A method and system for calculating the probability of charging an electric vehicle in a transformer area, and a storage medium

Abstract

The application discloses a kind of calculation method, system and storage medium of transformer area electric vehicle charging probability, method includes the following steps: constructing transformer area outgoing line and A, B, C phase active power and reactive power dataset;Calculate the probability of transformer area outgoing line and A, B, C phase active power and reactive power;Construct transformer area outgoing line and A, B, C phase voltage dataset;Calculate the probability value of transformer area outgoing line and A, B, C phase voltage;Introduce active and reactive influence factor, respectively calculate transformer area electric vehicle active charging probability and reactive charging probability;Based on the correlation analysis of transformer area electric vehicle historical voltage, introduce voltage influence factor, calculate transformer area electric vehicle voltage charging probability;Calculate transformer area electric vehicle charging probability.This method can evaluate transformer area electric vehicle charging state, reflect the uncertainty of transformer area electric vehicle charging state characteristic value, provide theoretical guidance for the calculation of transformer area electric vehicle charging probability.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of electric vehicle charging probability calculation, and particularly relates to a method and system for calculating electric vehicle charging probability of a transformer area and a storage medium. BACKGROUND

[0002] In a modern power system, a transformer area is a form of power grid in which distributed sources (small hydropower, small wind power, photovoltaic power generation) and loads (water, electricity, gas, cold, heat load) are integrated in a certain way within the transformer area. Electric vehicles and energy storage devices have dual attributes, that is, they can be a distributed power source or a distributed load. The transformer area is connected to the main power grid at voltage levels of 380V, 10kV, 35kV, etc. Under normal operating conditions, the transformer area is connected to the main power grid in parallel. When the load is heavy, the transformer area absorbs power from the main power grid. When the load is light, the transformer area may inject power into the main power grid. In the case of a local fault in the main power grid or in the case of a fault in a neighboring transformer area, the transformer area can operate as an isolated grid. Under the premise of ensuring the quality of electric energy, the transformer area provides electric power to the load from the distributed power sources within the transformer area, realizes normal power supply in the fault-free transformer area, reduces the power outage time, and improves the power supply reliability.

[0003] In a household, the charging and discharging power of an electric vehicle and an energy storage device is small, and the electric vehicle and the energy storage device are generally connected in single phase at a voltage level of 220V. The charging power is not greater than 10kW. However, due to different phase sequence connections of different households and transformer areas, and the randomness of household charging, the charging characteristics are different in different spatial areas, different phase sequences of transformer areas, and different time scales, such as different charging powers of transformer areas facing different areas, different charging powers of A, B, and C phases, and different charging powers in the time period of 1-24. Due to the single-phase charging and discharging of household electric vehicles and energy storage devices, and the significant differences in spatial, phase, and time characteristics, the three-phase power of the transformer area is often unbalanced, and this situation is more serious when the management is not in place, which has an adverse effect on the distribution network and the operation of the transformer area. In a public charging station, the charging and discharging power of an electric vehicle and an energy storage device is large, and the electric vehicle and the energy storage device are generally connected in three phases at a voltage level of 380V, which does not cause unbalanced three-phase power of the transformer area. However, due to the randomness of charging, the transformer area facing the public charging station also has significant differences in charging space, phase, and time characteristics.

[0004] The distribution area for distributed small hydropower is a distribution area mainly in the form of small hydropower supply. In the water, wind and light distribution area, most of the hydropower stations are runoff type, the dam generally has no water storage function, the reservoir has no water storage and water regulation capacity, the water energy utilization of the small hydropower station completely depends on the water value of the reservoir, and the generation state and output scale of the small hydropower unit also completely depend on the water value of the reservoir. In this case, in order to realize efficient utilization of water energy generation of small hydropower station, it is necessary to achieve the generation of water. The water value of the small hydropower station reservoir has randomness, and the water value in different hydrological periods is completely different. In the wet season, the water value is large, and in the dry season, the water value is small. Therefore, the river flow value of the small hydropower station basin often shows the minimum flow value, the maximum flow value, the average flow value, the multi-year average flow value, the calculated average flow value, the weighted average flow value, the mathematical average flow value and other table forms. Different flow value table forms are adopted, and different installed capacity values of the small hydropower station are obtained. Different installed capacity values often result in different power generation and power generation values of the small hydropower station in different hydrological periods, and the optimal leads to different water energy utilization rates, power generation equipment utilization rates and annual maximum utilization hours of power generation equipment of the small hydropower station.

[0005] The small hydropower-wind power station is a kind of station which integrates small hydropower and small wind power, certain capacity load and connects the distributed power and load in a certain way. In the small hydropower-wind power station, not only the reservoir inflow value, reservoir flow value and power generation flow value of the small hydropower station have uncertainty and randomness, but also the wind speed of the small wind power station has uncertainty and randomness. In the water, wind and light power station, most of the hydropower stations are runoff type, the dam generally has no water storage function, the reservoir has no water storage and water regulation capacity, and the water energy utilization of the small hydropower station completely depends on the reservoir inflow value. In this case, the small hydropower station must generate power according to the amount of water to realize efficient utilization of water energy. The reservoir inflow value of the small hydropower station has randomness, and the inflow value is completely different in different hydrological periods. The inflow value is large in the wet season and small in the dry season. Therefore, the river flow value of the small hydropower station often shows the minimum flow value, the maximum flow value, the average flow value, the multi-year average flow value, the calculated average flow value, the weighted average flow value, the mathematical average flow value and other table forms. The small hydropower station will obtain different installed capacity levels by using different flow value table forms. Different installed capacity levels often lead to different power generation power and power generation value of the small hydropower station in different hydrological periods, and the optimal leads to different water energy utilization rate, power generation equipment utilization rate and annual maximum utilization hours of power generation equipment of the small hydropower station. When the wind speed is less than the cut-in wind speed or greater than the cut-out wind speed, the wind turbine has no output power. When the wind speed is less than the rated wind speed for the cut-in wind speed, the wind turbine output power is less than the rated power. When the wind speed is less than the cut-out wind speed for the rated wind speed, the wind turbine outputs the rated power. The wind speed is completely different in different seasons of a year and completely different in different time periods of a day, and the wind speed has randomness, volatility and intermittency. Therefore, the wind speed of the small wind power station often shows the minimum wind speed, the maximum wind speed, the average wind speed, the multi-year average wind speed, the calculated average wind speed, the weighted average wind speed, the mathematical average wind speed and other table forms. The small wind power station will obtain different installed capacity levels by using different wind speed table forms. Different installed capacity levels often lead to different power generation power and power generation value of the small wind power station in different seasons, and the optimal leads to different wind energy utilization rate, power generation equipment utilization rate and annual maximum utilization hours of power generation equipment of the small wind power station.

