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

Abstract

The application discloses a reliability evaluation method and system for a power distribution network containing energy storage, and first establishes an evaluation index system with the effective load supply capacity of a system as the core; then adopts time sequence probability simulation and a Latin hypercube sampling method to generate typical daily random scenarios of power distribution facility faults and load fluctuations; further, in the sequential Monte Carlo simulation framework, system operation state evaluation is carried out through optimal power flow calculation, and statistical analysis of indexes such as equal annual value cost, power shortage expectation and loss of load probability is supported; in actual engineering application, scientific comparison of the economy and reliability of multiple energy storage configuration schemes can be carried out, and then reference is provided for the energy storage device configuration plan of the power distribution network.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power distribution network analysis, and particularly relates to a reliability evaluation method and system for a power distribution network containing energy storage. BACKGROUND

[0002] The power distribution network is at the end of the power system and directly connects power users. The peak-valley characteristics of the load have a negative impact on the stable operation of the power distribution network system. In addition, the line fault problems that often occur in some remote areas also bring great challenges to the stable power supply task of the power distribution network. Installing energy storage devices is an important measure to ensure the safe and reliable operation of the power distribution network, improve power supply quality, and promote the modernization of the power industry. In the long-term operation of the power distribution network, for a given energy storage configuration scheme, comprehensive and multi-level reliability evaluation is needed. A reasonably planned power distribution network containing energy storage needs to meet the requirements of continuous reliable power supply of the system and the requirements of economic and efficient operation of the system, especially the influence of energy storage devices on the reliability and economy of the system.

[0003] However, in the existing evaluation technology, only the improvement of certain aspects of the power distribution network after the access of energy storage is analyzed, or only the economic benefit of the power distribution network is modeled and analyzed, which cannot reflect the influence of the access of energy storage devices on the reliability of the power distribution network, and is not conducive to the comparison of multiple energy storage configuration schemes. The power distribution network containing energy storage involves a large number of parameters and complex operating conditions in the operation process, and it is a very difficult task to reasonably evaluate the economy and reliability of the system. Moreover, this evaluation process should be carried out before the power distribution network system accesses energy storage, and the data required for system evaluation cannot be obtained, so it is necessary to scientifically simulate the operation of the system and evaluate the economy and reliability of the system after the installation of energy storage according to the data obtained by simulation. SUMMARY

[0004] The technical problem to be solved by the present application is to provide a reliability evaluation method and system for a power distribution network containing energy storage to solve the problem of incomplete evaluation of a power distribution network containing energy storage and no evaluation process for energy storage configuration schemes.

[0005] The application adopts the following technical solutions:

[0006] A reliability evaluation method for a power distribution network containing energy storage, characterized in that it comprises the following steps:

[0007] S1, obtaining power distribution network load level information, power distribution network line fault probability information, power distribution network wind and light historical data, and power distribution network energy storage configuration information;

[0008] S2. Considering the impact of line equipment failure factors on the economy and reliability of distribution networks with energy storage, establish an evaluation index system with the effective load supply capacity of the system as the core.

[0009] S3. Based on the distribution network load level information, distribution network line fault probability information, distribution network wind and solar historical data, and distribution network energy storage configuration information obtained in step S1, establish an optimal power flow model for the distribution network including energy storage.

[0010] S4. The optimal power flow model of the distribution network with energy storage established in step S3 is evaluated using the sequential Monte Carlo evaluation method. Combined with the evaluation index system obtained in step S2, the evaluation index of the distribution network with energy storage is obtained for reliability evaluation and analysis.

[0011] Specifically, in step S2, the evaluation indicator system includes economic indicators and reliability indicators. The economic indicators include equivalent annual cost. Standardized cost of electricity Reliability metrics include expected low battery capacity. and the probability of load loss .

[0012] Furthermore, the first t Annual equivalent cost Specifically:

[0013]

[0014] in, This represents the system's equivalent annual cost in year t. This indicates the initial investment cost of the energy storage plan. Indicates the capital recovery coefficient; Indicates the system's first t Annual operating costs include the cost of purchasing electricity for the distribution network and the cost of load shedding penalties; For the system number t The annual maintenance cost here refers to the cost of repairing a broken line.

[0015] No. t Annual System Levelized Cost of Electricity for:

[0016]

[0017] in, Indicates the first t The system's total annual power generation.

[0018] Furthermore, insufficient battery power is expected. Specifically:

[0019]

[0020] wherein, represents the total number of simulated scenarios in the evaluation model; T represents the total number of simulated time periods; represents the time period interval; represents the scenario s the load loss of the system in the t time period;

[0021] the load loss probability is:

[0022]

[0023] wherein, represents the total load demand of the system in the evaluation time period.

[0024] Specifically, in step S3, the constraints of the optimal power flow model of the energy storage integrated distribution network include node power balance constraints, energy storage power station operation constraints, reactive power compensation device operation constraints, system power balance equations, voltage relaxation constraints, node voltage constraints, main substation transmission capacity constraints, and distribution line transmission power constraints. The objective function of the optimal power flow model of the energy storage integrated distribution network is:

[0025]

[0026] wherein, y represents the dispatching variable; j represents the node number of the distribution network system, represents all nodes of the system; t represents the number of time periods of the system, T represents the total number of time periods contained in the dispatching period, and also represents the time period set, represents the load loss of the system at the j node in the t time period.

[0027] Further, the node power balance constraint is:

[0028]

[0029] wherein, and represent the active power injection and the reactive power injection of the j node in the t time period, respectively; m represents the number of energy storage battery groups, represents the number of energy storage battery groups installed at the j node; and represent the active power injection and the reactive power injection of the jNode at the t The first period m Discharge and charging power of each energy storage battery pack; and Indicates the first j Node at the t Active and reactive loads during a given time period; Indicates the system at the node j Upper t Power loss during the period; Represents a node j Photovoltaic array in t Efforts during a specific time period; Represents a node j Wind power in t Efforts during a specific time period; Indicates the system at the node j Upper t The first period n The reactive power compensated by each reactive power compensation device. Indicates at node j Number of reactive power compensation devices installed;

[0030] The operational constraints of energy storage power stations include:

[0031] Energy storage battery charge and discharge power constraints:

[0032]

[0033]

[0034] in, Represents a node j The m Maximum charging power of each energy storage battery pack; Represents a node j The m The maximum discharge power of each energy storage battery pack;

[0035] Energy storage constraints of batteries:

[0036]

[0037]

[0038] in, Represents a node j The m The energy storage battery pack in the first t Storage capacity during a given period; and They are nodes j The mThe lower and upper limits of the storage capacity of each energy storage battery pack; Indicates the first j The node of the first m The charge / discharge efficiency of an energy storage battery pack; Indicates the system scheduling time interval;

