Collaborative Reliability Evaluation Method for Charging Stations and Distribution Networks with Interaction Fault Coupling between Sources and Loads

Abstract

The present invention relates to the technical field of power system reliability analysis, and specifically discloses a collaborative reliability assessment method for a charging station and a distribution network with source-load interaction fault coupling, including the following steps: collecting data parameters of distribution network lines and charging stations; based on the data parameters of the distribution network lines and charging stations, constructing an association probability matrix of line faults and charging station outages, integrating the Poisson distribution failure rate parameters of independent charging station faults to form a two-way joint probability model; constructing a hierarchical index system to form a multi-dimensional assessment system; using simulation to simulate the two-way triggering mechanism of distribution network line faults and charging station outages, analyzing the secondary faults of line overload caused by load transfer, and generating a reliability assessment report. The present invention breaks through the limitations of traditional one-way assessment, accurately restores the "fault-load transfer-overload" chain reaction, improves the dynamic simulation accuracy, and uses multi-dimensional indicators for scientific decision-making to provide a quantitative basis for reliability assessment.

Description

Technical Field

[0001] The present invention relates to the technical field of power system reliability analysis, and in particular to a collaborative reliability assessment method for charging stations and distribution networks with source-load interaction fault coupling. Background Art

[0002] With the rapid increase in the penetration rate of electric vehicles, as a centralized access point for new types of power loads, the charging stations show significant two-way coupling characteristics in the fault interaction with the distribution network. The traditional reliability assessment methods have the following technical bottlenecks:

[0003] Existing studies mostly adopt one-way causal models, only considering the impact of distribution network line faults on the power outage of charging stations, while ignoring the secondary impact of the charging load transfer caused by the faults of the distribution network and charging facilities themselves on the distribution network, as well as the coupling effect between the two. Traditional methods are based on a deterministic fault scenario set and cannot dynamically simulate the temporal correlation characteristics of source-load faults. For example, distribution network line faults and charging station outage events may trigger alternately, forming a dynamic fault propagation chain, and static models are difficult to capture such temporal coupling effects. Traditional power system reliability indicators (such as SAIDI and SAIFI) focus on the interruption statistics on the power supply side and lack the ability to quantify the experience of charging users on the user side and the losses on the coupling fault side. Although existing studies have pointed out that the faults of charging piles follow a Poisson distribution, they have not dynamically associated it with the failure rate model of the distribution network. In addition, the existing Monte Carlo method simulation does not consider the conditional triggering mechanism between fault events, resulting in a deviation in the assessment of the contribution degree of coupling faults.

[0004] For the above reasons, we have designed a collaborative reliability assessment method for charging stations and distribution networks with source-load interaction fault coupling to solve the above problems. Summary of the Invention

[0005] The purpose of the present invention is to solve the defects of one-way modeling, static analysis limitations, insufficient applicability of indicators, and insufficient refinement of models in the prior art, and to propose a collaborative reliability assessment method for charging stations and distribution networks with source-load interaction fault coupling, establish a two-way joint probability model of distribution network line faults and charging station outages, and quantify the spatio-temporal correlation characteristics of source-load interaction faults; design a hierarchical evaluation index system to simultaneously evaluate the grid side, power supply quality, and contribution degree of coupling faults; use an improved sequential Monte Carlo method simulation framework to dynamically simulate the fault propagation chain and the overload secondary faults caused by load transfer, calculate the reliability indicators of the hierarchical index system, and output the reliability assessment report of the hierarchical index system.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] A collaborative reliability assessment method for charging stations and distribution networks with source-load interaction fault coupling, comprising the following steps:

[0008] Step 1: Collect the data parameters of the distribution network lines and charging stations;

[0009] Step 2: Based on the data parameters of the distribution network lines and charging stations, construct an association probability matrix of line faults and charging station outages, and integrate the Poisson distribution failure rate parameters of independent charging station faults to form a two-way joint probability model;

[0010] Step 3: Construct a hierarchical index system to form a multi-dimensional evaluation system;

[0011] Step 4: Adopt a simulation to simulate the two-way trigger mechanism of distribution network line faults and charging station outages, analyze the secondary line overload faults caused by load transfer, and generate a reliability evaluation report.

[0012] As a further solution of the present invention, in Step 1, when collecting the data parameters of the distribution network lines and charging stations, the data parameters obtained for the distribution network lines and charging stations include:

[0013] Obtain the historical failure rate and topological structure parameters of the distribution network side lines, where the topological structure parameters include the node-branch incidence matrix, line impedance matrix, and line capacity matrix;

[0014] Obtain the number of charging piles, Poisson distribution failure rate parameters, failure rate, repair rate, and charging load on the charging station side.

