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Energies
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Volume II: Situation Awareness for Smart Distribution Systems
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Volume II: Situation Awareness for Smart Distribution Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F2: Distributed Energy System".

Deadline for manuscript submissions: closed (10 February 2023) | Viewed by 6895

Special Issue Editors

Dr. Leijiao Ge

E-Mail Website
Guest Editor
School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Interests: RFID; intelligent distribution network situational awareness technology; renewable energy grid integration optimization control technology; artificial intelligence empowering distribution networks/microgrids; and intelligent distribution power big data cloud computing technology
Special Issues, Collections and Topics in MDPI journals
Dr. Jun Yan

E-Mail Website
Guest Editor
Concordia Institute for Information Systems Engineering, Concordia University, Montréal, QC H3G 1M8, Canada
Interests: computational intelligence and cyber-physical security with applications in smart grids; smart cities; and other smart critical infrastructures
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yonghui Sun

E-Mail Website
Guest Editor
College of Energy and Electrical Engigeering, Hohai University, Nanjing 210098, China
Interests: power system state estimation; load forecasting; smart distribution network
Special Issues, Collections and Topics in MDPI journals
Dr. Bin Zhou

E-Mail Website
Guest Editor
College of Electrical and Information Engineering, Hunan University, Changsha 410012, China
Interests: smart grid analysis and planning; integrated energy power system modeling; energy management and optimal control; emergency management of power energy systems
Dr. Zhongguan Wang

E-Mail Website
Guest Editor
School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Interests: operation of renewable power generation; active distribution system energy management
Special Issues, Collections and Topics in MDPI journals
Dr. Junjun Xu

E-Mail Website
Guest Editor
College of Automation and College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
Interests: distribution network state estimation; self-healing control; uncertainty modeling approaches

Special Issue Information

Dear Colleagues,

As a key application of smart grid technologies, the smart distribution network (SDN) is expected to present a high diversity of equipment and complexity of operation patterns. Situational awareness (SA), which aims to provide critical visibility of the SDN, will be the enabling technology in the assurance of stable SDN operations, and inadequate SA has been identified as one of the causes of several recent large-scale electrical disturbances worldwide. One of the main reasons for the SA inadequacy has been the lack of accurate and reliable data-acquisition units at required locations. With the increase of new measurement units such as smart sensors, smart meters, and smart data concentrators, the availability of SDN data is being improved, but the massive data collected from the SDN may result in significant overheads in both communication and computation that leave the data to end up being unused.

To tackle such challenges and maximize the utility of the data, SA techniques will leverage a three-step approach through perception, comprehension, and prediction. SA perception is the data acquisition stage to obtain the required data for SDN analysis and control. The core technologies may include measurement optimization configuration technology, phase measurement unit (PMU) configuration optimization and data processing technologies, and advanced measurement system technology.

SA comprehension is the data analysis stage that extracts knowledge from the collected data and analyzes the SDN states in terms of stable operations, economy, reliability, flexibility, network power capacity, load transfer, load access capacity, and distributed generation capacity, among others. The core technologies may include power supply computation, distribution system flexibility analysis, survivability and vulnerability analysis, power flow analysis, SDN status estimation, non-intrusive load monitoring, non-intrusive load feature extraction, big data, and cloud computing technologies.

SA prediction is the data forecast stage that predicts the potential variations in the SDN states, such as the changes in load, distributed generation, and electric vehicles. SA can evaluate and prompt the security risks of the system and warn system operators. The specific core technologies include hierarchical load prediction, power output prediction considering uncertainty, electric vehicle zoning prediction considering randomness, system safety risks analysis, load demand forecasting, and early warning techniques.

The objective of this Special Issue is to address issues related to the challenges and solutions of SA in future SDNs, including but not limited to situation perception (e.g., non-intrusive load monitoring), situation comprehension (e.g., three-phase affine power flow analysis of distribution networks), situation prediction (e.g., load demand forecasting), and the evaluation of existing and emerging SA in place (e.g., the implementation of effectiveness evaluation). Applications of SDN situation orientation are also a focus of this Special Issue.

