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Situation Awareness for Smart Distribution Systems

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

Deadline for manuscript submissions: closed (25 February 2022) | Viewed by 25742

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Special Issue Editors

Dr. Leijiao Ge

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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

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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
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Prof. Dr. Yonghui Sun

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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. Zhongguan Wang

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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

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
Prof. Dr. Zhongguan Wang
Guest Editors

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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 (11 papers)

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Editorial

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3 pages, 174 KiB

Open AccessEditorial

Situational Awareness for Smart Distribution Systems

by Leijiao Ge, Jun Yan, Yonghui Sun and Zhongguan Wang

Energies 2022, 15(11), 4164; https://doi.org/10.3390/en15114164 - 06 Jun 2022

Cited by 2 | Viewed by 1393

Abstract

In recent years, the accelerating climate change and intensifying natural disasters have called for more renewable, resilient, and reliable energy from more distributed sources to more diversified consumers, resulting in a pressing need for advanced situational awareness of modern smart distribution systems [...] [...] Read more.

(This article belongs to the Special Issue Situation Awareness for Smart Distribution Systems)

Research

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24 pages, 5687 KiB

Open AccessArticle

Multiple Power Supply Capacity Planning Research for New Power System Based on Situation Awareness

by Dahu Li, Xiaoda Cheng, Leijiao Ge, Wentao Huang, Jun He and Zhongwei He

Energies 2022, 15(9), 3298; https://doi.org/10.3390/en15093298 - 30 Apr 2022

Cited by 10 | Viewed by 1555

Abstract

In the context of new power systems, reasonable capacity optimization of multiple power systems can not only reduce carbon emissions, but also improve system safety and stability. This paper proposes a situation awareness-based capacity optimization strategy for wind-photovoltaic-thermal power systems and establishes a bi-level model for system capacity optimization. The upper-level model considers environmental protection and economy, and carries out multi-objective optimization of the system capacity planning solution with the objectives of minimizing carbon emissions and total system cost over the whole life cycle of the system, further obtaining a set of capacity planning solutions based on the Pareto frontier. A Pareto optimal solution set decision method based on grey relativity analysis is proposed to quantitatively assess the comprehensive economic–environmental properties of the system. The capacity planning solutions obtained from the upper model are used as the input to the lower model. The lower model integrates system stability, environmental protection, and economy and further optimizes the set of capacity planning solutions obtained from the upper model with the objective of maximizing the inertia security region and the best comprehensive economic–environmental properties to obtain the optimal capacity planning scheme. The NSGA-II modified algorithm (improved NSGA-II algorithm based on dominant strength, INSGA2-DS) is used to solve the upper model, and the Cplex solver is called on to solve the lower model. Finally, the modified IEEE-39 node algorithm is used to verify that the optimized capacity planning scheme can effectively improve the system security and stability and reduce the carbon emissions and total system cost throughout the system life cycle. Full article

(This article belongs to the Special Issue Situation Awareness for Smart Distribution Systems)

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20 pages, 3330 KiB

Open AccessArticle

A DC Series Arc Fault Detection Method Based on a Lightweight Convolutional Neural Network Used in Photovoltaic System

by Yao Wang, Cuiyan Bai, Xiaopeng Qian, Wanting Liu, Chen Zhu and Leijiao Ge

Energies 2022, 15(8), 2877; https://doi.org/10.3390/en15082877 - 14 Apr 2022

Cited by 15 | Viewed by 2342

Abstract

Although photovoltaic (PV) systems play an essential role in distributed generation systems, they also suffer from serious safety concerns due to DC series arc faults. This paper proposes a lightweight convolutional neural network-based method for detecting DC series arc fault in PV systems to solve this issue. An experimental platform according to UL1699B is built, and current data ranging from 3 A to 25 A is collected. Moreover, test conditions, including PV inverter startup and irradiance mutation, are also considered to evaluate the robustness of the proposed method. Before fault detection, the current data is preprocessed with power spectrum estimation. The lightweight convolutional neural network has a lower computational burden for its fewer parameters, which can be ready for embedded microprocessor-based edge applications. Compared to similar lightweight convolutional network models such as Efficientnet-B0, B2, and B3, the Efficientnet-B1 model shows the highest accuracy of 96.16% for arc fault detection. Furthermore, an attention mechanism is combined with the Efficientnet-B1 to make the algorithm more focused on arc features, which can help the algorithm reduce unnecessary computation. The test results show that the detection accuracy of the proposed method can be up to 98.81% under all test conditions, which is higher than that of general networks. Full article

