Method for evaluating cooperative reliability of charging station and power distribution network based on source-load interaction fault coupling

Abstract

The invention relates to the technical field of power system reliability analysis, and particularly discloses a source-load interaction fault coupling charging station and power distribution network cooperation reliability evaluation method comprising the following steps: collecting data parameters of a power distribution network line and a charging station; based on data parameters of a power distribution network line and a charging station, an association probability matrix of line faults and charging station outage is constructed, Poisson distribution fault rate parameters of independent faults of the charging station are integrated, and a two-way joint probability model is formed; constructing a hierarchical index system, and forming a multi-dimensional evaluation system; a bidirectional trigger mechanism for simulating a power distribution network line fault and charging station shutdown is adopted, a line overload secondary fault caused by load transfer is analyzed, and a reliability evaluation report is generated. According to the method, the limitation of traditional one-way evaluation is broken through, the'fault-load transfer-overload 'chain reaction is accurately restored, the dynamic simulation precision is improved, a multi-dimensional index scientific decision is adopted, and a quantitative basis is provided for reliability evaluation.

Description

Technical Field

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

[0002] With the rapid increase in the penetration rate of electric vehicles, charging stations, as centralized access points for new power loads, have shown significant bidirectional coupling characteristics in their fault interaction with the distribution network. Traditional reliability assessment methods have the following technical bottlenecks:

[0003] Most existing studies use a one-way causal model, which only considers the impact of distribution network line faults on charging station power outages, while ignoring the secondary impact of charging load transfer caused by faults in the distribution network and charging facilities themselves on the distribution network, and the coupling effect between the two. Traditional methods are based on a deterministic set of fault scenarios and cannot dynamically simulate the temporal correlation characteristics of source-load faults. For example, distribution network line faults and charging station outages may be triggered alternately to form 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 power supply interruption statistics and lack the ability to quantify the experience of charging users and the losses on the coupled fault side. Although existing studies have pointed out that charging pile failures follow a Poisson distribution, they have not been dynamically associated with the distribution network failure rate model. In addition, the existing Monte Carlo method simulation does not consider the conditional triggering mechanism between fault events, resulting in a deviation in the evaluation of coupled fault contribution.

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

[0005] The purpose of the present invention is to solve the shortcomings of unidirectional modeling, static analysis limitations, insufficient applicability of indicators, and insufficient model refinement in the prior art, and 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 failure and charging station shutdown, and quantify the spatiotemporal correlation characteristics of source-load interaction faults; design a hierarchical evaluation index system to simultaneously evaluate power grid measurement, power supply quality, and coupled fault contribution; use an improved sequential Monte Carlo method simulation framework to dynamically simulate the fault propagation chain and overload secondary faults caused by load transfer, calculate the reliability index of the hierarchical index system, and output a reliability assessment report of the hierarchical index system.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions: A method for evaluating the collaborative reliability of a charging station and a distribution network with source-load interactive fault coupling comprises the following steps: Step 1: Collect data parameters of distribution network lines and charging stations; Step 2: Based on the data parameters of the distribution network lines and charging stations, a correlation probability matrix between line failures and charging station shutdowns is constructed, and the Poisson distribution failure rate parameters of independent charging station failures are integrated to form a two-way joint probability model. Step three: construct a hierarchical indicator system to form a multi-dimensional evaluation system; Step 4: Use simulation to simulate the bidirectional trigger mechanism of distribution network line failure and charging station shutdown, analyze the secondary failure of line overload caused by load transfer, and generate a reliability assessment report.

[0007] As a further solution of the present invention, step 1, collecting data parameters of the distribution network lines and the charging station, obtaining the data parameters of the distribution network lines and the charging station includes: Obtain the historical failure rate and topological parameters of the distribution network side lines, the topological parameters include the node-branch association matrix, the line impedance matrix and the line capacity matrix; Obtain the number of charging piles, Poisson distribution failure rate parameters, and failure rate, repair rate, and charging load on the charging station side.