Dr. Leijiao Ge
Dr. Jun Yan
Prof. Dr. Yonghui Sun
Dr. Bin Zhou  
Dr. Zhongguan Wang  
Dr. Junjun Xu
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

  • situation awareness (SA)
  • situation orientation (SO)
  • smart distribution networks (SDNs)
  • phasor measurement unit (PMU)
  • wide-area monitoring systems (WAMSs)
  • SA perception
  • SA comprehension
  • SA prediction
  • non-intrusive load monitoring
  • distributed generation
  • optimization configuration
  • stability analysis
  • blackout prevention
  • system resilience and restoration
  • distribution system planning monitoring, operation, and control
  • very high integration of renewable energy resources
  • cyber security in SDN operation and control
  • modelling of cyber-physical energy and communication systems
  • micro-PMUs and big data in SDN
  • big data
  • expert systems
  • integrated energy systems
  • ubiquitous IoT

Published Papers (5 papers)

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Research

17 pages, 4114 KiB  
Article
Research on Real-Time Dynamic Allocation Strategy of Energy Storage Battery Participating in Secondary Frequency Modulation of Distribution Network
by Yufei Rao, Gaojun Meng, Feng Zhang, Yue Chang, Junjun Xu and Congcong Qian
Energies 2023, 16(8), 3399; https://doi.org/10.3390/en16083399 - 12 Apr 2023
Viewed by 1098
Abstract
With the rapid growth of the power grid load and the continuous access of impact load, the range of power system frequency fluctuation has increased sharply, rendering it difficult to meet the demand for power system frequency recovery through primary frequency modulation alone. [...] Read more.
With the rapid growth of the power grid load and the continuous access of impact load, the range of power system frequency fluctuation has increased sharply, rendering it difficult to meet the demand for power system frequency recovery through primary frequency modulation alone. Given this headache, an optimal control strategy for battery energy storage participating in secondary frequency regulation of the power grid is proposed in this paper based on a double-layer structure. Besides, a coordinated control framework is constructed for energy storage battery joint units engaged in automatic generation control (AGC). At the dispatching level, the power allocation principle is set to coordinate the fast and slow resources of energy storage and conventional thermal power units, and the power decoupling of the two types of frequency modulation (FM) resources is completed. At the system level, a power allocation model representing the real-time frequency modulation capability of energy storage is established to realize the division of frequency modulation responsibilities of each unit and state of charge (SOC) consistency management, and the proposed control strategy is simulated and verified to provide a reference for the energy storage battery to participate in the secondary frequency modulation design of the power grid. Full article
(This article belongs to the Special Issue Volume II: Situation Awareness for Smart Distribution Systems)
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17 pages, 4520 KiB  
Article
Research on Hierarchical Control Strategy of ESS in Distribution Based on GA-SVR Wind Power Forecasting
by Linlin Yu, Gaojun Meng, Giovanni Pau, Yao Wu and Yun Tang
Energies 2023, 16(4), 2079; https://doi.org/10.3390/en16042079 - 20 Feb 2023
Cited by 2 | Viewed by 1262
Abstract
In recent years, the world has been actively promoting the development of wind power, photovoltaic, and other new energy. The inherent randomness and intermittency of wind power output have led to the reduction of supply-side controllability and stability, and the power system is [...] Read more.
In recent years, the world has been actively promoting the development of wind power, photovoltaic, and other new energy. The inherent randomness and intermittency of wind power output have led to the reduction of supply-side controllability and stability, and the power system is facing severe challenges. Aiming at the irregular fluctuation of wind power output and the restriction between the charge and discharge depth and service life of hybrid energy storage equipment, a hierarchical control strategy for a hybrid energy storage system based on improved GA-SVR wind power prediction is proposed. First of all, the short-term prediction of wind power output is carried out using Support Vector Regression (SVR), and the improved genetic algorithm is used for optimization. Then, the result obtained from the prediction calculation is used as the wind power output, and the internal initial power of each energy storage element is obtained through the hybrid energy storage capacity configuration method and further controlled through hierarchical control regulation. Finally, a simulation experiment is carried out on the proposed control strategy. The simulation algorithm shows that the proposed method can not only enhance the effective output of new energy but also extend the service life of energy storage and ensure the safe and stable operation of the power system. Full article
(This article belongs to the Special Issue Volume II: Situation Awareness for Smart Distribution Systems)
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18 pages, 5333 KiB  
Article
TADA: A Transferable Domain-Adversarial Training for Smart Grid Intrusion Detection Based on Ensemble Divergence Metrics and Spatiotemporal Features
by Pengyi Liao, Jun Yan, Jean Michel Sellier and Yongxuan Zhang
Energies 2022, 15(23), 8778; https://doi.org/10.3390/en15238778 - 22 Nov 2022
Cited by 4 | Viewed by 1270
Abstract
For attack detection in the smart grid, transfer learning is a promising solution to tackle data distribution divergence and maintain performance when facing system and attack variations. However, there are still two challenges when introducing transfer learning into intrusion detection: when to apply [...] Read more.