[0039] Reactive power compensation device operating constraints:

[0040]

[0041] in, and Representing nodes respectively j The n The lower limit and upper limit of the compensation power of each reactive power compensation device;

[0042] Power balance constraints at node 0 of substation:

[0043]

[0044] Power balance equations for all nodes except node 0:

[0045]

[0046] in, Indicates the first j The set consisting of all downstream nodes of a given node. k This is the number of the downstream node; Indicates in t Time period route from i Node transmission to j The active power of the node, Indicates in t Time period route from i Node transmission to j The reactive power of a node;

[0047] Voltage relaxation constraint:

[0048]

[0049] in, and They represent the lines respectively. ij The resistance and reactance values; Indicates the operating status of the line; M This represents a very large number, used to relax constraints; This represents the voltage value of node j during time period t; Indicates the node voltage reference value; Represents the set of line numbers;

[0050] Node voltage constraints:

[0051]

[0052] wherein, and denote the lower and upper voltage limits of the node j , respectively;

[0053] Main substation transmission capacity constraints:

[0054]

[0055] wherein, and denote the lower and upper active capacity limits of the substation, respectively; and denote the lower and upper reactive capacity limits of the substation, respectively;

[0056] Distribution line transmission power constraints:

[0057]

[0058]

[0059] wherein, denotes the maximum transmission capacity of the line ij , and is used to describe the on-off state.

[0060] Specifically, in step S4, the evaluation process of the sequential Monte Carlo is specifically:

[0061] S401, input the energy storage configuration scheme to be evaluated;

[0062] S402, initialize the scene running simulation parameters, and let the scene count bit ;

[0063] S403, after initializing the scene running simulation parameters, randomly sample the load in the time period, the new energy output and the line fault state, a total of t times, each time taking T , and simulating for scenarios; i

[0064] S404, according to the optimal power flow scheduling model of the power distribution network containing energy storage established in step S3, perform optimal power flow analysis and calculation under the first i scenario, and then according to the evaluation index system established in step S2, calculate the economic and reliability indexes of the first i scenarios; and statistically calculate the first i ​​For each scenario and economic and reliability index, if the index convergence condition is not met, then let Return to step S403; if the index convergence condition is met, perform system evaluation analysis and output the evaluation results.

[0065] Furthermore, in step S403, the Latin hypercube sampling method is used for sampling as follows:

[0066] S4031. The number of samplings of the random variable is denoted as... N Divide the probability interval of random variable X into N The system is divided into equidistant, non-overlapping subintervals, and random numbers are drawn from each subinterval according to a uniform distribution. If the cumulative probability distribution function of random variable X is known to be... Then, according to the inverse function of the cumulative probability distribution function... Obtain a random sample of X Obtain random quantity dimensional sample sequence;

[0067] S4032, When the number of random variables is M At that time, a sample with the same dimension as the initial sample is generated. dimensional random order matrix L The Choreski decomposition method is used to decompose random ordered matrices. L To make corrections, first, the random order matrix... L The corresponding correlation coefficient matrix is ​​denoted as And obtain the correlation coefficient matrix based on the historical observations of the random variables. ,exist and Given that all matrices are symmetric positive definite matrices, perform the Cholliski decomposition, and then, according to... Q , D and L Export the correlation matrix G When the correlation matrix G When the elements are not integers, based on the association matrix G The relative sizes of the elements in the original sample X are used to construct an association matrix in which all elements are positive numbers, and the elements in the original sample X are reordered based on the association matrix.

[0068] Specifically, in step S4, the stability criterion is used to determine whether the sequential Monte Carlo evaluation has converged. When the convergence index... CI Less than or equal to the convergence threshold When the evaluation process converges, the loop stops; otherwise, the loop continues, and the convergence criterion is... CI The calculation is as follows:

[0069]

[0070] wherein, is an index for measuring the fluctuation of the economy index under all scenarios, is an index for measuring the fluctuation of the reliability index under all scenarios.

[0071] In a second aspect, the embodiments of the present application provide a reliability evaluation system of a power distribution network with energy storage, comprising:

[0072] a data module, which obtains power distribution network load level information, power distribution network line fault probability information, wind and light historical data of the power distribution network, and power distribution network energy storage configuration information;

[0073] an index module, which considers the influence of line equipment failure factors on the economy and reliability of the power distribution network with energy storage, and establishes an evaluation index system with system effective load supply capacity as the core;

[0074] an optimization module, which establishes a power distribution network with energy storage optimal power flow model according to the power distribution network load level information, power distribution network line fault probability information, wind and light historical data of the power distribution network, and power distribution network energy storage configuration information obtained by the data module;

[0075] an evaluation module, which uses a sequential Monte Carlo evaluation method to evaluate the power distribution network with energy storage optimal power flow model established by the optimization module, and obtains the evaluation index of the power distribution network with energy storage for reliability evaluation analysis of the power distribution network with energy storage in combination with the evaluation index system obtained by the index module.

[0076] Compared with the prior art, the present application has at least the following beneficial effects:

[0077] The application discloses a reliability evaluation method for a power distribution network containing energy storage, which comprehensively considers various information of the power distribution network, fully utilizes load level, line fault probability, wind and light historical data and energy storage configuration scheme information for evaluation, so that the evaluation process is comprehensive; a full-range and multi-level index system is established for economic and reliability evaluation of the power distribution network containing energy storage, the influence of the energy storage device on the economy and reliability of the system is highlighted by mainly considering the influence of line equipment fault factors, and the selection of the energy storage configuration scheme is facilitated; an optimal power flow model of the power distribution network containing energy storage is established by using system parameter information of the power distribution network, the model gives detailed operation states of the power distribution network containing energy storage under a given scene, by solving the model, the fine description of the states of all elements of the system can be realized, and the optimal scheme under a specified standard can be obtained by changing the objective function; the model can also improve the utilization efficiency of the system and better realize the economic and reliability evaluation of the system; by introducing a sequential Monte Carlo framework, random sampling and scheduling models are combined under the premise of considering the time sequence characteristics of the system. In the reliability evaluation of the application, the time sequence characteristics are embodied in daily time sequence changes with daily scheduling as a cycle and annual time sequence characteristics of equipment based on long-term fault probability; a well-designed sequential Monte Carlo simulation framework can quickly obtain stable and real evaluation indexes, so that the final economic and reliability evaluation of the system is realized.