[0008] As a further preferred solution of the present invention, in step 2, in order to quantify the interactive impact of the faults of the distribution network and the charging station, a correlation probability matrix between line faults and charging station outages is constructed: , the elements in the association probability matrix represents the conditional probability of the charging station being shut down due to line failure, expressed as: ; The number of times the charging station was shut down due to line failure. is the total number of faults on the line; The Poisson distribution failure rate parameters of independent failures of charging stations are integrated to form a two-way joint probability model: Coupled failure rate, mapping line failure rate to charging station through the association probability matrix; Repair time constraint: when the simulation time exceeds the repair time of the line fault, the line fault rate is reset; A bidirectional joint probability model of charging station outage and grid coupling failure is constructed, and the expression is as follows: ; ; ; In the above formula, represents the failure probability of charging station shutdown, 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, Indicates the repair time of the fault in this state. The transition matrix representing the probability of a charging station changing between the faulty and repaired states satisfies, For charging stations at intervals The probability of maintaining normal operation within For charging stations at intervals The probability of transition from a normal state to a fault state; For charging stations at intervals The probability of recovering from a fault state to a normal state; For charging stations at intervals The probability of a sustained failure within is the repair rate; Dynamically update the probability of charging station outage failure , and solve the unavailability rate of charging stations : .

[0009] As a further preferred solution of the present invention, in step three, the hierarchical index system constructed includes: The average power outage frequency index measures the frequency of power outages and reflects the average number of power outages experienced by users each year. The calculation formula is: ; and are the failure rate and number of users at the line load point, is the set of all load points of the line; The average power outage duration indicator measures the duration of power outages and reflects the total power outage time of users each year. The calculation formula is: ; is the annual unavailability rate or power outage time of the line load point; The charging availability index measures the proportion of time that a charging station is available for use. The calculation formula is: ; is the power supply of the charging pile, is the power demand; The expected unsupplied energy indicator for charging measures the total amount of charging energy that cannot be provided due to a fault. The calculation formula is: ; is the total number of charging stations, is the total simulation running time, For charging stations Time outage status, is the time interval, For connection at the load point The average load; The coupling fault contribution index quantifies the impact of bidirectional faults on the system and measures the proportion of faults caused by source-load interaction to the total faults. The calculation formula is: ; is the number of coupling faults triggered by the distribution network and the charging station, is the total number of failures.

[0010] As a further preferred solution of the present invention, in step 4, an improved sequential Monte Carlo method is used to simulate the bidirectional triggering mechanism of the distribution network line failure and the charging station shutdown: Data reading and loading: loading the data parameters of the distribution network, including line data and load data, and loading the data parameters of the charging station, including the number of charging piles, failure rate, repair rate and charging load; Initialize the system state, set the total simulation time and the convergence threshold variance coefficient, and construct the distribution network-charging station joint state vector, which is expressed as: ; ; in, Line load point The fault state, and Respectively represent the first and Line load points Fault status; is the initial repair time of the line and charging station, Time for line repair, Time taken to repair the charging station; , as well as are the dynamic failure rates of charging stations, is the failure rate of the charging station itself.

[0011] Sampling the operating status of the line load points and the charging stations to obtain the operating status sequence of the line load points and the charging stations, and calculating the time interval from the start of normal operation to the occurrence of a fault at the line load point or the charging station and the time interval from the occurrence of a fault to the completion of repair and restoration of normal operation at the line load point or the charging station; Find the minimum value among the time intervals from the start of normal operation to the occurrence of failure at each faulty line load point or charging station, and record the time and corresponding equipment when the minimum value is found; A multiple time advancement mechanism considering distribution network line faults and charging station faults is adopted to generate event sequences, including distribution network line fault events, charging station fault events and coupled fault events, and the events are sorted in chronological order.

[0012] As a further preferred solution of the present invention, a dynamic fault propagation model is established to perform bidirectional coupling fault determination on the line and the charging station: Positive trigger: When a line load point fails, the following is executed for each charging station: If the conditional probability that the line failure causes the charging station to stop operating exists and is in (0,1], then the charging station is considered to be in a faulty state at this time; Reverse trigger: When the charging station is out of service, the following operations are performed: ; is the load transfer amount, Charging piles in charging stations exist The amount of charging load over time; When the charging station is out of service, the power distribution of the distribution network is solved using the power balance equation: ; in, , is the active and reactive power of the line load transfer, and is the line loss, and is the line node load; , are respectively the active and reactive power of the line before load transfer occurs; Analyze the secondary fault of line overload caused by load transfer, make overload judgment, and mark the line load point fault when the following conditions are met: ; in, is the apparent power.