[0015] As a further preferred solution of the present invention, in Step 2, to quantify the fault interaction effect between the distribution network and the charging station, construct an association probability matrix of line faults and charging station outages , and the element in the association probability matrix represents the conditional probability of a charging station outage caused by a line fault, and the expression is:

[0016] ;

[0017] is the number of charging station outages caused by line faults, is the total number of line faults;

[0018] Integrate the Poisson distribution failure rate parameters of independent charging station faults to form a two-way joint probability model:

[0019] Coupled failure rate, map the line failure rate to the charging station through the association probability matrix;

[0020] Repair time constraint, when the simulation time exceeds the repair time of the line fault, reset the line failure rate;

[0021] Construct a two-way joint probability model of charging station outages and grid coupled faults, and the expression is as follows:

[0022] ;

[0023] ;

[0024] ;

[0025] In the above formula, represents the probability of charging station outage failure, represents the probability of grid coupling failure, represents the line failure rate, represents the total number of charging stations, represents the Poisson failure rate of the charging station itself, represents the simulation time, represents the repair time of the failure in this state, represents the transition matrix that the probability of change of the charging station in the failure and repair states satisfies, is the probability that the charging station remains in normal operation within the time interval ; is the probability that the charging station transfers from the normal state to the failure state within the time interval ; is the probability that the charging station recovers from the failure state to the normal state within the time interval ; is the probability that the charging station continues to fail within the time interval , is the repair rate;

[0026] Dynamically update the probability of charging station outage failure , and solve the charging station unavailability rate :

[0027] .

[0028] As a further preferred solution of the present invention, in step three, the constructed hierarchical index system includes:

[0029] The average power outage frequency index, which measures the frequency of power supply interruption and reflects the average number of power outages experienced by users per year. The calculation formula is:

[0030] ;

[0031] and are respectively the failure rate and the number of users of the line load point, is the set of all load points of the line;

[0032] The average power outage duration index, which measures the duration of power outage and reflects the total power outage time of users per year. The calculation formula is:

[0033] ;

[0034] is the annual unavailability rate or power outage time of the line load point;

[0035] The charging availability rate index measures the proportion of time that a charging station is available for use. The calculation formula is:

[0036] ;

[0037] is the power supply of the th charging pile, is the demand power;

[0038] The charging expected non-powered energy index measures the total charging energy that fails to be provided due to faults. The calculation formula is:

[0039] ;

[0040] is the total number of charging stations, is the total simulation running time, is the outage state of the charging station at time, is the time interval, is the average load connected to the load point ;

[0041] The coupling fault contribution degree index quantifies the impact of two-way faults on the system and measures the proportion of faults caused by source-load interaction in the total faults. The calculation formula is:

[0042] ;

[0043] is the number of coupling faults triggered by the distribution network and the charging station, is the total number of faults.

[0044] As a further preferred solution of the present invention, in step four, an improved sequential Monte Carlo method is used to simulate the two-way triggering mechanism of distribution network line faults and charging station outages:

[0045] Data reading and loading: Load the data parameters of the distribution network, including line data and load data, and load the data parameters of the charging station, including the number of charging piles, failure rate, repair rate, and charging load;

[0046] System state initialization: Set the total simulation time and the convergence threshold variance coefficient, and construct the distribution network-charging station joint state vector. The expression is:

[0047] ;

[0048] ;

[0049] Wherein, is the fault state of the line load point ; and respectively represent the fault states of the first and the th line load points ; is the starting repair time of the line and the charging station, is the line repair time, is the charging station repair time; , and are all the dynamic failure rates of the charging stations, is the inherent failure rate of the charging station.

[0050] Sample the operating states of the line load points and the charging stations to obtain the sequences of the operating states of the line load points and the charging stations, and calculate the time intervals between the start of normal operation and the occurrence of faults of the line load points or the charging stations and the time intervals between the occurrence of faults and the completion of repair and the resumption of normal operation of the line load points or the charging stations;

[0051] Find the minimum value among the time intervals between the start of normal operation and the occurrence of faults of each faulty line load point or charging station, and record the time and the corresponding equipment when the minimum value is found;

[0052] Generate an event sequence by adopting a multiple time advancement mechanism considering the line faults and the charging station faults in the distribution network, including generating line fault events, charging station fault events and coupled fault events, and sorting the events in chronological order.