[0078] Further, since the reasonably planned power distribution network containing energy storage needs to meet the requirements of continuous reliable power supply of the system and economic and efficient operation of the system, especially the influence of the energy storage device on the reliability and economy of the system, the application mainly considers the influence of line equipment fault factors on the economy and reliability of the power distribution network containing energy storage, and establishes an evaluation system with effective load supply capacity as the core. In the economic indexes used, the annualized cost ACS describes the sum of the annualized initial investment, annual operation and maintenance cost and annual equipment replacement cost of the system, the cost of the power distribution network containing energy storage mainly includes energy storage planning investment cost, system operation cost and line breakage maintenance cost, and the introduction of the annualized cost ACS can clearly describe the total operation cost of the power distribution network containing energy storage; the levelized cost of electricity LCE describes the proportion of the annualized cost and the expected power supply, and the introduction of the index can distribute the annualized cost of the system to the unit power supply of the system. The reliability indexes include the expected power shortage EENS and the loss of load probability LPSP, and the use of the two indexes can evaluate the reliability of different energy storage configuration schemes by describing the expected value of the loss of load power of the system in a specified period and the ratio of the expected loss of load of the system to the total load of the system.

[0079] Further, in the economic indexes used, the annualized cost of the tth year The sum of the equal annual value of the initial investment of the system in the t year, the annual operation and maintenance cost and the annual equipment replacement cost is described, the cost of the energy storage distribution network system mainly includes the energy storage planning investment cost, the system operation cost and the line breakage maintenance cost, and the equal annual value cost is introduced The total operation cost of the energy storage distribution network system in each year in the medium and long term simulation process can be clearly described. The ratio of the equal annual value cost in the t year to the expected power supply is described, and the introduction of the index can represent the result of the equal annual value cost of the system being allocated to the system per unit of power supply in each year, so that the cost of the system operation in each year is more clearly described.

[0080] Further, in the reliability index used, the expected insufficient power EENS represents the expected value of the load loss of the system in a specified period, when the line fails, the distribution network system will have a load loss phenomenon due to the lack of self-provided power, and the load loss of the system can be effectively reduced by reasonably configuring the energy storage system, so that the EENS index can reflect the influence of different energy storage planning schemes on the reliability of the system; the load loss probability LPSP represents the ratio of the expected load loss of the system to the total load of the system, and the use of the index can more clearly compare different energy storage planning schemes.

[0081] Further, the optimal power flow model of the energy storage distribution network takes the minimum system load loss power as the objective function, and the system load loss power can be minimized by solving each dispatching variable, including the power purchase power of the distribution network system, the charging power and the discharging power of the energy storage device, so that the dispatching decision obtained can meet the reliability requirements of the system.

[0082] Further, the optimal power flow model of the energy storage distribution network includes node power balance constraints, energy storage power station operation constraints, reactive power compensation device operation constraints, system power balance equations, voltage relaxation constraints, node voltage constraints, main substation transmission capacity constraints and distribution line transmission power constraints, and the mathematical model of the optimal power flow of the energy storage distribution network can be expressed through the above constraints, which fully reflects the physical reality, so as to ensure the practicability of the final economic and reliability evaluation results.

[0083] Further, the sequential Monte Carlo simulation simulates the actual system operation result by generating a large number of samples and performing index statistics on the samples, and when the number of samples is sufficient, the statistical index converges to the true value of the index. In the economic and reliability evaluation of the present application, the time sequence characteristics are reflected in the daily time sequence change with daily scheduling as the period and the annual time sequence characteristics of the equipment based on the long-term failure probability. The well-designed sequential Monte Carlo simulation framework in the present application is helpful to quickly obtain stable and real evaluation indexes.

[0084] Further, in the scenario simulation process of the present application, the selection of a large number of random variables is involved, and the process of selecting and determining the values of these random variables is called random variable sampling. The random variable sampling method generally uses a simple random sampling method, that is, after the probability distribution of the random variable is given, a random number is generated by using a random number generation table, and then the value of the random variable is converted into the value of the random variable by using the probability distribution function. In the present application, in order to better reflect the physical reality process through the simulation process, the Latin hypercube sampling (LHS) method is used for sampling. Referring to Figure 3 It can be obtained that the sampling efficiency of the Latin hypercube sampling method is higher than that of the simple random sampling.

[0085] Further, the most important thing in the evaluation framework is how to judge the convergence of the evaluation index, and the present application uses a stability criterion to judge the convergence. By using the stability criterion, the changes of all economic indicators and reliability indicators under all simulation scenarios can be counted, and each indicator is given a certain convergence judgment weight, so that the conclusion of the evaluation index convergence is obtained, and the sequential Monte Carlo simulation process is stopped accordingly.

[0086] It can be understood that the beneficial effects of the above-mentioned second aspect can be referred to the related description in the above-mentioned first aspect, which will not be repeated here.

[0087] In summary, the present application first establishes an evaluation index system with the effective load supply capacity of the system as the core; then uses the time sequence probability simulation and the Latin hypercube sampling method to generate typical daily random scenarios of power distribution facility failures and load fluctuations; further, in the sequential Monte Carlo simulation framework, the system operation state is evaluated by optimal power flow calculation, and the statistical analysis of indexes such as equal annual cost, power shortage expectation, and loss of load probability is supported; the medium and long-term operation effect of the energy storage configuration scheme can be simulated, and the feasibility of the energy storage planning result is verified from the aspects of economy and reliability. In actual engineering application, a plurality of energy storage configuration schemes can be compared scientifically from the aspects of economy and reliability, and then the reference for the energy storage device configuration plan of the distribution network is provided.

[0088] The technical solutions of the present application will be further described in detail below with the aid of the drawings and examples. BRIEF DESCRIPTION OF DRAWINGS

[0089] Figure 1 It is a reliability evaluation method framework for a power distribution network containing energy storage;

[0090] Figure 2 It is a sequential Monte Carlo evaluation flowchart;

[0091] Figure 3 It is a comparison chart of the Latin hypercube sampling and the simple random sampling;

[0092] Figure 4 It is a line topology diagram of the example power distribution network;

[0093] Figure 5 The schematic diagram of each construction scheme for the embodiment network is shown in the figure.

[0094] Figure 6 The statistical diagram of the failure times of the random scene for the embodiment is shown in the figure. DETAILED DESCRIPTION

[0095] The technical solutions in the embodiments of the present application will be clearly and completely described in the embodiments of the present application in combination with the accompanying drawings. Obviously, the described embodiments are some of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0096] In the description of the present application, it should be understood that the terms “include” and “contain” indicate the existence of described features, whole, steps, operations, elements and / or components, but do not exclude the existence or addition of one or more other features, whole, steps, operations, elements, components and / or sets thereof.

[0097] It should also be understood that the terms used in the present application specification are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the present application specification and the appended claims, unless otherwise clear from the context, the singular forms “a”, “an” and “the” are intended to include the plural forms.

