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Abstract
With the large-scale integration of distributed renewable generation (DRG) and increasing proportion of power electronic equipment, the traditional power distribution network (DN) is evolving into an active distribution network (ADN). The operation state of an ADN, which is equipped with DRGs, could rapidly change among multiple states, which include steady, alert, and fault states. It is essential to manage large-scale DRG and enable the safe and economic operation of ADNs. In this paper, the current operation control strategies of ADNs under multiple states are reviewed with the interpretation of each state and the transition among the three aforementioned states. The multi-state identification indicators and identification methods are summarized in detail. The multi-state regulation capacity quantification methods are analyzed considering controllable resources, quantification indicators, and quantification methods. A detailed survey of optimal operation control strategies, including multiple state operations, is presented, and key problems and outlooks for the expansion of ADN are discussed.
A new round of energy revolutions is sweeping across the globe to deal with socioeconomic problems associated with energy production, delivery, and utilization, and realize sustainable energy development [
The International Energy Agency (IEA) has proposed that large-scale restructuring of global and regional energy system architectures is essential by 2040 [
It is imperative to develop an active distribution network (ADN) that includes DRG as its main component on a global scale [
Fig. 1 Diagram of an ADN.
An ADN has several operation states, and the control strategies differ accordingly under the prevailing operation states [
The primary goal of the ADN operation control strategy is to clarify the operation states and control objectives. First, it adopts hierarchical partitioning as a collaborative strategy to achieve global complementarity in an ADN by aiming to curb the limitations of existing control strategies [
With the ongoing changes in the power supply structure and load characteristics of ADN, power grid adjustment methods have undergone tremendous changes. The load characteristics have changed from inductive and resistive linear loads to large-scale nonlinear loads represented by power electronic devices. System regulation has changed from the voltage and frequency regulation on the power supply side to the coordinated regulation control of multiple energies, flexible loads, and energy storage located behind the meters. Finally, the grid stability adjustments have changed from relying on generator inertia to introducing advanced control strategies for ADN equipment. Research on ADN control strategies has posed several ongoing problems.
1) The operation state of the ADN is no longer limited to the binary stable and fault states. The DRG introduced in the ADN includes multiple types of energy, configurations, and fusion forms. The safe and stable operation domain of an ADN is significantly different from that of a traditional DN. Higher and more stringent requirements for the risk perception, exception warning, and stability control of ADN have been proposed. Accurate identification of the operation state of the ADN is a priority in an uncertain environment, which could otherwise result in the grid being unable to support flexible switching of ADN control strategies.
2) The difficulty in evaluating the resource regulation capacity gradually increases with an increasing proportion of DRG in the ADN. Multi-level DRG systems are widely distributed in ADNs. ADN dispatching requires the coordination of large and distributed resources with various regulation characteristics and response time. Such characteristics make it difficult to quantify the resource regulation capacity of the ADN under multiple and uncertain operation states and significantly limit the support ability and responsiveness of proliferated DRGs.
3) Massive DRGs in the ADN increase the complexity of control strategies. Owing to the complex and changeable operation states of ADN, traditional top-down control strategies have been challenged by flexible and uncertain interactions in ADNs. In traditional DN control strategies, it is difficult to ensure safe, economical, and reliable operations with large-scale DRG resource access. Traditional methods cannot achieve the optimal energy complementarity, synergistic energy supply, or shear linkage designated in ADNs under multiple states.
This study focuses on the development of optimal operation control strategies of the ADN from three aspects: operation state identification, regulation capacity quantification, and smart management and control. The key technical routes and control strategies of the ADN under multiple operation states, combined with the latest research results, are summarized in this paper. Subsequently, the corresponding research directions and prospects are presented. This study provides references and explores ideas for enhancing control strategies of the ADN.
Massive DRGs result in frequent changes in the ADN operation state. The operation state classification is the basis for state estimation and self-healing control [
In this study, the ADN operation state is divided into three different states, i.e., steady, alert, and fault, to facilitate the subsequent summary of state evaluation and control strategies and consider the reliability requirements of an ADN. Accordingly, multiple control objectives must be adopted for different operation states.
Under the steady state, no electrical component in the ADN can operate beyond its permissible limit. Under this state, the ADN operates with a large power flow safety margin, voltage safety margin, and strong anti-disturbance ability. This can satisfy the security and reliability requirements of power supply. The control objective under the steady state is supposed to improve the power quality, economic efficiency, and utilization of DRGs. The timescale is generally seconds class or longer. Common control measures such as topology reconfiguration, load voltage regulation, and demand response improve the power quality by reducing network losses.
Under the alert state, an ADN is still under the power supply state without failure, but some component operation limits are exceeded, such as voltage out-of-limit, security criterion violation, and local heavy overload. Since some areas of the ADN cannot guarantee the desired power quality, it follows that the system has potential safety hazards and poor anti-interference ability. The operation state of the ADN transits to the fault state if the abnormal indicators continue to deteriorate. The control objective in the alert state is to maximize the capacity margin of the ADN to prevent further degradation while satisfying the power demands to the greatest extent possible. The response timescale under the alert state usually depends on upper-level instructions to achieve flexible cooperative support.
The fault state indicates that the power supply to certain loads is interrupted owing to failure. The operations of the relay protection devices may cause partial loads to temporarily lose power and enter the fault recovery stage. Considering customer satisfaction, the control target of the fault state is to maximize the restoration load capacity as quickly as possible after troubleshooting. Generally, the control response time under the fault state must be within the allowed threshold moments of the current, frequency, and voltage. The operation time of the protection devices must be determined based on the specific situation instead of the speed requirement.