[0006] The small hydropower-wind-solar power station is a kind of power station which integrates three kinds of distributed power sources, i.e. small hydropower, small wind power and solar power, and connects the distributed power sources and loads in a certain way. In the small hydropower-wind-solar power station, not only the water inflow value, reservoir flow value and power generation flow value of the small hydropower station, but also the wind speed of the small wind power station and the solar intensity have uncertainty and randomness. In the small hydropower-wind-solar power station, most of the hydropower stations are runoff type, the dam generally has no water storage function, and the reservoir has no water storage and water regulation capacity. The water energy utilization of the small hydropower station completely depends on the water inflow value of the reservoir, and the generation state and output scale of the small hydropower unit also completely depend on the water inflow value of the reservoir. In this case, in order to realize the efficient utilization of water energy, the small hydropower station must generate electricity according to the water inflow value. However, the water inflow value of the small hydropower station has randomness, and the water inflow value is completely different in different hydrological periods. The water inflow value is large in the wet season and small in the dry season. Therefore, the river flow value of the small hydropower station often shows the minimum flow value, maximum flow value, average flow value, multi-year average flow value, calculated average flow value, weighted average flow value, mathematical average flow value and other table forms. By using different flow value table forms, the small hydropower station will obtain different installed capacity levels. Different installed capacity levels often lead to different power generation power and power generation value of the small hydropower station in different hydrological periods, and the optimal leads to different water energy utilization rate, power generation equipment utilization rate and annual maximum utilization hours of power generation equipment of the small hydropower station. When the wind speed is less than the cut-in wind speed or greater than the cut-out wind speed, the wind turbine has no output power. When the wind speed is less than the rated wind speed for the cut-in wind speed, the wind turbine output power is less than the rated power. When the wind speed is less than the cut-out wind speed for the rated wind speed, the wind turbine outputs the rated power. The wind speed is completely different in different seasons of a year and completely different in different time periods of a day, and the wind speed has randomness, volatility and intermittency. Therefore, the wind speed of the small wind power station often shows the minimum wind speed, maximum wind speed, average wind speed, multi-year average wind speed, calculated average wind speed, weighted average wind speed, mathematical average wind speed and other table forms. By using different wind speed table forms, the small wind power station will obtain different installed capacity levels. Different installed capacity levels often lead to different power generation power and power generation value of the small wind power station in different seasons, and the optimal leads to different wind energy utilization rate, power generation equipment utilization rate and annual maximum utilization hours of power generation equipment of the small wind power station. The greater the solar intensity, the greater the output power of the photovoltaic power generation system. The solar intensity is completely different in different seasons of a year and completely different in different time periods of a day, and the solar intensity has randomness, volatility and intermittency. Therefore, the solar intensity of the photovoltaic power station often shows the minimum solar intensity, maximum solar intensity, average solar intensity, multi-year average solar intensity, calculated average solar intensity, weighted average solar intensity, mathematical average solar intensity and other table forms. By using different solar intensity table forms, the photovoltaic power station will obtain different installed capacity levels.The power generation and value of photovoltaic power station in different seasons are different at different installed capacity levels, and the optimal causes the wind energy utilization rate, power generation equipment utilization rate, and annual maximum utilization hours of power generation equipment to be different.

[0007] The integration of different load levels and distributed power capacity scales in the transformer area changes the structure and power flow characteristics of the transformer area. Due to the access of small hydropower, small wind power, photovoltaic power and other distributed power sources, different voltage levels will be adopted due to the different capacity scales of the accessed power sources. Due to the randomness of electricity consumption, load power will change at different time and space scales, with obvious time period characteristics. At the same time, the output of wind power, photovoltaic power and other distributed power sources is intermittent, random and time period, and the output of small hydropower units is seasonal. Therefore, the balance between transformer area load power and power supply power is often difficult to maintain. When the load power is greater than the power supply power, the transformer area needs to obtain supplementary power from the main power grid, and when the load power is less than the power supply power, the remaining power needs to be injected into the main power grid, forming a random bidirectional power flow characteristic. The random bidirectional power flow characteristic has an impact on the voltage quality of the transformer area internal nodes. When the output of distributed power is large and the load is light, the node voltage of the local area in the transformer area will be too high, and when the output of distributed power is small and the load is heavy, the node voltage of the local area in the transformer area will be too low. Therefore, the limitation and requirement of the transformer area internal node voltage have an impact and restriction on the distributed power capacity configuration, operation mode and voltage control strategy in the transformer area, and the distributed power capacity configuration, operation mode and voltage control strategy in the transformer area need to consider the limitation and requirement of the transformer area internal node voltage. The transformer area is connected to the distribution network at different voltage levels, which will cause the node voltage of the distribution network to be too high or too low due to the different size of power absorbed or injected by the transformer area from the distribution network. The distributed power capacity configuration, operation mode and voltage control strategy in the transformer area need to consider the limitation and requirement of the distribution network node voltage.

[0008] The distribution type power supply system of a transformer area is a system with complex relations and interaction of random and fuzzy uncertainty events or parameters. Under the influence of various random and fuzzy events or parameters, the power generation and generation value of the distribution type power supply system of a transformer area become more random and fuzzy, which greatly affects the capacity configuration of the distribution type power supply system of a transformer area. In the past, the power generation and generation value of the distribution type power supply system of a transformer area were usually calculated by a deterministic method, and some were calculated by a probabilistic analysis method. The deterministic method usually calculates the power generation, generation value and installed capacity of the distribution type power supply system of a transformer area under the assumption that the inflow value and flow value of a small hydropower station, the regional sunshine intensity and wind speed are determined, and the influence of factors such as transformer area and distribution network voltage regulation requirements and flexible control mode is not considered. The calculation result is unique and deterministic, and often cannot reflect the actual situation of the power generation, generation value and installed capacity of the distribution type power supply system of a transformer area. The probabilistic analysis method usually calculates the power generation, generation value and installed capacity of the distribution type power supply system of a transformer area under the assumption that only a single factor such as the inflow value and flow value of a small hydropower station, the regional sunshine intensity and wind speed is an uncertain factor. The calculation result is a probability value with a certain confidence level. In fact, the power generation, generation value and installed capacity of the distribution type power supply system of a transformer area are affected by multiple uncertain factors. Moreover, these factors usually have random uncertainty or fuzzy uncertainty, or both random and fuzzy uncertainty, and often exist as random and fuzzy uncertainty events or parameters. Therefore, the existing technology for calculating the power generation, generation value and installed capacity of the distribution type power supply system of a transformer area does not fully consider the uncertainty and randomness of the influencing factors, and the applicability, practicality and applicability of the calculation method are difficult to meet.