For attack detection in the smart grid, transfer learning is a promising solution to tackle data distribution divergence and maintain performance when facing system and attack variations. However, there are still two challenges when introducing transfer learning into intrusion detection: when to apply transfer learning and how to extract effective features during transfer learning. To address these two challenges, this paper proposes a transferability analysis and domain-adversarial training (TADA) framework. The framework first leverages various data distribution divergence metrics to predict the accuracy drop of a trained model and decides whether one should trigger transfer learning to retain performance. Then, a domain-adversarial training model with CNN and LSTM is developed to extract the spatiotemporal domain-invariant features to reduce distribution divergence and improve detection performance. The TADA framework is evaluated in extensive experiments where false data injection (FDI) attacks are injected at different times and locations. Experiments results show that the framework has high accuracy in accuracy drop prediction, with an RMSE lower than 1.79%. Compared to the state-of-the-art models, TADA demonstrates the highest detection accuracy, achieving an average accuracy of 95.58%. Moreover, the robustness of the framework is validated under different attack data percentages, with an average F1-score of 92.02%. Full article
(This article belongs to the Special Issue Volume II: Situation Awareness for Smart Distribution Systems)
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19 pages, 28286 KiB  
Article
An Optimal Allocation Method of Distributed PV and Energy Storage Considering Moderate Curtailment Measure
by Gang Liang, Bing Sun, Yuan Zeng, Leijiao Ge, Yunfei Li and Yu Wang
Energies 2022, 15(20), 7690; https://doi.org/10.3390/en15207690 - 18 Oct 2022
Cited by 3 | Viewed by 1127
Abstract
Increasing distributed generations (DGs) are integrated into the distribution network. The risk of not satisfying operation constraints caused by the uncertainty of renewable energy output is increasing. The energy storage (ES) could stabilize the fluctuation of renewable energy generation output. Therefore, it can [...] Read more.
Increasing distributed generations (DGs) are integrated into the distribution network. The risk of not satisfying operation constraints caused by the uncertainty of renewable energy output is increasing. The energy storage (ES) could stabilize the fluctuation of renewable energy generation output. Therefore, it can promote the consumption of renewable energy. A distributed photovoltaic (PV) and ES optimal allocation method based on the security region is proposed. Firstly, a bi-level optimal allocation model of PV and ES is established. The outer layer is a nonlinear optimization model, taking the maximum power supply benefit as the objective function. The inner layer is a day-ahead economic dispatching model. Then, a quick model solving method based on the steady-state security region is proposed. An initial allocation scheme of PV and ES is determined with the redundancy capacity. In addition, the linear hyperplane coefficient of the security region is used to convert the nonlinear day-ahead economic dispatching model into a linear one. Finally, the proposed method is used to analyze the improved IEEE 33-node system. It is found that a moderate curtailment measure of distributed PV peak output and the allocation of energy storage have a significant effect on the power supply benefit of the distribution system. The optimal quota capacity of DG exceeds the sum of the maximum load and the branch capacity. In addition, the optimal allocation scheme is closely related to the cost and technical parameters of distributed PV and ES. Dynamic allocation schemes should be formulated for distribution network. Full article
(This article belongs to the Special Issue Volume II: Situation Awareness for Smart Distribution Systems)
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23 pages, 6822 KiB  
Article
Active Defense Research against False Data Injection Attacks of Power CPS Based on Data-Driven Algorithms
by Xiaoyong Bo, Zhaoyang Qu, Lei Wang, Yunchang Dong, Zhenming Zhang and Da Wang
Energies 2022, 15(19), 7432; https://doi.org/10.3390/en15197432 - 10 Oct 2022
Cited by 5 | Viewed by 1569
Abstract
The terminal equipment interconnection and the network communication environment are complex in power cyber–physical systems (CPS), and the frequent interaction between the information and energy flows aggravates the risk of false data injection attacks (FDIAs) in the power grid. This paper proposes an [...] Read more.
The terminal equipment interconnection and the network communication environment are complex in power cyber–physical systems (CPS), and the frequent interaction between the information and energy flows aggravates the risk of false data injection attacks (FDIAs) in the power grid. This paper proposes an active defense framework against FDIAs of power CPS based on data-driven algorithms in order to ensure that FDIAs can be efficiently detected and processed in real-time during power grid operation. First, the data transmission scenario and false data injection forms of power CPS were analyzed, and the FDIA mathematical model was expounded. Then, from a data-driven perspective, the algorithm improvement and process design were carried out for the three key links of data enhancement, attack detection, and data reconstruction. Finally, an active defense framework against FDIAs was proposed. The example analysis verified that the method proposed in this paper could effectively detect FDIAs and perform data reconstruction, providing a new idea for the active defense against FDIAs of power CPS. Full article
(This article belongs to the Special Issue Volume II: Situation Awareness for Smart Distribution Systems)
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