A DN under steady state is unable to maintain a normal state due to the uncertainty of DG output, load fluctuations, and external disturbances. The severity of a disturbance determines whether the operation state changes to the alert or fault state. The alert state is unstable, because further disturbances may change the operation state into a fault state. Therefore, the dispatcher should prioritize the safety and reliability of the system and take preventive measures to recover the operation state. When the DN is under a fault state, the operators improve the reliability in the non-fault area by operating adjacent tie switches to transfer the load or island microgrids. Accordingly, it can realize the transition from a fault state to an alert state.
Rapid and accurate operation state identification for the ADN is conducive to the stable operation of the distribution management system, thus ultimately improving the safety and economy of the DN operation. To accurately identify the operation state of an ADN, it is necessary to consider its operation characteristics, establish a reasonable multi-state evaluation indicator system for the DN, and adopt a systematic operation state identification method.
In the process of state identification for an ADN, the selection of evaluation indicators is the basis for identifying the operation state of the DN. Currently, relatively mature smart grid evaluation indicators include the IBM Smart Grid Maturity Model, DOE Smart Grid Development Evaluation Indicator System, EPRI Smart Grid Construction Project Cost/Benefit Evaluation Indicator System, and European Smart Grid Revenue Evaluation System [
A state identification evaluation indicator system has been developed and further enhanced by many academics in response to the aforementioned problems by using the conventional comprehensive evaluation indicator system of the DN and the operation characteristics of the ADN. A DN economic operation evaluation indicator system with 7 primary and 21 secondary indicators was created in [
With the increasing scale of the ADN and the massive integration of the DRG, the complexity and variability of the ADN operation situation have led to a large number of indicators for evaluating the operation state of the ADN. Generally, these indicators can be divided into six categories: spare capacity margin, independent power supply capacity, real-time controllability, fault risk rate, power supply quality, and reliability. The connotations and related indicators for each dimensional indicator are listed in
| Category | Connotation | Related indicator |
|---|---|---|
| Spare capacity margin | Reserve capacity of DN for load fluctuations and distributed resource fluctuations |
Probability area reserve, transformer power margin, line power margin, generator standby capacity, power plant standby capacity, and power structure standby capacity [ |
| Independent power supply capacity | Ability of the microgrid formed by DN to independently supply loads after losing external power supply |
Partition load average, partition independent, power supply duration, islanding imbalance, lost load ratio, and important load loss rate [ |
| Real-time controllability | Ability to control distributed resources, loads, and other controllable resources in real time through distribution automation and Internet of Things |
Controllable load ratio, DG power factor, electric vehicle (EV) state of charge, DG real-time power output, and load response rate [ |
| Fault risk rate | The maximum risk probability of failure of important power supply equipment in DN |
10 kV line failure rate, distribution transformer failure rate, switchgear failure rate, downtime rate, and repeat trip rate [ |
| Power supply quality | Comprehensive quality of DN power supply in terms of voltage deviation ratio, frequency deviation ratio, etc. |
Overvoltage risk, over total harmonic distortion, and frequency limit rate [ |
| Reliability | Ability of DN to provide electricity to consumers without interruption at acceptable quality standards and in required quantities |
System average interruption frequency index, system average interruption duration index, average service availability index, average power supply time, and expected energy not served [ |
In addition, to understand the correlation between the indicators and the overall operation state of the ADN, [
Researchers studying the indicator system for the operation state of an ADN have made some progress in their work. Various indicators have been established, and their selection has a comparatively developed theoretical foundation. However, the indicator systems suggested by most scholars only consider one or two operation states of the ADN and fail to fully consider the operation characteristics of the ADN under the three states, namely steady, alert, and fault. Therefore, research on state indicator systems for different operation states is ongoing, and it is still challenging to establish a systematic and feasible indicator system.
Traditional identification methods for the power system operation state are based on the effects of faults (outage indicators), while also considering the causes and modes of faults. With the rapid development of ADNs, the large number of distributed resources, and the high proportion of power electronic access, it is obvious that the traditional principles of operation state identification methods are not applicable to ADNs with increasing complexity and variation.
In terms of the operation state identification for the ADN, [
However, these online state identification methods rely on accurate information regarding the topology of the DN. When measurements are limited to real time, they cannot accurately identify the operation state of the DN. In [
To ensure the safe and stable operation of an ADN, it is crucial to accurately depict the current operation state and further state changing trends. However, the identification method for the DN operation state described above does not consider the future state of the ADN. Researchers have applied prediction models to the online identification process of the ADN operation state. In [
Regulation capacity quantification is crucial for ADNs to guide the dispatch of variable resources, thereby exploiting the resource adjustment potential to respond flexibly and quickly to uncertain power fluctuations. Furthermore, the quantifying results of the regulation capacity can provide the necessary boundary conditions for ADN control strategies. With respect to ADN operation under multiple operation states, the corresponding controllable resources are not completely consistent. The response speeds and regulation characteristics of the diverse resources under multiple states also differ. Therefore, quantifying the regulation capacity of ADNs that can satisfy diversified operation requirements under multiple operation states is becoming increasingly complex.
It is necessary to clarify the controllable resources and ranges under multiple operation states.