[0098] It should be further understood that the term “and / or” used in the present application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes these combinations, for example, A and / or B can represent three cases of A alone, A and B together, and B alone. In addition, the character “ / ” herein generally represents an “or” relationship between the front and rear associated objects.

[0099] It should be understood that although the terms first, second, third, etc. may be used in the embodiments of the present application to describe the preset ranges, etc., these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from each other. For example, the first preset range can also be referred to as the second preset range, and similarly, the second preset range can also be referred to as the first preset range without departing from the scope of the embodiments of the present application.

[0100] Depending on the context, the word "if" as used herein can be interpreted to mean "when" or "while" or "in response to determining" or "in response to detecting." Similarly, the phrase "if it is determined" or "if [a stated condition or event] is detected" can be interpreted to mean "when it is determined" or "in response to determining" or "when [a stated condition or event] is detected" or "in response to detecting [a stated condition or event]."

[0101] The various structural diagrams according to the disclosed embodiments of the present application are shown in the drawings. These diagrams are not drawn to scale, in which certain details are exaggerated for clarity of presentation and may be omitted. The shapes and relative sizes of the various regions, layers, and the relative positions of these regions / layers shown in the drawings are merely exemplary, and in actuality, they may be deviated due to manufacturing tolerances or technical limitations, and a person skilled in the art can additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0102] The present application provides a reliability evaluation method of a power distribution network containing energy storage, which establishes a comprehensive and multi-level evaluation index system; a scene construction method for operation simulation of the power distribution network containing energy storage is given, which includes element fault scene simulation method, wind power time series output simulation method, photovoltaic array time series output simulation method, and load scene simulation method of the power distribution network containing energy storage; during the scene simulation process, Latin hypercube sampling method is used for sampling to improve the sampling efficiency; an optimal power flow model of the power distribution network containing energy storage is established, and the scene simulation and the optimal power flow dispatching model are combined through a sequential Monte Carlo framework, and finally the reliability evaluation of the system is realized. In engineering application, technical personnel can use the method provided by the present application to evaluate the economic efficiency and reliability of several energy storage configuration schemes to be connected to the power distribution network system, so as to select a suitable energy storage configuration scheme for connecting the energy storage device to the power distribution network system. The present application relates to the evaluation problem of the power distribution network system containing energy storage, and can scientifically and effectively evaluate the selected energy storage configuration scheme, thereby providing a practical and effective technical route for the energy storage configuration of the power distribution network system in actual engineering.

[0103] Please refer to Figure 1 The reliability evaluation method of the power distribution network containing energy storage provided by the present application can reasonably evaluate a given energy storage configuration scheme and help the power distribution network system to reasonably connect the energy storage device, which comprises the following steps:

[0104] S1, obtaining relevant information required for reliability evaluation of the power distribution station containing energy storage;

[0105] The relevant information includes basic information of the power distribution network load level, basic information of the power distribution network line fault probability, historical data of wind and light of the power distribution network, and the energy storage configuration scheme to be evaluated of the power distribution network.

[0106] S2, establish a full range of multi-level index system as the basis for reliability evaluation of energy storage distribution network;

[0107] Reasonably planned energy storage distribution network system needs to meet the requirements of continuous reliable power supply of the system and the requirements of economic and efficient operation of the system, especially to reflect the influence of energy storage device on system reliability and economy. Therefore, the influence of line equipment failure factors on the economy and reliability of energy storage distribution network is mainly considered, and an evaluation index system is established with the core of system effective load supply capacity.

[0108] Among them, the economy index includes the equal annual cost And the levelized cost of electricity ; The reliability index includes the power shortage expectation And the loss of load probability .

[0109] The equal annual cost The meaning is the sum of the equal annual value of the initial investment of the system, the annual operation and maintenance cost and the annual equipment replacement cost, the equal annual cost t In the Specifically:

[0110]

[0111] Among them, Indicates the equal annual cost of the system in the Indicates the initial investment cost of the energy storage planning scheme; Indicates the capital recovery coefficient; Indicates the operation cost of the system in the t Including the purchase cost of electricity and the loss of load penalty cost of distribution network; The maintenance cost of the system in the t Here refers to the maintenance cost of line breakage.

[0112] The specific calculation formula of

[0113]

[0114] Among them, N Indicates the rated service life of energy storage equipment, in years; R Indicates the discount rate, which is 8%.

[0115] The levelized cost of electricity The meaning is the ratio of the equal annual cost to the expected power supply, which divides the equal annual cost of the system into the system per unit of supply power,

[0116] The calculation formula of the levelized cost of electricity is:

[0117]

[0118] wherein, represents the system levelized cost of electricity in the year t , unit: $ / kW·h; represents the total annual power generation of the system in the year t , unit: kW·h.

[0119] expected power shortage The index represents the expected value of the load loss of the system in a specified period. After the line fails, the distribution network system will lose load due to the lack of self-provided power. Reasonable configuration of the energy storage system will effectively reduce the load loss of the system. Therefore, the expected power shortage index can reflect the influence of different energy storage planning schemes on the reliability of the system.

[0120] expected power shortage Specifically,

[0121]

[0122] wherein, represents the total number of simulated scenarios in the evaluation model; T represents the total number of simulated time periods; represents the time period interval; represents the load loss power of the system in the s time period of the scenario. t

[0123] When T = 24 h, = 1 h, the daily expected power shortage is obtained.

[0124] load loss probability The meaning is the ratio of the expected load loss of the system to the total load of the system. This index can help compare different energy storage planning schemes.

[0125] Specifically,

[0126]

[0127] wherein, represents the total load demand of the system in the evaluation period.

[0128] S3, according to the distribution network load level information obtained in step S1, the distribution network line fault probability information, the historical data of the distribution network wind and light, and the distribution network energy storage configuration information, the system operating state under the given system scenario (i.e. load scenario and fault scenario) is solved as the target, and at the same time the minimum load loss power of the system is ensured, and the optimal power flow model of the distribution network containing energy storage is established.​

[0129] 1) Objective function

[0130] The objective function of this model is to minimize the system load loss power, which is:

[0131]

[0132] where, y is the scheduling variable, including the purchase power of the distribution system, the charging power and discharging power of the energy storage device, etc. j is the node number of the distribution system, set is all nodes of the system; t is the number of time periods of the system, T is the total number of time periods included in the scheduling period, and also represents the time period set. is the load loss power of the system at the j node in the t time period, and when there is no load loss, is equal to zero.

