Rapid reliability evaluation method and system for power distribution network containing energy storage

Abstract

The invention belongs to the technical field of power distribution network reliability evaluation, and discloses an energy storage-containing power distribution network rapid reliability evaluation method and system, and the method comprises the steps: carrying out the pre-scheduling of energy storage, and decomposing an energy storage continuous operation scene into an independent scheduling period scene through time sequence decoupling; performing adaptive scene clustering to obtain a typical scene; a potential load shedding condition is identified by adopting the direct current optimal power flow in the worst scene, all faults are screened, and a fault set is obtained; and constructing a reliability calculation model by using the screened fault set and the typical scene, and performing rapid reliability evaluation on the power distribution network based on the reliability calculation model. According to the method, the reliability evaluation speed is improved from the three aspects of energy storage pre-scheduling, self-adaptive scene clustering and rapid fault verification, the influence of the distributed power supply and energy storage can be considered, the adaptability is good, and the improvement of the reliability evaluation speed of the power distribution network is facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network reliability evaluation, and in particular to a method and system for rapid reliability evaluation of a distribution network containing energy storage. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] Distribution network reliability assessment is one of the foundations of system operation and planning. With the widespread integration of clean, low-carbon distributed generation (DG), the uncertainty and volatility of DG output pose new challenges to distribution network reliability assessment. The introduction of energy storage (ES) provides an effective approach to addressing these challenges. It can flexibly store and release electricity, assist in absorbing DG, smooth power fluctuations, and enhance system stability and reliability. Therefore, the ability to quickly and accurately assess the reliability of distribution networks incorporating DG and ES is of great significance.

[0004] Due to the volatility and randomness of DG output, actual output often deviates from predicted output, impacting system operation. Existing methods for distribution network reliability assessment consider the impact of distributed generation (DGs) by establishing output models for wind and photovoltaic power generation. These methods combine sampling states with timelines, employing Markov chains to characterize system failures and the uncertainty of DG output, and thus obtain the power under the sampling states. However, this assessment method, which considers DG time-series power, significantly increases computational complexity and is time-consuming. For reliability assessment of distribution networks with energy storage, existing methods consider the impact of remaining energy storage capacity on reliability. However, due to the continuous and strong temporal coupling of energy storage, the optimization computational complexity is very high. Furthermore, reliability assessment for power networks with large-scale energy storage systems under N-2 fault scenarios faces computational complexity challenges, especially when the grid exceeds 100 lines, where computational feasibility bottlenecks exist. Robust grid assessment methods based on redundant architectures still face theoretical limitations in scalability.

[0005] In summary, the current distribution network reliability assessment considering DG and energy storage is more complex and time-consuming than traditional reliability assessment. New methods need to be proposed to improve the assessment speed. Summary of the Invention

[0006] To address the above-mentioned issues, the present invention proposes a method and system for rapid reliability assessment of a distribution network containing energy storage, which improves the reliability assessment speed from three aspects: energy storage pre-dispatching, adaptive scenario clustering, and rapid fault verification. The method can consider the impact of distributed power sources and energy storage, has good adaptability, and helps to improve the speed of distribution network reliability assessment.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions: In a first aspect, the present invention provides a method for rapid reliability assessment of a distribution network including energy storage, comprising the following steps: Pre-dispatch energy storage and decompose the continuous operation scenario of energy storage into independent scheduling cycle scenarios through time series decoupling; Adaptively cluster independent scheduling cycle scenarios to obtain typical scenarios; The DC optimal power flow under the worst-case scenario is used to identify potential load shedding situations, screen all faults, retain valid faults, and obtain a fault set. The reliability calculation model is constructed using the screened fault sets and typical scenarios, and a rapid reliability assessment of the distribution network is performed based on the reliability calculation model.