[0009] Under the interaction and common influence of distributed power supply, electric vehicles and energy storage devices, and the random and fuzzy uncertainty of load, the peak and valley load difference is often formed in the transformer area, and the peak and valley load space, phase and time characteristics are also significantly different. In order to identify these characteristics, the traditional method is to use artificial method to verify the user information of the transformer area through regular inspection. The traditional method lacks real-time performance and is time-consuming and laborious. For the transformer area facing old streets, the line is complex, and the information cannot be updated in time, which causes the transformer area to have the problems of incomplete, imperfect and missing information. From the perspective of transformer area management, technicians often change the user connection mode, or need to modify the line to balance the transformer area load, which easily leads to unclear user line relationship and inaccurate attribution relationship record. These factors increase the difficulty and complexity of identifying the interaction and common influence characteristics of the distribution type power supply, electric vehicles and energy storage devices of the transformer area.

[0010] With the increasing number of users in the transformer area, the traditional method of manual transcription has been difficult to adapt. Using special recognition equipment has become a common practice, such as carrier communication method and pulse current method. Carrier signal and pulse signal are easy to be disturbed, and transformer is a greater obstacle to communication. Therefore, the feature recognition technology of special recognition equipment based on carrier communication method and pulse current method can only be applied within the same phase line range in the same transformer area.

[0011] With the increasing maturity of big data and artificial intelligence technology, data mining and artificial intelligence technology are gradually applied in the interaction and common influence feature recognition of distributed power supply, electric vehicles and energy storage devices in the transformer area, such as: obtaining transformer area and user data through smart meters, identifying the transformer area and phase of the user by using data mining method; Using transformer voltage data, using data mining method to identify the space-time correlation of equipment. However, due to the difference in gray correlation, the recognition accuracy is not high in some cases. In the case of very limited voltage time series data, and without considering the random and fuzzy uncertainty of distributed power generation, electric vehicles and energy storage devices charging and discharging and load in the transformer area, it is difficult to identify the dynamic state and dynamic characteristics of the transformer area based on big data and artificial intelligence feature recognition technology, and it is more difficult to identify the peak-valley load characteristics of the transformer area. SUMMARY

[0012] The main purpose of the present application is to overcome the shortcomings and deficiencies of the prior art, and provide a transformer area electric vehicle charging probability calculation method, system and storage medium. The method is aimed at the problem of low accuracy of electric vehicle charging probability caused by random and fuzzy uncertainty of electric vehicle charging and discharging and load in the transformer area, and uses the charging probability of transformer outlet and A, B and C phase active power and reactive power, combined with voltage probability to evaluate the charging state of electric vehicle in the transformer area, and provides guidance for transformer area electric vehicle charging probability calculation.

[0013] In order to achieve the above purpose, the technical scheme adopted by the present application is as follows:

[0014] The present application provides a transformer area electric vehicle charging probability calculation method, which comprises the following steps:

[0015] Obtain the historical data of electric vehicles in the transformer area, and construct the active power and reactive power data set of the transformer outlet and A, B and C phase;

[0016] According to the constructed active power and reactive power data set, the probability of active power and reactive power of transformer outlet and A, B and C phase is calculated;

[0017] Using the historical data of electric vehicles in the transformer area, construct the voltage data set of the transformer outlet and A, B and C phase;

[0018] According to the constructed voltage data set, the probability value of the voltage of the transformer outlet and A, B and C phase is calculated;

[0019] Based on the correlation analysis of the historical active and reactive power of electric vehicles in the transformer substation, active and reactive power influencing factors are introduced to calculate the active charging probability and reactive charging probability of electric vehicles in the transformer substation.

[0020] Based on the correlation analysis of the historical voltage of electric vehicles in the transformer area, a voltage influence factor is introduced to calculate the voltage charging probability of electric vehicles in the transformer area.

[0021] The charging probability of electric vehicles in the transformer area is calculated by combining the active charging probability, reactive charging probability, and voltage charging probability of electric vehicles in the transformer area.

[0022] As a preferred technical solution, the construction of the active and reactive power datasets for the transformer substation outgoing lines and phases A, B, and C specifically involves:

[0023] Obtain historical data of electric vehicles in the transformer area, select n years of historical data and N historical time periods, and construct a dataset of active power and reactive power of the a-th outgoing line and phases A, B, and C.

[0024] The active power dataset is represented as follows:

[0025] P Sa =[P Sa1 ,P Sa2 ,...,P SaN ]

[0026] P Aa =[P Aa1 ,P Aa2 ,...,P AaN ]

[0027] P Ba =[P Ba1 ,P Ba2 ,...,P BaN ]

[0028] P Ca =[P Ca1 ,P Ca2 ,...,P CaN ]

[0029] Among them, P Sa Let P be the active power dataset of the a-th outgoing line. Aa Let P be the active power dataset of phase A of the a-th outgoing line. Ba For the active power dataset of phase B of the a-th outgoing line, P Ca For the active power dataset of phase C of the a-th outgoing line, P SaN Let P be the active power dataset of the a-th outgoing line in time period N. AaN Let P be the active power dataset of phase A of the a-th outgoing line during time period N.BaN For the active power dataset of phase B of the a-th outgoing line in time period N, P CaN This is the active power dataset of phase C of the a-th outgoing line in time period N, where N is the number of historical time periods;

[0030] The reactive power dataset is represented as follows:

[0031] Q Sa =[Q Sa1 Q Sa2 ,...,Q SaN ]

[0032] Q Aa =[Q Aa1 Q Aa2 ,...,Q AaN ]

[0033] Q Ba =[Q Ba1 Q Ba2 ,...,Q BaN ]

[0034] Q Ca =[Q Ca1 Q Ca2 ,...,Q CaN ]

[0035] Among them, Q Sa Let Q be the reactive power dataset of the a-th outgoing line. Aa Let Q be the reactive power dataset of phase A of the a-th outgoing line. Ba Let Q be the reactive power dataset of phase B of the a-th outgoing line. Ca Let Q be the reactive power dataset of phase C of the a-th outgoing line. SaN Let Q be the reactive power dataset of the a-th outgoing line during time period N. AaN Let Q be the reactive power dataset of phase A of the a-th outgoing line during time period N. BaN Given the reactive power dataset of phase B of the a-th outgoing line during time period N, Q CaN This is the C-phase reactive power dataset for the a-th outgoing line during time period N.

