A method and system for evaluating reliability of regional power distribution network considering vehicle-to-grid interaction

Abstract

A regional power distribution network reliability evaluation method and system considering vehicle-to-grid interaction, the method comprising S1, data acquisition and processing; S2, simulation behavior data; S3, constructing a dynamic vehicle-to-grid interaction model; S4, power distribution network reliability evaluation index calculation; S5, power distribution network reliability comprehensive evaluation; the method comprehensively considers the influence of the travel behavior and charging and discharging characteristics of electric vehicles on the power distribution network through the dynamic vehicle-to-grid interaction model, and generates random samples conforming to the operation characteristics of the power distribution network and electric vehicles through Monte Carlo simulation, and comprehensively considers the randomness of the travel, charging and discharging behavior of electric vehicles.The application not only can dynamically describe the dual role of electric vehicles as loads and energy storage resources through the dynamic vehicle-to-grid interaction model, effectively improve the accuracy of regional power distribution network reliability evaluation, but also can generate random samples conforming to the operation characteristics of the power distribution network and electric vehicles through Monte Carlo simulation, effectively improve the accuracy and robustness of the evaluation.

Description

Technical Field

[0001] The present invention relates to a distribution network reliability assessment method, and in particular to a regional distribution network reliability assessment method and system considering vehicle-grid interaction. Background Art

[0002] With the increasing popularity of electric vehicles (EVs), vehicle-to-grid (V2G) interaction has become an important means of improving the reliability and stability of distribution networks. It not only allows EVs to draw energy from the grid for charging but also enables them to feed stored energy back to the grid during peak demand periods, thereby acting as distributed energy storage devices. This bidirectional energy flow mechanism can effectively alleviate the burden on the grid, balance power supply and demand, and reduce the impact of peak power demand on the grid. In particular, EVs, as mobile energy storage units, can provide additional stability to the distribution network during disasters or peak load conditions.

[0003] Although this method can improve grid stability, it still has the following drawbacks:

[0004] Most existing distribution network reliability assessment methods are based on static load data or single-dimensional models, which cannot fully reflect the impact of vehicle-grid interaction on the power grid system, resulting in poor accuracy in regional distribution network reliability assessment.

[0005] The information disclosed in this background technology section is only intended to increase understanding of the overall background of the application and should not be considered as an admission or any form of suggestion that the information constitutes the prior art already known to ordinary technicians in this field. Summary of the Invention

[0006] The purpose of the present invention is to overcome the shortcomings of poor accuracy of regional distribution network reliability assessment in the prior art, and to provide a regional distribution network reliability assessment method and system with better accuracy considering vehicle-grid interaction.

[0007] To achieve the above objectives, the technical solution of the present invention is:

[0008] In a first aspect, the present invention proposes a distribution network reliability assessment method considering vehicle-grid interaction, the assessment method comprising:

[0009] S1. Data collection and processing: obtaining data related to distribution network reliability assessment and constructing a time series dataset for dynamic evaluation of regional distribution network reliability considering vehicle-grid interaction;

[0010] S2, simulate behavioral data and generate random samples that meet the operating characteristics of the distribution network and electric vehicles through the Monte Carlo algorithm;

[0011] S3. Construct a dynamic vehicle-grid interaction model, which includes an electric vehicle behavior model and an electric vehicle total power model. Substitute a time series dataset for dynamic evaluation of regional distribution network reliability considering vehicle-grid interaction and a random sample that conforms to the operating characteristics of the distribution network and electric vehicles into the dynamic vehicle-grid interaction model to calculate the source-load state of the regional distribution network and the voltage amplitude of each node.

[0012] S4. Calculation of distribution network reliability assessment indicators. Based on the impact of large-scale access of electric vehicles on the reliability of the distribution network, a reliability assessment indicator system is constructed that includes system load loss probability, active power loss, and voltage deviation rate indicators. Random samples that meet the operating characteristics of the distribution network and electric vehicles are substituted into the reliability assessment indicator system to calculate the reliability assessment indicators.

[0013] S5. Comprehensive evaluation of distribution network reliability: A distribution network comprehensive evaluation model is constructed with the comprehensive evaluation score of distribution network reliability as the objective function, and the distribution network comprehensive evaluation model is solved to obtain the comprehensive evaluation score of distribution network reliability.

[0014] Said S1 comprises:

[0015] S1.1. Acquire data related to distribution network reliability assessment, including regional distribution network node load data, regional distribution network line topology data, node topology data, time series data of distribution network node loads, maximum charging power of each distribution network node, and maximum discharge power of each distribution network node. The regional distribution network node load data is acquired through the SCADA system, and the regional distribution network line topology data and node topology data are acquired through the PMS system.

[0016] The load of the distribution network node is arrive Time series data between moments include:

[0017] ;

[0018] ;

[0019] In the above formula, is the node i in the regional distribution network arrive The load at a given time, in MW; For regional distribution networks arrive The load at a certain time, N is the number of distribution network nodes;

[0020] The electric vehicle characteristics include the mth electric vehicle capacity , charging efficiency and discharge efficiency ;

[0021] The maximum charging power of the mth electric vehicle in the distribution network include:

[0022] ;

[0023] In the above formula, is the charging efficiency of the mth electric vehicle;

[0024] The maximum discharge power of the electric vehicle at the mth node of the distribution network include:

[0025] ;

[0026] In the above formula, is the discharge efficiency of the mth electric vehicle;

[0027] S1.2. Data preprocessing: cleaning, denoising and other preprocessing operations are performed on the collected load data to ensure the accuracy and availability of the data;

[0028] S1.3. Construct a time series dataset. Considering different operating conditions of the distribution network, such as normal and fault conditions, the pre-processed data related to the distribution network reliability assessment are integrated to construct a time series dataset for dynamic evaluation of regional distribution network reliability considering vehicle-grid interaction. The time series dataset should have time series characteristics and be able to reflect the operating status of the distribution network and electric vehicles at different times.

[0029] The S2 includes:

[0030] S2.1. Set simulation parameters, including the number of simulations K, random variables, and their ranges. Use the Monte Carlo algorithm to simulate the number of electric vehicles, their travel behavior, and their charging and discharging behavior.

[0031] S2.2. Substitute the simulation parameters into a random number generator to generate a large number of random samples that conform to the operating characteristics of the distribution network and electric vehicles, each of which corresponds to a possible distribution network operating state and electric vehicle charging and discharging condition.