| Controllable resource | Operation requirement | ||
|---|---|---|---|
| Steady state | Alert state | Fault state | |
| DRG (PV, WT, ) |
Satisfy power factor constraint -0.98-0.98 without generation curtailment [ |
Satisfy power factor constraint -0.95-0.95 without generation curtailment [ |
Adjust power factor arbitrarily and discard generation [ |
| Load (FL, EV, ) |
Manage by demand response [ |
Shed contracted interruptible load [ |
Shed regular load [ |
| Energy storage (BESS, MESS, ) |
Clip peak, fill valley, or improve power quality [ |
Provide emergency backup to reduce operation risk [ |
Guarantee important loads or isolated operation [ |
| Tie switch (SOP, FMSS, ) |
Reconfigure topology [ |
Reconfigure topology [ |
Isolate fault [ |
| State | Reference | Timescale | Schedulable resource | Quantification indicator | Quantification model |
|---|---|---|---|---|---|
| Steady state |
[ | Minute | FL, EV, and ESS | Available average regulation power | Sum of schedulable active power and remaining demand response capacity |
|
[ | Hour | FL, MT, EV, and ESS | Power supply capacity evaluation index | Multiple grid connection time with schedulable power | |
|
[ | Hour | FL and ESS | The maximum allowable volatility of net load | Available regulation capacity quantification model | |
|
[ | Hour | WT and OLTC | The maximum grid-connected capacity of DG | The maximum capacity optimization model | |
|
[ | Hour | BESS and heating system | Flexible resource power setpoint | Flexible resource control model | |
| Alert state |
[ | Second | FL, DG, and ESS | Recourse cost requirement | Extreme point |
|
[ | Hour | Tie switch | Available supply capacity | security operation model | |
|
[ | Hour | FMSS | Expected energy not supplied | Sum of expected power shortage | |
|
[ | Minute | PV, WT, OLTC, and Shunt CB | Dynamic reactive power reserve | P-Q capacity curve calculation | |
|
[ | Minute | PV, EV, CB, and OLTC | Reactive power reserve | Reactive power regulation of PVs and EVs | |
| Fault state |
[ | Minute | MBCV, SOP, and microgrid | Total restored active power | Multi-period restoration model |
|
[ | Hour | PV, WT, ESS, and tie switch | Load priority restoration set | Breadth-first search | |
|
[ | Second | FL, DG, ESS, and tie switch | Total restore load | MAS-based service restoration method | |
|
[ | Minute | PV, WT, and ESS | Reliability indicators in island mode | Adjustable interval optimization | |
|
[ | Hour | FL, DG, WT, and ESS | Load control capability | Sum of scheduled active load control in microgrids |
Under the steady state, most research has quantified the regulation capacity of DN to improve economic benefits and renewable energy utilization. Among these, quantification indicators are based on the active power of controllable resources or the capacity of the demand response. The quantification timescale was typically in hours.
The combination of real-time controllable capacity and remaining grid-connection time was adopted to evaluate the power supply capability of controllable resources [
To address the variability in PV generation, [
Currently, there are few studies and discussions on the quantification of the ADN regulation potential under the alert state. Many regulation measures that consider both economy and safety based on multiple timescales are essential for studying ADN control under steady and alert states [
In addition, under the active reconfiguration strategy of the ADN, the maximum total power supply capacity was quantified online based on the security criterion [
In the case of DN failure or insufficient power supply, a direct control strategy and an incentive demand response mechanism have been utilized to change the flexible loads. Considering the stability of island operations, [
To achieve the rapid recovery of critical loads, a multi-period recovery model was developed to maximize the total weighted loads restored by optimal routing of repair crews, MBCVs, and microgrids [
The controllable capacity quantification indicator under a fault state is generally exerted at the second or minute level. The source and storage sides primarily consider the load transfer capability of controllable resources in the ADN. The load side focuses on the load recovery priority and cuts off interruptible loads if necessary. In contrast to the ADN topology reconstruction in optimal economic operations, the network side prioritizes the security and reliability of network reconstruction and considers short-term island operations. In addition, most existing studies only focus on several factors that pertain to generation, grid, storage, and load, rather than providing a holistic view that covers the entire DN.
When an ADN operates under different states, different control objectives must be achieved from the perspectives of the system coordination, grid coordination, and station area autonomy. Owing to the large number of DGs connected to the ADN and different dynamic responses of DGs, the collaborative optimization control based on mutual supply or assistance control and the uninterruptible power supply control under extreme conditions need to be developed. Several studies have been conducted to address the control problems of the ADN under different operation states. From the perspective of coordinated source-network-load-storage control, this section analyzes the control objectives under steady, alert, and fault states. Relevant control strategies are introduced, which are presented in
| State | Control objective | Control strategy |
|---|---|---|
| Steady state | Economic operation |
Demand response control [ |
|
Network reconfiguration optimization and control [ | ||
| Reliable operation |
Source-network-load-storage control [ | |
|
Hierarchical scheduling and control [ | ||
| Alert state | Restoration to steady state |
Topology optimization and reorganization [ |
| Safe and stable operation |
Load transfer control [ | |
| Fault state | Uninterruptible power supply |
Fault diagnosis and clearance [ |
| Fault restoration |
Interconnection switch adjustment [ | |
|
Active support of distributed resources [ |
Under steady state, the optimal control objective is to achieve the minimum total operation cost and carbon emissions. Nevertheless, the uncertainty of renewable energy sources such as PV, WT, and FLs complicates optimization control, and makes effective optimization difficult. Existing studies have proposed several control strategies for the steady state, including the electricity price incentive, grid reconfiguration, ESS regulation, and voltage regulation.
The introduction of demand response into electricity market competition was proposed in [
Moreover, the economic benefit evaluation index for the ADN transformation from the current topology structure to the optimal topology structure was given in [
When an ADN operates under an alert state, the key parameters of DNs are within the critical stable operation range, whereas the safe operation margin is relatively low, which means that the system can be easily driven into an unstable state. Along with the connection of the DRG, the uncertainty of the DN is significantly increased, which poses significant challenges to the safe operation of the system. To guarantee the safety of the DN under an alert state and return it to a steady state as soon as possible, it is necessary to evaluate its operation state. With improvements in real-time monitoring equipment, real-time risk assessment is possible. In [
Existing control strategies for an alert state can be divided into two categories. One is topology optimization and reorganization, and the other is load transfer control. Both strategies rely on controllable DRGs, ESSs, and FLs for power regulation. From the perspective of topology optimization and reorganization, the conditional value-at-risk theory was introduced in [
The fault state control of an ADN can be divided into two stages: fault location and isolation, and recovery control. Accurate and reliable fault locations form the basis for effective fault isolation. The limited thermal capacity of DGs would reduce fault current injection, and bidirectional power flow would bring challenges to the fault location. The existing literature mainly focuses on two topics [
In addition to fault location and isolation strategies, the essence of fault recovery control ensures rapid recovery, stable operation, and maximum protection of load supply after grid faults. The control objectives during the recovery stage include the minimum power outage load, maximum feeder capacity margin, and minimum number of switching operations. Based on the importance level, several PV and WT units and ESSs were combined to establish a mixed integral linear model that aimed to restore the maximum economic value of the load to determine the optimal control strategy during the fault recovery period [
The objectives of the control strategies under multiple states include ensuring that the ADN operates efficiently and safely by optimizing the economy under a steady state, reducing the duration of the alert state, and minimizing the propagation range and processing time of the fault state [
With the large-scale integration of DRGs and FLs, the operation state of the ADN changes rapidly with spatial and temporal distributions. Currently, the identification of the operation states of an ADN must explore the following two challenges.