[0036] As a preferred technical solution, the probability of calculating the active and reactive power of the transformer substation outgoing lines and phases A, B, and C is specifically as follows:

[0037] Data on the active and reactive power of the outgoing lines and phases A, B, and C of the transformer substation are obtained from the metering automation system. Simulation is used to determine the mean μ of the active power of the outgoing line a in time period t, which follows a normal distribution. SPat and variance σ SPat, determine the mean μ of active power of the a-th outgoing line A, B, C phase data set in the t time period according to normal distribution rule change APat , μ BPat , μ CPat and variance σ APat , σ BPat , σ CPat ; determine the mean μ of reactive power of the a-th outgoing line data set in the t time period according to normal distribution rule change SQat and variance σ SQat , determine the mean μ of reactive power of the a-th outgoing line A, B, C phase data set in the t time period according to normal distribution rule change AQat , μ BQat , μ CQat and variance σ AQat , σ BQat , σ CQat ;

[0038] Determine the probability that the active power difference of the a-th outgoing line and A, B, C phase in time period t and time period t-1 is greater than the sum of the electric vehicle charging power configured on the a-th outgoing line, the calculation formula is:

[0039]

[0040]

[0041]

[0042]

[0043] Wherein, Pr{} is the probability function, N EVa is the number of electric vehicles configured on the a-th outgoing line, N AEVa is the number of electric vehicles configured on the a-th outgoing line A phase, N BEVa is the number of electric vehicles configured on the a-th outgoing line B phase, N CEVa is the number of electric vehicles configured on the a-th outgoing line C phase, k SEVa , k AEVa , k BEVa , k CEVa respectively the a-th outgoing line and A, B, C phase active power difference and the sum of the electric vehicle charging active power configured on the a-th outgoing line is the limited coefficient, P SEVa , P AEVa , P BEVa , P CEVa respectively the a-th outgoing line and A, B, C phase electric vehicle charging active power;

[0044] Determine the probability that the reactive power difference between the a-th outgoing line and A, B, C phases at time period t and time period t-1 is greater than the sum of the electric vehicle charging reactive power configured on the a-th outgoing line, the calculation formula is:

[0045]

[0046]

[0047]

[0048]

[0049] Wherein, Q SEVa , Q AEVa , Q BEVa , Q CEVa are the electric vehicle charging reactive power configured on the a-th outgoing line and A, B, C phases respectively.

[0050] As a preferred technical solution, the historical data of the electric vehicle in the transformer area is used to construct the voltage data set of the outgoing line and A, B, C phases in the transformer area, specifically:

[0051] According to the historical data of the electric vehicle in the transformer area, select n years of historical data and N historical time periods, and construct the voltage data set of the a-th outgoing line and A, B, C phases, denoted as:

[0052] V Sa =[V Sa1 ,V Sa2 ,...,V SaN ]

[0053] V Aa =[V Aa1 ,V Aa2 ,...,V AaN ]

[0054] V Ba =[V Ba1 ,V Ba2 ,...,V BaN ]

[0055] V Ca =[V Ca1 ,V Ca2 ,...,V CaN ]

[0056] Wherein, V Sa is the voltage data set of the a-th outgoing line, V Aa is the A-phase voltage data set of the a-th outgoing line, V Ba is the B-phase voltage data set of the a-th outgoing line, V Ca is the C-phase voltage data set of the a-th outgoing line, and VSaN V is the voltage data set of the a-th outgoing line in time period N AaN V is the A-phase voltage data set of the a-th outgoing line in time period N BaN V is the B-phase voltage data set of the a-th outgoing line in time period N CaN V is the C-phase voltage data set of the a-th outgoing line in time period N, and N is the number of historical time periods.

[0057] As a preferred technical solution, the probability value of the calculated substation outgoing line and A, B, and C phase voltage is specifically:

[0058] The data information of the substation outgoing line and A, B, and C phase voltage is obtained from the metering automation system, and the mean value μ SVat and the variance σ SVat of the voltage of the a-th outgoing line data set in the t-th time period are determined according to the normal distribution rule, the mean values μ AVat , μ BVat , and μ CVat and the variances σ AVat , σ BVat , and σ CVat of the A, B, and C phase data sets of the a-th outgoing line in the t-th time period are determined according to the normal distribution rule.

[0059] The probability that the voltage value of the a-th outgoing line and A, B, and C phase in the t-th time period is lower than the rated voltage value of the a-th outgoing line and A, B, and C phase is determined, and the calculation formula is:

[0060] p SVt = Pr{V Sat < k V V R}

[0061] p AVt = Pr{V Aat < k V V R}

[0062] p BVt = Pr{V Bat < k V V R}

[0063] p CVt = Pr{V Cat < k V V R}

[0064] wherein Pr{} is a probability function, k V <1 is the coefficient of voltage drop caused by electric vehicle charging, V R is the rated voltage.

[0065] As a preferred technical scheme, the active power charging probability calculation formula of the transformer area electric vehicle is:

[0066]

[0067] Wherein, k SP is an active power influence factor, erf(y) is an error function, and the expression is:

[0068]

[0069] The reactive power charging probability calculation formula of the transformer area electric vehicle is:

[0070]

[0071] Wherein, k SQ is a reactive power influence factor.

[0072] As a preferred technical scheme, the voltage charging probability calculation formula of the transformer area electric vehicle is:

[0073]

[0074] Wherein, k SV is an active power influence factor.

[0075] As a preferred technical scheme, the charging probability calculation formula of the transformer area electric vehicle is:

[0076]

[0077] Wherein, is a weight coefficient.

[0078] On the other hand, the application provides a transformer area electric vehicle charging probability calculation system, which is applied to the transformer area electric vehicle charging probability calculation method and comprises a power data set construction module, a power probability calculation module, a voltage data set construction module, a voltage probability calculation module, a power charging probability calculation module, a voltage charging probability module and an automobile charging probability obtaining module.

[0079] The power data set construction module is used for constructing transformer area outgoing line and A, B and C phase active power and reactive power data sets according to historical data of the transformer area electric vehicle.

[0080] The power probability calculation module is used for calculating the probability of transformer area outgoing line and A, B and C phase active power and reactive power.

[0081] The voltage data set construction module is used for constructing transformer area outgoing line and A, B and C phase voltage data sets according to historical data of the transformer area electric vehicle.

[0082] The voltage probability calculation module is used for calculating the probability values of the outgoing line and A, B and C phase voltages of the transformer area.

[0083] The power charging probability calculation module introduces active and reactive power influence factors based on the correlation analysis of historical active power and reactive power of the electric vehicles in the transformer area, and calculates the active charging probability and the reactive charging probability of the electric vehicles in the transformer area.