[0053] As a further preferred solution of the present invention, establish a dynamic fault propagation model to perform two-way coupled fault determination on the line and the charging station:

[0054] Forward trigger: When a line load point fails, perform for each charging station: If the conditional probability that the line fault causes the charging station to shut down exists and is in (0, 1], it is considered that the charging station is in a fault state at this time;

[0055] Reverse trigger: When the charging station shuts down, perform the following operations:

[0056] ;

[0057] is the load transfer amount, is the charging load of the charging pile in the charging station at time;

[0058] When the charging station is out of service, solve the power distribution of the distribution network line using the power balance equation:

[0059] ;

[0060] where and are the active and reactive powers of the load transfer on the line, and are the line losses, and are the load of the line nodes; and are the active and reactive powers of the line before the load transfer occurs, respectively;

[0061] Analyze the secondary line overload fault caused by load transfer, perform overload judgment, and mark the line load point fault when the following conditions are met:

[0062] ;

[0063] where is the apparent power.

[0064] As a further preferred solution of the present invention, in step four, the process of generating the reliability evaluation report includes:

[0065] Judge and accumulate and count the corresponding fault time and fault times after the fault;

[0066] Convergence judgment: If it converges, calculate the reliability index; if it does not converge, increase the simulation rounds and resample for the next fault generation event;

[0067] Calculate, update and count the reliability indexes of the hierarchical index system;

[0068] Output the reliability evaluation report of the hierarchical index system after the simulation ends.

[0069] The present invention breaks through the limitations of traditional one-way evaluation, simultaneously captures the bidirectional coupling effect of "power grid faults affecting charging stations" and "charging station outages impacting the power grid", considers the collaborative reliability evaluation under the bidirectional fault coupling of the distribution network (source side) and electric vehicle charging stations (load side), designs the hierarchical multi-dimensional evaluation index system to work together by establishing the fault interaction probability model between the distribution network and the charging station, and uses the improved sequential Monte Carlo method for dynamic simulation to accurately restore the "fault - load transfer - overload" chain reaction, improve the dynamic simulation accuracy, achieve the accurate quantitative evaluation of source-load interaction faults, and provide a quantitative basis for reliability evaluation. Description of the Drawings

[0070] Figure 1Schematic flowchart of the collaborative reliability assessment method for a charging station and a distribution network with source-load interaction faults proposed in the embodiments of the present invention;

[0071] Figure 2 Schematic diagram of a distribution network with a charging station built based on IEEE33 in the embodiments of the present invention;

[0072] Figure 3 Voltage distribution map of IEEE33 nodes in the embodiments of the present invention;

[0073] Figure 4 Schematic diagram of comparative analysis of voltage margins of IEEE33 nodes in the embodiments of the present invention;

[0074] Figure 5 Schematic diagram of reliability index analysis in the embodiments of the present invention. Specific implementation manners

[0075] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments.

[0076] Research shows that when a charging station shuts down due to equipment failure, the sudden increase in the load of adjacent charging stations may cause the distribution network lines to be overloaded, forming a "fault-load transfer-overload" chain reaction, which may further lead to load shedding. In reality, however, the more complex situation of their fault coupling has not been well captured. Charging users are more sensitive to short-term interruptions than traditional residential loads. There is an urgent need to define new evaluation indicators for the grid side, user side, and coupling side, and construct a collaborative reliability assessment method for charging stations and distribution networks.

[0077] The present invention establishes a two-way joint probability model for distribution network line faults and charging station outages to quantify the spatio-temporal correlation characteristics of source-load interaction faults; designs a hierarchical evaluation index system to simultaneously evaluate the power supply quality on the grid side and user side and the contribution degree of coupling faults on the system side; and develops an improved sequential Monte Carlo method simulation framework to dynamically simulate the fault propagation chain and the secondary overload faults caused by load transfer.

[0078] The Markov model adopted in the present invention is a mathematical model based on stochastic processes, which is used to describe the dynamic transitions of a system between different states. It has the property of "memorylessness", that is, the future state of the system depends only on the current state and is independent of the past path. This property makes the Markov model very efficient in modeling systems with randomness and dynamics and is widely used in reliability analysis. The sequential Monte Carlo method is a numerical analysis technique based on Monte Carlo simulation, which is used to handle stochastic processes in dynamic systems. It approximates the state distribution of the system by generating a large number of random samples and gradually updates these samples to reflect the evolution of the system over time. The sequential Monte Carlo method is particularly suitable for dealing with non-linear and non-Gaussian systems and can effectively estimate the state and parameters of the system. Combined with the reliability assessment of the distribution network, the sequential Monte Carlo method can dynamically evaluate the operating state of the system by simulating the failure and repair processes of equipment, so as to calculate the reliability index more accurately.