[0032] The S3 includes:

[0033] S3.1. Calculate an electric vehicle behavior model. The electric vehicle behavior model is a mathematical relationship model between the electric vehicle's charge and discharge power, battery SOC, charging time, and other factors, based on the electric vehicle's battery characteristics, charging facility type, and grid-side power supply capacity. The electric vehicle behavior model includes:

[0034] ;

[0035] ;

[0036] In the above formula, is the charging power of the mth electric vehicle at time t, The unit is MW; is the discharge power of the mth electric vehicle at time t, The unit is MW; x(t) is the mark value of whether the electric vehicle is in the charging state. If x(t) is 1, it means the electric vehicle is in the charging state, otherwise it is in the non-charging state or the traveling state. y(t) is the mark value of whether the electric vehicle is in the discharging state. If y(t) is 1, it means the electric vehicle is in the discharging state, otherwise it is in the non-discharging state or the traveling state. The expressions of x(t) and y(t) are:

[0037] ;

[0038] ;

[0039] S3.2. Construct a total electric vehicle power model. The total electric vehicle power model is used to combine with the electric vehicle behavior model to obtain the total power demand or supply of all electric vehicles in the region to the distribution network at any time t. The total electric vehicle power model includes:

[0040] ;

[0041] In the above formula, is the total power of electric vehicles in the regional distribution network at time t, in MW; M is the total number of electric vehicles;

[0042] S3.3. Construct a node voltage model, which is used to perform iterative calculation of distribution network power flow for different electric vehicle timing simulation states to obtain the voltage amplitude of each node ;

[0043] S3.4、Analyze the source and load status of regional distribution network. When the system can meet the load demand, the regional distribution network source-load state flag I takes 0; when When the system cannot meet the load demand, the regional distribution network source-load state flag I takes 1; For all nodes in the regional distribution network arrive Time load.

[0044] The S4 includes:

[0045] The reliability evaluation indicators include system load loss probability, active power loss rate and voltage deviation rate indicators;

[0046] The system load loss probability includes:

[0047] ;

[0048] In the above formula, K is the number of Monte Carlo simulations; I means that when the system cannot meet the load demand, I=1, otherwise I=0;

[0049] The active energy loss rate includes:

[0050] ;

[0051] The active energy loss in the Xth simulation includes:

[0052] ;

[0053] In the above formula, is the system active power loss rate; T is the simulation time period;

[0054] The voltage deviation rate indicators include:

[0055] ;

[0056] ;

[0057] ;

[0058] In the above formula, is the voltage deviation of distribution network node i at time t, is the simulated voltage of distribution network node i at time t, is the rated voltage of each node in the distribution network. The rated voltage of distribution networks with the same voltage level is the same; is the average voltage deviation rate, N is the number of distribution network nodes, is the maximum voltage deviation required by the distribution network, is the voltage deviation rate assessment score;

[0059] In S5, the comprehensive evaluation model includes:

[0060] ;

[0061] In the above formula, is the comprehensive assessment score; is the indicator weight, and .

[0062] In a second aspect, the present invention proposes a distribution network reliability assessment system that takes vehicle-grid interaction into consideration, the assessment system comprising:

[0063] The system includes a data acquisition and processing module, a simulation behavior data module, a dynamic vehicle-grid interaction model construction module, a distribution network reliability evaluation index calculation module and a distribution network reliability comprehensive evaluation module;

[0064] The data acquisition and processing module is used to obtain data related to the distribution network reliability assessment and construct a time series data set for dynamic evaluation of regional distribution network reliability considering vehicle-grid interaction;

[0065] The simulation behavior data module is used to generate random samples that meet the operating characteristics of the distribution network and electric vehicles through the Monte Carlo algorithm;

[0066] The dynamic vehicle-grid interaction model construction module is used to construct a dynamic vehicle-grid interaction model, which includes an electric vehicle behavior model and an electric vehicle total power model. A time series dataset for dynamic evaluation of regional distribution network reliability considering vehicle-grid interaction and random samples that meet the operating characteristics of the distribution network and electric vehicles are substituted into the dynamic vehicle-grid interaction model to calculate the source-load state of the regional distribution network and the voltage amplitude of each node.

[0067] The distribution network reliability assessment index calculation module is used to construct a reliability assessment index system including system load loss probability, active power loss, and voltage deviation rate indicators based on the impact of large-scale access of electric vehicles on the reliability of the distribution network, and substitute random samples that meet the operating characteristics of the distribution network and electric vehicles into the reliability assessment index system to calculate the reliability assessment index;

[0068] The distribution network reliability comprehensive evaluation module is used to construct a distribution network comprehensive evaluation model with the comprehensive evaluation score of the distribution network reliability as the objective function, and solve the distribution network comprehensive evaluation model to obtain the comprehensive evaluation score of the distribution network reliability.

[0069] The specific operations of the data acquisition and processing module include:

[0070] Acquire data related to distribution network reliability assessment, including regional distribution network node load data, regional distribution network line topology data, node topology data, time series data of distribution network node loads, maximum charging power of each distribution network node, and maximum discharge power of each distribution network node. The regional distribution network node load data is acquired through the SCADA system, and the regional distribution network line topology data and node topology data are acquired through the PMS system.

[0071] The load of the distribution network node is arrive Time series data between moments include:

[0072] ;

[0073] ;

[0074] In the above formula, is the node i in the regional distribution network arrive The load at a given time, in MW; For regional distribution networks arrive The load at a certain time, N is the number of distribution network nodes;

[0075] The electric vehicle characteristics include the mth electric vehicle capacity , charging efficiency and discharge efficiency ;

[0076] The maximum charging power of the mth electric vehicle in the distribution network include:

[0077] ;

[0078] In the above formula, is the charging efficiency of the mth electric vehicle;

[0079] The maximum discharge power of the electric vehicle at the mth node of the distribution network include:

[0080] ;

[0081] In the above formula, is the discharge efficiency of the mth electric vehicle;

[0082] Data preprocessing: cleaning, denoising and other preprocessing operations are performed on the collected load data to ensure the accuracy and availability of the data;

[0083] Construct a time series dataset, consider different operating conditions such as normal and faulty distribution network, integrate the preprocessed data related to distribution network reliability assessment, and construct a time series dataset for dynamic assessment of regional distribution network reliability considering vehicle-grid interaction. The time series dataset should have time series characteristics and be able to reflect the operating status of the distribution network and electric vehicles at different times.

