Research

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17 pages, 3916 KiB

Open AccessArticle

A Self-Healing Strategy for Modern Distribution Networks

by Cleberton Reiz, Caio E. M. Pereira and Jonatas B. Leite

Energies 2023, 16(16), 5890; https://doi.org/10.3390/en16165890 - 09 Aug 2023

Viewed by 709

Electrical distribution companies have been investing in modernizing their structures, especially operation automation. The integration of information technologies and communications makes fast power restoration during fault events, providing better profit to companies and a more reliable and safe distribution network for customers. A [...] Read more.

Electrical distribution companies have been investing in modernizing their structures, especially operation automation. The integration of information technologies and communications makes fast power restoration during fault events, providing better profit to companies and a more reliable and safe distribution network for customers. A self-healing strategy can be implemented for protection and control devices to work cooperatively, achieving the global purpose of automatic distribution system restoration. Thus, this work proposes a methodology for short-circuit fault detection, isolation of the faulted section, and restoration of downstream sections using neighbor feeders. The protection devices use standardized IEC and ANSI/IEEE functions to sensitize faults in the system and to promote adequate isolation, allowing the consequent restorative process. A genetic algorithm optimizes the devices’ parameters used in the protection scheme, making fastest the isolation process and ensuring the protection system coordination and selectivity. Results obtained using Simulink^® allows for verifying the proposed methodology’s behavior and efficiency. Full article

(This article belongs to the Special Issue Current Research and Future Development in Intelligent Power Distribution Systems: Planning, Operation and Control)

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16 pages, 5445 KiB

Open AccessArticle

Capacity Optimal Allocation Method and Frequency Division Energy Management for Hybrid Energy Storage System Considering Grid-Connected Requirements in Photovoltaic System

by Wei Li, Ruixin Jin, Xiaoyong Ma and Guozun Zhang

Energies 2023, 16(10), 4154; https://doi.org/10.3390/en16104154 - 17 May 2023

Cited by 2 | Viewed by 951

Abstract

The coordination between a hybrid energy storage system (HESS) and photovoltaic (PV) power station can significantly reduce grid-connected PV power fluctuations. This study proposes a HESS capacity optimal allocation method considering the grid-connected PV requirements. Firstly, based on the power fluctuation requirements in [...] Read more.

The coordination between a hybrid energy storage system (HESS) and photovoltaic (PV) power station can significantly reduce grid-connected PV power fluctuations. This study proposes a HESS capacity optimal allocation method considering the grid-connected PV requirements. Firstly, based on the power fluctuation requirements in the PV power station grid-connected regulations, the maximum power point tracking working point switching control is performed for the PV power station, from which the grid-connected PV power and HESS power are obtained. Then, a capacity optimal allocation method and frequency division energy management strategy (EMS) for HESS is proposed to find the energy response and power response of each energy storage source. Furthermore, a multi-objective optimization function with HESS cutoff frequency as the independent variable is constructed, and the input cost of HESS and the life loss of the lithium battery are optimized. Finally, the overall strategy is compared and analyzed under the scenarios of three typical PV power fluctuations. Simulation results show that the control strategy has a good smoothing effect on PV power fluctuations. From the perspective of the annual comprehensive input cost, HESS realizes the optimal capacity allocation when the cutoff frequency is 0.0066 Hz. Full article

(This article belongs to the Special Issue Current Research and Future Development in Intelligent Power Distribution Systems: Planning, Operation and Control)

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16 pages, 2204 KiB

Open AccessArticle

Non-Intrusive Arc Fault Detection and Localization Method Based on the Mann–Kendall Test and Current Decomposition

by Wenqian Jiang, Bo Liu, Zhou Yang, Hanju Cai, Xiuqing Lin and Da Xu

Energies 2023, 16(10), 3988; https://doi.org/10.3390/en16103988 - 09 May 2023

Cited by 2 | Viewed by 1114

Abstract

In recent years, electrical fires caused by arc faults have been increasing, seriously affecting the safety of people’s lives and property. Considering the complex arc fault characteristics of actual low-voltage users, the non-intrusive arc fault detection and localization method is studied. First, the [...] Read more.