Although several identification indicators have been proposed to evaluate the operation state of an ADN, a widely recognized indicator system for identifying the operation states of an ADN is still lacking. By summarizing the existing indicators and considering the distinctive features of the ADN, six important categories should be considered, which are explained in
When the state identification indicators are determined, the next task involves providing the value ranges for each indicator under the steady, alert, and fault states. However, only a few studies have focused on this topic. It is challenging to determine the value ranges under various operation states because multiple ADNs have multiple operation rules and strategies. However, it is meaningful to provide a typical reference. In addition, historical operation data as well as predictive data should be considered simultaneously when identifying online operation states; therefore, various data mining methods can be used to identify the operation states of the ADN.
Regulation capacity quantification is necessary to provide boundary conditions for ADN control strategies. In general, there has been a paucity of studies on the quantification evaluation of the controllable potential of the ADN. It is imperative to explore how to incorporate state transitions into the regulation capacity quantification of the ADN.
The main idea of the regulation capacity quantification of an ADN is to calculate the individual controllable ability of each resource and then aggregate them to obtain the cluster regulation ability. However, the existing quantification indicators often ignore the acceptable dynamic operation limits of the ADN. The operation constraints of a DN are likely to cause a slight reduction in the controllable potential. Therefore, the regulation capacity quantification on multi-timescale should fully consider the dynamic operating limits of the ADN.
Current quantification methods for the controllable potential do not consider the influence of the adjustment direction. To quantify the controllable ability precisely, the upward and downward regulation capacities of the ADN need to be calculated separately. However, the existing research works focus only on one or several factors in terms of generation, grid, storage, and load resources. Therefore, quantifying the regulation capacity of the generation, grid, load, and storage resources under multiple states is a major challenge for further research.
The ADN operation control objective under multiple states guarantees reliable operation and improves the operation efficiency. The integration of DRGs brings challenges to the ADN under multiple states while simultaneously increasing control complexity. How to guarantee stable and safe operation under multiple states and smooth state switching among multiple states remains to be solved.
Although several control strategies have been proposed to deal with the serious problems of ADNs, the existing literature focuses on one or two operation states separately, and cannot deal with the switching period among various operation states. Transient switching between multiple states is complex. For example, several differences exist in the control objectives between the steady and fault states. How to identify the two states and design a proper control is a problem. It is feasible to ensure smooth state switching by setting appropriate margins among multiple states. In
Fig. 2 Transitions among ADN operation states.
Considering the integration of DRGs and flexible loads, all the controllable sources must be coordinated to achieve smart control. Furthermore, a hierarchical control strategy can be designed according to the dynamic response of DRGs at multiple timescales. Specifically, from the perspective of control strategies under the alert state, existing research works have introduced the conditional value-at-risk theory to quantify the operation risk of an ADN. However, its applicability and accuracy must be further considered.
Concurrently, the coordinate control among multiple sources and loads such as EVs should also be considered.
This paper presented an overview of the optimal operation control strategies for an ADN under multiple states. First, the concepts of the steady, alert, and fault states were explained, as well as the control objectives under the three states. Second, current research advances in state identification indicators and identification strategies were summarized. Third, the regulation capacity quantification methods were reviewed from the perspectives of controllable resources, quantification indicators, and quantification methods. Fourth, various operation control strategies for the three states were summarized. Finally, key problems and outlooks were presented to advanced related research.
Based on the proposed review presented in this paper, we believe that with the large-scale integration of DRGs, FL, etc., smart control will play a key role in the safe and economic operation of ADNs. However, many critical problems regarding state identification, regulation capacity quantification, and control strategies remain unsolved. It would be especially beneficial if a smart control framework could be established to manage ADNs more efficiently.