[0079] The proposed method for collaborative reliability assessment of charging stations and distribution networks considering source-load interaction faults is a multi-dimensional collaborative assessment method that forms a hierarchical reliability index system based on dynamic fault propagation chain modeling and construction of a bidirectional association probability matrix to meet the reliability assessment of distribution networks with high-penetration electric vehicle charging loads in charging stations.

[0080] Refer to Figure 1 , the method for collaborative reliability assessment of charging stations and distribution networks includes the following steps:

[0081] Step 1, data collection and parameter integration.

[0082] To implement this method, multi-dimensional data of the distribution network and charging stations need to be obtained first. Collect data parameters of distribution network lines and charging stations, obtain data parameters of distribution network lines and loads (such as node voltages), and collect topological structure parameters of distribution network side lines, including reading node-branch association matrices, line impedance matrices (unit: ΩKm) and line capacity matrices (unit: kVA). The topological data is stored in the form of an adjacency list, supporting dynamic switching between radial and loop network structures. The line impedance values are obtained through a standard parameter library such as the IEEE 33-node system; Figure 2 Figure 20 is a schematic diagram of a distribution network with charging stations built based on IEEE 33. The historical failure rate of distribution network lines needs to be extracted from the operation and maintenance database, usually in the unit of annual failure times.

[0083] A test case is constructed based on the IEEE 33-node system to verify the effectiveness of the proposed method. The IEEE 33-node system is a typical distribution network test system, which contains multiple nodes and multiple distribution network lines and can effectively simulate the operation of the actual distribution network. In the test case, different fault scenarios are set, including distribution network line faults and charging station independent faults, and the improved sequential Monte Carlo method simulation method is used for simulation analysis. The effectiveness and accuracy of the proposed method are verified through reliability indexes and charging service availability indexes.

[0084] Obtain the number of charging piles on the charging station side and the Poisson distribution failure rate parameters, as well as the failure rate, repair rate, and charging load.

[0085] The power curve is divided by 30 minutes of time to reflect the load characteristics during peak and valley periods. Since existing literature shows that the faults of charging facilities conform to the Poisson distribution, the Poisson distribution failure rate parameter is obtained by fitting historical operation and maintenance data as 3 times / year, and the annual failure rate value of a single charging pile can be calculated to be approximately 0.15 through the Poisson distribution formula. The Poisson distribution formula is as follows:

[0086] ;

[0087] Among them, in the Poisson distribution, the random variable exactly takes the value of probability; in the embodiment: is the number of times the charging pile fails within a certain time period; is a specific failure times value (such as =0 means no failure, =1 means 1 failure occurs, etc.); is the parameter of the Poisson distribution, representing the average failure rate of the charging pile; is the time period length.

[0088] Step 2, construction of the joint probability model and two-way coupling mechanism.

[0089] Based on the data parameters of the distribution network line and the charging station, the historical fault correlation data of the distribution network line and the charging station, and the two-state Markov chain, construct the correlation probability matrix of line faults and charging station outages, and integrate the Poisson distribution failure rate parameters of charging station independent faults to form a two-way joint probability model. The process is as follows:

[0090] To quantify the fault interaction effect between the distribution network and the charging station, construct the correlation probability matrix of line faults and charging station outages , the rows of which represent the fault lines of the power system, and the columns represent the charging station faults. The elements inside indicate the probability of the line fault causing the charging station outage, and the matrix elements in the correlation probability matrix Indicates the line The conditional probability that a fault causes the charging station to shut down is expressed as:

[0091] ;

[0092] In the formula, is the number of times the charging station shuts down due to a line fault, is the total number of line faults.

[0093] Integrate the Poisson distribution failure rate parameters of the charging station's independent faults to form a two-way joint probability model:

[0094] The coupled failure rate maps the failure rate of the line load point to the charging station through the associated probability matrix. For the Poisson failure rate of the charging station itself, the acquisition method is the annual average failure rate of a single charging pile obtained through Poisson distribution generation. Since the faults of the charging piles in the charging station can be regarded as independent events, so , is the number of charging piles in the charging station ; Repair time constraint: When , that is, the simulation time exceeds the repair time of the line fault, trigger the repair mechanism to reset the line failure rate.