[0008] As an optional implementation, pre-scheduling of energy storage is achieved by optimizing the charge and discharge power of each energy storage to smooth the net load. The specific formula is as follows:

[0009]

[0010] in, is the smoothed load value, , For the actual and used scenario reduction of new energy output scenarios, , is the energy storage charging and discharging state variable, , , , , , is the energy storage charge and discharge capacity at time t, the maximum charge and discharge capacity, the energy storage capacity and the charge and discharge efficiency, , is the energy storage amount at the beginning of the energy storage and at the end of the dispatch cycle, , are the values ​​of node load and renewable energy at time t.

[0011] As an optional implementation, adaptive scene clustering includes non-extreme, sub-extreme, and extreme scenes, using the following formula as the classification standard:

[0012] in, k is the conversion factor.

[0013] As an optional implementation, all faults are screened using a fast fault screening model, specifically:

[0014]

[0015]

[0016]

[0017]

[0018]

[0019]

[0020]

[0021] in, is the active load reduction of node i at time t on the typical day of sudden fault event e, and Line The upstream and downstream nodes of For the line In a typical scenario The active power under is the node output power, To smooth the new energy output, is the load factor, is the load power, is the output of the judgment function, 0 means that node i has no load, represents reactance, represents the phase angle, Indicates voltage, Represents a very small number, and denote the original and filtered fault sets, respectively.

[0022] As an optional implementation, the reliability calculation model constructed is specifically as follows:

[0023]

[0024] .

[0025] As an optional implementation, the distribution network reliability evaluation indicators include system average power outage frequency, system average power outage time, power shortage expectation, and power supply reliability rate.

[0026] In a second aspect, the present invention provides a rapid reliability assessment system for a distribution network including energy storage, comprising: The energy storage pre-dispatch module is configured to: pre-dispatch energy storage and decompose the continuous operation scenario of energy storage into independent scheduling cycle scenarios through time series decoupling; The adaptive scenario clustering module is configured to: perform adaptive scenario clustering on independent scheduling period scenarios to obtain typical scenarios; The fast fault verification module is configured to: use the DC optimal power flow under the worst-case scenario to identify potential load shedding conditions, screen all faults, retain valid faults, and obtain a fault set; The reliability assessment module is configured to: construct a reliability calculation model using the screened fault set and typical scenarios, and perform a rapid reliability assessment on the distribution network based on the reliability calculation model.

[0027] In a third aspect, the present invention provides an electronic device comprising a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein the computer instructions, when executed by the processor, perform the method described in the first aspect.

[0028] In a fourth aspect, the present invention provides a computer-readable storage medium for storing computer instructions, wherein when the computer instructions are executed by a processor, the method described in the first aspect is performed.

[0029] In a fifth aspect, the present invention provides a computer program product, comprising a computer program, which implements the method described in the first aspect when executed by a processor.

[0030] Compared with the prior art, the present invention has the following beneficial effects: The present invention proposes a method and system for rapid reliability assessment of a distribution network containing energy storage. First, by pre-scheduling the energy storage, the temporary coupled operation of the energy storage is decomposed into parallel independent scenarios, the DG output is smoothed, the fluctuation is reduced, and the reliability assessment calculation speed is improved. Secondly, considering the influence of DG, an adaptive scenario clustering technology is proposed to address its uncertainty problem. While eliminating normal scenarios, extreme scenarios are retained according to a certain criterion, which solves the problem that traditional clustering will cause considerable calculation errors. Then, a rapid fault verification process is proposed to determine whether a cut occurs, which is crucial for filtering a large number of all possible emergencies. An improved reliability calculation model based on linear AC power flow is constructed. Finally, LHS is used for distribution network state sampling to provide a fault analysis and modeling process for reliability assessment. The example shows that the proposed method can improve the convergence speed and evaluation efficiency.

[0031] Advantages of additional aspects of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0033] Figure 1 A flowchart of a method for rapid reliability assessment of a distribution network including energy storage provided in Example 1 of the present invention; Figure 2 It is a two-state sampling model for equipment; Figure 3 It is the equipment timing operation diagram; Figure 4 It is the schematic diagram of random sampling and LHS; Figure 5 Schematic diagram for island division; Figure 6 This is the IEEE RBTS Bus6 F4 feeder structure diagram; Figure 7 is the convergence analysis diagram; Figure 8 This is the adaptive clustering result diagram. DETAILED DESCRIPTION

[0034] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0035] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0036] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments of the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but includes other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0037] In the absence of conflict, the embodiments of the present invention and the features thereof may be combined with each other.