References
X. Zhou, Z. Lu, Y. Liu et al., “Development models and key technologies of future grid in China,” Proceedings of the CSEE, vol. 34, no. 29, pp. 4999-5008, Oct. 2014. [Baidu Scholar]
REN21. (2022, Jan.). Renewables 2022 Global State Report. [Online]. Available: https://www.doc88.com/p-28761584917079.html [Baidu Scholar]
W. Nicole. (2022, Jan.). Renewables 2022 Global State Report China Factsheet. [Online]. Available: https://www.unep.org/resources/report/renewables-2022-global-status-report [Baidu Scholar]
A. Androniceanu and O. M. Sabie, “Overview of green energy as a real strategic option for sustainable development,” Energies, vol. 15, no. 22, p. 8573, Nov. 2022. [Baidu Scholar]
W. Sheng, Technology and Application of Renewable Energy Power Generation Cluster. Beijing: Science Press, 2020. [Baidu Scholar]
R. J. Lowe and P. Drummond, “Solar, wind and logistic substitution in global energy supply to 2050 – barriers and implications,” Renewable and Sustainable Energy Reviews, vol. 153, p. 111720, Jan. 2022. [Baidu Scholar]
F. Bai, Y. Cui, R. Yan et al., “Frequency response of PV inverters towards high renewable penetrated distribution networks,” CSEE Journal of Power and Energy Systems, vol. 8, no. 2, pp. 465-475, Feb. 2022. [Baidu Scholar]
J. Le, G. Qi, L. Zhao et al., “Distributed secondary voltage optimal control of active distribution network and the system stability investigation,” Journal of Electrical Engineering & Technology, vol. 18, no. 3, pp. 1573-1586, May 2023. [Baidu Scholar]
J. Hou, Z. Liu, S. Wang et al., “Intelligent coordinated damping control in active distribution network based on PSO,” Energy Reports, vol. 8, pp. 1302-1312, Apr. 2022. [Baidu Scholar]
B. N. Miranda-Blanco, E. Díaz-Dorado, C. Carrillo et al., “State estimation for wind farms including the wind turbine generator models,” Renewable Energy, vol. 71, pp. 453-465, Feb. 2014. [Baidu Scholar]
Q. Shi, F. Li, M. Olama et al., “Network reconfiguration and distributed energy resource scheduling for improved distribution system resilience,” International Journal of Electrical Power & Energy Systems, vol. 124, p. 106355, Jan. 2021. [Baidu Scholar]
M. Markovic, A. Sajadi, A. Florita et al., “Voltage estimation in low-voltage distribution grids with distributed energy resources,” IEEE Transactions on Sustainable Energy, vol. 12, no. 3, pp. 1640-1650, Jul. 2021. [Baidu Scholar]
H. Sun, B. Wang, W. Li et al., “Research on inertia system of frequency response for power system with high penetration electronics,” Proceedings of the CSEE, vol. 40, no. 16, pp. 5179-5191, Jun. 2020. [Baidu Scholar]
J. Fang, Y. Tang, H. Li et al., “The role of power electronics in future low inertia power systems,” in Proceedings of IEEE International Power Electronics and Application Conference and Exposition (IEEE PEAC), Shenzhen, China, Nov. 2018, pp. 261-266. [Baidu Scholar]
T. T. Hoang, Q. T. Tran, and Y. Besanger, “An advanced protection scheme for medium-voltage distribution networks containing low-voltage microgrids with high penetration of photovoltaic systems,” International Journal of Electrical Power & Energy Systems, vol. 139, p. 107988, Jul. 2022. [Baidu Scholar]
Z. Ma, H. Zhang, H. Zhao et al., “New mission and challenge of power distribution and consumption system under dual-carbon target,” Proceedings of the CSEE, vol. 42, no. 19, pp. 6931-6945, Jul. 2022. [Baidu Scholar]
J. Wu, K. Lin, F. Wu et al., “Improved unscented Kalman filter based interval dynamic state estimation of active distribution network considering uncertainty of photovoltaic and load,” Frontiers in Energy Research, vol. 10, p. 1054162, Jan. 2023. [Baidu Scholar]
Y. Guo, Q. Wu, H. Gao et al., “MPC-based coordinated voltage regulation for distribution networks with distributed generation and energy storage system,” IEEE Transactions on Sustainable Energy, vol. 10, no. 4, pp. 1731-1739, Oct. 2019. [Baidu Scholar]
P. Li, J. Ji, H. Ji et al., “MPC-based local voltage control strategy of DGs in active distribution networks,” IEEE Transactions on Sustainable Energy, vol. 11, no. 4, pp. 2911-2921, Oct. 2020. [Baidu Scholar]
Y. Li, J. Lou, K. Sun et al., “Source-grid-load-storage interactive power quality characteristic of active distribution network,” Frontiers in Energy Research, vol. 10, p. 1023474, Sept. 2022. [Baidu Scholar]
T. Wei, X. Chu, D. Yang et al., “Power balance control of RES integrated power system by deep reinforcement learning with optimized utilization rate of renewable energy,” Energy Reports, vol. 8, pp. 544-553, Aug. 2022. [Baidu Scholar]
T. E. D. Liacco, “The adaptive reliability control system,” IEEE Transactions on Power Apparatus and Systems, vol. PAS-86, no. 5, pp. 517-531, May 1967. [Baidu Scholar]
Y. Zhang and X. Meng, “Research on operation evaluation technology system of smart distribution grid,” in Proceeding of 2011 IEEE Power Engineering and Automation Conference, Wuhan, China, Sept. 2011, pp. 542-546. [Baidu Scholar]
L. Fang and C. Chen, “Self-healing scheme in alert operating state for smart distribution systems,” Journal of Electrical Engineering & Technology, vol. 14, no. 3, pp. 1117-1126, May 2019. [Baidu Scholar]
C. Li, X. Liu, W. Zhang et al., “Assessment method and indexes of operating states classification for distribution system with distributed generations,” IEEE Transactions on Smart Grid, vol. 7, no. 1, pp. 481-490, Jan. 2016. [Baidu Scholar]