[0095] Construct a two-way joint probability model for the shutdown of the charging station and the coupled faults of the power grid, and its expression is as follows:

[0096] ;

[0097] ;

[0098] ;

[0099] In the above formula, represents the shutdown fault probability of the charging station, represents the grid coupling fault probability, represents the failure rate of the line load point , represents the total number of charging stations, represents the simulation time, represents the repair time of this state fault, represents the transition matrix satisfied by the probability of the charging station changing between the fault and repair states, is the probability that the charging station remains in normal operation within the time interval ; The probability that the charging station transfers from the normal state to the fault state within the time interval ; The probability that the charging station recovers from the fault state to the normal state within the time interval ; The probability that the charging station remains faulty within the time interval ; is the repair rate.

[0100] Dynamically update the outage fault probability of the charging station , and solve the unavailability rate of the charging station :

[0101] .

[0102] Step 3: Design of the hierarchical index system and calculation logic.

[0103] Construct a hierarchical index system to form a multi-dimensional evaluation system. The hierarchical index system includes three types of indicators: the user side, the distribution network side, and the economic side, forming a multi-dimensional evaluation system.

[0104] Select the average outage frequency index and the average outage duration index of the distribution network side indicators, and define the charging availability rate index, the expected un-supplied energy index for charging on the user side, and the coupled fault contribution degree index on the economic side.

[0105] The average outage frequency index SAIFI (system average interruption frequency index) of the distribution network side system measures the frequency of power supply interruptions, reflects the average number of power outages experienced by users per year, and evaluates the power supply continuity of the power grid. Its calculation formula is:

[0106] ;

[0107] In the above formula, and are the failure rate and the number of users of the line load point respectively, and is the set of all load points of the system line.

[0108] The average outage duration index SAIDI (system average interruption duration index) of the distribution network side system measures the duration of power outages, reflects the total power outage time of users per year, and is used to evaluate the recovery ability and power supply stability of the power grid. The calculation formula is:

[0109] ;

[0110] Among them, is the annual unavailability rate or power outage time of the line load point .

[0111] The charging availability index on the user side, ACAI (Average charging availability index), measures the proportion of time that the charging station is available for use and reflects the probability that users can successfully charge; its significance lies in directly relating to the user experience and showing the reliability of the charging service. The calculation formula is:

[0112] ;

[0113] Among them, is the power supply of the th charging pile, is the demand power, and 8760 is the number of hours in a year.

[0114] The charging expected non-supplied energy index on the user side, CENS (Charging energy not supplied), measures the total charging energy that fails to be provided due to faults. Its significance lies in quantifying economic losses and energy gaps and helping to evaluate the impact of charging service interruptions. The calculation formula is:

[0115] ;

[0116] In the formula, is the total number of charging stations, is the total simulation running time, is the outage state of the charging station at time, is the time interval (time step), is the average load (unit: kW) connected to the line load point . The average load of the system can be obtained by multiplying the system demand by the load factor, or by calculating the total power required during the research period based on the load duration curve and then dividing by the research period. Finally, the average load of the load point can be calculated through a certain proportion allocation.

[0117] The coupling fault contribution index on the economic side (system coupling side), CFCI (Coupling fault contribution index), quantifies the impact of two-way faults on the system and measures the proportion of faults caused by source-load interaction in the total faults; its significance lies in revealing the degree of mutual influence of faults between the power grid and the charging station and helping to identify key coupling points. The calculation formula is:

[0118] ;

[0119] Among them, is the number of coupled faults triggered by the distribution network and the charging station, is the total number of faults; during implementation, the triggering path of each fault needs to be recorded. For example, if a line fault causes the charging station to shut down, it is recorded as a forward coupling, and if the charging station shuts down and causes the line to be overloaded, it is recorded as a reverse coupling.

[0120] Step 4, Simulate the reliability assessment of dynamic fault propagation using the improved sequential Monte Carlo method.

[0121] Simulation of the improved sequential Monte Carlo method: Using random number generation, according to the fault and repair probability distributions of the distribution network lines and charging stations, fault events are generated. Each event contains information such as the fault type, occurrence time, and associated equipment; the generated fault events are sorted according to the occurrence time to form an initial event queue. Each event in the event queue is processed sequentially in time order to simulate the fault occurrence process in the actual system; for each fault event, according to the fault type and associated equipment, calculate the impact of the fault on the system, including the power outage range, power outage duration, and lost load, etc. And use DistFlow for power flow calculation to judge whether there is overload, because DistFlow is applicable to radial distribution networks, takes into account the resistance and reactance of the lines, and can calculate the power flow in the distribution system more accurately. At the same time, update the state of the system to provide a basis for the processing of subsequent events.