[0013] As a further preferred solution of the present invention, in step 4, the process of generating a reliability assessment report includes: After determining the fault, the corresponding fault time and fault frequency are accumulated and counted; Convergence judgment: If converged, reliability index calculation is performed; if not converged, the simulation round is increased and re-sampled for the next fault generation event; Calculate, update and count the reliability indicators of the hierarchical indicator system; After the simulation is completed, a reliability evaluation report of the hierarchical indicator system is output.

[0014] The present invention breaks through the limitations of traditional one-way evaluation, and simultaneously captures the bidirectional coupling effects of "grid fault affecting charging stations" and "charging station shutdown impacting the grid", and considers the collaborative reliability evaluation under the bidirectional fault coupling of the distribution network (source side) and the electric vehicle charging station (load side). By establishing a fault interaction probability model between the distribution network and the charging station, a hierarchical multidimensional evaluation index system is designed for synergy, and an improved sequential Monte Carlo method is used for dynamic simulation to accurately restore the "fault-load transfer-overload" chain reaction. The dynamic simulation accuracy is improved, and the accurate quantitative evaluation of source-load interaction faults is achieved, providing a quantitative basis for reliability evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a flow chart of a method for evaluating the collaborative reliability of a charging station and a distribution network with source-load interactive fault coupling proposed in an embodiment of the present invention; Figure 2 This is a schematic diagram of a distribution network including charging stations built based on IEEE33 in an embodiment of the present invention; Figure 3 IEEE33 node voltage distribution diagram in an embodiment of the present invention; Figure 4 Schematic diagram of IEEE33 node voltage margin comparison and analysis in an embodiment of the present invention; Figure 5 Schematic diagram of reliability index analysis in an embodiment of the present invention. DETAILED DESCRIPTION

[0016] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0017] Research shows that when a charging station stops operating due to equipment failure, the surge in load at adjacent charging stations may cause the distribution network lines to overload, forming a "fault-load transfer-overload" chain reaction, which in turn leads to load shedding. In reality, however, the more complex situation of the two being coupled due to faults is not well captured. Charging users are more sensitive to short-term interruptions than traditional residential loads. It is urgent to define new evaluation indicators for the grid side, user side, and coupling side, and to build a collaborative reliability evaluation method for charging stations and distribution networks.

[0018] The present invention establishes a bidirectional joint probability model of distribution network line failures and charging station outages to quantify the spatiotemporal correlation characteristics of source-load interaction failures; designs a hierarchical evaluation index system to simultaneously evaluate the power grid measurement, user-side power supply quality, and system-side coupling fault contribution; develops an improved sequential Monte Carlo method simulation framework to dynamically simulate the fault propagation chain and overload secondary failures caused by load transfer.

[0019] The Markov model adopted in the present invention is a mathematical model based on random processes, which is used to describe the dynamic transition of the system between different states. It has "no memory", that is, the future state of the system depends only on the current state, and has nothing to do with the past path. This characteristic 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 deal with random 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. Sequential Monte Carlo is particularly suitable for processing nonlinear 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 status of the system by simulating the failure and repair process of the equipment, thereby more accurately calculating the reliability index.

[0020] The collaborative reliability assessment method for charging stations and distribution networks taking into account source-load interaction fault coupling proposed in this embodiment is to meet the reliability assessment of distribution networks suitable for charging stations with high penetration electric vehicle charging loads. The scheme particularly involves a multi-dimensional collaborative assessment method based on dynamic fault propagation chain modeling, bidirectional correlation probability matrix construction, and the formation of a hierarchical reliability index system.

[0021] Reference Figure 1 The charging station and distribution network collaborative reliability assessment method comprises the following steps:

[0022] Step 1: Data collection and parameter integration.

