A distributed charging pile cloud energy storage system operation status monitoring system and method

Abstract

The present invention relates to the field of charging pile energy storage, and specifically to a distributed charging pile cloud energy storage system operation status supervision system and method, comprising a historical charging event acquisition module, an optimal charging event set construction module, a preference index analysis module, a scoring selection parameter analysis module and a maximum power grid demand warning module; the historical charging event acquisition module acquires historical charging events recorded by each charging station in a monitoring area; the optimal charging event set construction module selects the optimal charging events recorded by each charging station in the monitoring area for each user vehicle and forms an optimal charging event set; the preference index analysis module analyzes the first preference index and the second preference index of multiple user vehicles at each multiple set corresponding to the charging station; the scoring selection parameter analysis module generates the scoring selection parameters of the same user vehicle for each charging station; the maximum power grid demand warning module is used to analyze the maximum power grid demand required by the charging station in a unit time period.

Description

Technical Field

[0001] The present invention relates to the field of charging pile energy storage technology, and in particular to a distributed charging pile cloud energy storage system operation status monitoring system and method. Background Art

[0002] In recent years, with the enhancement of environmental awareness and the rapid development of the new energy vehicle market, smart charging piles, as one of the transportation infrastructures of the new era, play an increasingly important role. Due to the characteristics of electric vehicles such as high speed, heavy load, and long travel distance, electric vehicle charging and swapping stations not only bring convenient services to users, but also face challenges such as high charging demand, uneven distribution of charging load time, and large peak-to-valley difference in load. With the development of smart grid and renewable energy technologies in recent years, a photovoltaic storage charging and discharging integrated charging station with distributed power generation and energy storage technology has been proposed and is considered to be an effective solution to the problem of large-scale charging of electric vehicles. The photovoltaic storage charging and discharging integrated charging pile can not only alleviate the impact of a large number of electric vehicle charging on the power grid, but also use new energy to reduce carbon dioxide emissions, and can realize the multi-energy complementary cycle of clean energy. This system can also solve the problem of insufficient land resources, realize self-use of photovoltaic power, improve the utilization rate of new energy, reduce carbon emissions, smooth peaks and fill valleys, and maximize economic benefits; however, there is still a problem with this charging method, that is, the electric energy of the charging station is converted based on the real-time power supply needs of vehicles. When the photovoltaic storage energy is not enough, it is still necessary to purchase a certain amount of electricity from the power grid in real time. Therefore, when there is insufficient photovoltaic storage energy and the number of vehicles demanding electricity is concentrated, it will still cause a certain amount of pressure on the power grid. Summary of the invention

[0003] The purpose of the present invention is to provide a distributed charging pile cloud energy storage system operating status monitoring system and method to solve the problems raised in the above background technology.

[0004] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for supervising the operation status of a distributed charging pile cloud energy storage system, comprising the following analysis steps:

[0005] Step S100: Obtain historical charging events recorded by each charging station in the monitoring area, where the charging station is a photovoltaic storage charging and discharging integrated charging station, and select the optimal charging events recorded by each charging station in the monitoring area for each user's vehicle based on the historical charging events to form an optimal charging event set;

[0006] Step S200: Based on the optimal charging event set of each charging station in the monitoring area, when the number of the same user vehicle recorded in the optimal charging event set is greater than one, mark the user vehicle as a multi-user vehicle, and the optimal charging event set recorded by the multi-user vehicle as a multi-set; analyze the first preference index of the multi-user vehicle corresponding to the charging station in each multi-set;

[0007] Step S300: extracting the historical charging times of the user's vehicle at the charging station and combining it with the optimal charging event, analyzing the second preference index of each user's vehicle at the corresponding charging station; generating a scoring selection parameter for the same user's vehicle for each charging station based on the second preference index and the first preference index;

[0008] The first preference index is based on the actual charging efficiency of the vehicle and the charging pressure of the charging station, while the second preference index is based on the actual user choice. The two different dimensions are used to make data judgments on the user's choice of charging station, thereby improving the user's high-precision prediction based on the available charging stations.

[0009] Step S400: monitor the real-time user vehicles in the responsive area of ​​each charging station within a unit time period in real time, determine whether the real-time user vehicle is a historically recorded user vehicle, and based on the judgment result combined with the score selection parameter, extract the real-time power supply situation of the charging station to analyze the maximum grid demand required by the charging station within the unit time period.