[0084] The voltage charging probability module introduces a voltage influence factor based on the correlation analysis of historical voltages of the electric vehicles in the transformer area, and calculates the voltage charging probability of the electric vehicles in the transformer area.

[0085] The vehicle charging probability obtaining module is used for calculating the charging probability of the electric vehicles in the transformer area in combination with the active charging probability, the reactive charging probability and the voltage charging probability of the electric vehicles in the transformer area.

[0086] In still another aspect, the application provides a computer readable storage medium storing a program, which, when executed by a processor, implements the above-mentioned method for calculating the charging probability of the electric vehicles in the transformer area.

[0087] Compared with the prior art, the application has the following advantages and beneficial effects:

[0088] The application provides a method, system and storage medium for calculating the charging probability of the electric vehicles in the transformer area, which constructs a data set from historical data, analyzes correlation information, introduces influence factors, and completes the calculation of the charging probability, so as to evaluate the charging state of the electric vehicles in the transformer area, reflect the uncertainty of the characteristic values of the charging state of the electric vehicles in the transformer area, provide theoretical guidance for the calculation of the charging probability of the electric vehicles in the transformer area, and provide necessary technical support for the operation and maintenance of the distribution network. BRIEF DESCRIPTION OF DRAWINGS

[0089] In order to more clearly illustrate the technical solutions in the embodiments of the application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative effort.

[0090] Figure 1 The flow chart of the method for calculating the charging probability of the electric vehicles in the transformer area in the embodiment of the application;

[0091] Figure 2 The structural diagram of the system for calculating the charging probability of the electric vehicles in the transformer area in the embodiment of the application;

[0092] Figure 3 The structural diagram of the computer readable storage medium in the embodiment of the application. DETAILED DESCRIPTION

[0093] In the interest of an enabling disclosure of the embodiments of the application, the detailed description will often be presented in terms of exemplary implementations of the application. It should be appreciated that these implementations are presented by way of example only, and that the application is not limited to the disclosed implementations. In the description below, numerous specific details are set forth in order to provide an understanding of the embodiments of the application. However, it will be apparent to one of ordinary skill in the art that the embodiments of the application can be practiced without these specific details. In other instances, well-known methods, procedures, components, and circuits have not been described in detail so as not to unnecessarily obscure aspects of the embodiments of the application.

[0094] Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase "in an embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are they necessarily all directed to the same embodiment, or to one or more feature, structure, or characteristic of a particular embodiment. It will be apparent to those skilled in the art from the

[0095] As shown in FIG. 1, the embodiment provides a method for calculating the probability of electric vehicle charging in a transformer area, including the following steps: Figure 1

[0096] S1, constructing a transformer area outgoing line and A, B, C phase active power and reactive power data set according to historical data of electric vehicles in the transformer area, specifically:

[0097] According to the historical data of electric vehicles in the transformer area, n years of historical data and N historical time periods are selected to construct the a-th outgoing line and A, B, C phase active power and reactive power data set;

[0098] The active power data set is represented as:

[0099] P Sa a Sa1 a Sa2 a SaN a

[0100] P Aa a Aa1 a Aa2 a AaN a

[0101] P Ba a Ba1 a Ba2 a BaN a

[0102] P Ca a Ca1 a Ca2 a CaN a

[0103] P Ca a Ca1 a Ca2 a CaN a

[0103] wherein, PSa P is the active power data set of the a-th outgoing line, Aa P is the A-phase active power data set of the a-th outgoing line, Ba P is the B-phase active power data set of the a-th outgoing line, Ca P is the C-phase active power data set of the a-th outgoing line, SaN P is the active power data set of the a-th outgoing line at time period N, AaN P is the A-phase active power data set of the a-th outgoing line at time period N, BaN P is the B-phase active power data set of the a-th outgoing line at time period N, CaN P is the C-phase active power data set of the a-th outgoing line at time period N, N is the number of historical time periods; in this embodiment, N = n*7860 historical time periods are selected;

[0104] The reactive power data set is represented as:

[0105] Q Sa = [Q Sa1 , Q Sa2 ,..., Q SaN ]

[0106] Q Aa = [Q Aa1 , Q Aa2 ,..., Q AaN ]

[0107] Q Ba = [Q Ba1 , Q Ba2 ,..., Q BaN ]

[0108] Q Ca = [Q Ca1 , Q Ca2 ,..., Q CaN ]

[0109] Q Sa is the reactive power data set of the a-th outgoing line, Q Aa is the A-phase reactive power data set of the a-th outgoing line, Q Ba is the B-phase reactive power data set of the a-th outgoing line, Q Ca is the C-phase reactive power data set of the a-th outgoing line, Q SaN is the reactive power data set of the a-th outgoing line at time period N, Q AaN is the A-phase reactive power data set of the a-th outgoing line at time period N, Q BaN is the B-phase reactive power data set of the a-th outgoing line at time period N, Q CaN is the C-phase reactive power data set of the a-th outgoing line at time period N.

[0110] S2, the probability of the active power and the reactive power of the transformer area outgoing line and A, B, C phase is calculated, specifically:

[0111] The data information of the active power and the reactive power of the transformer area outgoing line and A, B, C phase is obtained from the metering automation system, the mean value μ of the active power of the a-th outgoing line data set in the t-th period is determined according to the normal distribution law SPat And the variance σ SPat The mean value μ of the active power of the A, B, C phase data set of the a-th outgoing line in the t-th period is determined according to the normal distribution law APat , μ BPat , μ CPat And the variance σ APat , σ BPat , σ CPat The mean value μ of the reactive power of the a-th outgoing line data set in the t-th period is determined according to the normal distribution law SQat And the variance σ SQat The mean value μ of the reactive power of the A, B, C phase data set of the a-th outgoing line in the t-th period is determined according to the normal distribution law AQat , μ BQat , μ CQat And the variance σ AQat , σ BQat , σ CQat ;

[0112] The probability that the active power difference of the a-th outgoing line and A, B, C phase in the t-th period and the t-1-th period is greater than the sum of the charging power of the electric vehicles configured on the a-th outgoing line is determined, and the calculation formula is:

[0113]

[0114]

[0115]

[0116]

[0117] Wherein, p SPt , p APt , p BPt , p CPt The probability that the active power difference of the a-th outgoing line and A, B, C phase in the t-th period and the t-1-th period is greater than the sum of the charging power of the electric vehicles configured on the a-th outgoing line is determined, and the calculation formula is: EVa N AEVa The number of electric vehicles configured on the A phase of the a-th outgoing line, N BEVa The number of electric vehicles configured on the B phase of the a-th outgoing line, NCEVa The number of electric vehicles configured on the a-th outgoing line C phase, k SEVa , k AEVa , k BEVa , k CEVa , P SEVa , P AEVa , P BEVa , P CEVa The active power of the electric vehicle charging configured on the a-th outgoing line and the A, B, and C phases, respectively.