[0133] 2) Node power balance constraint

[0134] This model uses a linear flow model and does not consider the network loss power of the line. The node power balance constraint of the system is:

[0135]

[0136] where, and represent the active power injection and reactive power injection of the j node in the t time period, respectively; m is the number of energy storage battery groups, is the number of energy storage battery groups installed at the j node; and represent the discharging power and charging power of the j energy storage battery group at the t node in the m time period, respectively; and represent the active load and reactive load of the j node in the t time period; is the load loss power of the system at the j node in the t time period; is the output of the photovoltaic array at the j node in the t time period; representing the node j wind power on t the output of the time period; representing the system on the node j the first t the reactive power compensated by the first n reactive power compensation device in the time period, representing the installation of the reactive power compensation device number on the node j .

[0137] 3) Energy storage power station operation constraints

[0138]

[0139]

[0140] The above two formulas are the charge and discharge power constraints of the energy storage battery. Among them, representing the node j the first m the maximum charging power of the first energy storage battery group on the node . j representing the node m the first the maximum discharging power of the first energy storage battery group on the node

[0141] .

[0142]

[0143] The above two formulas are the energy storage constraints of the energy storage battery. In the formula, representing the node j the first m the storage capacity of the first energy storage battery group in the time period t . and are the lower limit and upper limit of the storage capacity of the first energy storage battery group on the node j . m representing the first the charging and discharging efficiency of the first energy storage battery group of the node j . m representing the system scheduling time interval, 1h. 4) Reactive power compensation device operation constraints

[0144]

[0145] Among them,

[0146] and represent the first the first j the first nThe lower limit and upper limit of compensation power of the reactive power compensation device.

[0147] 5) System power balance equation

[0148]

[0149] The above formula represents the power balance constraint of the 0th substation node.

[0150]

[0151] The above formula represents the power balance equation of the remaining nodes except the 0th node. Because the 0th node has no line injection power, it is different in form from the constraints of other nodes.

[0152] In the above two formulas, represents the set of all downstream nodes of the j th node, k is the number of downstream nodes; represents the active power transmitted from the t th node to the i th node on the line during the j th time period, represents the reactive power transmitted from the t th node to the i th node on the line during the j th time period.

[0153] 6) Voltage relaxation constraint

[0154]

[0155] where, and represent the resistance value and reactance value of the line ij , respectively; represents the operating state of the line, equal to 0 when the line is in a fault state, and the above constraint is naturally satisfied; equal to 1 when the line is in a normal operating state, and the above constraint normally functions; M represents a very large number, used for relaxation constraint, to achieve a unified description of the voltage drop constraint in the on-off state; represents the voltage value of node j at time period t; represents the node voltage reference value, usually taken as 1.0 p.u.; represents the line number set.

[0156] 7) Node voltage constraint

[0157]

[0158] where, and Vmin and Vmax, respectively, represent the lower and upper voltage limits of the node j

[0159] 8) Transmission capacity constraints of the main substation

[0160]

[0161] where, and represent the lower and upper active capacity limits of the substation, respectively; and represent the lower and upper reactive capacity limits of the substation, respectively.

[0162] 9) Transmission power constraints of the distribution line

[0163]

[0164]

[0165] where, represents the maximum transmission capacity of the line ij , in MVA, which is the apparent power. The rest of the loads are the same as the aforementioned constraints. is used to describe the on-off state, when , the aforementioned constraints work normally, when , the line power can only be equal to zero, realizing the modeling of the line in the off-line state.

[0166] The above model gives the detailed operating state of the energy storage integrated distribution network system under a given scenario. By solving the model, not only can the fine description of the state of each element of the system be realized, but also the optimal scheme under the specified standard can be obtained by changing the objective function. The model can improve the utilization efficiency of the system and better realize the economic and reliability evaluation of the system.

[0167] S4, using the sequential Monte Carlo evaluation method to evaluate the optimal power flow model of the energy storage integrated distribution network established in step S3, calculate the economic and reliability indicators of the energy storage configuration scheme of the distribution network, combine the evaluation index system obtained in step S2, use the evaluation index of the energy storage integrated distribution network and perform evaluation analysis.

[0168] The sequential Monte Carlo simulation is a common explanation of producing a large number of samples, and then performing index statistics on the samples. When the number of samples is sufficient, the statistical index will converge to the true value of the index. Sequential refers to the time sequence characteristics of the system in the sample generation process.

[0169] ​In the reliability assessment of this invention, timing characteristics are reflected in daily timing changes with a daily scheduling cycle, and annual timing characteristics of the equipment based on long-term failure probability. A well-designed sequential Monte Carlo simulation process can quickly obtain stable and realistic evaluation indicators.

[0170] The most crucial aspect of the evaluation process is determining the convergence of the evaluation metrics. This invention employs a stability criterion to determine convergence. Using CI to represent the convergence metric, the convergence determination is expressed as follows:

[0171]

[0172] in, This represents the set convergence threshold, when... CI If the value is less than or equal to this threshold, the evaluation process converges and the loop stops; otherwise, the loop continues.

[0173] Convergence index CI The calculation formula is as follows:

[0174]

[0175]

[0176] in, Indicates the previous i The first scenario statistics obtained the first k The values ​​of the economic indicators are as follows: K One economic indicator; Indicates the first k The calibration value of an economic indicator serves to transform a unit-based indicator quantity into a dimensionless quantity, thereby achieving indicator normalization. Indicates the previous i The first scenario statistics obtained the first m The value of each reliability metric, Indicates the first m The calibrated values ​​of the economic indicators are as follows: M Several reliability assessment indicators. The selection of calibration values ​​not only normalizes each indicator but also serves as a weight for determining indicator convergence.

[0177] Please see Figure 3 The sequential Monte Carlo assessment process consists of the following steps:

[0178] S401, Input the energy storage configuration scheme to be evaluated.

[0179] S402. Initialize the simulation parameters and set the scene counter.

[0180] S403, targeting iSimulate a scenario: after initializing the scenario parameters, simulate the period load, new energy output and line fault state, a total of t times of sampling, each taking T times of sampling. .

[0181] S404, according to the optimal power flow scheduling model of the energy storage power distribution network established in step S3, perform optimal power flow analysis and calculation under the first i scenario, and then calculate the economic and reliability indicators of the first i scenario according to the evaluation index system established in step S2. Statistics of the economic and reliability indicators of the first i scenario, if the index convergence condition is not met, let , return to step S403; if the index convergence condition is met, perform system evaluation analysis and output the evaluation result of the system.