[0010] Furthermore, step S100 includes the following specific steps:

[0011] Step S110: The historical charging event records the charging mode implemented by the user's vehicle based on the charging station. The charging mode includes charging by using the power of the photovoltaic power generation system, the power of the energy storage system and the power purchased from the power grid. The power of the photovoltaic power generation system has a higher priority than the power of the energy storage system and the power purchased from the power grid. The historical charging event also records the endpoint power Q and charging time T of the user's vehicle. The endpoint power refers to the initial power of the user's vehicle and the power at the end. The formula is used:

[0012] Y i =a1*(Q 2i -Q 1i / T i )+a2*P i

[0013] Calculate the charging characteristic value Y of the i-th user vehicle in each charging station i , where Q 2i represents the power of the i-th user's vehicle at the end of charging, Q 1i represents the initial charging capacity of the i-th user's vehicle, T i represents the charging time of the i-th user’s vehicle, P i represents the power consumption pressure characteristic value of the i-th user vehicle corresponding to the charging stage, and a1 represents the unit charging capacity (Q 2i -Q 1i / T i ) corresponding to the reference coefficient, a2 represents the corresponding reference coefficient of the voltage characteristic value;

[0014] Step S120: When the power source for the charging stage of the user vehicle is only the electric energy from the photovoltaic-storage power generation system, the power consumption pressure eigenvalue is K1; when the power source for the charging stage of the user vehicle is only the electric energy from the energy storage system, the power consumption pressure eigenvalue is K2; when the power source for the charging stage of the user vehicle is only the electric energy purchased from the power grid, the power consumption pressure eigenvalue is K3, where 1≥K1>K2>K3>0;

[0015] When the power source for the charging stage of the user vehicle is the electric energy from the photovoltaic-storage power generation system and the electric energy from the energy storage system, the power consumption pressure eigenvalue is K1 - b1, where K2 < K1 - b1 < K1; when the power source for the charging stage of the user vehicle is the electric energy from the energy storage system and the electric energy purchased from the power grid, the power consumption pressure eigenvalue is K2 - b1, where K3 < K2 - b1 < K2; when the power source for the charging stage of the user vehicle is the electric energy from the photovoltaic-storage power generation system, the electric energy from the energy storage system, and the electric energy purchased from the power grid, the power consumption pressure eigenvalue is K2 - b2, where K3 < K2 - b2 < K2 - b1; P = {K1, K2, K3, K1 - b1, K2 - b1, K2 - b2};

[0016] Step S130: When the number of records of the same user vehicle in the same charging station is not one, extract the maximum value Y of the charging characteristics of the user imax The corresponding charging event is used as the optimal charging event, and the optimal charging events of all users recorded in the same charging station form an optimal charging event set.

[0017] The purpose of analyzing the optimal charging event is to achieve a two-way evaluation of high-energy-efficiency charging events from two aspects: the charging station and the user vehicle, which not only reduces the risk of centralized power supply of the charging piles but also enables the user to retain a better charging experience, so as to prepare for the user to better select the corresponding charging piles when there is a choice.

[0018] Further, Step S200 includes the following specific steps:

[0019] Step S210: When the number of optimal charging event sets where the same user vehicle is recorded is one, output the first preference index X = 1;

[0020] Step S220: Extract the unit charging amount W j and the power consumption pressure eigenvalue P j recorded in the charging station corresponding to the jth multi-user vehicle multi-set, and mark the maximum value W max of the unit charging amount and the maximum value P max of the power consumption pressure eigenvalue in each multi-user vehicle corresponding multi-set. Using the formula:

[0021] X j = (Wj / W max )*(P j / P max )

[0022] Calculate the first preference index X of the corresponding charging station when each multivariate user vehicle is in the jth multivariate set j .

[0023] Furthermore, step S300 includes the following specific processes:

[0024] Step S310: Taking the optimal charging event set recorded by each charging station in the monitoring area as the analysis object, extract the number of charging event records m1 of each user vehicle at the corresponding charging station and the charging characteristic value Y of each historical charging event; excluding the optimal charging event, the charging characteristic values ​​of the historical charging events recorded by the corresponding user vehicle in the same charging station are compared with the charging characteristic value of the optimal charging event one by one to obtain a first difference C, C=Ymax-Y;

[0025] Step S320: extracting historical charging events with a first difference value less than or equal to a first difference threshold as similar charging events, obtaining the number of records of similar charging events m2, and calculating the second preference index Z of each user vehicle at the corresponding charging station using the formula: Z=m2 / m1;

[0026] Step S330: Obtain the first preference index X and the second preference index Z corresponding to the same charging station for each user vehicle, and use the formula: F=r1*X+r2*Z to calculate the score selection parameter F for each user vehicle facing the corresponding charging station, where r1 represents the reference coefficient corresponding to the first preference index, and r2 represents the reference coefficient corresponding to the second preference index.