[0122] Adopt the improved sequential Monte Carlo method to simulate the two-way triggering mechanism of distribution network line faults and charging station outages, embed the power flow calculation model to analyze the secondary faults of line overload caused by load transfer, conduct quantitative calculations for reliability assessment, and generate a reliability assessment report. The specific process is as follows:

[0123] Data reading and loading: Load the distribution network data parameters of IEEE33 and the data parameters of charging piles, such as Figure 3 and Figure 4 shown, which includes line data and load data, such as the IEEE33 node voltage; load the data parameters of the charging station, including the number of charging piles, failure rate, repair rate, and charging load. Expand the access of charging stations in this system: For example, connect charging stations CS1, CS2, and CS3 to nodes 9, 19, and 29 respectively, and the parameters are shown in Table 1.

[0124] Table 1 Data parameter table after nodes are connected to charging stations

[0125]

[0126] System state initialization: Set the total simulation duration equivalent years, the convergence threshold variance coefficient , construct a distribution network - charging station joint state vector , where: represents the fault status of lines (0 represents normal, 1 represents fault); represents

[0127] the outage status of

[0128] charging stations, and initializes the event queue.

[0129] ;

[0130] where, is the fault status of the line load point ; and respectively represent the fault status of the line load points of the 1st and the th lines; ; is the starting repair time of the line and the charging station, is the repair time of the line, is the repair time of the charging station; , and are all the dynamic failure rates of the charging stations, is the inherent failure rate of the charging station.

[0131] The Monte Carlo method is used to sample the operating states of the line load points and the charging stations, obtain the sequences of the operating states of the line load points and the charging stations, and calculate the TTF (Time To Failure, which refers to the time interval from the start of normal operation of the line load point or the charging station to the occurrence of a fault) and TTR (Time To Repair, which refers to the time interval from the occurrence of a fault in the line load point or the charging station to the completion of repair and the restoration of normal operation) of the line load point or the charging station.

[0132] Find the minimum value among the TTFs of all faults. Generally, the device with the minimum TTF is regarded as the device that fails in this simulation, and record the time when the minimum value is found and the corresponding device.

[0133] In a system, multiple devices are running simultaneously, and each device has its own TTF, that is, their respective predicted fault times. During simulation, it is necessary to find out which device has the shortest TTF, that is, the one that fails first. The device that fails earliest determines the first fault time of the system. This method simplifies the reliability analysis of multi-device systems.

[0134] Initial fault dynamic event chain generation, in which a multiple time advancement mechanism considering distribution network line faults and charging station faults is adopted to generate event sequences, including generating distribution network line fault events, charging station fault events, and coupled fault events, and sorting the generated events in chronological order.

[0135] Establish a dynamic fault propagation model to conduct two-way coupled fault determination for lines and charging stations:

[0136] Forward triggering: When a line load point fails, for each charging station Execute: If the conditional probability that the line fault causes the charging station to shut down exists and is in (0, 1], then the charging station is considered to be in a fault state at this time; record relevant data such as the fault time, repair time, and load of the distribution network and charging stations.

[0137] Reverse triggering: When a charging station shuts down, perform the following operations:

[0138] ;

[0139] where is the load transfer amount, is the charging load of the charging piles in the charging station at time;

[0140] When a charging station shuts down, call the DistFlow power flow calculation model to solve the power distribution of the distribution network (the reactive power Q is small and can be ignored, and the lines here include the downstream branches), and use the following power balance equation:

[0141] ;

[0142] where and are the active and reactive powers of the line with load transfer, and are the line losses, and are the line node loads; and are the active and reactive powers of the line before load transfer.

[0143] Embed the DistFlow power flow calculation model, analyze the secondary line overload faults caused by load transfer, conduct overload judgment, and mark the line load point as a fault when the following conditions are met:

[0144] ;

[0145] Among them, is the apparent power. Considering that manufacturers will reserve a safety margin, usually set at ±5% during design. For example, the protection devices allowed by the power system usually use 1.05 - 1.2 times the rated current or current as the threshold to cope with short-term overload or environmental fluctuations. However, long-term exceeding will directly consume this margin, putting the equipment into the "critical overload" state. Therefore, in this example, the current threshold is selected as 1.05, and it is determined that if this ratio exceeds 1.05 times, load shedding is initiated, directly resulting in the load of the charging station being cut off, thus indirectly becoming a fault.

[0146] Perform quantitative calculations for reliability assessment to generate a reliability assessment report:

[0147] After a fault is judged, accumulate and count its corresponding fault time and number of faults;

[0148] Based on the DistFlow power flow calculation model, perform convergence judgment: if convergence occurs, calculate reliability indicators and record data; if not, do not record data, increase the simulation round, resample for the next fault generation event, recalculate TTR and TTF, and count fault factors.