Federal Ministry of Economics and Technology. (2008, May). E-Energy: ICT-based energy system of the future. [Online]. Available: https://prod.iea.org/policies/1509-e-energy-ict-based-energy-system-of-the-future [Baidu Scholar]
L. Ma and X. Zhang, “Economic operation evaluation of active distribution network based on fuzzy Borda method,” IEEE Access, vol. 8, pp. 29508-29517, Feb. 2020. [Baidu Scholar]
L. Ge, Y. Li, X. Zhu et al., “An evaluation system for HVDC protection systems by a novel indicator framework and a self-learning combination method,” IEEE Access, vol. 8, pp. 152053-152070, Aug. 2020. [Baidu Scholar]
B. Park, Z. Zhou, A. Botterud et al., “Probabilistic zonal reserve requirements for improved energy deliverability with wind power,” IEEE Transactions on Power Systems, vol. 35, no. 6, pp. 4324-4334, Nov. 2020. [Baidu Scholar]
V. I. Zatsepina, E. P. Zatsepin, V. R. Lovchiy et al., “Analysis of power supply systems’ reliability of according to the time-to-failure distribution functions,” in Proceedings of 2019 1st International Conference on Control Systems, Mathematical Modelling, Automation and Energy Efficiency, Lipetsk, Russia, Jul. 2019, pp. 664-668. [Baidu Scholar]
W. Xie, Y. Zhang, W. Gu et al., “Minimum bending distance based evaluation method for subarea independent power supply capability of smart distribution network,” in Proceedings of 2018 International Conference on Power System Technology, Guangzhou, China, Nov. 2018, pp. 1177-1182. [Baidu Scholar]
S. Zhang, W. Pei, Y. Yang et al., “Calculation and analysis of total supply capability of AC/DC hybrid distribution network considering load balance,” in Proceedings of 2020 23rd International Conference on Electrical Machines and Systems, Hamamatsu, Japan, Nov. 2020, pp. 320-324. [Baidu Scholar]
K. Chen, W. Wu, B. Zhang et al., “A method to evaluate total supply capability of distribution systems considering network reconfiguration and daily load curves,” IEEE Transactions on Power Systems, vol. 31, no. 3, pp. 2096-2104, May 2016. [Baidu Scholar]
R. Sioshansi and P. Denholm, “Benefits of colocating concentrating solar power and wind,” IEEE Transactions on Sustainable Energy, vol. 4, no. 4, pp. 877-885, Oct. 2013. [Baidu Scholar]
M. D. A. Al-falahi, S. D. G. Jayasinghe, and H. Enshaei, “A review on recent size optimization methodologies for standalone solar and wind hybrid renewable energy system,” Energy Conversion and Management, vol. 143, pp. 252-274, Jul. 2017. [Baidu Scholar]
A. Ramadan, “Permanent magnet fault current limiter for the power grid,” Energy Procedia, vol. 134, pp. 768-775, Oct. 2017. [Baidu Scholar]
M. Hasheminamin, V. Agelidis, V. Salehi et al., “Index-based assessment of voltage rise and reverse power flow phenomena in a distribution feeder under high PV penetration,” IEEE Journal of Photovoltaics, vol. 5, no. 4, pp. 1158-1168, Jul. 2015. [Baidu Scholar]
S. Elphick, P. Ciufo, G. Drury et al., “Large scale proactive power-quality monitoring: an example from Australia,” IEEE Transactions on Power Delivery, vol. 32, no. 2, pp. 881-889, Apr. 2017. [Baidu Scholar]
S. Su, Y. Hu, L. He et al., “An assessment procedure of distribution network reliability considering photovoltaic power integration,” IEEE Access, vol. 7, pp. 60171-60185, May 2019. [Baidu Scholar]
S. Ahmad, S. Sardar, B. Noor et al., “Analyzing distributed generation impact on the reliability of electric distribution network,” International Journal of Advanced Computer Science and Applications, vol. 7, no. 10, pp. 217-221, Oct. 2016. [Baidu Scholar]
J. C. Cebrian, J. Giacomini, C. A. Carneiro et al., “A novel methodology to estimate probability density function of voltage sag duration and failure rates on power distribution systems,” IEEE Access, vol. 11, pp. 16863-16874, Feb. 2023. [Baidu Scholar]
X. Liu, J. Yu, W. Lv et al., “Network security situation: from awareness to awareness-control,” Journal of Network and Computer Applications, vol. 139, no. 9, pp. 15-30, Aug. 2019. [Baidu Scholar]
X. Xu, X. He, Q. Ai et al., “A correlation analysis method for operation state of distribution network based on random matrix theory,” Power System Technology, vol. 40, no. 3, pp. 781-790, Mar. 2016. [Baidu Scholar]
H. Tan, L. Wang, R. Dai et al., “An operation status correlation analysis method of integrated electricity-water system based on random matrix theory,” in Proceeding of 2021 IEEE Sustainable Power and Energy Conference (iSPEC), Nanjing, China, Dec. 2021, pp. 3815-3820. [Baidu Scholar]
R. Salim, K. Oliveira, A. Filomena et al., “Hybrid fault diagnosis scheme implementation for power distribution systems automation,” IEEE Transactions on Power Delivery, vol. 23, no. 4, pp. 1846-1856, Oct. 2008. [Baidu Scholar]
M. Cai, “An intelligence identification method for distribution network operation state,” Hunan Electric Power, vol. 35, no. 2, pp. 21-24, Mar. 2015. [Baidu Scholar]
J. Zhang, Z. He, S. Lin et al., “An ANFIS-based fault classification approach in power distribution system,” International Journal of Electrical Power & Energy Systems, vol. 49, pp. 243-252, Jul. 2013. [Baidu Scholar]
M. Pau, F. Ponci, A. Monti et al., “Distributed state estimation for multi-feeder distribution grids,” IEEE Open Journal of Instrumentation and Measurement, vol. 1, pp. 1-12, Aug. 2022. [Baidu Scholar]
Z. Soltani, S. Ma, M. Khorsand et al., “Simultaneous robust state estimation, topology error processing, and outage detection for unbalanced distribution systems,” IEEE Transactions on Power Systems, vol. 38, no. 3, pp. 2018-2034, May 2023. [Baidu Scholar]