In recent years, electrical fires caused by arc faults have been increasing, seriously affecting the safety of people’s lives and property. Considering the complex arc fault characteristics of actual low-voltage users, the non-intrusive arc fault detection and localization method is studied. First, the characteristics of arc current waveforms are analyzed, and event detection based on the Mann–Kendall Test is performed for the difference between the current waveforms of two adjacent cycles, rather than using the current waveforms directly. Then, the current waveforms of the two segments are calculated via subtraction to obtain the current waveform of the electric appliances causing the event. A current feature parameter database of the normal and arc currents is constructed via harmonic analysis, and a multi-appliance current decomposition model considering the sparse operation characteristics of appliances is established; thus, the arc localization problem is transformed into an optimization problem. Finally, a genetic algorithm is used to optimize the differential current decomposition results, and then, locate the arc fault. A household arc fault simulation experiment is carried out for the common electric appliances of actual low-voltage users. The experimental results show that the proposed non-intrusive arc fault detection and localization method is effective. Full article

(This article belongs to the Special Issue Current Research and Future Development in Intelligent Power Distribution Systems: Planning, Operation and Control)

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27 pages, 8911 KiB

Open AccessArticle

Impact of the Operation of Distribution Systems on the Resilience Assessment of Transmission Systems under Ice Disasters

by Zhiwei Wang, Xiao Ma, Song Gao, Changjiang Wang and Shuguang Li

Energies 2023, 16(9), 3845; https://doi.org/10.3390/en16093845 - 29 Apr 2023

Viewed by 943

Abstract

Ice disasters, such as ice storms, can cause serious damage to power systems. To understand ice disasters’ influences on power systems, this paper introduces a resilience evaluation frame for transmission and distribution systems during ice disasters. First, we built a vulnerability model for [...] Read more.

Ice disasters, such as ice storms, can cause serious damage to power systems. To understand ice disasters’ influences on power systems, this paper introduces a resilience evaluation frame for transmission and distribution systems during ice disasters. First, we built a vulnerability model for transmission and distribution systems under ice disaster weather. Then, we established an optimal load power shedding model for transmission and distribution systems. After this, according to the vulnerability model and the optimal power load power shedding model, we generated the fault scenario set of a system in the influence of an ice disaster. According to the curve of system resilience, we propose two resilience evaluation indices of transmission and distribution systems under ice disaster weather. Finally, we verified the efficacy and rationalization of the established resilience evaluation framework with an example in which a transmission and distribution system is coupled with a six-bus transmission system and two distribution systems. This study highlights the necessity of resilience assessment of transmission and distribution systems during ice disasters. Full article

(This article belongs to the Special Issue Current Research and Future Development in Intelligent Power Distribution Systems: Planning, Operation and Control)

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18 pages, 10720 KiB

Open AccessArticle

Secondary Frequency Regulation Control Strategy with Electric Vehicles Considering User Travel Uncertainty

by Xiaohong Dong, Yang Ma, Xiaodan Yu, Xiangyu Wei, Yanqi Ren and Xin Zhang

Energies 2023, 16(9), 3794; https://doi.org/10.3390/en16093794 - 28 Apr 2023

Viewed by 973

Abstract

The premise of electric vehicles (EVs) participating in the frequency regulation (FR) of power systems is to satisfy the charging demands of users. In view of problems such as the uncertainty of EV users’ departure time and the increase in power supply pressure [...] Read more.

The premise of electric vehicles (EVs) participating in the frequency regulation (FR) of power systems is to satisfy the charging demands of users. In view of problems such as the uncertainty of EV users’ departure time and the increase in power supply pressure due to disordered charging in the frequency regulation process of EV clusters, a secondary frequency regulation control strategy with EVs considering user travel uncertainty is proposed. Firstly, EV charging history was analyzed, a reliability parameter was introduced to describe the user travel uncertainty, and an individual EV controllable domain model based on reliability correction was constructed. Then, EV clusters were grouped according to charging urgency and state of charge (SOC), and the controllable capacity of EV clusters was determined. Finally, EV frequency regulation capability parameters and charging urgency parameters were defined to determine the EV frequency regulation priority list, combined with the EV state grouping and priority list, and the EV cluster frequency control strategy was proposed. The simulation results show that the proposed strategy can satisfy the charging demands of users under uncertain travel conditions, reduce the power supply pressure of the power system caused by EVs entering the forced charging state, and effectively suppress frequency deviation. Full article