[0149] Perform reliability indicator calculation updates and count SAIDI, SAIFI, ACAI, CENS, and CFCI reliability indicators;

[0150] As Figure 5 shown in and Table 2, after the simulation ends, output the reliability assessment report of the hierarchical index system.

[0151] Table 2 Calculation Table of SAIDI, SAIFI, ACAI, CENS, and CFCI Reliability Indicators

[0152]

[0153] Hierarchical index assessment: Show the reliability indicator values of ACAI, CFCI, CENS and the traditional indicators SAIDI and SAIFI, as shown in Table 2. It can be seen from Table 2 that the indicators on the grid side all indicate a decrease in the power supply capacity of the distribution network, while the indicators on the user side highlight that the coupling fault causes the charging availability ACAI to drop to 93.45%, and CENS is 505.38 MWh, indicating a loss in the charging supply capacity.

[0154] The experimental results prove the accurate simulation of the fault dynamic propagation process by using some reliability indexes and the improved sequential Monte Carlo simulation method. By introducing the overload duration threshold and the two-way trigger mechanism, the chain reaction of "fault - load transfer - overload" is accurately restored, significantly improving the accuracy and credibility of the evaluation. The verification based on the IEEE 33 standard test system shows that the proportion of evaluation coupling faults in the scenario with dense charging stations of this method reaches 13.33%, demonstrating good engineering practicability. The evaluation method proposed by the present invention has broad application prospects. It can provide reliable decision-making support for power grid companies, charging station operators and urban planning departments, help optimize charging reliability, improve the reliability of distribution networks, and enhance the charging experience of users, thus promoting the healthy and sustainable development of the electric vehicle industry.

[0155] This method considers the collaborative reliability evaluation under the two-way fault coupling effect of the distribution network (source side) and the electric vehicle charging station (load side). By establishing the fault interaction probability model of the distribution network and the charging station, designing a hierarchical multi-dimensional evaluation index system, and using the improved sequential Monte Carlo method for dynamic simulation, the accurate quantitative evaluation of the source-load interaction fault is realized.

[0156] Conclusion statement: The above embodiments fully present the technical details and operation processes of the present invention, covering all links of data modeling, dynamic simulation, index calculation and optimization decision-making. Any method improvement based on the core idea of the present invention (such as integrating an artificial intelligence prediction module and extending to the AC-DC hybrid distribution network scenario) belongs to the category of equivalent replacement and should be included in the scope of the claims of the present invention.

Claims

1. A collaborative reliability evaluation method for a charging station and a distribution network with source-load interaction faults coupling, characterized in that, It includes the following steps: Step 1: Collect the data parameters of the distribution network lines and charging stations; Step 2: Based on the data parameters of the distribution network lines and charging stations, construct the correlation probability matrix of line faults and charging station outages, and integrate the Poisson distribution failure rate parameters of independent charging station faults to form a two-way joint probability model; Construct the correlation probability matrix of line faults and charging station outages as , and the elements in the correlation probability matrix represent the conditional probability of a charging station outage caused by a line fault, and the expression is: ； is the number of times the charging station is shut down due to line faults, is the total number of line faults; Integrate the Poisson distribution failure rate parameters of independent charging station faults to form a two-way joint probability model: Couple the failure rate, and map the line failure rate to the charging station through the correlation probability matrix; Repair time constraint: When the simulation time exceeds the repair time of the line fault, reset the line failure rate; Construct a two-way joint probability model for charging station outages and grid coupled faults, and the expression is as follows: ； ； ； In the above formula, represents the outage failure probability of the charging station, represents the grid coupling failure probability, represents the line failure rate, represents the total number of charging stations, represents the Poisson failure rate of the charging station itself, represents the simulation time, represents the repair time of the failure, represents the transition matrix that the probability of change in the charging station between the failure and repair states satisfies, is the probability that the charging station remains in normal operation within the time interval ; is the probability that the charging station transfers from the normal state to the failure state within the time interval ; is the probability that the charging station recovers from the failure state to the normal state within the time interval ; is the probability that the charging station remains in failure within the time interval ; is the repair rate; Dynamically update the outage fault probability of the charging station and solve the unavailability rate of the charging station : ； Step 3: Construct a hierarchical index system to form a multi-dimensional evaluation system; Step 4: Adopt simulation to simulate the two-way trigger mechanism of distribution network line faults and charging station outages, analyze the secondary faults of line overload caused by load transfer, and generate a reliability evaluation report.