[0182] In the process of simulating scenarios in step S403, a large number of random variables are selected, and the process of selecting and determining the values of these random variables is called random variable sampling, that is, random number generation. The random variable sampling method generally uses the simple random sampling method, that is, after the probability distribution of the random variable is given, a random number is randomly generated using a random number generation table, and then the value of the random variable is converted into a random variable by the probability distribution function. In the present application, in order to improve the sampling efficiency, the Latin hypercube sampling (LHS) method is used for sampling. Referring to Figure 3 Compared with simple random sampling, the sampling efficiency of the Latin hypercube sampling method is higher. The Latin hypercube sampling method mainly includes two steps of stratified sampling and random ordering:

[0183] Stratified sampling can ensure that sample points fully cover the joint probability space of multi-dimensional random variables. Let the sampling number of random variables be N , for random variable X, the probability interval can be divided into N equally spaced non-overlapping subintervals, and random numbers are extracted on each subinterval according to uniform distribution. If the cumulative probability distribution function of X is known , then the random sample of X can be obtained according to the inverse function of the cumulative probability distribution function, so that the dimensional sample sequence of random quantities is obtained. A commonly used interval median Latin hypercube stratified sampling method is given, and its formula is:

[0184]

[0185] Random ordering is used to disrupt the sample order in the initial sampling. The initial sample obtained by stratified sampling is strictly arranged according to the cumulative probability of the sample. Therefore, for a single random variable, random ordering is more in line with the actual sampling situation. For sampling of multiple random variables, there is often correlation between different random variables, and random ordering can reconstruct the correlation between random variables.

[0186] When the number of random variables is M , the method of random ordering is to generate a random order matrix with the same dimension as the initial sample. L The Cholesky decomposition method is used to correct the random order matrix L , so as to realize the correlation control of the sampling result.

[0187] First, the corresponding correlation coefficient matrix of the random order matrix L is denoted as , and the correlation coefficient matrix is obtained according to the historical observation value of the random variable. Under the premise that and are symmetric positive definite matrices, Cholesky decomposition is performed on them:

[0188]

[0189] Among them, Q , D are the lower triangular matrices obtained by decomposing and respectively.

[0190] Then, the correlation matrix Q is derived according to D , L : G

[0191]

[0192] When the elements in G are not integers, a correlation matrix with all positive elements can be constructed based on the relative size of the elements in G , and the elements in the original sample X are reordered according to the correlation matrix.

[0193] In another embodiment of the present application, a reliability evaluation system for a power distribution network with energy storage is provided, which can be used to implement the reliability evaluation method for the power distribution network with energy storage. Specifically, the reliability evaluation system for the power distribution network with energy storage includes a data module, an index module, an optimization module, and an evaluation module.

[0194] ​The data module obtains power distribution network load level information, power distribution network line fault probability information, wind and light historical data of the power distribution network, and power distribution network energy storage configuration information.

[0195] The index module considers the influence of line equipment fault factors on the economy and reliability of the power distribution network with energy storage, and establishes an evaluation index system with system effective load supply capacity as the core.

[0196] The optimization module establishes an optimal power flow model of the power distribution network with energy storage according to the power distribution network load level information, power distribution network line fault probability information, wind and light historical data of the power distribution network, and power distribution network energy storage configuration information obtained by the data module.

[0197] The evaluation module uses a sequential Monte Carlo evaluation method to evaluate the optimal power flow model of the power distribution network with energy storage established by the optimization module, and obtains the evaluation index of the power distribution network with energy storage for reliability evaluation and analysis of the power distribution network with energy storage by combining the evaluation index system obtained by the index module.

[0198] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be described clearly and completely below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of the present application.

[0199] In a remote area with low power supply reliability, energy storage equipment needs to be constructed in the power distribution network, and the reliability of the existing energy storage configuration scheme needs to be evaluated. The line topology of the power distribution network is as shown in Figure 4 .

[0200] Two energy storage configuration schemes A and B are set based on node numbers, and scheme C without energy storage is taken as a control. The energy storage configuration scale of the three schemes is shown in Table 1:

[0201] Table 1 Summary of Energy Storage Configuration Schemes

[0202]

[0203] The detailed configuration data of each node of the power distribution network energy storage configuration scheme A and the configuration scheme B is shown in Table 2:

[0204] Table 2 Detailed Configuration Capacity of Each Node of Energy Storage Configuration Scheme

[0205]

[0206] Based on the above case information, random scenarios were generated. The total number of time periods for each scenario was set to 24, with a time interval of one hour (i.e., 24 scenarios per day). Several sets of scenarios were randomly generated until the metric converged. After 2000 random scenario generation cycles, the convergence metric reached convergence. The normalized total load value for the 2000 load scenarios is as follows: Figure 5 As shown. Figure 5 The random load in the data fully covers load scenarios ranging from 55% to 100% of maximum load.

[0207] Please see Figure 6 The graph shows the total number of failures in a given scenario, with a failure rate set at 1% during testing. When the failure rate is 1%, the expected total number of failures for 30 lines in 2000 random scenarios is 600, and the total number of failures in the random case is 544, demonstrating the sufficiency of the random scenarios. Furthermore, the graph shows that with a single line failure rate of 1%, a maximum of three lines will fail simultaneously. Based on these data, the N-1, N-2, and N-3 failure rates for this case can be obtained, as shown in Table 3.

[0208] Table 3 Failure Rate Statistics for Each Failure Type

[0209]

[0210] Failure rate statistics show that although the failure rate of a single line is only 1%, the N-1 failure rate for the entire distribution network reaches 22.5%. This indicates that the system failure rate is greater than the failure rate of a single component. Furthermore, the failure rate for N-3 is 0.3%.

[0211] Based on the above random scenario, a reliability assessment was conducted on the given energy storage configuration scheme. The total lifespan of the energy storage equipment was set at 20 years, the operating cost of electricity was 0.5 yuan / kWh, the cost of load shedding was 12.5 yuan / kWh, and the cost of line fault repair was 20,000 yuan / time. The landed cost of the energy storage equipment was 3 million yuan / MWh. The results are shown in Table 4.

[0212] Table 4 Results of Reliability and Economic Evaluation Indicators

[0213]

[0214] From Table 4, it can be found that the reliability of scheme A and scheme B with energy storage is obviously improved compared with scheme C without energy storage. Among them, the loss of load probability index is reduced from 2.13% of scheme C to 0.74% of scheme A and 0.51% of scheme B; the expected shortage of electricity is reduced from 6832.1 MWh of scheme C to 3534.4 MWh of scheme A and 2318.6 MWh of scheme B. Scheme A is the optimal configuration scheme of this case, which realizes the trade-off between reliability and economy. By converting the loss of load into electricity price penalty, the comprehensive income and social income of the system are converted into economic benefits. In this measurement scale, it can be found that the average cost of per kilowatt-hour of scheme A is 1.1002 yuan, the average cost of per kilowatt-hour of scheme B is 1.1727 yuan, and the average cost of per kilowatt-hour of scheme C is 0.8140 yuan. In this embodiment, the energy storage scheme A is better than scheme B, which can be used as the existing best scheme.