(This article belongs to the Special Issue Situation Awareness for Smart Distribution Systems)

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18 pages, 4181 KiB

Open AccessArticle

Two-Stage Energy Management Strategies of Sustainable Wind-PV-Hydrogen-Storage Microgrid Based on Receding Horizon Optimization

by Jiarui Wang, Dexin Li, Xiangyu Lv, Xiangdong Meng, Jiajun Zhang, Tengfei Ma, Wei Pei and Hao Xiao

Energies 2022, 15(8), 2861; https://doi.org/10.3390/en15082861 - 14 Apr 2022

Cited by 19 | Viewed by 2428

Abstract

Hydrogen and renewable electricity-based microgrid is considered to be a promising way to reduce carbon emissions, promote the consumption of renewable energies and improve the sustainability of the energy system. In view of the fact that the existing day-ahead optimal operation model ignores the uncertainties and fluctuations of renewable energies and loads, a two-stage energy management model is proposed for the sustainable wind-PV-hydrogen-storage microgrid based on receding horizon optimization to eliminate the adverse effects of their uncertainties and fluctuations. In the first stage, the day-ahead optimization is performed based on the predicted outpower of WT and PV, the predicted demands of power and hydrogen loads. In the second stage, the intra-day optimization is performed based on the actual data to trace the day-ahead operation schemes. Since the intra-day optimization can update the operation scheme based on the latest data of renewable energies and loads, the proposed two-stage management model is effective in eliminating the uncertain factors and maintaining the stability of the whole system. Simulations show that the proposed two-stage energy management model is robust and effective in coordinating the operation of the wind-PV-hydrogen-storage microgrid and eliminating the uncertainties and fluctuations of WT, PV and loads. In addition, the battery storage can reduce the operation cost, alleviate the fluctuations of the exchanged power with the power grid and improve the performance of the energy management model. Full article

(This article belongs to the Special Issue Situation Awareness for Smart Distribution Systems)

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25 pages, 8365 KiB

Open AccessArticle

Short-Term Load Forecasting Model of Electric Vehicle Charging Load Based on MCCNN-TCN

by Jiaan Zhang, Chenyu Liu and Leijiao Ge

Energies 2022, 15(7), 2633; https://doi.org/10.3390/en15072633 - 04 Apr 2022

Cited by 26 | Viewed by 3096

Abstract

The large fluctuations in charging loads of electric vehicles (EVs) make short-term forecasting challenging. In order to improve the short-term load forecasting performance of EV charging load, a corresponding model-based multi-channel convolutional neural network and temporal convolutional network (MCCNN-TCN) are proposed. The multi-channel convolutional neural network (MCCNN) can extract the fluctuation characteristics of EV charging load at various time scales, while the temporal convolutional network (TCN) can build a time-series dependence between the fluctuation characteristics and the forecasted load. In addition, an additional BP network maps the selected meteorological and date features into a high-dimensional feature vector, which is spliced with the output of the TCN. According to experimental results employing urban charging station load data from a city in northern China, the proposed model is more accurate than artificial neural network (ANN), long short-term memory (LSTM), convolutional neural networks and long short-term memory (CNN-LSTM), and TCN models. The MCCNN-TCN model outperforms the ANN, LSTM, CNN-LSTM, and TCN by 14.09%, 25.13%, 27.32%, and 4.48%, respectively, in terms of the mean absolute percentage error. Full article

(This article belongs to the Special Issue Situation Awareness for Smart Distribution Systems)