[0023] To implement this method, you need to first obtain multi-dimensional data of the distribution network and charging stations. Collect the data parameters of the distribution network lines and charging stations, obtain the data parameters of the distribution network lines and loads (such as node voltage), and collect the topological structure parameters of the distribution network side lines, including reading the node-branch association matrix and line impedance matrix through the IEEE33 data file. (Unit: ΩKm) and line capacity matrix (Unit: kVA). Topological data is stored in the form of an adjacency table, supporting dynamic switching between radial and ring network structures. Line impedance values ​​are obtained through standard parameter libraries such as the IEEE33 node system; Figure 2This is a schematic diagram of a distribution network with charging stations based on IEEE33. The historical failure rate of the distribution network lines needs to be extracted from the operation and maintenance database, usually in units of annual failure times.

[0024] A test case is constructed based on the IEEE33 node system to verify the effectiveness of the proposed method. The IEEE33 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 failure and independent charging station failure, and the simulation analysis is carried out using the improved sequential Monte Carlo method simulation method. The effectiveness and accuracy of the proposed method are verified by reliability indicators and charging service availability indicators.

[0025] Get the number of charging piles on the charging station side and Poisson distribution failure rate parameters as well as failure rate, repair rate and charging load.

[0026] The power curve is divided into 30-minute time periods to reflect the load characteristics during peak and valley periods. As existing literature has shown that the failure of charging facilities conforms to the Poisson distribution, the Poisson distribution failure rate parameter is obtained by fitting historical operation and maintenance data to be 3 times / year. The annual failure rate of a single charging pile can be calculated by the Poisson distribution formula to be approximately 0.15. The Poisson distribution formula is: ; in, In the Poisson distribution, the random variable The value is exactly The probability of; in the embodiment: The number of times the charging pile fails within a certain period of time; For a specific number of failures (such as =0 means no fault, =1 means 1 fault occurred, etc.); is the parameter of Poisson distribution, which indicates the average failure rate of charging piles; The length of the time period.

[0027] Step 2: Joint probability model construction and bidirectional coupling mechanism.

[0028] Based on the data parameters of the distribution network lines and charging stations, the historical fault correlation data of the distribution network lines and charging stations, and the two-state Markov chain, the correlation probability matrix between line faults and charging station shutdowns is constructed, and the Poisson distribution failure rate parameters of independent faults of charging stations are integrated to form a two-way joint probability model. The process is as follows: In order to quantify the interactive impact of faults between the distribution network and the charging station, a probability matrix of the association between line faults and charging station outages is constructed. , whose rows represent faulty power lines and columns represent faulty charging stations. The elements inside represent the probability of a charging station being shut down due to line faults. The matrix elements in the associated probability matrix Indicates line Fault causing charging station The conditional probability of outage is expressed as: ; In the formula, The number of times the charging station was shut down due to line failure. is the total number of faults on the line.

[0029] The Poisson distribution failure rate parameters of independent failures of charging stations are integrated to form a two-way joint probability model: The coupling failure rate is used to convert the line load point failure rate into Map to charging station , for the Poisson failure rate of the charging station itself , the method of obtaining is the annual average failure rate of a single charging pile It is obtained by Poisson distribution. Since the charging pile failure of the charging station can be regarded as an independent event, , For charging stations The number of charging piles; repair time constraint: when , i.e. simulation The time exceeds the repair time of the line fault , triggering the repair mechanism to reset the line failure rate.

[0030] A bidirectional joint probability model of charging station outage and grid coupling failure probability is constructed, and its expression is as follows: ; ; ; In the above formula, represents the failure probability of charging station shutdown, represents the grid coupling failure probability, Indicates line load point Failure rate, represents the total number of charging stations, represents the simulation time, Indicates the repair time of the fault in this state. The transition matrix representing the probability of a charging station changing between the faulty and repaired states satisfies, For charging stations at intervals The probability of maintaining normal operation within For charging stations at intervals The probability of transition from a normal state to a fault state; For charging stations at intervals The probability of recovering from a fault state to a normal state; For charging stations at intervals The probability of a sustained failure within is the repair rate.

[0031] Dynamically update the probability of charging station outage failure , and solve the unavailability rate of charging stations : .