[0215] In summary, the reliability evaluation method and system of the power distribution network with energy storage according to the present application firstly establishes an evaluation index system with the effective load supply capacity of the system as the core; then adopts time sequence probability simulation and Latin hypercube sampling method to generate typical day random scenarios of power distribution facility failure and load fluctuation; and further, in the framework of sequential Monte Carlo simulation, the system operation state is evaluated through optimal power flow calculation, and the statistical analysis of indexes such as equal annual cost, expected shortage of electricity, and loss of load probability is supported. The method can simulate the medium and long term operation effect of the energy storage configuration scheme, and check the feasibility of the energy storage planning result from the aspects of economy and reliability. In actual engineering application, a plurality of energy storage configuration schemes can be compared scientifically from the aspects of economy and reliability, and then the reference for the energy storage device configuration plan of the power distribution network is provided.

[0216] Those skilled in the art will appreciate that embodiments of the present application can be provided as methods, systems, or computer program products. Accordingly, the present application can be embodied in the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application can be embodied in the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk memory, CD-ROM, optical memory, etc.) having computer-usable program code embodied thereon.

[0217] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart or flowsheet block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the steps in one or more flowcharts and / or blocks

[0218] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart or flowsheet block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the steps in one or more flowcharts and / or blocks

[0219] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart or flowsheet block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the steps in one or more flowcharts and / or blocks

[0220] The above merely provides the technical idea of the present application, and cannot be used to limit the protection scope of the present application. Any modification made according to the technical idea of the present application, and based on the technical solutions, shall fall within the protection scope of the present application.

Claims

1. A reliability assessment method for a distribution network including energy storage, characterized in that, Includes the following steps: S1. Obtain information on the load level of the distribution network, the probability of line faults in the distribution network, historical wind and solar data of the distribution network, and energy storage configuration information of the distribution network. S2. Considering the impact of line equipment failure factors on the economy and reliability of distribution networks with energy storage, establish an evaluation index system with the effective load supply capacity of the system as the core. S3. Based on the distribution network load level information, distribution network line fault probability information, historical wind and solar power data of the distribution network, and distribution network energy storage configuration information obtained in step S1, establish an optimal power flow model for the distribution network including energy storage. The constraints of the optimal power flow model for the distribution network including energy storage include node power balance constraints, energy storage power station operation constraints, reactive power compensation device operation constraints, system power balance equations, voltage relaxation constraints, node voltage constraints, main substation transmission capacity constraints, and distribution line transmission power constraints. The objective function of the optimal power flow model for the distribution network including energy storage is: in, y The scheduling variable is represented; j Indicates the node number of the distribution network system. Represents all nodes in the system; t Indicates the number of time periods in the system. T This represents the total number of time periods included in the scheduling period, and also represents the set of time periods. Indicates the system at the 1st j The node at the node t Power loss during the period; The node power balance constraint is: in, and They represent the first j Node at the t Active power injection and reactive power injection during different time periods; m This indicates the serial number of the energy storage battery pack. Indicates the first j The number of energy storage battery packs installed at the node; and They represent the first j Node at the t The first period m Discharge and charging power of each energy storage battery pack; and Indicates the first j Node at the t Active and reactive loads during a given time period; Indicates the system at the node j Upper t Power loss during the period; Represents a node j Photovoltaic array in t Efforts during a specific time period; Represents a node j Wind power in t Efforts during a specific time period; Indicates the system at the node j Upper t The first period n The reactive power compensated by each reactive power compensation device. Indicates at node j Number of reactive power compensation devices installed; The operational constraints of energy storage power stations include: Energy storage battery charge and discharge power constraints: in, Represents a node j The m Maximum charging power of each energy storage battery pack; Represents a node j The m The maximum discharge power of each energy storage battery pack; Energy storage constraints of batteries: in, Represents a node j The m The energy storage battery pack in the first t Storage capacity during a given period; and They are nodes j The m The lower and upper limits of the storage capacity of each energy storage battery pack; Indicates the first j The node of the first m The charge / discharge efficiency of an energy storage battery pack; Indicates the system scheduling time interval; Reactive power compensation device operating constraints: in, and Representing nodes respectively j The n The lower limit and upper limit of the compensation power of each reactive power compensation device; Power balance constraints at node 0 of substation: Power balance equations for all nodes except node 0: in, Indicates the first j The set consisting of all downstream nodes of a given node. k This is the number of the downstream node; Indicates in t Time period route from i Node transmission to j The active power of the node, Indicates in t Time period route from i Node transmission to j The reactive power of a node; Voltage relaxation constraint: in, and They represent the lines respectively. ij The resistance and reactance values; Indicates the operating status of the line; M This represents a very large number, used to relax constraints; This represents the voltage value of node j during time period t; Indicates the node voltage reference value; Represents the set of line numbers; Node voltage constraints: in, and Representing nodes respectively j The lower and upper voltage limits; Transmission capacity constraints of main substation: in, and These represent the lower and upper limits of the active power capacity of the substation, respectively. and These represent the lower and upper limits of the reactive power capacity of the substation, respectively. Power transmission constraints of power distribution lines: in, Indicates the line ij Maximum transmission capacity Used to describe on / off states; S4. The optimal power flow model of the distribution network with energy storage established in step S3 is evaluated using the sequential Monte Carlo evaluation method. Combined with the evaluation index system obtained in step S2, the evaluation index of the distribution network with energy storage is obtained for reliability evaluation and analysis.

2. The reliability assessment method for distribution networks including energy storage according to claim 1, characterized in that, In step S2, the evaluation index system includes economic indicators and reliability indicators. The economic indicators include equivalent annual cost. Standardized cost of electricity ; Reliability metrics include expected low battery capacity and the probability of load loss .

3. The reliability assessment method for distribution networks including energy storage according to claim 2, characterized in that, No. t Annual equivalent cost Specifically: in, This represents the system's equivalent annual cost in year t. This indicates the initial investment cost of the energy storage plan. Indicates the capital recovery coefficient; Indicates the system's first t Annual operating costs include the cost of purchasing electricity for the distribution network and the cost of load shedding penalties; For the system number t The annual maintenance cost here refers to the cost of repairing a broken line. No. t Annual System Levelized Cost of Electricity for: in, Indicates the first t The system's total annual power generation.

4. The reliability assessment method for distribution networks including energy storage according to claim 2, characterized in that, Low battery expectation Specifically: in, This indicates the total number of scenarios simulated in the evaluation model; T Indicates the total number of simulation periods; Indicates time interval; Representing a scene s Download the system t Power loss during the period; Probability of load loss for: in, This indicates the total load demand of the system during the assessment period.