[0118] Determine the probability that the reactive power difference between the a-th outgoing line and the A, B, and C phases at time period t and time period t-1 is greater than the sum of the electric vehicle charging power configured on the a-th outgoing line, and the calculation formula is:

[0119]

[0120]

[0121]

[0122]

[0123] Where, p SQt , p AQt , p BQt , p CQt The probability that the reactive power difference between the a-th outgoing line and the A, B, and C phases at time period t and time period t-1 is greater than the sum of the electric vehicle charging power configured on the a-th outgoing line, Q SEVa , Q AEVa , Q BEVa , Q CEVa The reactive power of the electric vehicle charging configured on the a-th outgoing line and the A, B, and C phases, respectively.

[0124] S3, according to the historical data of the electric vehicle in the transformer area, construct the voltage data set of the outgoing line and the A, B, and C phases in the transformer area, specifically:

[0125] According to the historical data of the electric vehicle in the transformer area, select n years of historical data and N historical time periods, and construct the voltage data set of the a-th outgoing line and the A, B, and C phases, denoted as:

[0126] V Sa =[V Sa1 ,V Sa2 ,...,V SaN ]

[0127] V Aa =[VAa1 ,V Aa2 ,...,V AaN ]

[0128] V Ba =[V Ba1 ,V Ba2 ,...,V BaN ]

[0129] V Ca =[V Ca1 ,V Ca2 ,...,V CaN ]

[0130] Wherein, V Sa is the voltage data set of the a-th outgoing line, V Aa is the A-phase voltage data set of the a-th outgoing line, V Ba is the B-phase voltage data set of the a-th outgoing line, V Ca is the C-phase voltage data set of the a-th outgoing line, V SaN is the voltage data set of the a-th outgoing line at period N, V AaN is the A-phase voltage data set of the a-th outgoing line at period N, V BaN is the B-phase voltage data set of the a-th outgoing line at period N, V CaN is the C-phase voltage data set of the a-th outgoing line at period N, and N is the number of historical periods.

[0131] S4, calculate the probability values of the voltage of the outgoing line and A, B and C phases of the transformer station, specifically:

[0132] Obtain the data information of the voltage of the outgoing line and A, B and C phases of the transformer station from the metering automation system, and determine the mean value μ SVat and the variance σ SVat of the voltage of the a-th outgoing line data set at the t-th period according to the normal distribution rule, determine the mean values μ AVat , μ BVat , μ CVat and the variances σ AVat , σ BVat , σ CVat of the A, B and C phase data sets of the a-th outgoing line at the t-th period according to the normal distribution rule;

[0133] Determine the probability that the voltage value of the a-th outgoing line and A, B and C phases at period t is lower than the rated voltage value of the a-th outgoing line and A, B and C phases, and the calculation formula is:

[0134] p SVt =Pr{V Sat <k V V R}

[0135] p AVt = Pr{V Aat <k V V R}

[0136] p BVt = Pr{V Bat <k V V R}

[0137] p CVt = Pr{V Cat <k V V R}

[0138] wherein, Pr{} is a probability function, k V <1 is a coefficient of voltage deviation caused by electric vehicle charging, V R is a rated voltage.

[0139] S5, based on the correlation analysis of the historical active power and reactive power of the electric vehicle in the transformer area, the active and reactive influence factors are introduced, and the active charging probability and the reactive charging probability of the electric vehicle in the transformer area are calculated respectively, wherein the active charging probability calculation formula of the electric vehicle in the transformer area is:

[0140]

[0141] wherein, k SP is an active influence factor, erf(y) is an error function, and the expression is:

[0142]

[0143] The reactive charging probability calculation formula of the electric vehicle in the transformer area is:

[0144]

[0145] wherein, k SQ is a reactive influence factor.

[0146] S6, based on the correlation analysis of the historical voltage of the electric vehicle in the transformer area, the voltage influence factor is introduced, and the voltage charging probability of the electric vehicle in the transformer area is calculated, and the calculation formula is:

[0147]

[0148] wherein, k SV is an active influence factor.

[0149] S7, the charging probability of the electric vehicle in the transformer area is calculated by combining the active charging probability, the reactive charging probability and the voltage charging probability of the electric vehicle in the transformer area, and the expression is:

[0150]

[0151] wherein, are weight coefficients.

[0152] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited by the order of the described actions, because according to the present application, certain steps can be performed in other orders or simultaneously.

[0153] Based on the same idea as the calculation method of the electric vehicle charging probability of a transformer area in the above embodiment, the present application also provides a calculation system of the electric vehicle charging probability of a transformer area, which can be used to execute the calculation method of the electric vehicle charging probability of a transformer area. For the sake of simple description, in the structural schematic diagram of an embodiment of the calculation system of the electric vehicle charging probability of a transformer area, only the parts related to the embodiments of the present application are shown, and those skilled in the art can understand that the illustrated structure does not constitute a limitation on the device, and can include more or fewer components than the illustrated, or combine certain components, or different component arrangements.

[0154] As Figure 2 shown, another embodiment of the present application provides a calculation system of the electric vehicle charging probability of a transformer area, which includes the following modules:

[0155] The power dataset construction module is used to construct the active power and reactive power datasets of the A, B and C phases of the transformer outgoing line according to the historical data of the electric vehicle of the transformer area;

[0156] The power probability calculation module is used to calculate the probability of the active power and reactive power of the A, B and C phases of the transformer outgoing line;

[0157] The voltage dataset construction module is used to construct the voltage datasets of the A, B and C phases of the transformer outgoing line according to the historical data of the electric vehicle of the transformer area;

[0158] The voltage probability calculation module is used to calculate the probability value of the voltage of the A, B and C phases of the transformer outgoing line;

[0159] The power charging probability calculation module introduces the active and reactive influence factors based on the correlation analysis of the historical active power and reactive power of the electric vehicle of the transformer area, and calculates the active charging probability and the reactive charging probability of the electric vehicle of the transformer area;

[0160] The voltage charging probability module introduces the voltage influence factor based on the correlation analysis of the historical voltage of the electric vehicle of the transformer area, and calculates the voltage charging probability of the electric vehicle of the transformer area;

[0161] The automobile charging probability obtaining module is configured to calculate the charging probability of the electric automobile in the transformer area by combining the active charging probability, the reactive charging probability and the voltage charging probability of the electric automobile in the transformer area.