[0032] Step three: Design of hierarchical indicator system and calculation logic.

[0033] Construct a hierarchical indicator system to form a multi-dimensional evaluation system. The hierarchical indicator system includes three types of indicators: user side, distribution network side and economic side, forming a multi-dimensional evaluation system.

[0034] The average power outage frequency index and the average power outage duration index on the distribution network side are selected, and the charging availability index, the expected unpowered energy index on the user side and the coupling fault contribution index on the economic side are defined.

[0035] The average power outage frequency index SAIFI (system average interruption frequency index) on the distribution network side measures the frequency of power outages, reflects the average number of power outages experienced by users each year, and evaluates the power supply continuity of the power grid. Its calculation formula is: ; In the above formula, and Line load points The failure rate and number of users, For all load points of the system line A collection of .

[0036] The average interruption duration index (SAIDI) of the distribution network side measures the duration of power outages and reflects the total power outage time of users each year. It is significant in evaluating the recovery capacity and power supply stability of the power grid. The calculation formula is: ; in, Line load point Annual unavailability or power outage time.

[0037] The average charging availability index (ACAI) on the user side measures the proportion of time that a charging station is available for use and reflects the probability that a user can successfully charge. It is directly related to the user experience and shows the reliability of the charging service. The calculation formula is: ; in, For the The power supply of each charging station, is the electricity demand and 8760 is the number of hours in a year.

[0038] The user-side charging expected unsupplied energy indicator CENS (Charging energy not supplied) measures the total amount of charging energy that cannot be provided due to a fault. Its significance is to quantify economic losses and energy gaps and help evaluate the impact of charging service interruptions. The calculation formula is: ; In the formula, is the total number of charging stations, is the total simulation running time, For charging stations exist Time outage status, is the time interval (time step), For connection at the line load point The average load (in kW) of the system can be obtained by multiplying the system demand by the load factor, or by calculating the total amount of electricity required during the study period based on the load duration curve and dividing it by the study period. Finally, the average load of the load point can be calculated by allocating it in a certain proportion.

[0039] The coupling fault contribution index (CFCI) on the economic side (system coupling side) quantifies the impact of bidirectional faults on the system and measures the proportion of faults caused by source-load interaction to total faults. It is significant in revealing the degree of mutual influence between faults in the power grid and charging stations and helping to identify key coupling points. The calculation formula is: ; in, is the number of coupling 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, a line fault that causes the charging station to shut down is recorded as forward coupling, and a charging station shutdown that causes line overload is recorded as reverse coupling.

[0040] Step 4: Improved Sequential Monte Carlo method is used to simulate dynamic fault propagation reliability assessment.

[0041] Improved Sequential Monte Carlo Simulation: Random number generation is used to generate fault events according to the fault and repair probability distribution of distribution network lines and charging stations. Each event contains information such as fault type, occurrence time, and associated equipment; the generated fault events are sorted by occurrence time to form an initial event queue. Each event in the event queue is processed in chronological order to simulate the fault occurrence process in the actual system; for each fault event, the impact of the fault on the system is calculated according to the fault type and associated equipment, including the scope of power outage, duration of power outage, and load loss. DistFlow is used to perform flow calculation to determine whether it is overloaded, because DistFlow is suitable for radial distribution networks, taking into account the resistance and reactance of the lines, and can more accurately calculate the power flow in the distribution system. At the same time, the state of the system is updated to provide a basis for the processing of subsequent events.

[0042] The improved sequential Monte Carlo method is used to simulate the bidirectional trigger mechanism of distribution network line failure and charging station shutdown, and the power flow calculation model is embedded to analyze the secondary failure of line overload caused by load transfer, and the reliability evaluation is quantitatively calculated to generate a reliability evaluation report. The specific process is as follows: Data reading and loading: Load the IEEE33 distribution network data parameters and charging pile data parameters, such as Figure 3 and Figure 4 As shown, it contains line data and load data, such as IEEE33 node voltage; loading charging station data parameters, including the number of charging piles, failure rate, repair rate, and charging load. In this system, charging station access is extended: for example, charging stations CS1, CS2, and CS3 are connected to nodes 9, 19, and 29 respectively, and the parameters are shown in Table 1.