(This article belongs to the Special Issue Current Research and Future Development in Intelligent Power Distribution Systems: Planning, Operation and Control)

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13 pages, 4948 KiB

Open AccessArticle

Research on Multiple Load Short-Term Forecasting Model of Integrated Energy Distribution System Based on Mogrifier-Quantum Weighted MELSTM

by Peng Song and Zhisheng Zhang

Energies 2023, 16(9), 3697; https://doi.org/10.3390/en16093697 - 25 Apr 2023

Cited by 1 | Viewed by 1194

Abstract

Accurate and efficient short-term forecasting of multiple loads is of great significance to the operation control and scheduling of integrated energy distribution systems. In order to improve the effect of load forecasting, a mogrifier-quantum weighted memory enhancement long short-term memory (Mogrifier-QWMELSTM) neural network [...] Read more.

Accurate and efficient short-term forecasting of multiple loads is of great significance to the operation control and scheduling of integrated energy distribution systems. In order to improve the effect of load forecasting, a mogrifier-quantum weighted memory enhancement long short-term memory (Mogrifier-QWMELSTM) neural network forecasting model is proposed. Compared with the conventional LSTM neural network model, the model proposed in this paper has three improvements in model structure and model composition. First, the mogrifier is added to make the data fully interact with each other. This addition can help enhance the correlation between the front and rear data and improve generalization, which is the main disadvantage of LSTM neural network. Second, the memory enhancement mechanism is added on the forget gate to realize the extraction and recovery of forgotten information. The addition can help improve the gradient transmission ability in the learning process of the neural network, make the neural network remain sensitive to distant data information, and enhance the memory ability. Third, the model is composed of quantum weighted neurons. Compared with conventional neurons, quantum weighted neurons have significant advantages in nonlinear data processing and parallel computing, which help to improve the accuracy of load forecasting. The simulation results show that the weighted mean accuracy of the proposed model can reach more than 97.5% in summer and winter. Moreover, the proposed model has good forecasting effect on seven typical days in winter, which shows that the model has good stability. Full article

(This article belongs to the Special Issue Current Research and Future Development in Intelligent Power Distribution Systems: Planning, Operation and Control)

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15 pages, 2871 KiB

Open AccessArticle

An Online Control Method of Reactive Power and Voltage Based on Mechanism–Data Hybrid Drive Model Considering Source–Load Uncertainty

by Xu Huang, Guoqiang Zu, Qi Ding, Ran Wei, Yudong Wang and Wei Wei

Energies 2023, 16(8), 3501; https://doi.org/10.3390/en16083501 - 18 Apr 2023

Cited by 1 | Viewed by 984

Abstract

The uncertainty brought about by the high proportion of distributed generations poses great challenges to the operational safety of novel distribution systems. Therefore, this paper proposes an online reactive power and voltage control method that integrates source–load uncertainty and a mechanism–data hybrid drive [...] Read more.

The uncertainty brought about by the high proportion of distributed generations poses great challenges to the operational safety of novel distribution systems. Therefore, this paper proposes an online reactive power and voltage control method that integrates source–load uncertainty and a mechanism–data hybrid drive (MDHD) model. Based on the concept of a mechanism and data hybrid drive, the mechanism-driven deterministic reactive power optimization strategy and the stochastic reactive power optimization strategy are used as training data. By training the data-driven CNN–GRU network model offline, the influence of source–load uncertainty on reactive power optimization can be effectively assessed. On this basis, according to the online source and load predicted data, the proposed hybrid-driven model can be applied to quickly obtain the reactive power optimization strategy to enable fast control of voltage. As observed in the case studies, compared with the traditional deterministic and stochastic reactive power optimization models, the hybrid-driven model not only satisfies the real-time requirement of online voltage control, but also has stronger adaptability to source–load uncertainty. Full article

(This article belongs to the Special Issue Current Research and Future Development in Intelligent Power Distribution Systems: Planning, Operation and Control)

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16 pages, 2832 KiB

Open AccessArticle

An EV Charging Guidance Strategy Based on the Hierarchical Comprehensive Evaluation Method

by Cong Zhang, Qun Gao, Ke Peng and Yan Jiang

Energies 2023, 16(7), 3113; https://doi.org/10.3390/en16073113 - 29 Mar 2023

Viewed by 933

Abstract

With the increasing number of electric vehicles (EVs), the randomness of the charging load will have an increasing impact on the distribution network (DN) and road network. Different guidance strategies lead to different network-related capabilities of fast charging stations (FCSs). In this paper, [...] Read more.