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15 pages, 2488 KiB

Open AccessArticle

Short Text Classification for Faults Information of Secondary Equipment Based on Convolutional Neural Networks

by Jiufu Liu, Hongzhong Ma, Xiaolei Xie and Jun Cheng

Energies 2022, 15(7), 2400; https://doi.org/10.3390/en15072400 - 24 Mar 2022

Cited by 9 | Viewed by 1780

Abstract

As the construction of smart grids is in full swing, the number of secondary equipment is also increasing, resulting in an explosive growth of power big data, which is related to the safe and stable operation of power systems. During the operation of the secondary equipment, a large amount of short text data of faults and defects are accumulated, and they are often manually recorded by transportation inspection personnel to complete the classification of defects. Therefore, an automatic text classification based on convolutional neural networks (CNN) is proposed in this paper. Firstly, the topic model is used to mine the global features. At the same time, the word2vec word vector model is used to mine the contextual semantic features of words. Then, the improved LDA topic word vector and word2vec word vector are combined to absorb their respective advantages and utilizations. Finally, the validity and accuracy of the model is verified using actual operational data from the northwest power grid as case study. Full article

(This article belongs to the Special Issue Situation Awareness for Smart Distribution Systems)

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15 pages, 3254 KiB

Open AccessArticle

A Novel Denoising Auto-Encoder-Based Approach for Non-Intrusive Residential Load Monitoring

by Xi He, Heng Dong, Wanli Yang and Jun Hong

Energies 2022, 15(6), 2290; https://doi.org/10.3390/en15062290 - 21 Mar 2022

Cited by 4 | Viewed by 1720

Abstract

Mounting concerns pertaining to energy efficiency have led to the research of load monitoring. By Non-Intrusive Load Monitoring (NILM), detailed information regarding the electric energy consumed by each appliance per day or per hour can be formed. The accuracy of the previous residential load monitoring approach relies heavily on the data acquisition frequency of the energy meters. It brings high overall cost issues, and furthermore, the differentiating algorithm becomes much more complicated. Based on this, we proposed a novel non-Intrusive residential load disaggregation method that only depends on the regular data acquisition speed of active power measurements. Additionally, this approach brings some novelties to the traditionally used denoising Auto-Encoder (dAE), i.e., the reconfiguration of the overlapping parts of the sliding windows. The median filter is used for the data processing of the overlapping window. Two datasets, i.e., the Reference Energy Disaggregation Dataset (REDD) and TraceBase, are used for test and validation. By numerical testing of the real residential data, it proves that the proposed method is superior to the traditional Factorial Hidden Markov Model (FHMM)-based approach. Furthermore, the proposed method can be used for energy data, disaggregation disregarding the brand and model of each appliance. Full article

(This article belongs to the Special Issue Situation Awareness for Smart Distribution Systems)

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18 pages, 3152 KiB

Open AccessArticle

Distributionally Robust Joint Chance-Constrained Dispatch for Electricity–Gas–Heat Integrated Energy System Considering Wind Uncertainty

by Hui Liu, Zhenggang Fan, Haimin Xie and Ni Wang

Energies 2022, 15(5), 1796; https://doi.org/10.3390/en15051796 - 28 Feb 2022

Cited by 10 | Viewed by 1771

Abstract

With the increasing penetration of wind power, the uncertainty associated with it brings more challenges to the operation of the integrated energy system (IES), especially the power subsystem. However, the typical strategies to deal with wind power uncertainty have poor performance in balancing economy and robustness. Therefore, this paper proposes a distributionally robust joint chance-constrained dispatch (DR-JCCD) model to coordinate the economy and robustness of the IES with uncertain wind power. The optimization dispatch model is formulated as a two-stage problem to minimize both the day-ahead and the real-time operation costs. Moreover, the ambiguity set is generated using Wasserstein distance, and the joint chance constraints are used to ensure that the safety constraints (e.g., ramping limit and transmission limit) can be satisfied jointly under the worst-case probability distribution of wind power. The model is remodeled as a mixed-integer tractable programming issue, which can be solved efficiently by ready-made solvers using linear decision rules and linearization methods. Case studies on an electricity–gas–heat regional integrated system, which includes a modified IEEE 24-bus system, 20 natural gas-nodes, and 6 heat-node system, are investigated for verification. Numerical simulation results demonstrate that the proposed DR-JCCD approach effectively coordinates the economy and robustness of IES and can offer operators a reasonable energy management scheme with an acceptable risk level. Full article

(This article belongs to the Special Issue Situation Awareness for Smart Distribution Systems)