[0027] Furthermore, step S400 includes the following specific steps:

[0028] Step S410: The responsive area refers to the maximum area that the user vehicle system can search and respond to for the user vehicle based on the charging pile cloud energy storage system when interconnected;

[0029] Step S420: Taking each charging station as a monitoring station, when the real-time user vehicle that responds to the monitoring station in the responsive area of ​​the unit time period is not a historical record user vehicle, extracting all charging station distances recorded by the vehicle system corresponding to the real-time user vehicle, and when the distance between the monitoring station and the real-time user vehicle is less than or equal to the average distance of all charging stations, marking the real-time user vehicle as a real-time target vehicle, otherwise, marking the real-time user vehicle as a real-time exiled vehicle;

[0030] When the real-time user vehicle is a historical record vehicle, extract the scoring selection parameter F of the monitoring station corresponding to the real-time user vehicle. When the number of scoring selection parameters recorded by the real-time user vehicle is one, mark the real-time user vehicle as a real-time target vehicle; when the number of scoring selection parameters recorded by the real-time user vehicle is greater than one, calculate the average scoring selection parameter of all charging stations recorded by the real-time user vehicle. When the scoring selection parameter corresponding to the monitoring station is greater than or equal to the average scoring selection parameter, mark the real-time user vehicle as a real-time target vehicle; otherwise, mark the real-time user vehicle as a real-time exile vehicle;

[0031] Step S430: Extract all real-time target vehicles marked by the monitoring station within a unit time period, obtain the total remaining electric energy E0 of the real-time photovoltaic power generation system and energy storage system recorded by the monitoring station, and the average power consumption e0 of the historical records of the real-time target vehicles; calculate the estimated total power consumption E of all real-time target vehicles, E = ∑e0; the analysis of the total remaining electric energy includes the estimated amount of power supply to the existing real-time vehicles by the real-time photovoltaic power generation system and energy storage system;

[0032] Step S440: When E0 ≥ E, the charging pile cloud energy storage system continues to monitor; when E0 < E, calculate the maximum grid demand E1 required by the monitoring station within a unit time period, E1 = E - E0; and at the end of the unit time period, the charging pile cloud energy storage system sends a power purchase reminder to the grid with the maximum grid demand.

[0033] Analyzing the maximum grid demand that can be responded to within a unit time period is to weaken the situation of the grid charging peak, estimate in advance the user vehicles that may go to the corresponding charging station and combine the actual remaining electric energy in the station, and purchase electricity during off-peak hours in advance when power needs to be purchased from the grid to store, reducing the risk of the pressure on the grid energy caused by centralized power purchase.

[0034] A distributed charging pile cloud energy storage system operation status supervision system, including a historical charging event acquisition module, an optimal charging event set construction module, a preference index analysis module, a scoring selection parameter analysis module, and a maximum grid demand early warning module;

[0035] The historical charging event acquisition module is used to obtain the historical charging events recorded by each charging station in the monitoring area;

[0036] The optimal charging event set construction module is used to screen out the optimal charging events of each user vehicle recorded by each charging station in the monitoring area and form an optimal charging event set;

[0037] The preference index analysis module is used to analyze the first preference index and the second preference index of multiple user vehicles at the corresponding charging stations in each multiple set;

[0038] The scoring selection parameter analysis module is used to generate the scoring selection parameters of the same user vehicle for each charging station;

[0039] The maximum grid demand warning module is used to extract the real-time power supply situation of the charging station and analyze the maximum grid demand required by the charging station in a unit time period.

[0040] Furthermore, the optimal charging event set construction module includes a historical charging event extraction unit, a charging characteristic value calculation unit, a power consumption pressure characteristic value analysis unit and an optimal charging event determination unit;

[0041] The historical charging event extraction unit is used to record the charging mode implemented by the user's vehicle based on the charging station, as well as the terminal power and charging time of the user's vehicle;

[0042] The charging characteristic value calculation unit is used to calculate the charging characteristic value of the user vehicle in each charging station based on the data in the historical charging event extraction unit;

[0043] The power pressure characteristic value analysis unit is used to analyze the characteristic value corresponding to the power source of the user's vehicle in the corresponding charging stage;

[0044] The optimal charging event determination unit is used to extract the charging event corresponding to the maximum value of the user's charging characteristic as the optimal charging event, and to form an optimal charging event set by recording the optimal charging events of all users in the same charging station.