With the increasing number of electric vehicles (EVs), the randomness of the charging load will have an increasing impact on the distribution network (DN) and road network. Different guidance strategies lead to different network-related capabilities of fast charging stations (FCSs). In this paper, a hierarchical and comprehensive evaluation method is proposed for the network-related capability of FCSs. Based on the comprehensive evaluation method, a charging guidance strategy is proposed to improve the network-related capability of FCSs. Finally, the network connection capability of FCSs under four strategies is comprehensively evaluated to verify the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Current Research and Future Development in Intelligent Power Distribution Systems: Planning, Operation and Control)

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15 pages, 2298 KiB

Open AccessArticle

An Ultra-Short-Term PV Power Forecasting Method for Changeable Weather Based on Clustering and Signal Decomposition

by Jiaan Zhang, Yan Hao, Ruiqing Fan and Zhenzhen Wang

Energies 2023, 16(7), 3092; https://doi.org/10.3390/en16073092 - 28 Mar 2023

Cited by 1 | Viewed by 1198

Abstract

Photovoltaic (PV) power shows different fluctuation characteristics under different weather types as well as strong randomness and uncertainty in changeable weather such as sunny to cloudy, cloudy to rain, and so on, resulting in low forecasting accuracy. For the changeable type of weather, [...] Read more.

Photovoltaic (PV) power shows different fluctuation characteristics under different weather types as well as strong randomness and uncertainty in changeable weather such as sunny to cloudy, cloudy to rain, and so on, resulting in low forecasting accuracy. For the changeable type of weather, an ultra-short-term photovoltaic power forecasting method is proposed based on affinity propagation (AP) clustering, complete ensemble empirical mode decomposition with an adaptive noise algorithm (CEEMDAN), and bi-directional long and short-term memory network (BiLSTM). First, the PV power output curve of the standard clear-sky day was extracted monthly from the historical data, and the photovoltaic power was normalized according to it. Second, the changeable days were extracted from various weather types based on the AP clustering algorithm and the Euclidean distance by considering the mean and variance of the clear-sky power coefficient (CSPC). Third, the CEEMDAN algorithm was further used to decompose the data of changeable days to reduce its overall non-stationarity, and each component was forecasted based on the BiLSTM network, so as to obtain the PV forecasting value in changeable weather. Using the PV dataset obtained from Alice Springs, Australia, the presented method was verified by comparative experiments with the BP, BiLSTM, and CEEMDAN-BiLSTM models, and the MAPE of the proposed method was 2.771%, which was better than the other methods. Full article

(This article belongs to the Special Issue Current Research and Future Development in Intelligent Power Distribution Systems: Planning, Operation and Control)

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24 pages, 3074 KiB

Open AccessArticle

An Optimal Scheduling Method of Shared Energy Storage System Considering Distribution Network Operation Risk

by Jiahao Chen, Bing Sun, Yuan Zeng, Ruipeng Jing, Shimeng Dong and Jingran Wang

Energies 2023, 16(5), 2411; https://doi.org/10.3390/en16052411 - 02 Mar 2023

Viewed by 1103

Abstract

Shared energy storage systems (SESS) have been gradually developed and applied to distribution networks (DN). There are electrical connections between SESSs and multiple DN nodes; SESSs could significantly improve the power restoration potential and reduce the power interruption cost during fault periods. Currently, [...] Read more.