T. Zhang, J. Zhang, H. Xue et al., “Prediction of distribution network operation trend based on the secondary modal decomposition and LSTM-MFO algorithm,” in Proceedings of 2021 6th Asia Conference on Power and Electrical Engineering (ACPEE), Chongqing, China, Apr. 2021, pp. 520-525. [Baidu Scholar]
Y. Wu, J. Lin, and H. Lin, “Standards and guidelines for grid-connected photovoltaic generation systems: a review and comparison,” IEEE Transactions on Industry Applications, vol. 53, no. 4, pp. 3205-3216, Jul. 2017. [Baidu Scholar]
T. Jiang, C. Y. Chung, P. Ju et al., “A multi-timescale allocation algorithm of energy and power for demand response in smart grids: a Stackelberg game approach,” IEEE Transactions on Sustainable Energy, vol. 13, no. 3, pp. 1580-1593, Jul. 2022. [Baidu Scholar]
M. Afzalan and F. Jazizadeh, “Data-driven identification of consumers with deferrable loads for demand response programs,” IEEE Embedded Systems Letters, vol. 12, no. 2, pp. 54-57, Jun. 2020. [Baidu Scholar]
H. Yang, C. Li, R. Huang et al., “Bi-level energy trading model incorporating large-scale biogas plant and demand response aggregator,” Journal of Modern Power Systems and Clean Energy, vol. 11, no. 2, pp. 567-578, Mar. 2023. [Baidu Scholar]
T. Ding, Z. Wang, W. Jia et al., “Multiperiod distribution system restoration with routing repair crews, mobile electric vehicles, and soft-open-point networked microgrids,” IEEE Transactions on Smart Grid, vol. 11, no. 6, pp. 4795-4808, Nov. 2020. [Baidu Scholar]
Z. Yemeni, H. Wang, W. M. Ismael et al., “CFDDR: a centralized faulty data detection and recovery approach for WSN with faults identification,” IEEE Systems Journal, vol. 16, no. 2, pp. 3001-3012, Jun. 2022. [Baidu Scholar]
Y. Wen, W. Li, G. Huang et al., “Frequency dynamics constrained unit commitment with battery energy storage,” IEEE Transactions on Power Systems, vol. 31, no. 6, pp. 5115-5125, Nov. 2016. [Baidu Scholar]
H. Ye and Z. Li, “Robust security-constrained unit commitment and dispatch with recourse cost requirement,” IEEE Transactions on Power Systems, vol. 31, no. 5, pp. 3527-3536, Sept. 2016. [Baidu Scholar]
H. Wang, Z. Chen, H. Zhao et al., “Fault recovery strategy of distribution network considering uncertainty of wind-wind load,” Power System Technology, vol. 46, no. 11, pp. 4356-4364, Nov. 2022. [Baidu Scholar]
P. Prabawa and D. H. Choi, “Multi-agent framework for service restoration in distribution systems with distributed generators and static/mobile energy storage systems,” IEEE Access, vol. 8, pp. 51736-51752, Mar. 2020. [Baidu Scholar]
T. Zhong, H. Zhang, Y. Li et al., “Bayesian learning-based multi-objective distribution power network reconfiguration,” IEEE Transactions on Smart Grid, vol. 12, no. 2, pp. 1174-1184, Mar. 2021. [Baidu Scholar]
S. M. Razavi, H. R. Momeni, M. R. Haghifam et al., “Multi-objective optimization of distribution networks via daily reconfiguration,” IEEE Transactions on Power Delivery, vol. 37, no. 2, pp. 775-785, Apr. 2022. [Baidu Scholar]
F. S. Gazijahani, J. Salehi, M. Shafie-khah et al., “Spatiotemporal splitting of distribution networks into self-healing resilient microgrids using an adjustable interval optimization,” IEEE Transactions on Industrial Informatics, vol. 17, no. 8, pp. 5218-5229, Aug. 2021. [Baidu Scholar]
X. Wang, X. Sheng, W. Qiu et al., “Fault reconfiguration strategies of active distribution network with uncertain factors for maximum supply capacity enhancement,” IEEE Access, vol. 10, pp. 72373-72380, Feb. 2022. [Baidu Scholar]
A. Primadianto and C. Lu, “A review on distribution system state estimation,” IEEE Transactions on Power Systems, vol. 32, no. 5, pp. 3875-3883, Sept. 2017. [Baidu Scholar]
W. Huang, W. Xiong, L. Hua et al., “Multi-objective optimal scheduling of active distribution network based on dynamic scheduling priority,” Transactions of China Electrotechnical Society, vol. 33, no. 15, pp. 3486-3498, Aug. 2018. [Baidu Scholar]
H. Wang, S. Wang, Z. Pan et al., “Optimization scheduling method for distribution network flexibility improvement with high penetration distributed power supply,” Automation of Electric Power Systems, vol. 42, no. 15, pp. 86-93, Aug. 2018. [Baidu Scholar]
L. F. Ochoa, C. J. Dent, and G. P. Harrison, “Distribution network capacity assessment: variable DG and active networks,” IEEE Transactions on Power Systems, vol. 25, no. 1, pp. 87-95, Feb. 2010. [Baidu Scholar]
N. Karthikeyan, J. R. Pillai, B. Bak-Jensen et al., “Predictive control of flexible resources for demand response in active distribution networks,” IEEE Transactions on Power Systems, vol. 34, no. 4, pp. 2957-2969, Jul. 2019. [Baidu Scholar]
Y. Liu, X. Xu, P. Qiu et al., “Optimal capacity allocation of flexible multi-state switches based on overload risk of distribution network,” Proceedings of the CSEE, vol. 40, no. 11, pp. 3418-3429, Jun. 2020. [Baidu Scholar]
X. Yan, X. Xu, R. Li et al., “Dynamic optimization of multi-time scale reactive power in distribution network with renewable distributed power supply based on model predictive control,” Transactions of China Electrotechnical Society, vol. 34, no. 10, pp. 2022-2037, Jun. 2019. [Baidu Scholar]
X. Sun, J. Qiu, and J. Zhao, “Real-time volt/var control in active distribution networks with data-driven partition method,” IEEE Transactions on Power Systems, vol. 36, no. 3, pp. 2448-2461, May 2021. [Baidu Scholar]