[0084] The specific operations of the simulation behavior data module include:

[0085] Setting simulation parameters, including the number of simulations K, random variables, and the range of values ​​of the random variables, and simulating the number of electric vehicles, travel behavior, and charging and discharging behavior of electric vehicles using a Monte Carlo algorithm;

[0086] The simulation parameters are substituted into a random number generator to generate a large number of random samples that conform to the operating characteristics of the distribution network and electric vehicles. Each of the random samples corresponds to a possible distribution network operating state and electric vehicle charging and discharging condition.

[0087] The specific operations of the dynamic vehicle-grid interaction model construction module include:

[0088] Calculate the electric vehicle behavior model, which is a mathematical relationship model between the charging and discharging power of the electric vehicle and factors such as battery SOC and charging time based on the battery characteristics of the electric vehicle, the type of charging facilities, and the power supply capacity of the power grid. The electric vehicle behavior model includes:

[0089] ;

[0090] ;

[0091] In the above formula, is the charging power of the mth electric vehicle at time t, The unit is MW; is the discharge power of the mth electric vehicle at time t, The unit is MW; x(t) is the mark value of whether the electric vehicle is in the charging state. If x(t) is 1, it means the electric vehicle is in the charging state, otherwise it is in the non-charging state or the traveling state. y(t) is the mark value of whether the electric vehicle is in the discharging state. If y(t) is 1, it means the electric vehicle is in the discharging state, otherwise it is in the non-discharging state or the traveling state. The expressions of x(t) and y(t) are:

[0092] ;

[0093] ;

[0094] Construct an electric vehicle total power model, which is used to combine with the electric vehicle behavior model to obtain the total power demand or supply of all electric vehicles in the area to the distribution network at any time t. The electric vehicle total power model includes:

[0095] ;

[0096] In the above formula, is the total power of electric vehicles in the regional distribution network at time t, in MW; M is the total number of electric vehicles;

[0097] Construct a node voltage model, which is used to perform iterative calculation of distribution network power flow for different electric vehicle timing simulation states to obtain the voltage amplitude of each node ;

[0098] Analyze the source and load status of the regional distribution network. When the system can meet the load demand, the regional distribution network source-load state flag I takes 0; when When the system cannot meet the load demand, the regional distribution network source-load state flag I takes 1; For all nodes in the regional distribution network arrive Time load.

[0099] In the distribution network reliability evaluation index calculation module:

[0100] The reliability evaluation indicators include system load loss probability, active power loss rate and voltage deviation rate indicators;

[0101] The system load loss probability includes:

[0102] ;

[0103] In the above formula, K is the number of Monte Carlo simulations; I means that when the system cannot meet the load demand, I=1, otherwise I=0;

[0104] The active energy loss rate includes:

[0105] ;

[0106] The active energy loss in the Xth simulation includes:

[0107] ;

[0108] In the above formula, is the system active power loss rate; T is the simulation time period;

[0109] The voltage deviation rate indicators include:

[0110] ;

[0111] ;

[0112] ;

[0113] In the above formula, is the voltage deviation of distribution network node i at time t, is the simulated voltage of distribution network node i at time t, is the rated voltage of each node in the distribution network. The rated voltage of distribution networks with the same voltage level is the same; is the average voltage deviation rate, N is the number of distribution network nodes, is the maximum voltage deviation required by the distribution network, is the voltage deviation rate assessment score;

[0114] In the distribution network reliability comprehensive evaluation module, the comprehensive evaluation model includes:

[0115] ;

[0116] In the above formula, is the comprehensive assessment score; is the indicator weight, and .

[0117] Compared with the prior art, the present invention has the following beneficial effects:

[0118] 1. This invention proposes a method for assessing the reliability of a regional distribution network that considers vehicle-grid interaction. This method constructs a dynamic vehicle-grid interaction model that comprehensively considers the impact of electric vehicles' travel behavior and charging and discharging characteristics on the distribution network. Furthermore, through probability density functions and behavioral simulation, it dynamically describes the dual role of electric vehicles as both loads and energy storage resources, making the assessment more accurate and realistic. Therefore, this design can dynamically describe the dual role of electric vehicles as both loads and energy storage resources through the dynamic vehicle-grid interaction model, effectively improving the accuracy of regional distribution network reliability assessments.

[0119] 2. In this method for assessing the reliability of a regional distribution network that considers vehicle-grid interaction, a Monte Carlo simulation is used to analyze a large number of random scenarios, generating random samples that conform to the operating characteristics of the distribution network and electric vehicles. This method comprehensively considers the randomness of electric vehicle travel, charging, and discharging behaviors. This method not only quantifies the uncertainty of electric vehicle behavior but also simulates various possible extreme load scenarios, thereby identifying potential reliability risks in the system. Therefore, this design can generate random samples that conform to the operating characteristics of the distribution network and electric vehicles through Monte Carlo simulation, effectively improving the accuracy and robustness of the assessment.

[0120] 3. In the regional distribution network reliability assessment method of the present invention that considers vehicle-grid interaction, the distribution network reliability assessment index includes load loss probability. Load loss probability is a probabilistic assessment index that can comprehensively quantify the risk of system failure. Therefore, this design can comprehensively quantify the risk of system failure through probabilistic assessment indicators, effectively expanding the comprehensiveness of reliability assessment.

[0121] 4. This invention incorporates probabilistic assessment and risk analysis into a regional distribution network reliability assessment method that considers vehicle-grid interaction. This method innovatively uses probabilistic assessment metrics (such as load loss probability (LOLP) and other reliability indicators) to comprehensively quantify system failure risk. This approach can help power companies and system planners better understand the level of risk facing the system and formulate appropriate investment and management strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0122] Figure 1 It is a flow chart of the regional distribution network reliability assessment method considering vehicle-grid interaction in the present invention.

[0123] Figure 2 It is a principle block diagram of the reliability evaluation index system in the present invention.

[0124] Figure 3 It is a structural schematic diagram of the regional distribution network grid structure schematic diagram in the present invention.

[0125] Figure 4 It is a structural diagram of the regional distribution network reliability assessment system considering vehicle-grid interaction in the present invention.