Shared energy storage systems (SESS) have been gradually developed and applied to distribution networks (DN). There are electrical connections between SESSs and multiple DN nodes; SESSs could significantly improve the power restoration potential and reduce the power interruption cost during fault periods. Currently, a major challenge exists in terms of how to consider both the efficiency of the operation and the reliability cost when formulating the SESS scheduling scheme. A SESS optimal scheduling method that considers the DN operation risk is proposed in this paper. First, a multi-objective day-ahead scheduling model for SESS is developed, where the user’s interruption cost is regarded as the reliability cost and it is the product of the occurrence probability of the expected accident and the loss of power outage. Then, an island partition model with SESS was established in order to accurately calculate the reliability cost. Via the maximum island partition and island optimal rectification, the SESS was carefully integrated into the power restoration system. Furthermore, in order to minimize the comprehensive operation cost, an improved genetic algorithm for the island partition was designed to solve the complex SESS optimal scheduling model. Finally, a case study on the improved PG&E 69 bus system was analyzed. Moreover, we found that the DN’s comprehensive operation cost decreased by 6.6% using the proposed method. Full article

(This article belongs to the Special Issue Current Research and Future Development in Intelligent Power Distribution Systems: Planning, Operation and Control)

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21 pages, 7605 KiB

Open AccessArticle

Multi-Timescale Optimal Dispatching Strategy for Coordinated Source-Grid-Load-Storage Interaction in Active Distribution Networks Based on Second-Order Cone Planning

by Yang Mi, Yuyang Chen, Minghan Yuan, Zichen Li, Biao Tao and Yunhao Han

Energies 2023, 16(3), 1356; https://doi.org/10.3390/en16031356 - 27 Jan 2023

Cited by 9 | Viewed by 1489

Abstract

In order to cope with the efficient consumption and flexible regulation of resource scarcity due to grid integration of renewable energy sources, a scheduling strategy that takes into account the coordinated interaction of source, grid, load, and storage is proposed. In order to [...] Read more.

In order to cope with the efficient consumption and flexible regulation of resource scarcity due to grid integration of renewable energy sources, a scheduling strategy that takes into account the coordinated interaction of source, grid, load, and storage is proposed. In order to improve the accuracy of the dispatch, a BP neural network approach modified by a genetic algorithm is used to predict renewable energy sources and loads. The non-convex, non-linear optimal dispatch model of the distribution grid is transformed into a mixed integer programming model with optimal tides based on the second-order cone relaxation, variable substitution, and segmental linearization of the Big M method. In addition, the uncertainty of distributed renewable energy output and the flexibility of load demand re-response limit optimal dispatch on a single time scale, so the frequency of renewable energy and load forecasting is increased, and an optimal dispatch model with complementary time scales is developed. Finally, the IEEE 33-node distribution system was tested to verify the effectiveness of the proposed optimal dispatching strategy. The simulation results show an 18.28% improvement in the economy of the system and a 24.39% increase in the capacity to consume renewable energy. Full article

(This article belongs to the Special Issue Current Research and Future Development in Intelligent Power Distribution Systems: Planning, Operation and Control)

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Review

Jump to: Research

15 pages, 861 KiB

Open AccessReview

An Overview of Non-Intrusive Load Monitoring Based on V-I Trajectory Signature

by Jiangang Lu, Ruifeng Zhao, Bo Liu, Zhiwen Yu, Jinjiang Zhang and Zhanqiang Xu

Energies 2023, 16(2), 939; https://doi.org/10.3390/en16020939 - 13 Jan 2023

Cited by 3 | Viewed by 1909

Abstract

Non-intrusive load monitoring (NILM) can obtain fine-grained electricity consumption information of each appliance by analyzing the voltage and current data measured at a single point on the bus, which is of great significance for promoting and improving the efficiency and sustainability of the [...] Read more.

Non-intrusive load monitoring (NILM) can obtain fine-grained electricity consumption information of each appliance by analyzing the voltage and current data measured at a single point on the bus, which is of great significance for promoting and improving the efficiency and sustainability of the power grid and enhancing the energy efficiency of users. NILM mainly includes data collection and preprocessing, event detection, feature extraction, and appliance identification. One of the most critical steps in NILM is signature extraction, which is the basis for all algorithms to achieve good state detection and energy disaggregation. With the generalization of machine learning algorithms, different algorithms have also been used to extract unique signatures of appliances. Recently, the development and deployment of the voltage–current (V-I) trajectory signatures applied for appliance identification motivated us to present a comprehensive review in this domain. The V-I trajectory signatures have the potential to be an intermediate domain between computer vision and NILM. By identifying the V-I trajectory, we can detect the operating state of the appliance. We also summarize existing papers based on V-I trajectories and look forward to future research directions that help to promote the field’s development. Full article

(This article belongs to the Special Issue Current Research and Future Development in Intelligent Power Distribution Systems: Planning, Operation and Control)

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