[0038] Example 1 like Figure 1As shown, this embodiment provides a method for rapid reliability assessment of a distribution network including energy storage, comprising the following steps: Pre-dispatch energy storage and decompose the continuous operation scenario of energy storage into independent scheduling cycle scenarios through time series decoupling; Adaptively cluster independent scheduling cycle scenarios to obtain typical scenarios; The DC optimal power flow under the worst-case scenario is used to identify potential load shedding situations, screen all faults, retain valid faults, and obtain a fault set. Using the screened fault sets and typical scenarios, a reliability calculation model based on linear AC power flow is constructed, and a rapid reliability assessment of the distribution network is performed based on the reliability calculation model.

[0039] The scheme of the present invention is described in detail below: 1. Energy storage pre-dispatch Due to the scheduling of ES, especially the continuous strong temporal coupling, the optimization computational complexity is very high. Therefore, a method of decoupling the time series is proposed to decompose the continuous operation scenario of energy storage into scenarios with independent scheduling cycles. Considering the role of ES in peak and valley regulation, ES can be omitted from the optimal power flow model through pre-scheduling. Therefore, this pre-scheduling is achieved by optimizing the charge and discharge power of each ES to smooth the net load. The specific formula is as follows:

[0040]

[0041] Where, is the smoothed load value, , is the actual new energy output scenario used for scenario reduction, which can be obtained through equations (1) and (2). , is the energy storage charging and discharging state variable, , , , , , It is the energy storage charge and discharge capacity at time t, the maximum charge and discharge capacity, the energy storage capacity and the charge and discharge efficiency. , It is the amount of energy stored at the beginning of the energy storage and at the end of the dispatch cycle. , is the value of the node load and renewable energy at time t. Through this algorithm, the charging and discharging plan of the energy storage at each moment can be obtained in advance. This can reduce the computational complexity of the distribution network operation scenario, and only the reliability analysis at the moment of the fault is required.

[0042] ES pre-scheduling is the basis for further accelerating the calculation speed. Each scenario is calculated independently, so the source and load scenarios can be clustered to obtain a typical scenario, which significantly reduces the calculation amount of reliability analysis. Reliability assessment is performed using the traditional K-Means clustering method, which employs adaptive scenario reduction with extreme value retention. Clustering scenarios can encounter extreme cases, such as low output from smoothed renewable energy generation and high load. Load losses primarily occur in these extreme cases. Consequently, traditional clustering can result in significant computational errors.

[0043] To address this issue, it is recommended to retain the most extreme scenarios without clustering them and cluster the less extreme scenarios. Here, we propose a method that clusters 75% of the scenarios into 10 centers, clusters 20% of the scenarios into 10 centers, and retains the remaining 5% of the scenarios intact. The resulting classifications correspond to non-extreme, sub-extreme, and extreme scenarios, respectively. We propose a suitable criterion for this classification, as shown below.

[0044]

[0045] Where, k The conversion factor is set to 2.4 in this invention. A larger value indicates a more extreme situation. In extreme situations, the risk of power imbalance may be greater.

[0046] 3. Rapid fault detection After reducing the scenarios, the proposed method will quickly screen out fault conditions. Considering the target requirements of N-2 safety assessment, traditional assessment methods are very time-consuming. The core idea of ​​this method is to speed up the calculation process by eliminating non-critical fault conditions. To determine whether a certain condition will cause load shedding, it is only necessary to analyze the load reduction under the worst scenario. The worst scenario is defined as the scenario with the highest From a probabilistic perspective, this scenario is more likely to result in load shedding than any other scenario. More importantly, if this scenario does not trigger load shedding, then other scenarios will most likely not result in load shedding either. Furthermore, to speed up verification, the DC Optimal Power Flow (DCOPF) model can be used instead of the complex linear AC Optimal Power Flow (ACOPF) model. The specific calculation formula is as follows:

[0047]

[0048]

[0049]

[0050]

[0051]

[0052]

[0053]

[0054] Where, is the active load reduction of node i at time t on the typical day of sudden fault event e, and Line The upstream and downstream nodes of For the line In a typical scenario The active power under is the node output power, To smooth the new energy output, is the load factor, is the load power. is the output of the judgment function, 0 means that node i has no load, represents reactance, represents the phase angle, Indicates voltage, Represents a very small number, and represents the original and filtered incident sets.

[0055] The main structure of the rapid fault screening model is similar to that of the traditional model, but with several key differences: energy storage pre-dispatch (ESpre-dispatch), time decoupling, scenario reduction, and DC optimal power flow (DCOPF). In some scenarios, the smoothed net load is negative (requiring energy storage charging), but charging power cannot be guaranteed under certain severe faults, potentially rendering the problem unsolvable. To address this, a relaxation formulation is employed instead of the equational form. Since the objective function is to minimize load shedding, the corresponding variables are determined through the optimization process.

[0056] 4. Reliability index calculation correction The final stage can be implemented reliability assessment analysis. This assessment method is based on the system optimization of the three correction links mentioned above, and only targets emergencies that occur during the filtering process. Consider and adopt the smoothed scheduling solution This replaces the original ES scheduling mechanism and replaces the traditional time series method with independent scenario analysis within the set S. It is worth noting that this model still maintains consistency with the traditional modeling method in some features. The formula is as follows:

[0057]

[0058]

[0059] Compared to traditional reliability assessment methods, the optimization model constructed in this study has undergone significant structural improvements, significantly reducing computational complexity. Specifically: 1) Variables and constraints related to energy storage scheduling are completely eliminated; 2) the time series analysis method is simplified into a parallel multi-scenario analysis framework; 3) representative scenarios are used instead of the original typical day 24-hour calculation model; and 4) various contingency factors are screened to the greatest extent possible. Regarding the calculation of reliability indicators, this study uses the optimized probability distribution of unexpected events and scenario probabilities, a calculation principle consistent with traditional methods.

[0060] 5. Reliability Assessment Model LHS improved sampling: The two-state model of the device is as follows Figure 2 As shown, and are the failure rate and repair rate of the equipment respectively. Both the failure rate and repair rate of the equipment obey the exponential distribution, and the probability density function is as follows:

[0061] Where, is the failure rate or repair rate.

[0062] Integrate the above formula:

[0063] The inverse transformation obtains the trouble-free working time TTF (Time To Failure) and the troubleshooting time TTR (Time To Repair)

[0064]

[0065] Where, TTF and TTR are the normal operation time and fault duration of the equipment sampling, is a pseudo-randomly sampled random number.

[0066] Using the Monte Carlo method to extract the device state duration can obtain the device timing operation scenario, such as Figure 3 shown.

[0067] The Monte Carlo sampling process allows samples to fall anywhere within the input distribution. When performed with a small number of iterations, clustering can occur, resulting in a large number of samples and long computational times. LHS offers advantages such as a simple sampling process, good sample representativeness, and high speed. Its key concept is to perform an average stratification of the probability distribution over the (0,1) interval. Random numbers are drawn from each stratum without duplication. The number of random numbers drawn is the number of strata, significantly reducing the number of sample data and the number of iterations. Figure 4 This is the sampling principle diagram.

[0068] The LHS sampling method is used instead of random sampling and combined with the Monte Carlo method to extract the TTR and TTF of the equipment. The derivation process is as follows:

[0069] Where: is the average duration, MTTF and MTTR are the average TTF and average TTR.

[0070] Number of state cycles N within the simulation period T:

[0071] Where N is the number of LHS sampling strata, Due to the rounding function, the number of sampling strata for different power equipment is also different.

[0072] The state duration sampling formula obtained by combining LHS with Monte Carlo method is:

[0073]

[0074] Where R represents the Rth layer to be extracted, which is a random integer between [1, 2, .., N].