[0126] Figure 5 This is a schematic diagram of the equipment structure in Example 4. DETAILED DESCRIPTION

[0127] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Example

[0128] See also Figure 1 and Figure 2 A distribution network reliability assessment method considering vehicle-grid interaction is provided, the assessment method comprising:

[0129] S1. Data collection and processing: obtaining data related to distribution network reliability assessment and constructing a time series dataset for dynamic evaluation of regional distribution network reliability considering vehicle-grid interaction;

[0130] S2, simulate behavioral data and generate random samples that meet the operating characteristics of the distribution network and electric vehicles through the Monte Carlo algorithm;

[0131] S3. Construct a dynamic vehicle-grid interaction model, which includes an electric vehicle behavior model and an electric vehicle total power model. Substitute a time series dataset for dynamic evaluation of regional distribution network reliability considering vehicle-grid interaction and a random sample that conforms to the operating characteristics of the distribution network and electric vehicles into the dynamic vehicle-grid interaction model to calculate the source-load state of the regional distribution network and the voltage amplitude of each node.

[0132] S4. Calculation of distribution network reliability assessment indicators. Based on the impact of large-scale access of electric vehicles on the reliability of the distribution network, a reliability assessment indicator system is constructed that includes system load loss probability, active power loss, and voltage deviation rate indicators. Random samples that meet the operating characteristics of the distribution network and electric vehicles are substituted into the reliability assessment indicator system to calculate the reliability assessment indicators.

[0133] S5. Comprehensive evaluation of distribution network reliability: A distribution network comprehensive evaluation model is constructed with the comprehensive evaluation score of distribution network reliability as the objective function, and the distribution network comprehensive evaluation model is solved to obtain the comprehensive evaluation score of distribution network reliability.

[0134] S1 mainly involves the collection of electric vehicle behavior data and distribution network load data, carrying out distribution network reliability assessment considering vehicle-grid interaction, and determining the assessment time. and period T, and process the data to construct an evaluation time series dataset. S1 includes:

[0135] S1.1. Acquire data related to distribution network reliability assessment, including regional distribution network node load data, regional distribution network line topology data, node topology data, time series data of distribution network node loads, maximum charging power of each distribution network node, and maximum discharge power of each distribution network node. The regional distribution network node load data is acquired through the SCADA system, and the regional distribution network line topology data and node topology data are acquired through the PMS system.

[0136] The load of the distribution network node is arrive Time series data between moments include:

[0137] ;

[0138] ;

[0139] In the above formula, is the node i in the regional distribution network arrive The load at a given time, in MW; For regional distribution networks arrive The load at a certain time, N is the number of distribution network nodes;

[0140] The electric vehicle characteristics include the mth electric vehicle capacity , charging efficiency and discharge efficiency ;

[0141] The maximum charging power of the mth electric vehicle in the distribution network include:

[0142] ;

[0143] In the above formula, is the charging efficiency of the mth electric vehicle;

[0144] The maximum discharge power of the electric vehicle at the mth node of the distribution network include:

[0145] ;

[0146] In the above formula, is the discharge efficiency of the mth electric vehicle;

[0147] S1.2. Data preprocessing: cleaning, denoising and other preprocessing operations are performed on the collected load data to ensure the accuracy and availability of the data;

[0148] S1.3. Construct a time series dataset. Considering different operating conditions of the distribution network, such as normal and fault conditions, the pre-processed data related to the distribution network reliability assessment are integrated to construct a time series dataset for dynamic evaluation of regional distribution network reliability considering vehicle-grid interaction. The time series dataset should have time series characteristics and be able to reflect the operating status of the distribution network and electric vehicles at different times.

[0149] The S2 uses the Monte Carlo algorithm to set simulation parameters (simulation times K, random variables and their value ranges), simulates the number of electric vehicles, electric vehicle travel behavior, charging and discharging behavior, and substitutes the results of all simulation times calculated in step S3 to calculate the electric vehicle power, node voltage and other indicators of the regional distribution network, and substitutes the results of all simulation times into step S5 to calculate the comprehensive weight. The S2 includes:

[0150] S2.1. Set simulation parameters, including the number of simulations K, random variables, and their ranges. Use the Monte Carlo algorithm to simulate the number of electric vehicles, their travel behavior, and their charging and discharging behavior.

[0151] S2.2. Substitute the simulation parameters into a random number generator to generate a large number of random samples that conform to the operating characteristics of the distribution network and electric vehicles, each of which corresponds to a possible distribution network operating state and electric vehicle charging and discharging condition.

[0152] S3 mainly establishes an electric vehicle behavior model, an electric vehicle total power model, a node voltage, and a regional distribution network source-load state model, and is used for numerical calculation of the distribution network reliability assessment process for vehicle-grid interaction. S3 includes:

[0153] S3.1. Calculate an electric vehicle behavior model. The electric vehicle behavior model is a mathematical relationship model between the electric vehicle's charge and discharge power, battery SOC, charging time, and other factors, based on the electric vehicle's battery characteristics, charging facility type, and grid-side power supply capacity. The electric vehicle behavior model includes:

[0154] ;

[0155] ;

[0156] In the above formula, is the charging power of the mth electric vehicle at time t, The unit is MW; is the discharge power of the mth electric vehicle at time t, The unit is MW; x(t) is the mark value of whether the electric vehicle is in the charging state. If x(t) is 1, it means the electric vehicle is in the charging state, otherwise it is in the non-charging state or the traveling state. y(t) is the mark value of whether the electric vehicle is in the discharging state. If y(t) is 1, it means the electric vehicle is in the discharging state, otherwise it is in the non-discharging state or the traveling state. The expressions of x(t) and y(t) are:

[0157] ;

[0158] ;

[0159] S3.2. Construct a total electric vehicle power model. The total electric vehicle power model is used to combine with the electric vehicle behavior model to obtain the total power demand or supply of all electric vehicles in the region to the distribution network at any time t. The total electric vehicle power model includes:

[0160] ;

[0161] In the above formula, is the total power of electric vehicles in the regional distribution network at time t, in MW; M is the total number of electric vehicles;

[0162] S3.3. Construct a node voltage model, which is used to perform iterative calculation of distribution network power flow for different electric vehicle timing simulation states to obtain the voltage amplitude of each node ;

[0163] S3.4、Analyze the source and load status of regional distribution network. When the system can meet the load demand, the regional distribution network source-load state flag I takes 0; when When the system cannot meet the load demand, the regional distribution network source-load state flag I takes 1; For all nodes in the regional distribution network arrive Time load.