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13 pages, 1945 KiB

Open AccessArticle

Electric Heating Load Forecasting Method Based on Improved Thermal Comfort Model and LSTM

by Jie Sun, Jiao Wang, Yonghui Sun, Mingxin Xu, Yong Shi, Zifa Liu and Xingya Wen

Energies 2021, 14(15), 4525; https://doi.org/10.3390/en14154525 - 27 Jul 2021

Cited by 3 | Viewed by 1409

Abstract

The accuracy of the electric heating load forecast in a new load has a close relationship with the safety and stability of distribution network in normal operation. It also has enormous implications on the architecture of a distribution network. Firstly, the thermal comfort model of the human body was established to analyze the comfortable body temperature of a main crowd under different temperatures and levels of humidity. Secondly, it analyzed the influence factors of electric heating load, and from the perspective of meteorological factors, it selected the difference between human thermal comfort temperature and actual temperature and humidity by gray correlation analysis. Finally, the attention mechanism was utilized to promote the precision of combined adjunction model, and then the data results of the predicted electric heating load were obtained. In the verification, the measured data of electric heating load in a certain area of eastern Inner Mongolia were used. The results showed that after considering the input vector with most relative factors such as temperature and human thermal comfort, the LSTM network can realize the accurate prediction of the electric heating load. Full article

(This article belongs to the Special Issue Situation Awareness for Smart Distribution Systems)

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22 pages, 3835 KiB

Open AccessArticle

An Energy Management Optimization Method for Community Integrated Energy System Based on User Dominated Demand Side Response

by Yiqi Li, Jing Zhang, Zhoujun Ma, Yang Peng and Shuwen Zhao

Energies 2021, 14(15), 4398; https://doi.org/10.3390/en14154398 - 21 Jul 2021

Cited by 13 | Viewed by 1933

Abstract

With the development of integrated energy systems (IES), the traditional demand response technologies for single energy that do not take customer satisfaction into account have been unable to meet actual needs. Therefore, it is urgent to study the integrated demand response (IDR) technology for integrated energy, which considers consumers’ willingness to participate in IDR. This paper proposes an energy management optimization method for community IES based on user dominated demand side response (UDDSR). Firstly, the responsive power loads and thermal loads are modeled, and aggregated using UDDSR bidding optimization. Next, the community IES is modeled and an aggregated building thermal model is introduced to measure the temperature requirements of the entire community of users for heating. Then, a day-ahead scheduling model is proposed to realize the energy management optimization. Finally, a penalty mechanism is introduced to punish the participants causing imbalance response against the day-ahead IDR bids, and the conditional value-at-risk (CVaR) theory is introduced to enhance the robustness of the scheduling model under different prediction accuracies. The case study demonstrates that the proposed method can reduce the operating cost of the community under the premise of fully considering users’ willingness, and can complete the IDR request initiated by the power grid operator or the dispatching department. Full article

(This article belongs to the Special Issue Situation Awareness for Smart Distribution Systems)

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Review

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24 pages, 2837 KiB

Open AccessReview

Smart Distribution Network Situation Awareness for High-Quality Operation and Maintenance: A Brief Review

by Leijiao Ge, Yuanliang Li, Yuanliang Li, Jun Yan and Yonghui Sun

Energies 2022, 15(3), 828; https://doi.org/10.3390/en15030828 - 24 Jan 2022

Cited by 45 | Viewed by 4564

Abstract

In order to meet the requirements of high-tech enterprises for high power quality, high-quality operation and maintenance (O&M) in smart distribution networks (SDN) is becoming increasingly important. As a significant element in enhancing the high-quality O&M of SDN, situation awareness (SA) began to excite the significant interest of scholars and managers, especially after the integration of intermittent renewable energy into SDN. Specific to high-quality O&M, the paper decomposes SA into three stages: detection, comprehension, and projection. In this paper, the state-of-the-art knowledge of SND SA is discussed, a review of critical technologies is presented, and a five-layer visualization framework of the SDN SA is constructed. SA detection aims to improve the SDN observability, SA comprehension is associated with the SDN operating status, and SA projection pertains to the analysis of the future SDN situation. The paper can provide researchers and utility engineers with insights into the technical achievements, barriers, and future research directions of SDN SA. Full article

(This article belongs to the Special Issue Situation Awareness for Smart Distribution Systems)

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