2. The collaborative reliability assessment method for a charging station and a distribution network with source-load interaction fault coupling according to claim 1, wherein Step 1: Collect the data parameters of the distribution network lines and charging stations. The data parameters obtained for the distribution network lines and charging stations include: Obtain the historical failure rate and topological structure parameters of the distribution network side lines. The topological structure parameters include the node-branch incidence matrix, line impedance matrix, and line capacity matrix; Obtain the number of charging piles, Poisson distribution failure rate parameters, failure rate, repair rate, and charging load on the charging station side.

3. The method for collaborative reliability assessment of a charging station and a distribution network with source-load interaction fault coupling according to claim 1, wherein The hierarchical index system constructed in Step 3 includes: Average power outage frequency index, which measures the frequency of power supply interruptions and reflects the average number of power outages experienced by users per year. The calculation formula is: ； and are the failure rate and the number of users at the line load point respectively, is the set of all load points on the line; Average power outage duration index, which measures the duration of power outages and reflects the total power outage time of users per year. The calculation formula is: ； is the annual unavailability rate or power outage time of the line load point; Charging availability index, which measures the proportion of time that the charging station is available for use. The calculation formula is: ； is the power supply of the nth charging pile, is the required power; Charging expected unsupplied energy index, which measures the total charging energy that fails to be provided due to faults. The calculation formula is: ； is the total number of charging stations, is the total duration of the simulation run, is the outage state of the charging station at time, is the time interval, is the average load connected to the load point ; Coupled fault contribution index, which quantifies the impact of two-way faults on the system and measures the proportion of faults caused by source-load interaction in the total faults. The calculation formula is: ； is the number of coupled faults triggered by the interaction between the distribution network and the charging station, is the total number of faults.

4. The method for collaborative reliability assessment of a charging station and a distribution network with source-load interaction fault coupling according to claim 1, characterized in that, Step 4: Adopt the improved sequential Monte Carlo method to simulate the two-way trigger mechanism of distribution network line faults and charging station outages: Data reading and loading: Load the data parameters of the distribution network, including line data and load data, and load the data parameters of the charging station, including the number of charging piles, failure rate, repair rate, and charging load; System state initialization: Set the total simulation duration and the convergence threshold variance coefficient, and construct a distribution network-charging station joint state vector. The expression is: ； ； Among them, is the fault state of the line load, and respectively represent the fault states of the load points of the first and the th line loads; is the fault state; is the starting repair time of the line and the charging station, is the time taken for line repair, is the time taken for charging station repair; , and are all the dynamic failure rates of the charging station, is the inherent failure rate of the charging station; Sample the operating states of line load points and charging stations to obtain a sequence of the operating states of line load points and charging stations, and calculate the time interval from the start of normal operation to the occurrence of a fault for line load points or charging stations and the time interval from the occurrence of a fault to the completion of repair and restoration of normal operation for line load points or charging stations; Find the minimum value among the time intervals from the start of normal operation to the occurrence of a fault for each faulty line load point or charging station, and record the time and the corresponding equipment when the minimum value is found. Generate an event sequence using a multiple time-advancing mechanism that considers distribution network line faults and charging station faults, including generating distribution network line fault events, charging station fault events, and coupled fault events, and sorting the events in chronological order.

5. The coordinated reliability assessment method for a charging station and a distribution network with source-load interaction fault coupling according to claim 4, wherein: Establish a dynamic fault propagation model to perform two-way coupled fault determination on the line and the charging station: Forward trigger: When a line load point fails, for each charging station, execute: If the conditional probability of the charging station being out of service due to the line fault exists and is in the range of (0, 1], then the charging station is considered to be in a fault state at this time; Reverse trigger: When the charging station is out of service, perform the following operations: ； is the load transfer amount, is the charging load of the charging piles in the charging station at the charging load at time; When the charging station is out of service, solve the power distribution of the distribution network line using the power balance equation: ； Among them, and are the active and reactive powers for load transfer occurring on the line, and are the line losses, and are the line node loads; and are respectively the active and reactive powers before load transfer occurs on the line; Analyze the secondary fault of line overload caused by load transfer, perform overload judgment, and mark the line load point as faulty when the following conditions are met: ； Among them, is the apparent power.

6. The method for collaborative reliability assessment of a charging station and a distribution network with source-load interaction fault coupling according to claim 1, wherein In step four, the process of generating a reliability assessment report includes: After determining the fault, accumulate and count the corresponding fault time and number of faults; Convergence judgment: If convergence is achieved, calculate the reliability index; if not, increase the number of simulation rounds and resample for the next fault generation event; Calculate, update, and count the reliability indexes of the hierarchical index system; Output the reliability assessment report of the hierarchical index system after the simulation ends.

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