[0164] The S4 includes:

[0165] The reliability evaluation indicators include system load loss probability, active power loss rate and voltage deviation rate indicators;

[0166] The system load loss probability is calculated by analyzing the probability of the distribution network being unable to meet load demand due to faults or load fluctuations under different operating conditions, using methods such as Monte Carlo simulation to obtain a system load loss probability index value, including:

[0167] ;

[0168] In the above formula, K is the number of Monte Carlo simulations; I means that when the system cannot meet the load demand, I=1, otherwise I=0;

[0169] The active energy loss rate is a statistical measure of the total amount of active energy that cannot be normally supplied to the load due to factors such as distribution network failures and improper charging and discharging of electric vehicles within a certain period of time. It is calculated by integrating the energy data for each period, including:

[0170] ;

[0171] The active energy loss in the Xth simulation includes:

[0172] ;

[0173] In the above formula, is the system active power loss rate; T is the simulation time period;

[0174] The voltage deviation rate index is obtained by using distribution network simulation software to calculate the distribution network flow and obtain the deviation between the voltage of each node in the distribution network and the rated voltage. The voltage deviation rate index is calculated and the average deviation measures the degree of voltage deviation of the regional distribution network, including:

[0175] ;

[0176] ;

[0177] ;

[0178] In the above formula, is the voltage deviation of distribution network node i at time t, is the simulated voltage of distribution network node i at time t, is the rated voltage of each node in the distribution network. The rated voltage of distribution networks with the same voltage level is the same; is the average voltage deviation rate, N is the number of distribution network nodes, is the maximum voltage deviation required by the distribution network, is the voltage deviation rate assessment score;

[0179] Construct a reliability assessment index system by integrating the aforementioned system load loss probability, active power loss, and voltage deviation rate indicators to form a distribution network reliability assessment index system. This system should be able to comprehensively and objectively assess the reliability of the distribution network under vehicle-grid interaction.

[0180] In S5, the comprehensive evaluation model is combined with the distribution network reliability evaluation index system and adopts multi-index comprehensive evaluation methods such as the hierarchical analysis method and the fuzzy comprehensive evaluation method to construct a comprehensive distribution network evaluation model. The model should be able to organically integrate reliability indicators of different dimensions to comprehensively evaluate the overall reliability of the distribution network, including:

[0181] ;

[0182] In the above formula, is the comprehensive assessment score; is the indicator weight, and ;

[0183] Set corresponding weights for different indicators. The weights can be determined based on expert experience, hierarchical analysis method and other methods to ensure The system load loss probability index, which has a greater impact on the power supply stability of the distribution network, can be given a relatively high weight; and the voltage deviation rate index can be given an appropriate weight based on the degree of its impact on the user's power experience;

[0184] The comprehensive evaluation calculation is to substitute the various indicator values ​​obtained through Monte Carlo simulation into the distribution network comprehensive evaluation model, and perform weighted calculation according to the set indicator weights to obtain the comprehensive reliability evaluation result of the distribution network under the condition of vehicle-grid interaction. Example

[0185] See also Figure 4 , a distribution network reliability assessment system considering vehicle-grid interaction, the system comprising a data acquisition and processing module, a simulation behavior data module, a dynamic vehicle-grid interaction model construction module, a distribution network reliability assessment index calculation module and a distribution network reliability comprehensive assessment module;

[0186] The data acquisition and processing module is used to obtain data related to the distribution network reliability assessment and construct a time series data set for dynamic evaluation of regional distribution network reliability considering vehicle-grid interaction;

[0187] The simulation behavior data module is used to generate random samples that meet the operating characteristics of the distribution network and electric vehicles through the Monte Carlo algorithm;

[0188] The dynamic vehicle-grid interaction model construction module is used to construct a dynamic vehicle-grid interaction model, which includes an electric vehicle behavior model and an electric vehicle total power model. A time series dataset for dynamic evaluation of regional distribution network reliability considering vehicle-grid interaction and random samples that meet the operating characteristics of the distribution network and electric vehicles are substituted into the dynamic vehicle-grid interaction model to calculate the source-load state of the regional distribution network and the voltage amplitude of each node.

[0189] The distribution network reliability assessment index calculation module is used to construct a reliability assessment index system including system load loss probability, active power loss, and voltage deviation rate indicators based on the impact of large-scale access of electric vehicles on the reliability of the distribution network, and substitute random samples that meet the operating characteristics of the distribution network and electric vehicles into the reliability assessment index system to calculate the reliability assessment index;

[0190] The distribution network reliability comprehensive evaluation module is used to construct a distribution network comprehensive evaluation model with the comprehensive evaluation score of the distribution network reliability as the objective function, and solve the distribution network comprehensive evaluation model to obtain the comprehensive evaluation score of the distribution network reliability.

[0191] The specific operations of the data acquisition and processing module include:

[0192] Acquire data related to distribution network reliability assessment, including regional distribution network node load data, regional distribution network line topology data, node topology data, time series data of distribution network node loads, maximum charging power of each distribution network node, and maximum discharge power of each distribution network node. The regional distribution network node load data is acquired through the SCADA system, and the regional distribution network line topology data and node topology data are acquired through the PMS system.

[0193] The load of the distribution network node is arrive Time series data between moments include:

[0194] ;

[0195] ;

[0196] In the above formula, is the node i in the regional distribution network arrive The load at a given time, in MW; For regional distribution networks arrive The load at a certain time, N is the number of distribution network nodes;

[0197] The electric vehicle characteristics include the mth electric vehicle capacity , charging efficiency and discharge efficiency ;

[0198] The maximum charging power of the mth electric vehicle in the distribution network include:

[0199] ;

[0200] In the above formula, is the charging efficiency of the mth electric vehicle;

[0201] The maximum discharge power of the electric vehicle at the mth node of the distribution network include:

[0202] ;

[0203] In the above formula, is the discharge efficiency of the mth electric vehicle;

[0204] Data preprocessing: cleaning, denoising and other preprocessing operations are performed on the collected load data to ensure the accuracy and availability of the data;

[0205] Construct a time series dataset, consider different operating conditions such as normal and faulty distribution network, integrate the preprocessed data related to distribution network reliability assessment, and construct a time series dataset for dynamic assessment of regional distribution network reliability considering vehicle-grid interaction. The time series dataset should have time series characteristics and be able to reflect the operating status of the distribution network and electric vehicles at different times.