M. A. Gilani, A. Kazemi, and M. Ghasemi, “Distribution system resilience enhancement by microgrid formation considering distributed energy resources,” Energy, vol. 191, p. 116442, Jan. 2020. [Baidu Scholar]
J. Wang, Y. Pang, and G. Hu, “Social profit optimization with demand response management in electricity market: a multi-timescale leader-following approach,” IEEE Transactions on Control Systems Technology, vol. 30, no. 6, pp. 2534-2546, Nov. 2022. [Baidu Scholar]
M. K. Arpanahi, M. E. H. Golshan, and P. Siano, “A comprehensive and efficient decentralized framework for coordinated multiperiod economic dispatch of transmission and distribution systems,” IEEE Systems Journal, vol. 15, no. 2, pp. 2583-2594, Jun. 2021. [Baidu Scholar]
J. V. Gautam, H. B. Prajapati, V. K. Dabhi et al., “Empirical study of job scheduling algorithms in Hadoop MapReduce,” Cybernetics and Information Technologies, vol. 21, no. 1, pp. 146-163, Jan. 2017. [Baidu Scholar]
M. Lavorato, J. Franco, M. J. Rider et al., “Imposing radiality constraints in distribution system optimization problems,” IEEE Transactions on Power Systems, vol. 27, no. 1, pp. 172-180, Jan. 2012. [Baidu Scholar]
P. Li, H. Ji, C. Wang et al., “Coordinated control method of voltage and reactive power for active distribution networks based on soft open point,” IEEE Transactions on Sustainable Energy, vol. 8, no. 4, pp. 1430-1442, Oct. 2017. [Baidu Scholar]
L. He, Y. Li, X. Chu et al., “Single-phase to ground fault line identification for medium voltage islanded microgrids with neutral ineffectively grounded modes,” IEEE Transactions on Smart Grid, vol. 13, no. 6, pp. 4312-4326, Nov. 2022. [Baidu Scholar]
J. Sang, Y. Zhao, and J. Wang, “A fault detection method considering fault mode information,” in Proceedings of 2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes, Yibin, China, Sept. 2023, pp. 1-4. [Baidu Scholar]
B. Fan and X. Wang, “Fault recovery analysis of grid-forming inverters with priority-based current limiters,” IEEE Transactions on Power Systems, vol. 38, no. 6, pp. 5102-5112, Nov. 2023. [Baidu Scholar]
Q. Li, Y. Xu, C. Ren et al., “A probabilistic data-driven method for response-based load shedding against fault-induced delayed voltage recovery in power systems,” IEEE Transactions on Power Systems, vol. 38, no. 4, pp. 3491-3503, Jul. 2023. [Baidu Scholar]
X. Yang, Z. Zhou, Y. Zhang et al., “Resilience-oriented co-deployment of remote-controlled switches and soft open points in distribution networks,” IEEE Transactions on Power Systems, vol. 38, no. 2, pp. 1350-1365, Mar. 2023. [Baidu Scholar]
F. Shen, Q. Wu, and Y. Xue, “Review of service restoration for distribution networks,” Journal of Modern Power Systems and Clean Energy, vol. 8, no. 1, pp. 1-14, Mar. 2020. [Baidu Scholar]
W. Huang, N. Zhang, J. Yang et al., “Optimal configuration planning of multi-energy systems considering distributed renewable energy,” IEEE Transactions on Smart Grid, vol. 10, no. 2, pp. 1452-1464, Mar. 2019. [Baidu Scholar]
Z. Lin, F. Wen, Y. Ding et al., “WAMS-based coherency detection for situational awareness in power systems with renewables,” IEEE Transactions on Power Systems, vol. 33, no. 5, pp. 5410-5426, Sept. 2018. [Baidu Scholar]
J. Hu, W. Hu, J. Chen et al., “Fault location and classification for distribution systems based on deep graph learning methods,” Journal of Modern Power Systems and Clean Energy, vol. 11, no. 1, pp. 35-51, Jan. 2023. [Baidu Scholar]
S. A. Arefifar, M. S. Alam, and A. Hamadi, “A review on self-healing in modern power distribution systems,” Journal of Modern Power Systems and Clean Energy, vol. 11, no. 6, pp. 1719-1733, Nov. 2023. [Baidu Scholar]
Y. Seyedi and H. Karimi, “Coordinated protection and control based on synchrophasor data processing in smart distribution networks,” IEEE Transactions on Power Systems, vol. 33, no. 1, pp. 634-645, Jan. 2018. [Baidu Scholar]
E. O. Kontis, T. A. Papadopoulos, A. I. Chrysochos et al., “On the applicability of exponential recovery models for the simulation of active distribution networks,” IEEE Transactions on Power Delivery, vol. 33, no. 6, pp. 3220-3222, Dec. 2018. [Baidu Scholar]
X. Shen, Z. Shuai, W. Huang et al., “Power management for islanded hybrid AC/DC microgrid with low-bandwidth communication,” IEEE Transactions on Energy Conversion, vol. 36, no. 4, pp. 2646-2658, Dec. 2021. [Baidu Scholar]
W. Sheng, K. Liu, Z. Li et al., “Cyber-physical-systems collaborative fault recovery and network reconstruction method based on double layer optimization,” CSEE Journal of Power and Energy Systems, vol. 9, no. 1, pp. 380-392, Jan. 2023. [Baidu Scholar]
J. Lu, Y. Tang, D. Zeng et al., “Fault prediction of electromagnetic launch system based on knowledge prediction time series,” IEEE Transactions on Industry Applications, vol. 57, no. 2, pp. 1830-1839, Mar. 2021. [Baidu Scholar]
J. Nikolic, N. Jubatyrov, and E. Pournaras, “Self-healing dilemmas in distributed systems: fault correction vs. fault tolerance,” IEEE Transactions on Network and Service Management, vol. 18, no. 3, pp. 2728-2741, Sept. 2021. [Baidu Scholar]
C. Lv, R. Liang, and Y. Chai, “Decentralized bilateral risk-based self-healing strategy for power distribution network with potentials from central energy stations,” Journal of Modern Power Systems and Clean Energy, vol. 11, no. 1, pp. 179-190, Jan. 2023. [Baidu Scholar]