[0045] Further, the preference index analysis module includes a first preference index calculation unit and a second preference index calculation unit;

[0046] The first preference index calculation unit is used to extract the unit charging amount and power pressure characteristic values ​​recorded by the charging station corresponding to the multi-user set where the multi-user vehicle is located, and calculate the first preference index of the charging station corresponding to each multi-user set where the multi-user vehicle is located;

[0047] The second preference index calculation unit is used to take the optimal charging event set recorded by each charging station in the monitoring area as the analysis object, extract the number of charging event records of each user vehicle at the corresponding charging station recorded by the analysis object, and calculate the second preference index of each user vehicle at the corresponding charging station based on the analysis of the first difference.

[0048] Further, the maximum grid demand warning module includes a responsive area determination unit, a real-time target vehicle marking unit, a site power analysis unit, and a maximum grid demand calculation unit;

[0049] The responsive area determination unit is used to determine the maximum area that the user vehicle system can search and respond to for the user vehicle based on the charging pile cloud energy storage system when interconnected;

[0050] The real-time target vehicle marking unit is used to analyze each charging station as a monitoring station, determine the relationship between the real-time user vehicle and the historical record user vehicle to determine the real-time target vehicle;

[0051] The site power analysis unit is used to analyze the total remaining power of the real-time photovoltaic power generation system and the energy storage system recorded by the monitoring site, as well as the average power consumption of the real-time target vehicle's historical records, to estimate the estimated total power consumption of the real-time target vehicle;

[0052] The maximum grid demand calculation unit is used to calculate the maximum grid demand required by the monitoring site within a unit time period based on the data recorded in the site power analysis unit; and at the end of the unit time period, the charging pile cloud energy storage system reminds the grid to purchase electricity with the maximum grid demand.

[0053] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention analyzes the charging events recorded by the integrated photovoltaic storage and charging charging station, responds to the charging events in the monitoring area where there may be a two-way selection relationship between user vehicles and charging stations, and estimates the user vehicles that may have power demand within a unit time period of each charging station based on this, performs advance prediction analysis, and estimates the total demand based on the historical charging demand of the corresponding vehicle, and compares it with the power of the remaining energy storage system and the photovoltaic charging system in the current charging station, so as to achieve reasonable allocation while reducing the power purchased by the power grid. When there is a difference between the power of the remaining energy storage system and the photovoltaic charging system and the estimated total demand, advance purchase is made to avoid the pressure on the power grid caused by the purchase of vehicles when they are concentrated, thereby weakening the peak charging situation of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0055] Figure 1 It is a structural schematic diagram of a distributed charging pile cloud energy storage system operation status monitoring system of the present invention. DETAILED DESCRIPTION

[0056] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] See also Figure 1, the present invention provides a technical solution: a distributed charging pile cloud energy storage system operation status monitoring system, including a historical charging event acquisition module, an optimal charging event set construction module, a preference index analysis module, a scoring selection parameter analysis module and a maximum power grid demand warning module;

[0058] The historical charging event acquisition module is used to obtain the historical charging events recorded by each charging station in the monitoring area;

[0059] The optimal charging event set building module is used to filter out the optimal charging events recorded by each charging station in the monitoring area for each user's vehicle and form an optimal charging event set;

[0060] The preference index analysis module is used to analyze the first preference index and the second preference index of the multiple user vehicles at the charging station corresponding to each multiple set;

[0061] The scoring selection parameter analysis module is used to generate scoring selection parameters for the same user vehicle facing each charging station;

[0062] The maximum grid demand warning module is used to extract the real-time power supply situation of the charging station and analyze the maximum grid demand required by the charging station in a unit time period.