[0043] Table 1 Data parameter table after the node is connected to the charging station

[0044] System status initialization: set the total simulation time Equivalent year, convergence threshold variance coefficient , construct the distribution network-charging station joint state vector ,in: express The fault status of the line (0 means normal, 1 means fault); express The outage status of each charging station is checked and the event queue is initialized.

[0045] The joint state vector expression is: ; ; in, Line load point The fault state, and Respectively represent the first and Line load points Fault status; is the initial repair time of the line and charging station, Time for line repair, Time taken to repair the charging station; , as well as are the dynamic failure rates of charging stations, is the failure rate of the charging station itself.

[0046] The Monte Carlo method is used to sample the operating status of line load points and charging stations to obtain the operating status sequence of line load points and charging stations, and the TTF (Time To Failure, which refers to the time interval from the start of normal operation of the line load point or charging station to the occurrence of a failure) and TTR (Time To Repair, which refers to the time interval from the occurrence of a failure to the completion of repair and resumption of normal operation) of the line load point or charging station are calculated.

[0047] Find the minimum value among the TTFs of various faults. Generally, the device with the smallest TTF is taken as the device that fails in this simulation. Record the time when the minimum value is found and the corresponding device.

[0048] In a system, multiple devices are running at the same time, and each device has its own TTF, that is, its own expected failure time. When simulating, it is necessary to find out which device has the shortest TTF, that is, the one that fails first. The device that fails first determines the first failure time of the system. This method simplifies the reliability analysis of multi-device systems.

[0049] The initial fault dynamic event chain is generated, in which a multiple time advancement mechanism considering distribution network line faults and charging station faults is used 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.

[0050] A dynamic fault propagation model is established to perform bidirectional coupling fault determination on the line and charging station: Positive trigger: When the line load point In case of failure, each charging station Execution: Conditional probability of a charging station being shut down if a line failure occurs If it exists and is in (0,1], the charging station is considered to be in a faulty state at this time; the relevant data such as the fault time, repair time and load of the distribution network and the charging station are recorded.

[0051] Reverse trigger: When the charging station During an outage, do the following: ; in, is the load transfer amount, Charging piles in charging stations exist The amount of charging load over time; When charging station When the system is shut down, the DistFlow power flow calculation model is called to solve the power distribution of the distribution network (the reactive power Q is small and can be ignored, and the lines here all include downstream branches), using the following power balance equation: ; in, , is the active and reactive power of the line load transfer, and is the line loss, and is the line node load; and It is the active and reactive power of the line before load transfer occurs.

[0052] Embed the DistFlow power flow calculation model to analyze the secondary fault of line overload caused by load transfer, make overload judgment, and mark the line load point when the following conditions are met Fault: ; in, For apparent power, considering that manufacturers reserve a safety margin of ±5% during design, for example, the protection device allowed by the power system usually uses 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 overload will directly consume this margin and put the equipment into a "critical overload" state. Therefore, in this example, the current threshold is selected as 1.05, and it is determined that if the ratio exceeds 1.05 times, the load shedding is initiated, which directly leads to the removal of the charging station load, and indirectly turns into a fault.

[0053] Perform quantitative calculations for reliability assessment and generate reliability assessment reports: After determining the fault, the corresponding fault time and fault frequency are accumulated and counted; Based on the DistFlow power flow calculation model, convergence judgment is performed: if converged, reliability index calculation is performed and data is recorded; if not converged, data is not recorded, simulation rounds are increased to resample the next fault generation event, TTR and TTF are recalculated, and fault factors are counted.

[0054] Calculate and update reliability indicators and count SAIDI, SAIFI, ACAI, CENS and CFCI reliability indicators; like Figure 5 As shown in Table 2, after the simulation, a reliability evaluation report of the hierarchical indicator system is output.