[0206] The specific operations of the simulation behavior data module include:

[0207] Setting simulation parameters, including the number of simulations K, random variables, and the range of values ​​of the random variables, and simulating the number of electric vehicles, travel behavior, and charging and discharging behavior of electric vehicles using a Monte Carlo algorithm;

[0208] The simulation parameters are substituted into a random number generator to generate a large number of random samples that conform to the operating characteristics of the distribution network and electric vehicles. Each of the random samples corresponds to a possible distribution network operating state and electric vehicle charging and discharging condition.

[0209] The specific operations of the dynamic vehicle-grid interaction model construction module include:

[0210] Calculate the electric vehicle behavior model, which is a mathematical relationship model between the charging and discharging power of the electric vehicle and factors such as battery SOC and charging time based on the battery characteristics of the electric vehicle, the type of charging facilities, and the power supply capacity of the power grid. The electric vehicle behavior model includes:

[0211] ;

[0212] ;

[0213] In the above formula, is the charging power of the mth electric vehicle at time t, The unit is MW; is the discharge power of the mth electric vehicle at time t, The unit is MW; x(t) is the mark value of whether the electric vehicle is in the charging state. If x(t) is 1, it means the electric vehicle is in the charging state, otherwise it is in the non-charging state or the traveling state. y(t) is the mark value of whether the electric vehicle is in the discharging state. If y(t) is 1, it means the electric vehicle is in the discharging state, otherwise it is in the non-discharging state or the traveling state. The expressions of x(t) and y(t) are:

[0214] ;

[0215] ;

[0216] Construct an electric vehicle total power model, which is used to combine with the electric vehicle behavior model to obtain the total power demand or supply of all electric vehicles in the area to the distribution network at any time t. The electric vehicle total power model includes:

[0217] ;

[0218] In the above formula, is the total power of electric vehicles in the regional distribution network at time t, in MW; M is the total number of electric vehicles;

[0219] Construct a node voltage model, which is used to perform iterative calculation of distribution network power flow for different electric vehicle timing simulation states to obtain the voltage amplitude of each node ;

[0220] Analyze the source and load status of the regional distribution network. When the system can meet the load demand, the regional distribution network source-load state flag I takes 0; when When the system cannot meet the load demand, the regional distribution network source-load state flag I takes 1; For all nodes in the regional distribution network arrive Time load.

[0221] In the distribution network reliability evaluation index calculation module:

[0222] The reliability evaluation indicators include system load loss probability, active power loss rate and voltage deviation rate indicators;

[0223] The system load loss probability includes:

[0224] ;

[0225] In the above formula, K is the number of Monte Carlo simulations; I means that when the system cannot meet the load demand, I=1, otherwise I=0;

[0226] The active energy loss rate includes:

[0227] ;

[0228] The active energy loss in the Xth simulation includes:

[0229] ;

[0230] In the above formula, is the system active power loss rate; T is the simulation time period;

[0231] The voltage deviation rate indicators include:

[0232] ;

[0233] ;

[0234] ;

[0235] In the above formula, is the voltage deviation of distribution network node i at time t, is the simulated voltage of distribution network node i at time t, is the rated voltage of each node in the distribution network. The rated voltage of distribution networks with the same voltage level is the same; is the average voltage deviation rate, N is the number of distribution network nodes, is the maximum voltage deviation required by the distribution network, is the voltage deviation rate assessment score;

[0236] In the distribution network reliability comprehensive evaluation module, the comprehensive evaluation model includes:

[0237] ;

[0238] In the above formula, is the comprehensive assessment score; is the indicator weight, and . Example

[0239] According to step S1, the source and data status of the distribution network data are determined to form a reliability assessment time series data set;

[0240] Select Figure 3 The regional distribution network shown in the figure has 5 nodes and 30 electric vehicles. The battery capacity and charging and discharging efficiency of the electric vehicles are known. The typical daily load peak period from 8:00 to 14:00 is selected to carry out the distribution network reliability assessment. The load of each node in the distribution network, the behavior of electric vehicles, the voltage level and other data are known. A time series dataset for distribution network reliability assessment considering vehicle-grid interaction is constructed, as shown in Table 1:

[0241] ;

[0242] According to S2, the Monte Carlo algorithm parameters are set, the number of simulations is 1000, and the behavior and charging and discharging status of 30 electric vehicles in the distribution network are simulated. The electric vehicle parameters are shown in Table 2 and substituted into S3 to calculate the electric vehicle load power and node voltage;

[0243] ;

[0244] According to S3, combined with electric vehicle parameters, the electric vehicle behavior data simulated by the Monte Carlo algorithm, the distribution network structure is shown in Figure 3 , substitute the electric vehicle load power, node voltage model, etc. in step S3, obtain the distribution network electric vehicle load power, node voltage, and the number of times the regional load demand is not met under 1000 simulation conditions, and substitute them into S4.

[0245] According to S4, substitute the calculation results of S3 1000 times into S4 In the calculation model, we get The values ​​are 35 , 15 , 50 , and substitute it into S5.

[0246] According to S5, the Considering that reliability assessment focuses more on the load supply capacity of electric vehicles for regional distribution networks, the value is set , substituted into the comprehensive evaluation model, and the comprehensive evaluation is obtained =0.3×(1-0.35)+0.4×(1-0.15)+0.3×(1-0.50)=0.685. It can be seen that the ability of this area to support the reliability of the distribution network through vehicle-grid interaction is relatively low. Example

[0247] See also Figure 5 , a regional distribution network reliability assessment device considering vehicle-grid interaction, the device comprising a processor and a memory;

[0248] The memory is used to store computer program code and transmit the computer program code to the processor;

[0249] The processor is configured to execute the regional distribution network reliability assessment method considering vehicle-grid interaction described in Example 1 according to the instructions in the computer program code.

[0250] A computer medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method for evaluating the reliability of a regional distribution network considering vehicle-grid interaction described in Example 1 is implemented.

[0251] The above description is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiment. Any equivalent modifications or changes made by ordinary technicians in this field based on the contents disclosed in the present invention should be included in the protection scope recorded in the claims.