[0162] It should be noted that the one transformer area electric automobile charging probability calculation system of the present application corresponds to the one transformer area electric automobile charging probability calculation method of the present application, and the technical features and advantages described in the embodiment of the one transformer area electric automobile charging probability calculation method are applicable to the embodiment of the one transformer area electric automobile charging probability calculation system, and the specific content can be referred to the description in the method embodiment of the present application, which will not be repeated here.

[0163] In addition, in the embodiment of the one transformer area electric automobile charging probability calculation system of the above-mentioned embodiment, the logical division of each program module is only illustrative, and in actual application, the above-mentioned function allocation can be completed by different program modules according to the needs, for example, the configuration requirements of the corresponding hardware or the convenience of software implementation, that is, the internal structure of the one transformer area electric automobile charging probability calculation system is divided into different program modules to complete all or part of the functions described above.

[0164] As shown in Figure 3 In one embodiment, a computer readable storage medium is provided, which stores a program in the memory, and the program is executed by a processor to implement the one transformer area electric automobile charging probability calculation method, specifically:

[0165] According to the historical data of the electric automobile in the transformer area, the A, B and C phase active power and reactive power data sets of the transformer outgoing line are constructed;

[0166] The probability of the A, B and C phase active power and reactive power of the transformer outgoing line is calculated;

[0167] According to the historical data of the electric automobile in the transformer area, the A, B and C phase voltage data sets of the transformer outgoing line are constructed;

[0168] The probability value of the A, B and C phase voltage of the transformer outgoing line is calculated;

[0169] Based on the correlation analysis of the historical active power and reactive power of the electric automobile in the transformer area, the active and reactive influence factors are introduced, and the active charging probability and the reactive charging probability of the electric automobile in the transformer area are calculated respectively;

[0170] Based on the correlation analysis of the historical voltage of the electric automobile in the transformer area, the voltage influence factor is introduced, and the voltage charging probability of the electric automobile in the transformer area is calculated;

[0171] The charging probability of the electric automobile in the transformer area is calculated by combining the active charging probability, the reactive charging probability and the voltage charging probability of the electric automobile in the transformer area.

[0172] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program instructing relevant hardware. The program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiment methods. Any reference to memory, storage, database or other medium used in the embodiments provided by the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0173] Any combination of the technical features of the above embodiments can be made. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combination of the technical features does not exist, it should be considered as the scope of the present application.

[0174] The above embodiments are the preferred embodiments of the present application, but the embodiments of the present application are not limited to the above embodiments, and any changes, modifications, substitutions, combinations and simplifications made without departing from the spirit and principles of the present application shall be equivalent replacement methods and shall be within the scope of protection of the present application.

Claims

1. A method for calculating the probability of charging an electric vehicle in a transformer area, characterized by, The method comprises the following steps: obtaining historical data of the electric vehicle in the transformer area, and constructing a data set of active power and reactive power of the outgoing line and A, B and C phases in the transformer area; calculating the probability of active power and reactive power of the outgoing line and A, B and C phases in the transformer area according to the constructed data set of active power and reactive power; constructing a data set of voltage of the outgoing line and A, B and C phases in the transformer area using the historical data of the electric vehicle in the transformer area; calculating the probability value of voltage of the outgoing line and A, B and C phases in the transformer area according to the constructed data set of voltage; based on the correlation analysis of the historical active power and reactive power of the electric vehicle in the transformer area, introducing active and reactive power influence factors, and respectively calculating the active charging probability and the reactive charging probability of the electric vehicle in the transformer area; based on the correlation analysis of the historical voltage of the electric vehicle in the transformer area, introducing a voltage influence factor, and calculating the voltage charging probability of the electric vehicle in the transformer area; combining the active charging probability, the reactive charging probability and the voltage charging probability of the electric vehicle in the transformer area to calculate the charging probability of the electric vehicle in the transformer area.

2. The method of claim 1, wherein the method comprises: The data set of active power and reactive power of the outgoing line and A, B and C phases in the transformer area is constructed in particular as follows: Obtain historical data of the transformer area electric vehicle, select n year historical data and N a historical period, construct the first a line and A, B, C phase active power and reactive power data set; The data set of active power is expressed as: P Sa = [ P Sa1 , P Sa2 ,…, P SaN ]， P Aa = [ P Aa1 , P Aa2 ,…, P AaN ]， P Ba = [ P Ba1 , P Ba2 ,…, P BaN ]， P Ca = [ P Ca1 , P Ca2 ,…, P CaN ]， wherein, P Sa the active power data set for the first a phase of the first P Aa the active power data set for the A phase of the first a phase of the first P Ba the active power data set for the B phase of the first a phase of the first P Ca the active power data set for the C phase of the first a phase of the first P SaN the active power data set for the first a phase over the time period N P AaN the A phase active power data set for the first a phase over the time period N P BaN the B phase active power data set for the first a phase over the time period N P CaN the C phase active power data set for the first a phase over the time period N N is a number of historical time periods;​​​​ The data set of reactive power is expressed as: Q Sa = [ Q Sa1 , Q Sa2 ,…, Q SaN ]， Q Aa = [ Q Aa1 , Q Aa2 ,…, Q AaN ]， Q Ba = [ Q Ba1 , Q Ba2 ,…, Q BaN ]， Q Ca = [ Q Ca1 , Q Ca2 ,…, Q CaN ]， in, Q Sa For the first a Data set of reactive power of outgoing lines Q Aa For the first a Data set of reactive power of phase A of the outgoing line. Q Ba For the first a Data set of reactive power of phase B of the outgoing line. Q Ca For the first a Data set of C-phase reactive power of outgoing lines. Q SaN For the first a The line of players will be drawn during the specified time period. N The reactive power dataset on the internet Q AaN For the first a The line of players will be drawn during the specified time period. N The A-phase reactive power dataset on the data, Q BaN For the first a The line of players will be drawn during the specified time period. N The B-phase reactive power dataset on the above, Q CaN For the first a The line of players will be drawn during the specified time period. N The C-phase reactive power dataset.