[0075] Failure analysis: The basic idea of ​​system reliability analysis is to determine whether the load node has power outage after a fault occurs, whether power is restored after the power outage, and calculate the time it takes to restore power. Due to the presence of switch protection components and DG in the system, when components at different locations fail, the power supply status of the load point will be different, that is, the fault area will be different. Figure 5 As shown in the figure, assuming that the isolation switch element QS2 fails, the fault recovery process is as follows: 1) The line-end circuit breakers QF1 and QF2 are immediately disconnected, and all system loads LP1-LP5 are de-energized.

[0076] 2) Fault location and fault isolation: QS2 fault can be determined based on relevant measurement elements, and then QS1 and QS3 are disconnected to isolate QS2 from the system.

[0077] 3) When the fault is restored, QF1 closes, LP1 resumes power, and LP2 and LP3 are disconnected until the faulty component is repaired. The power supply status of loads LP4 and LP5 is closely related to the DG. Without DG and ES, their power supply status is the same as LP2 and LP3. However, with DG and ES installed, they form an island area (green box). Once the fault is isolated, power can be restored, eliminating the need to wait until the fault is repaired, thus shortening the outage.

[0078] The island area is divided based on the relative size of the smoothed DG output and the load demand. If the DG output is greater than the LP4 and LP5 load demands, the load will resume power supply; otherwise, the load will be shut down.

[0079] Reliability indicators: Reliability indicators mainly include system average outage frequency (SAIFI), system average outage duration (SAIDI), expected energy shortage (EENS), and power supply reliability index (ASAI). The calculation formula is as follows:

[0080]

[0081]

[0082]

[0083] Where: 、 、 、 They are the failure rate of the load point, the number of users at the load point, the average power outage time at the load point, and the average load power at the load point.

[0084] 6. Case Analysis This embodiment is tested based on the modified IEEE RBTS Bus6 F4 system. The specific modifications are as follows: Figure 6 As shown in Figure 2, DG and ES are installed on 27 nodes, 47 nodes and 52 nodes respectively. The convergence accuracy is set to The following schemes are set up to compare and analyze the effectiveness of the method proposed in the present invention.

[0085] Option 1: Use random sampling and do not install DG and energy storage; Option 2: Use LHS without installing DG and energy storage; Option 3: Use LHS, install DG and energy storage, and no energy storage pre-dispatch; Option 4: Use LHS, install DG and energy storage, and have energy storage pre-dispatch.

[0086] Table 1. Failure repair rate of components;

[0087] The average simulation time is as follows: Table 2 Time consumption of different solutions

[0088] As shown in Table 2, a comparison between Schemes 1 and 2 shows that LHS sampling takes less time than random sampling. This is because the LHS principle is stratified sampling, which ensures that fewer samples meet the requirements of sample representativeness and convergence, resulting in fewer useless repetitive samples. Random sampling, on the other hand, requires a large number of samples to meet convergence requirements. A comparison between Schemes 3 and 4 shows that the energy storage pre-dispatch proposed in this invention can effectively shorten simulation time. This is because after performing energy storage pre-dispatch, energy storage does not need to be considered in reliability calculations, reducing computational complexity and thus simulation time. A comparison between Schemes 1 and 2 and Schemes 3 and 4 shows that after considering DG, distribution network reliability assessment becomes more complex and time-consuming.

[0089] Table 3 Reliability indicators of different schemes

[0090] As shown in Table 3, installing DGs and ESs can effectively improve distribution network reliability overall. This is because during power outages, DGs and ESs can provide power to some of the outaged loads, reducing load outage duration. Schemes 1 and 2 differ only in the sampling method; all other conditions are identical. When convergence conditions are met, the sampling method does not affect the reliability index, so their reliability indexes are similar, which indirectly confirms the correctness of the proposed sampling method. A comparison of Schemes 3 and 4 shows that the energy storage pre-dispatch proposed in this invention can effectively improve system power supply reliability. This is because the energy storage pre-dispatch strategy of this invention can smooth the net load, utilize the synergistic effect of energy storage and DGs to maximize load power supply, and improve DG utilization efficiency.