[0055] Table 2 Calculation table of reliability indexes of SAIDI, SAIFI, ACAI, CENS and CFCI

[0056] Hierarchical indicator evaluation: The reliability index values ​​of ACAI, CFCI, CENS and traditional indicators SAIDI and SAIFI are shown in Table 2. As can be seen from Table 2, the indicators on the grid side all indicate that the power supply capacity of the distribution network has declined, while the indicators on the user side highlight that the coupling fault caused the charging availability ACAI to drop to 93.45%, while CENS was 505.38 MWh, indicating a loss in charging supply capacity.

[0057] The experimental results prove that some reliability indicators and improved sequential Monte Carlo simulation methods can accurately simulate the dynamic propagation process of faults. By introducing the overload duration threshold and bidirectional trigger mechanism, the chain reaction of "fault-load transfer-overload" is accurately restored, which significantly improves the accuracy and credibility of the evaluation. The verification based on the IEEE33 standard test system shows that the proportion of coupled faults in the evaluation of this method in the dense charging station scenario reaches 13.33%, showing good engineering practicality. The evaluation method proposed in this 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 distribution network reliability, and improve user charging experience, thereby promoting the healthy and sustainable development of the electric vehicle industry.

[0058] This method considers the collaborative reliability evaluation under the coupling of bidirectional faults between the distribution network (source side) and the electric vehicle charging station (load side). By establishing a fault interaction probability model between the distribution network and the charging station, a hierarchical multidimensional evaluation index system is designed, and the improved sequential Monte Carlo method is used for dynamic simulation to achieve accurate quantitative evaluation of source-load interaction faults.

[0059] Conclusion statement: The above implementation methods fully present the technical details and operation process of the present invention, covering 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 artificial intelligence prediction module and expanding to AC / DC hybrid distribution network scenarios) 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 assessment method for charging stations and distribution networks with source-load interaction fault coupling, characterized in that: The following steps are involved: Step 1: Collect data parameters of distribution network lines and charging stations; Step 2: Based on the data parameters of the distribution network lines and charging stations, a correlation probability matrix between line failures and charging station shutdowns is constructed, and the Poisson distribution failure rate parameters of independent charging station failures are integrated to form a two-way joint probability model. Step three: construct a hierarchical indicator system to form a multi-dimensional evaluation system; Step 4: Use simulation to simulate the bidirectional trigger mechanism of distribution network line failure and charging station shutdown, analyze the secondary failure of line overload caused by load transfer, and generate a reliability assessment report.

2. The method for evaluating the reliability of charging stations and distribution networks in a source-load interactive fault coupling manner according to claim 1 is characterized in that: Step 1: Collect data parameters of distribution network lines and charging stations. Acquiring data parameters of distribution network lines and charging stations includes: Obtain the historical failure rate and topological parameters of the distribution network side lines, the topological parameters include the node-branch association matrix, the line impedance matrix and the line capacity matrix; Obtain the number of charging piles, Poisson distribution failure rate parameters, and failure rate, repair rate, and charging load on the charging station side.

3. The method for evaluating the collaborative reliability of charging stations and distribution networks with source-load interactive fault coupling according to claim 1 is characterized in that: In step 2, in order to quantify the interactive impact of faults between the distribution network and the charging station, a correlation probability matrix between line faults and charging station outages is constructed. , the elements in the association probability matrix represents the conditional probability of the charging station being shut down due to line failure, expressed as: ； The number of times the charging station was shut down due to line failure. is the total number of faults on the line; The Poisson distribution failure rate parameters of independent failures of charging stations are integrated to form a two-way joint probability model: Coupled failure rate, mapping line failure rate to charging station through the association probability matrix; Repair time constraint: when the simulation time exceeds the repair time of the line fault, the line fault rate is reset; A bidirectional joint probability model of charging station outage and grid coupling failure is constructed, and the expression is as follows: ； ； ； In the above formula, represents the failure probability of charging station shutdown, 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, Indicates the repair time of the fault in this state. The transition matrix representing the probability of a charging station changing between the faulty and repaired states satisfies, For charging stations at intervals The probability of maintaining normal operation within For charging stations at intervals The probability of transition from a normal state to a fault state; For charging stations at intervals The probability of recovering from a fault state to a normal state; For charging stations at intervals The probability of a sustained failure within is the repair rate; Dynamically update the probability of charging station outage failure , and solve the unavailability rate of charging stations : 。 4. The method for evaluating the collaborative reliability of charging stations and distribution networks with source-load interactive fault coupling according to claim 1, characterized in that: Step 3: The hierarchical indicator system constructed includes: The average power outage frequency index measures the frequency of power outages and reflects the average number of power outages experienced by users each year. The calculation formula is: ； and are the failure rate and number of users at the line load point, is the set of all load points of the line; The average power outage duration indicator measures the duration of power outages and reflects the total power outage time of users each year. The calculation formula is: ； is the annual unavailability rate or power outage time of the line load point; The charging availability index measures the proportion of time that a charging station is available for use. The calculation formula is: ； is the power supply of the charging pile, is the power demand; The expected unsupplied energy indicator for charging measures the total amount of charging energy that cannot be provided due to a fault. The calculation formula is: ； is the total number of charging stations, is the total simulation running time, For charging stations Time outage status, is the time interval, For connection at the load point The average load; The coupling fault contribution index quantifies the impact of bidirectional faults on the system and measures the proportion of faults caused by source-load interaction to the total faults. The calculation formula is: ； is the number of coupling faults triggered by the distribution network and the charging station, is the total number of failures.