[0063] The optimal charging event set construction module includes a historical charging event extraction unit, a charging characteristic value calculation unit, a power consumption pressure characteristic value analysis unit and an optimal charging event determination unit;

[0064] The historical charging event extraction unit is used to record the charging mode implemented by the user's vehicle based on the charging station, as well as the terminal power and charging time of the user's vehicle;

[0065] The charging characteristic value calculation unit is used to calculate the charging characteristic value of the user vehicle in each charging station based on the data in the historical charging event extraction unit;

[0066] The power pressure characteristic value analysis unit is used to analyze the characteristic value corresponding to the power source of the user's vehicle in the corresponding charging stage;

[0067] The optimal charging event determination unit is used to extract the charging event corresponding to the maximum value of the user's charging characteristic as the optimal charging event, and to form an optimal charging event set by recording the optimal charging events of all users in the same charging station.

[0068] The preference index analysis module includes a first preference index calculation unit and a second preference index calculation unit;

[0069] The first preference index calculation unit is used to extract the unit charging amount and power pressure characteristic values ​​recorded by the charging station corresponding to the multi-user set where the multi-user vehicle is located, and calculate the first preference index of the charging station corresponding to each multi-user set where the multi-user vehicle is located;

[0070] The second preference index calculation unit is used to take the optimal charging event set recorded by each charging station in the monitoring area as the analysis object, extract the number of charging event records of each user vehicle at the corresponding charging station recorded by the analysis object, and calculate the second preference index of each user vehicle at the corresponding charging station based on the analysis of the first difference.

[0071] The maximum grid demand warning module includes a responsive area determination unit, a real-time target vehicle marking unit, a site power analysis unit, and a maximum grid demand calculation unit;

[0072] The responsive area determination unit is used to determine the maximum area that the user vehicle system can search and respond to for the user vehicle based on the charging pile cloud energy storage system when interconnected;

[0073] The real-time target vehicle marking unit is used to analyze each charging station as a monitoring station, determine the relationship between the real-time user vehicle and the historical record user vehicle to determine the real-time target vehicle;

[0074] The site power analysis unit is used to analyze the total remaining power of the real-time photovoltaic power generation system and the energy storage system recorded by the monitoring site, as well as the average power consumption of the real-time target vehicle's historical records, to estimate the estimated total power consumption of the real-time target vehicle;

[0075] The maximum grid demand calculation unit is used to calculate the maximum grid demand required by the monitoring site within a unit time period based on the data recorded in the site power analysis unit; and at the end of the unit time period, the charging pile cloud energy storage system reminds the grid to purchase electricity with the maximum grid demand.

[0076] A method for monitoring the operating status of a distributed charging pile cloud energy storage system includes the following analysis steps:

[0077] Step S100: Obtain historical charging events recorded by each charging station in the monitoring area, where the charging station is a photovoltaic storage charging and discharging integrated charging station, and select the optimal charging events recorded by each charging station in the monitoring area for each user's vehicle based on the historical charging events to form an optimal charging event set;

[0078] The photovoltaic storage charging and discharging integrated charging station is composed of a power supply and distribution system, an energy storage system, a photovoltaic power generation system, and a charging system. The system includes a solar cell array, a photovoltaic inverter, a battery, a bidirectional AC device, a charging pile, and a DC-DC converter. In the integrated station, the power generated by the photovoltaic power generation system first meets the needs of the charging station. When the load demand is not met, the energy storage system discharges. If it still cannot meet the demand, it purchases electricity from the power grid. When the photovoltaic output is in excess, the excess electricity can be used to charge the energy storage system.

[0079] Step S200: Based on the optimal charging event set of each charging station in the monitoring area, when the number of the same user vehicle recorded in the optimal charging event set is greater than one, mark the user vehicle as a multi-user vehicle, and the optimal charging event set recorded by the multi-user vehicle as a multi-set; analyze the first preference index of the multi-user vehicle corresponding to the charging station in each multi-set;

[0080] Step S300: extracting the historical charging times of the user's vehicle at the charging station and combining it with the optimal charging event, analyzing the second preference index of each user's vehicle at the corresponding charging station; generating a scoring selection parameter for the same user's vehicle for each charging station based on the second preference index and the first preference index;

[0081] The first preference index is based on the actual charging efficiency of the vehicle and the charging pressure of the charging station, while the second preference index is based on the actual user choice. The two different dimensions are used to make data judgments on the user's choice of charging station, thereby improving the user's high-precision prediction based on the available charging stations.

[0082] Step S400: monitor the real-time user vehicles in the responsive area of ​​each charging station within a unit time period in real time, determine whether the real-time user vehicle is a historically recorded user vehicle, and based on the judgment result combined with the score selection parameter, extract the real-time power supply situation of the charging station to analyze the maximum grid demand required by the charging station within the unit time period.