[0091] By performing multiple simulations with 1000 years as the unit, we can see that the load point failure rate gradually converges with the increase of years. Figure 7 The variance of the average power outage time under different partial load times is shown. It can be seen that the LHS converges faster. The reason is that the LHS principle is stratified sampling, and the sample representativeness is good. The reduced samples can meet the convergence conditions, so the convergence speed is faster.

[0092] Figure 8This is the clustering result. It can be seen that the clustering method proposed in the present invention retains extreme scenarios, reduces the number of typical scenarios, makes the scenarios involved in the calculation more representative, and reduces the amount of calculation.

[0093] This paper proposes a rapid reliability assessment method for distribution networks with energy storage. First, by pre-dispatching energy storage, DG output is smoothed, reducing fluctuations and improving the reliability assessment calculation speed. Second, the impact of DGs is considered. To address their uncertainty, an adaptive scenario clustering technique is proposed, taking into account extreme scenarios. Finally, a rapid fault verification process is proposed, and an improved reliability calculation model based on linear AC power flow is constructed. Finally, LHS is applied to distribution network state sampling to provide a fault analysis and modeling process for reliability assessment. Numerical examples demonstrate that the proposed method can improve convergence speed and assessment efficiency.

[0094] Example 2 This embodiment provides a rapid reliability assessment system for a distribution network including energy storage, including: The energy storage pre-dispatch module is configured to: pre-dispatch energy storage and decompose the continuous operation scenario of energy storage into independent scheduling cycle scenarios through time series decoupling; The adaptive scenario clustering module is configured to: perform adaptive scenario clustering on independent scheduling period scenarios to obtain typical scenarios; The fast fault verification module is configured to: use the DC optimal power flow under the worst-case scenario to identify potential load shedding conditions, screen all faults, retain valid faults, and obtain a fault set; The reliability assessment module is configured to: use the screened fault set and typical scenarios to build a reliability calculation model based on linear AC power flow, and perform rapid reliability assessment on the distribution network based on the reliability calculation model.

[0095] It should be noted that the above modules correspond to the steps in Example 1, and the examples and application scenarios implemented by the above modules and the corresponding steps are the same, but are not limited to the contents disclosed in the above Example 1. It should be noted that the above modules can be executed in a computer system as part of the system.

[0096] In further embodiments, there is also provided: An electronic device includes a memory and a processor, and computer instructions stored in the memory and executed by the processor, wherein when the computer instructions are executed by the processor, the method in embodiment 1 is performed. For the sake of brevity, no further details are given here.

[0097] It should be understood that in this embodiment, the processor may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), off-the-shelf field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0098] A computer-readable storage medium is used to store computer instructions, and when the computer instructions are executed by a processor, the method in embodiment 1 is completed.

[0099] The method in Example 1 can be directly executed by a hardware processor, or by a combination of hardware and software modules in the processor. The software module can be located in a storage medium mature in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in the memory, and the processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, it will not be described in detail here.

[0100] A computer program product includes a computer program, wherein the computer program implements the method in embodiment 1 when executed by a processor.

[0101] The present invention also provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer-executable instructions, such as instructions contained in program modules, which are executed in a device on a real or virtual processor of a target to perform the process / method described above. Generally, program modules include routines, programs, libraries, objects, classes, components, data structures, etc. that perform specific tasks or implement specific abstract data types. In various embodiments, the functionality of program modules can be combined or divided between program modules as needed. The machine-executable instructions for the program modules can be executed in local or distributed devices. In distributed devices, program modules can be located in local and remote storage media.

[0102] The computer program code for implementing the method of the present invention can be written in one or more programming languages. These computer program codes can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the computer or other programmable data processing device, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on a computer, partially on a computer, as an independent software package, partially on a computer and partially on a remote computer, or entirely on a remote computer or server.