Claims

1. A distribution network reliability assessment method considering vehicle-grid interaction, characterized by: The evaluation method includes: S1. Data collection and processing: obtaining data related to distribution network reliability assessment and constructing a time series dataset for dynamic evaluation of regional distribution network reliability considering vehicle-grid interaction; S2, simulate behavioral data and generate random samples that meet the operating characteristics of the distribution network and electric vehicles through the Monte Carlo algorithm; S3. Construct a dynamic vehicle-grid interaction model, which includes an electric vehicle behavior model and an electric vehicle total power model. Substitute a time series dataset for dynamic evaluation of regional distribution network reliability considering vehicle-grid interaction and a random sample that conforms to the operating characteristics of the distribution network and electric vehicles into the dynamic vehicle-grid interaction model to calculate the source-load state of the regional distribution network and the voltage amplitude of each node. S4. Calculation of distribution network reliability assessment indicators. Based on the impact of large-scale access of electric vehicles on the reliability of the distribution network, a reliability assessment indicator system is constructed that includes system load loss probability, active power loss, and voltage deviation rate indicators. Random samples that meet the operating characteristics of the distribution network and electric vehicles are substituted into the reliability assessment indicator system to calculate the reliability assessment indicators. S5. Comprehensive evaluation of distribution network reliability: construct a distribution network comprehensive evaluation model with the comprehensive evaluation score of distribution network reliability as the objective function, and solve the distribution network comprehensive evaluation model to obtain the comprehensive evaluation score of distribution network reliability; The S4 includes: The reliability evaluation indicators include system load loss probability, active power loss rate and voltage deviation rate indicators; The system load loss probability includes: ； In the above formula, K is the number of Monte Carlo simulations; I means that when the system cannot meet the load demand, I=1, otherwise I=0; The active energy loss rate includes: ； The active energy loss in the Xth simulation includes: ； In the above formula, is the system active power loss rate; T is the simulation time period; The voltage deviation rate indicators include: ； ； ； In the above formula, is the voltage deviation of distribution network node i at time t, is the simulated voltage of distribution network node i at time t, is the rated voltage of each node in the distribution network. The rated voltage of distribution networks with the same voltage level is the same; is the average voltage deviation rate, N is the number of distribution network nodes, is the maximum voltage deviation required by the distribution network, is the voltage deviation rate assessment score; In S5, the comprehensive evaluation model includes: ； In the above formula, is the comprehensive assessment score; is the indicator weight, and .

2. A distribution network reliability assessment method considering vehicle-grid interaction according to claim 1, characterized in that: Said S1 comprises: S1.

1. Acquire data related to distribution network reliability assessment, including regional distribution network node load data, regional distribution network line topology data, node topology data, time series data of distribution network node loads, maximum charging power of each distribution network node, and maximum discharge power of each distribution network node. The regional distribution network node load data is acquired through the SCADA system, and the regional distribution network line topology data and node topology data are acquired through the PMS system. The load of the distribution network node is arrive Time series data between moments include: ； ； In the above formula, is the node i in the regional distribution network arrive The load at a given time, in MW; For regional distribution networks arrive The load at a certain time, N is the number of distribution network nodes; The electric vehicle characteristics include the mth electric vehicle capacity , charging efficiency and discharge efficiency ; The maximum charging power of the mth electric vehicle in the distribution network include: ； In the above formula, is the charging efficiency of the mth electric vehicle; The maximum discharge power of the electric vehicle at the mth node of the distribution network include: ； In the above formula, is the discharge efficiency of the mth electric vehicle; S1.

2. Data preprocessing: cleaning, denoising and other preprocessing operations are performed on the collected load data to ensure the accuracy and availability of the data; S1.

3. Construct a time series dataset. Considering different operating conditions of the distribution network, such as normal and fault conditions, the pre-processed data related to the distribution network reliability assessment are integrated to construct a time series dataset for dynamic evaluation of regional distribution network reliability considering vehicle-grid interaction. The time series dataset should have time series characteristics and be able to reflect the operating status of the distribution network and electric vehicles at different times.

3. The method for evaluating distribution network reliability considering vehicle-grid interaction according to claim 2, characterized in that: The S2 includes: S2.

1. Setting simulation parameters, including the number of simulations K, random variables, and the range of random variables, to simulate the number of electric vehicles, electric vehicle travel behavior, and charging and discharging behavior using a Monte Carlo algorithm; S2.

2. Substitute the simulation parameters into a random number generator to generate a large number of random samples that conform to the operating characteristics of the distribution network and electric vehicles, each of which corresponds to a possible distribution network operating state and electric vehicle charging and discharging condition.

4. The method for evaluating distribution network reliability considering vehicle-grid interaction according to claim 3, characterized in that: The S3 includes: S3.

1. Calculate an electric vehicle behavior model. The electric vehicle behavior model is a mathematical relationship model between the electric vehicle's charge and discharge power, battery SOC, charging time, and other factors, based on the electric vehicle's battery characteristics, charging facility type, and grid-side power supply capacity. The electric vehicle behavior model includes: ； ； In the above formula, is the charging power of the mth electric vehicle at time t, The unit is MW; is the discharge power of the mth electric vehicle at time t, The unit is MW; x(t) is the mark value of whether the electric vehicle is in the charging state. If x(t) is 1, it means the electric vehicle is in the charging state, otherwise it is in the non-charging state or the traveling state. y(t) is the mark value of whether the electric vehicle is in the discharging state. If y(t) is 1, it means the electric vehicle is in the discharging state, otherwise it is in the non-discharging state or the traveling state. The expressions of x(t) and y(t) are: ； ； S3.

2. Construct a total electric vehicle power model. The total electric vehicle power model is used to combine with the electric vehicle behavior model to obtain the total power demand or supply of all electric vehicles in the region to the distribution network at any time t. The total electric vehicle power model includes: ； In the above formula, is the total power of electric vehicles in the regional distribution network at time t, in MW; M is the total number of electric vehicles; S3.

3. Construct a node voltage model, which is used to perform iterative calculation of distribution network power flow for different electric vehicle timing simulation states to obtain the voltage amplitude of each node ; S3.4、Analyze the source and load status of regional distribution network. When the system can meet the load demand, the regional distribution network source-load state flag I takes 0; when When the system cannot meet the load demand, For all nodes in the regional distribution network arrive Time load.

