the proposed dynamic aggregation strategy, and provide technical support for the operation situation awareness of the load side of the new power system. Full article
(This article belongs to the Special Issue Situational Awareness and Protection Technologies for Low-Carbon Economic Operation of New Power Systems)
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20 pages, 2652 KiB  
Article
A Current Differential Protection Scheme for Distribution Networks with Inverter-Interfaced Distributed Generators Considering Delay Behaviors of Sequence Component Extractors
by Gang Wang, Min Huang, Hao Bai, Jie Li, Ruotian Yao, Haoming Wang and Chengxin Li
Electronics 2023, 12(23), 4727; https://doi.org/10.3390/electronics12234727 - 21 Nov 2023
Viewed by 1085
Abstract
The high-level proliferation of inverter-interfaced distributed generators (IIDGs) in modern distribution networks (DNs) has changed system topologies and fault current signatures, which compromises the protective relays in DNs. Investigating IIDG fault behaviors-based protection scheme will benefit the grid’s safety and stability. This paper [...] Read more.
The high-level proliferation of inverter-interfaced distributed generators (IIDGs) in modern distribution networks (DNs) has changed system topologies and fault current signatures, which compromises the protective relays in DNs. Investigating IIDG fault behaviors-based protection scheme will benefit the grid’s safety and stability. This paper proposes a novel current differential protection (CDP) scheme that considers the delay behaviors of positive- and negative-sequence component extractors for IIDGs in DNs. A frequency-domain analytical model of the fault current for a grid-connected IIDG with the PQ control strategy and a low-voltage ride-through (LVRT) capability is investigated. The dynamic behavior of the IIDGs considering the sequence-component extractor based on the Pade approximation is presented, where the T/4 delay extractor of the IIDGs causes a two-stage behavior in the fault transient process. It is found that a 5 ms error between the measured and actual values after the fault will affect the transient characteristics of the IIDGs. The transient current generated by the IIDGs during grid faults contains a large number of low-order harmonic components within the range of 0–200 Hz, which is significantly different to the current provided by the power grid. Therefore, the proposed CDP scheme uses protective relays at both terminals to obtain the required transient electric quantity using the Prony method. By constructing the frequency-characteristics ratio (FCR) and the exchanging FCR between two terminal relays, the developed protection criteria are implemented. The accuracy of the fault analysis method, whose maximum computational error is below 0.1%, and the feasibility of the proposed protection scheme are demonstrated by using a 10 kV DN in a PSCAD/EMTDC simulation, which can be applied to various fault conditions and traditional DNs without IIDGs. Full article
(This article belongs to the Special Issue Situational Awareness and Protection Technologies for Low-Carbon Economic Operation of New Power Systems)
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21 pages, 5228 KiB  
Essay
Convolution Power Ratio Based on Single-Ended Protection Scheme for HVDC Transmission Lines
by Guangqiang Peng, Lixin Chen, Jiyang Wu, Huimin Jiang, Zhijie Wang and Haifeng Li
Electronics 2023, 12(23), 4883; https://doi.org/10.3390/electronics12234883 - 4 Dec 2023
Cited by 1 | Viewed by 924
Abstract
In order to solve the problems of insufficient abilities to withstand transition resistance under remote faults and difficulties in identifying internal and external faults for HVDC transmission line protection, a new single-ended protection scheme based on time-domain convolutional power was proposed. In this [...] Read more.
In order to solve the problems of insufficient abilities to withstand transition resistance under remote faults and difficulties in identifying internal and external faults for HVDC transmission line protection, a new single-ended protection scheme based on time-domain convolutional power was proposed. In this scheme, the ratio of time-domain convolution power at different frequencies is used to detect internal and external faults, and the long window convolution power is used to form the pole selection criteria. Due to the integration of transient power fault characteristics at high and low frequencies, this scheme amplifies the characteristic differences between internal and external faults caused by DC line boundaries and has a strong ability to withstand transition resistance. Based on PSCAD/EMTDC, simulation verification was conducted on the Yunnan–Guangzhou ±800 kV HVDC project. The results show that the proposed single-ended protection scheme can effectively identify fault poles, as well as internal and external faults. It has strong resistance to transition resistance and certain anti-interference ability and has strong adaptability to DC line boundaries, which meets the protection requirements of HVDC transmission systems for high speed, selectivity and reliability. Full article
(This article belongs to the Special Issue Situational Awareness and Protection Technologies for Low-Carbon Economic Operation of New Power Systems)
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