[0103] In the context of the present invention, computer program code or related data can be carried by any appropriate carrier to enable a device, apparatus, or processor to perform the various processes and operations described above. Examples of carriers include signals, computer-readable media, and the like. Examples of signals include electrical, optical, radio, acoustic, or other forms of propagated signals, such as carrier waves, infrared signals, and the like.

[0104] Those skilled in the art will appreciate that the units and algorithm steps of the various examples described in conjunction with this embodiment can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0105] Although the above describes the specific embodiments of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without any creative work are still within the scope of protection of the present invention.

Claims

1. A method for rapid reliability assessment of a distribution network containing energy storage, characterized in that: The following steps are involved: Pre-dispatch energy storage and decompose the continuous operation scenario of energy storage into independent scheduling cycle scenarios through time series decoupling; Adaptively cluster independent scheduling cycle scenarios to obtain typical scenarios; The DC optimal power flow under the worst-case scenario is used to identify potential load shedding situations, screen all faults, retain valid faults, and obtain a fault set. The reliability calculation model is constructed using the screened fault sets and typical scenarios, and a rapid reliability assessment of the distribution network is performed based on the reliability calculation model.

2. A method for rapid reliability assessment of a distribution network containing energy storage according to claim 1, characterized in that: Pre-dispatching of energy storage is achieved by optimizing the charge and discharge power of each energy storage to smooth the net load. The specific formula is as follows: in, is the smoothed load value, , For the actual and used scenario reduction of new energy output scenarios, , is the energy storage charging and discharging state variable, , , , , , is the energy storage charge and discharge capacity at time t, the maximum charge and discharge capacity, the energy storage capacity and the charge and discharge efficiency, , is the energy storage amount at the beginning of the energy storage and at the end of the dispatch cycle, , are the values ​​of node load and renewable energy at time t.

3. A method for rapid reliability assessment of a distribution network containing energy storage according to claim 1, characterized in that: Adaptive scenario clustering includes non-extreme, sub-extreme, and extreme scenarios, using the following formula as the classification criteria: in, k is the conversion factor.

4. A method for rapid reliability assessment of a distribution network containing energy storage according to claim 1, characterized in that: All faults are screened using a fast fault screening model, specifically: in, is the active load reduction of node i at time t on the typical day of sudden fault event e, and Line The upstream and downstream nodes of For the line In a typical scenario The active power under is the node output power, To smooth the new energy output, is the load factor, is the load power, is the output of the judgment function, 0 means that node i has no load, represents reactance, represents the phase angle, Indicates voltage, Represents a very small number, and denote the original and filtered fault sets, respectively.

5. A method for rapid reliability assessment of a distribution network containing energy storage according to claim 1, characterized in that: The reliability calculation model constructed is as follows: 。 6. A method for rapid reliability assessment of a distribution network with energy storage according to claim 1, characterized in that: The distribution network reliability evaluation indicators include the average system outage frequency, the average system outage time, the power shortage expectation and the power supply reliability rate.

7. A rapid reliability assessment system for a distribution network with energy storage, characterized in that: include: The energy storage pre-dispatch module is configured to: pre-dispatch energy storage and decompose the continuous operation scenario of energy storage into independent scheduling cycle scenarios through time series decoupling; The adaptive scenario clustering module is configured to: perform adaptive scenario clustering on independent scheduling period scenarios to obtain typical scenarios; The fast fault verification module is configured to: use the DC optimal power flow under the worst-case scenario to identify potential load shedding conditions, screen all faults, retain valid faults, and obtain a fault set; The reliability assessment module is configured to: construct a reliability calculation model using the screened fault set and typical scenarios, and perform a rapid reliability assessment on the distribution network based on the reliability calculation model.

8. An electronic device, characterized in that: The method comprises a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method according to any one of claims 1 to 6 is completed.

9. A computer-readable storage medium, characterized in that Used to store computer instructions, which, when executed by a processor, complete the method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The invention comprises a computer program, which is used to implement the method according to any one of claims 1 to 6 when the computer program is executed by a processor.

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