5. The reliability assessment method for distribution networks including energy storage according to claim 1, characterized in that, In step S4, the evaluation process for sequential Monte Carlo is as follows: S401, Input the energy storage configuration scheme to be evaluated; S402. Initialize scene simulation parameters, set scene counter bits. ; S403. After initializing the scene simulation parameters, ... t Random sampling was conducted on time-period load, renewable energy output, and line fault status, with a total of [number] samples taken. T Each time, take... ,against i Simulate each scenario; S404. Based on the optimal power flow scheduling model for the distribution network including energy storage established in step S3, in the... i Perform optimal power flow analysis calculations in each scenario, and then calculate the pre-optimal power flow based on the evaluation index system established in step S2. i Each scenario and its economic and reliability indicators; before statistics i For each scenario and economic and reliability index, if the index convergence condition is not met, then let Return to step S403; if the index convergence condition is met, perform system evaluation analysis and output the evaluation results.

6. The reliability assessment method for distribution networks including energy storage according to claim 5, characterized in that, In step S403, the Latin hypercube sampling method is used for sampling as follows: S4031. The number of samplings of the random variable is denoted as... N Divide the probability interval of random variable X into N The system is divided into equidistant, non-overlapping subintervals, and random numbers are drawn from each subinterval according to a uniform distribution. If the cumulative probability distribution function of random variable X is known to be... Then, according to the inverse function of the cumulative probability distribution function... Obtain a random sample of X Obtain random quantity dimensional sample sequence; S4032, When the number of random variables is M At that time, a sample with the same dimension as the initial sample is generated. dimensional random order matrix L The Choreski decomposition method is used to decompose random ordered matrices. L To make corrections, first, the random order matrix... L The corresponding correlation coefficient matrix is ​​denoted as And obtain the correlation coefficient matrix based on the historical observations of the random variables. ,exist and Given that all matrices are symmetric positive definite matrices, perform the Cholliski decomposition, and then, according to... Q , D and L Export the correlation matrix G When the correlation matrix G When the elements are not integers, based on the association matrix G The relative sizes of the elements in the original sample X are used to construct an association matrix in which all elements are positive numbers, and the elements in the original sample X are reordered based on the association matrix.

7. The reliability assessment method for distribution networks including energy storage according to claim 1, characterized in that, In step S4, the stability criterion is used to determine whether the sequential Monte Carlo evaluation has converged. When the convergence index... CI Less than or equal to the convergence threshold When the evaluation process converges, the loop stops; otherwise, the loop continues, and the convergence criterion is... CI The calculation is as follows: in, As an indicator to measure the volatility of economic indicators across all scenarios, It is an indicator used to measure the volatility of reliability metrics across all scenarios.

8. A reliability assessment system for a distribution network including energy storage, characterized in that, include: The data module acquires information on the load level of the distribution network, the probability of line faults in the distribution network, historical wind and solar data of the distribution network, and energy storage configuration information of the distribution network. The indicator module considers the impact of line equipment failure factors on the economy and reliability of distribution networks with energy storage, and establishes an evaluation indicator system with the effective load supply capacity of the system as the core. The optimization module, based on the distribution network load level information, distribution network line fault probability information, historical wind and solar data of the distribution network, and distribution network energy storage configuration information obtained from the data module, establishes an optimal power flow model for the distribution network including energy storage. The constraints of this optimal power flow model include node power balance constraints, energy storage station operation constraints, reactive power compensation device operation constraints, system power balance equations, voltage relaxation constraints, node voltage constraints, main substation transmission capacity constraints, and distribution line transmission power constraints. The objective function of the optimal power flow model for the distribution network including energy storage is: in, y The scheduling variable is represented; j Indicates the node number of the distribution network system. Represents all nodes in the system; t Indicates the number of time periods in the system. T This represents the total number of time periods included in the scheduling period, and also represents the set of time periods. Indicates the system at the 1st j The node at the node t Power loss during the period; The node power balance constraint is: in, and They represent the first j Node at the t Active power injection and reactive power injection during different time periods; m This indicates the serial number of the energy storage battery pack. Indicates the first j The number of energy storage battery packs installed at the node; and They represent the first j Node at the t The first period m Discharge and charging power of each energy storage battery pack; and Indicates the first j Node at the t Active and reactive loads during a given time period; Indicates the system at the node j Upper t Power loss during the period; Represents a node j Photovoltaic array in t Efforts during a specific time period; Represents a node j Wind power in t Efforts during a specific time period; Indicates the system at the node j Upper t The first period n The reactive power compensated by each reactive power compensation device. Indicates at node j Number of reactive power compensation devices installed; The operational constraints of energy storage power stations include: Energy storage battery charge and discharge power constraints: in, Represents a node j The m Maximum charging power of each energy storage battery pack; Represents a node j The m The maximum discharge power of each energy storage battery pack; Energy storage constraints of batteries: in, Represents a node j The m The energy storage battery pack in the first t Storage capacity during a given period; and They are nodes j The m The lower and upper limits of the storage capacity of each energy storage battery pack; Indicates the first j The node of the first m The charge / discharge efficiency of an energy storage battery pack; Indicates the system scheduling time interval; Reactive power compensation device operating constraints: in, and Representing nodes respectively j The n The lower limit and upper limit of the compensation power of each reactive power compensation device; Power balance constraints at node 0 of substation: Power balance equations for all nodes except node 0: in, Indicates the first j The set consisting of all downstream nodes of a given node. k This is the number of the downstream node; Indicates in t Time period route from i Node transmission to j The active power of the node, Indicates in t Time period route from i Node transmission to j The reactive power of a node; Voltage relaxation constraint: in, and They represent the lines respectively. ij The resistance and reactance values; Indicates the operating status of the line; M This represents a very large number, used to relax constraints; This represents the voltage value of node j during time period t; Indicates the node voltage reference value; Represents the set of line numbers; Node voltage constraints: in, and Representing nodes respectively j The lower and upper voltage limits; Transmission capacity constraints of main substation: in, and These represent the lower and upper limits of the active power capacity of the substation, respectively. and These represent the lower and upper limits of the reactive power capacity of the substation, respectively. Power transmission constraints of power distribution lines: in, Indicates the line ij Maximum transmission capacity Used to describe on / off states; The evaluation module uses the sequential Monte Carlo evaluation method to evaluate the optimal power flow model of the distribution network with energy storage established by the optimization module. Combined with the evaluation index system obtained by the index module, the evaluation index of the distribution network with energy storage is obtained for reliability evaluation and analysis.

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