5. A distribution network reliability assessment system considering vehicle-grid interaction, characterized by: The system includes a data acquisition and processing module, a simulation behavior data module, a dynamic vehicle-grid interaction model construction module, a distribution network reliability evaluation index calculation module and a distribution network reliability comprehensive evaluation module; The data acquisition and processing module is used to obtain data related to the distribution network reliability assessment and construct a time series data set for dynamic evaluation of regional distribution network reliability considering vehicle-grid interaction; The simulation behavior data module is used to generate random samples that meet the operating characteristics of the distribution network and electric vehicles through the Monte Carlo algorithm; The dynamic vehicle-grid interaction model construction module is used to construct a dynamic vehicle-grid interaction model, which includes an electric vehicle behavior model and an electric vehicle total power model. A time series dataset for dynamic evaluation of regional distribution network reliability considering vehicle-grid interaction and random samples that meet the operating characteristics of the distribution network and electric vehicles are substituted into the dynamic vehicle-grid interaction model to calculate the source-load state of the regional distribution network and the voltage amplitude of each node. The distribution network reliability assessment index calculation module is used to construct a reliability assessment index system including system load loss probability, active power loss, and voltage deviation rate indicators based on the impact of large-scale access of electric vehicles on the reliability of the distribution network, and substitute random samples that meet the operating characteristics of the distribution network and electric vehicles into the reliability assessment index system to calculate the reliability assessment index; The distribution network reliability comprehensive evaluation module is used to construct a distribution network comprehensive evaluation model with the comprehensive evaluation score of the distribution network reliability as the objective function, and solve the distribution network comprehensive evaluation model to obtain the comprehensive evaluation score of the distribution network reliability; In the distribution network reliability evaluation index calculation module: The reliability evaluation indicators include system load loss probability, active power loss rate and voltage deviation rate indicators; The system load loss probability includes: ； In the above formula, K is the number of Monte Carlo simulations; I means that when the system cannot meet the load demand, I=1, otherwise I=0; The active energy loss rate includes: ； The active energy loss in the Xth simulation includes: ； In the above formula, is the system active power loss rate; T is the simulation time period; The voltage deviation rate indicators include: ； ； ； In the above formula, is the voltage deviation of distribution network node i at time t, is the simulated voltage of distribution network node i at time t, is the rated voltage of each node in the distribution network. The rated voltage of distribution networks with the same voltage level is the same; is the average voltage deviation rate, N is the number of distribution network nodes, is the maximum voltage deviation required by the distribution network, is the voltage deviation rate assessment score; In the distribution network reliability comprehensive evaluation module, the comprehensive evaluation model includes: ； In the above formula, is the comprehensive assessment score; is the indicator weight, and .

6. A distribution network reliability assessment method considering vehicle-grid interaction according to claim 5, characterized in that: The specific operations of the data acquisition and processing module include: Acquire data related to distribution network reliability assessment, including regional distribution network node load data, regional distribution network line topology data, node topology data, time series data of distribution network node loads, maximum charging power of each distribution network node, and maximum discharge power of each distribution network node. The regional distribution network node load data is acquired through the SCADA system, and the regional distribution network line topology data and node topology data are acquired through the PMS system. The load of the distribution network node is arrive Time series data between moments include: ； ； In the above formula, is the node i in the regional distribution network arrive The load at a given time, in MW; For regional distribution networks arrive The load at a certain time, N is the number of distribution network nodes; The electric vehicle characteristics include the mth electric vehicle capacity , charging efficiency and discharge efficiency ; The maximum charging power of the mth electric vehicle in the distribution network include: ； In the above formula, is the charging efficiency of the mth electric vehicle; The maximum discharge power of the electric vehicle at the mth node of the distribution network include: ； In the above formula, is the discharge efficiency of the mth electric vehicle; Data preprocessing: cleaning, denoising and other preprocessing operations are performed on the collected load data to ensure the accuracy and availability of the data; Construct a time series dataset, consider different operating conditions such as normal and faulty distribution network, integrate the preprocessed data related to distribution network reliability assessment, and construct a time series dataset for dynamic assessment of regional distribution network reliability considering vehicle-grid interaction. The time series dataset should have time series characteristics and be able to reflect the operating status of the distribution network and electric vehicles at different times.

7. The method for evaluating distribution network reliability considering vehicle-grid interaction according to claim 6, characterized in that: The specific operations of the simulation behavior data module include: Setting simulation parameters, including the number of simulations K, random variables, and the range of values ​​of the random variables, and simulating the number of electric vehicles, travel behavior, and charging and discharging behavior of electric vehicles using a Monte Carlo algorithm; The simulation parameters are substituted into a random number generator to generate a large number of random samples that conform to the operating characteristics of the distribution network and electric vehicles. Each of the random samples corresponds to a possible distribution network operating state and electric vehicle charging and discharging condition.

8. The method for evaluating distribution network reliability considering vehicle-grid interaction according to claim 7, characterized in that: The specific operations of the dynamic vehicle-grid interaction model construction module include: Calculate the electric vehicle behavior model, which is a mathematical relationship model between the charging and discharging power of the electric vehicle and factors such as battery SOC and charging time based on the battery characteristics of the electric vehicle, the type of charging facilities, and the power supply capacity of the power grid. The electric vehicle behavior model includes: ； ； In the above formula, is the charging power of the mth electric vehicle at time t, The unit is MW; is the discharge power of the mth electric vehicle at time t, The unit is MW; x(t) is the mark value of whether the electric vehicle is in the charging state. If x(t) is 1, it means the electric vehicle is in the charging state, otherwise it is in the non-charging state or the traveling state. y(t) is the mark value of whether the electric vehicle is in the discharging state. If y(t) is 1, it means the electric vehicle is in the discharging state, otherwise it is in the non-discharging state or the traveling state. The expressions of x(t) and y(t) are: ； ； Construct an electric vehicle total power model, which is used to combine with the electric vehicle behavior model to obtain the total power demand or supply of all electric vehicles in the area to the distribution network at any time t. The electric vehicle total power model includes: ； In the above formula, is the total power of electric vehicles in the regional distribution network at time t, in MW; M is the total number of electric vehicles; Construct a node voltage model, which is used to perform iterative calculation of distribution network power flow for different electric vehicle timing simulation states to obtain the voltage amplitude of each node ; Analyze the source and load status of the regional distribution network. When the system can meet the load demand, the regional distribution network source-load state flag I takes 0; when When the system cannot meet the load demand, the regional distribution network source-load state flag I takes 1; For all nodes in the regional distribution network arrive Time load.

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