5. The method for evaluating the reliability of charging stations and distribution networks in a source-load interactive fault coupling manner according to claim 1, characterized in that: Step 4: Use the improved sequential Monte Carlo method to simulate the bidirectional triggering mechanism of distribution network line failure and charging station shutdown: Data reading and loading: loading the data parameters of the distribution network, including line data and load data, and loading the data parameters of the charging station, including the number of charging piles, failure rate, repair rate and charging load; Initialize the system state, set the total simulation time and the convergence threshold variance coefficient, and construct the distribution network-charging station joint state vector, which is expressed as: ； ； in, is the fault state of the line load, and Respectively represent the first and Line load points Fault status; is the initial repair time of the line and charging station, Time for line repair, Time taken to repair the charging station; , as well as are the dynamic failure rates of charging stations, is the failure rate of the charging station itself; Sampling the operating status of the line load points and the charging stations to obtain the operating status sequence of the line load points and the charging stations, and calculating the time interval from the start of normal operation to the occurrence of a fault at the line load point or the charging station and the time interval from the occurrence of a fault to the completion of repair and restoration of normal operation at the line load point or the charging station; Find the minimum value among the time intervals from the start of normal operation to the occurrence of failure at each faulty line load point or charging station, and record the time and corresponding equipment when the minimum value is found; A multiple time advancement mechanism considering distribution network line faults and charging station faults is adopted to generate event sequences, including distribution network line fault events, charging station fault events and coupled fault events, and the events are sorted in chronological order.

6. The method for evaluating the reliability of charging stations and distribution networks in a source-load interactive fault coupling according to claim 5 is characterized in that: A dynamic fault propagation model is established to perform bidirectional coupling fault determination on the line and charging station: Positive trigger: When a line load point fails, the following is executed for each charging station: If the conditional probability that the line failure causes the charging station to stop operating exists and is in (0,1], then the charging station is considered to be in a faulty state at this time; Reverse trigger: When the charging station is out of service, the following operations are performed: ； is the load transfer amount, Charging piles in charging stations exist The amount of charging load over time; When the charging station is out of service, the power distribution of the distribution network is solved using the power balance equation: ； in, , is the active and reactive power of the line load transfer, and is the line loss, and is the line node load; , are respectively the active and reactive power of the line before load transfer occurs; Analyze the secondary fault of line overload caused by load transfer, make overload judgment, and mark the line load point fault when the following conditions are met: ； in, is the apparent power.

7. The method for evaluating the reliability of charging stations and distribution networks in a source-load interactive fault coupling manner according to claim 1, characterized in that: In step 4, the process of generating a reliability assessment report includes: After determining the fault, the corresponding fault time and fault frequency are accumulated and counted; Convergence judgment: If converged, reliability index calculation is performed; if not converged, the simulation round is increased and re-sampled for the next fault generation event; Calculate, update and count the reliability indicators of the hierarchical indicator system; After the simulation is completed, a reliability evaluation report of the hierarchical indicator system is output.

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