3. The method of claim 2, wherein the method further comprises: The probability of active power and reactive power of the outgoing line and A, B and C phases in the transformer area is calculated in particular as follows: Data on the active and reactive power of the transformer substation outgoing lines and phases A, B, and C are obtained from the metering automation system. Simulation is then used to determine the... a The outgoing dataset is in the first... t The mean of active power during a time period that varies according to a normal distribution. μ SPat and variance σ SPat Determine the first a Data sets of phases A, B, and C of the outgoing lines in the first... t The mean of active power during a time period that varies according to a normal distribution. μ APat , μ BPat , μ CPat and variance σ APat , σ BPat , σ CPat ; Determine the first a The outgoing dataset is in the first... t The mean value of reactive power during a time period that varies according to a normal distribution. μ SQat and variance σ SQat Determine the first a Data sets of phases A, B, and C of the outgoing lines in the first... t The mean value of reactive power during a time period that varies according to a normal distribution. μ AQat , μ BQat , μ CQat and variance σ AQat , σ BQat , σ CQat ; determining the first a line and the sum of the charging powers of the electric vehicles on the first t line and the sum of the charging powers of the electric vehicles on the first t -1, the probability that the active power difference is greater than the sum of the charging powers of the electric vehicles on the first a line, is calculated by the following formula: ， ， ， ， Wherein, Pr{} is a probability function, N EVa is the number of electric vehicles configured on the first a outlet, N AEVa is the number of electric vehicles configured on the first a outlet A phase, N BEVa is the number of electric vehicles configured on the first a outlet B phase, N CEVa is the number of electric vehicles configured on the first a outlet C phase, k SEVa , k AEVa , k BEVa , k CEVa are respectively the first a outlet and A, B, C phase active power difference value and the sum of the first a outlet line electric vehicle charging active power limit coefficient, P SEVa , P AEVa , P BEVa , P CEVa are respectively the first a outlet and A, B, C phase electric vehicle charging active power; Determine the first a The outgoing lines and phases A, B, and C during the time period t and time period t -1 The reactive power difference is greater than the configuration in the first a The probability of the sum of reactive power of electric vehicle charging on each outgoing line is calculated using the following formula: ， ， ， ， Wherein, Q SEVa , Q AEVa , Q BEVa , Q CEVa Respectively configure the first a Line and A, B, C phase on the electric vehicle charging reactive power.

4. The method of claim 3, wherein the method further comprises: The data set of voltage of the outgoing line and A, B and C phases in the transformer area is constructed in particular as follows using the historical data of the electric vehicle in the transformer area: According to the historical data of the transformer area electric vehicle, select n year historical data and N historical period, construct the first a outlet and A, B, C phase voltage data set, represented as: V Sa = [ V Sa1 , V Sa2 ,…, V SaN ]， V Aa = [ V Aa1 , V Aa2 ,…, V AaN ]， V Ba = [ V Ba1 , V Ba2 ,…, V BaN ]， V Ca = [ V Ca1 , V Ca2 ,…, V CaN ]， wherein, V Sa the voltage data set for the a th phase conductor, V Aa the voltage data set for the a th phase conductor, V Ba the voltage data set for the a th phase conductor, V Ca the voltage data set for the a th phase conductor, V SaN the voltage data set for the a th phase conductor over the time period N , V AaN the voltage data set for the a th phase conductor over the time period N , V BaN the voltage data set for the a th phase conductor over the time period N , V CaN the voltage data set for the a th phase conductor over the time period N , N is the number of historical time periods.

5. The method of claim 4, wherein the method further comprises: The probability value of voltage of the outgoing line and A, B and C phases in the transformer area is calculated in particular as follows: The data information of the area outlet and A, B, C phase voltage is acquired from the metering automation system, the mean value of the voltage of the first time period is determined according to the normal distribution law a of the first outlet data set t , μ SVat and the variance σ SVat of the first time period, the mean value of the voltage of the first time period is determined according to the normal distribution law a of the A, B, C phase data set of the first outlet t , μ AVat , μ BVat , μ CVat and the variance σ AVat , σ BVat , σ CVat of the first time period. Determine the probability that the voltage value of the A, B, and C phases at the time period t is lower than the rated voltage value of the first outgoing line and the A, B, and C phases. a t Determine the probability that the voltage value of the A, B, and C phases at the time period t is lower than the rated voltage value of the first outgoing line and the A, B, and C phases. a Determine the probability that the voltage value of the A, B, and C phases at the time period t is lower than​ P SVt = Pr{ V Sat < k V V R}， P AVt = Pr{ V Aat < k V V R}， P BVt = Pr{ V Bat < k V V R}， P CVt = Pr{ V Cat < k V V R}， Wherein, Pr{} is a probability function, k V <1 is a coefficient of voltage drop caused by electric vehicle charging, V R is a rated voltage.

6. The method of claim 5, wherein the method further comprises: The formula for calculating the active charging probability of the electric vehicle in the transformer area is: ， ， wherein k SP is the active influence factor, erf is the error function, and the expression is: y is the error function, and the expression is: ， The formula for calculating the reactive charging probability of the electric vehicle in the transformer area is: ， ， wherein, k SQ is the reactive influence factor.

7. The method of claim 6, wherein the method further comprises: The formula for calculating the voltage charging probability of the electric vehicle in the transformer area is: ， ， wherein, k SV is the active influence factor.

8. The method according to claim 7, wherein, The expression for calculating the charging probability of the electric vehicle in the transformer area is: ， wherein , , are weight coefficients.

9. A system for calculating the probability of electric vehicle charging in a transformer area, characterized by, The method for calculating the charging probability of the electric vehicle in the transformer area according to any one of claims 1-8 comprises a power data set construction module, a power probability calculation module, a voltage data set construction module, a voltage probability calculation module, a power charging probability calculation module, a voltage charging probability module and an automobile charging probability obtaining module. The power data set construction module is used to construct a data set of active power and reactive power of the outgoing line and A, B and C phases in the transformer area according to the historical data of the electric vehicle in the transformer area. The power probability calculation module is used to calculate the probability of active power and reactive power of the outgoing line and A, B and C phases in the transformer area. The voltage data set construction module is used to construct a data set of voltage of the outgoing line and A, B and C phases in the transformer area according to the historical data of the electric vehicle in the transformer area. The voltage probability calculation module is used to calculate the probability value of voltage of the outgoing line and A, B and C phases in the transformer area. The power charging probability calculation module introduces active and reactive power influence factors based on the correlation analysis of the historical active power and reactive power of the electric vehicle in the transformer area, and respectively calculates the active charging probability and the reactive charging probability of the electric vehicle in the transformer area. The voltage charging probability module introduces a voltage influence factor based on the correlation analysis of the historical voltage of the electric vehicle in the transformer area, and calculates the voltage charging probability of the electric vehicle in the transformer area. The automobile charging probability obtaining module is used for calculating the charging probability of the electric automobile in the transformer area in combination with the active charging probability, the reactive charging probability and the voltage charging probability of the electric automobile.

10. A computer-readable storage medium storing a program, characterized in that, The program is executed by the processor to realize the method for calculating the charging probability of the electric automobile in the transformer area according to any one of claims 1-8.

Images