[0083] Step S100 includes the following specific steps:

[0084] Step S110: The historical charging event records the charging mode implemented by the user's vehicle based on the charging station. The charging mode includes charging by using the power of the photovoltaic power generation system, the power of the energy storage system and the power purchased from the power grid. The power of the photovoltaic power generation system has a higher priority than the power of the energy storage system and the power purchased from the power grid. The historical charging event also records the endpoint power Q and charging time T of the user's vehicle. The endpoint power refers to the initial power of the user's vehicle and the power at the end. The formula is used:

[0085] Y i =a1*(Q 2i -Q 1i / T i )+a2*P i

[0086] Calculate the charging characteristic value Y of the i-th user vehicle in each charging station i , where Q 2i represents the power of the i-th user's vehicle at the end of charging, Q 1i represents the initial charging capacity of the i-th user's vehicle, T i represents the charging time of the i-th user’s vehicle, Pi represents the power consumption pressure characteristic value corresponding to the charging stage of the i-th user vehicle, and a1 represents the reference coefficient corresponding to the unit charge amount (Q 2i -Q 1i / T i ), and a2 represents the reference coefficient corresponding to the power consumption pressure characteristic value;

[0087] Step S120: When the power source for the charging stage of the user vehicle is only the electric energy of the photovoltaic energy storage power generation system, the power consumption pressure characteristic value is K1; when the power source for the charging stage of the user vehicle is only the electric energy of the energy storage system, the power consumption pressure characteristic value is K2; when the power source for the charging stage of the user vehicle is only the electric energy purchased from the power grid, the power consumption pressure characteristic value is K3, where 1≥K1>K2>K3>0; and K1, K2, and K3 are all values after normalization;

[0088] When the power source for the charging stage of the user vehicle is the electric energy of the photovoltaic energy storage power generation system and the electric energy of the energy storage system, the power consumption pressure characteristic value is K1 - b1, where K2 < K1 - b1 < K1; when the power source for the charging stage of the user vehicle is the electric energy of the energy storage system and the electric energy purchased from the power grid, the power consumption pressure characteristic value is K2 - b1, where K3 < K2 - b1 < K2; when the power source for the charging stage of the user vehicle is the electric energy of the photovoltaic energy storage power generation system, the electric energy of the energy storage system, and the electric energy purchased from the power grid, the power consumption pressure characteristic value is K2 - b2, where K3 < K2 - b2 < K2 - b1; P = {K1, K2, K3, K1 - b1, K2 - b1, K2 - b2};

[0089] Step S130: When the number of records of the same user vehicle in the same charging station is not one, extract the maximum charging characteristic value Y imax corresponding charging event as the optimal charging event, and form an optimal charging event set by recording all the optimal charging events of users in the same charging station.

[0090] The purpose of analyzing the optimal charging event is to achieve a two-way evaluation of high-energy charging events from two aspects: the charging station and the user vehicle, which not only reduces the risk of centralized power supply of the charging piles but also enables users to retain a better charging experience, thus preparing for users to better select the corresponding charging piles when there is a choice.

[0091] Step S200 includes the following specific steps:

[0092] Step S210: When the number of optimal charging event sets where the same user vehicle record is located is one, output the first preference index X = 1;

[0093] Step S220: Extract the unit charge amount W recorded by the charging station corresponding to the j-th multi-element set where the multi-user vehicle is located jand the power pressure characteristic value P j , and mark the maximum unit charge capacity W in the multivariate set corresponding to each multivariate user vehicle max and the maximum value of the characteristic value of power consumption pressure P max , using the formula:

[0094] X j =(W j / W max )*(P j / P max )

[0095] Calculate the first preference index X of the corresponding charging station when each multivariate user vehicle is in the jth multivariate set j .

[0096] Step S300 includes the following specific processes:

[0097] Step S310: Taking the optimal charging event set recorded by each charging station in the monitoring area as the analysis object, extract the number of charging event records m1 of each user vehicle at the corresponding charging station and the charging characteristic value Y of each historical charging event; excluding the optimal charging event, the charging characteristic values ​​of the historical charging events recorded by the corresponding user vehicle in the same charging station are compared with the charging characteristic value of the optimal charging event one by one to obtain a first difference C, C=Ymax-Y;

[0098] Step S320: extracting historical charging events with a first difference value less than or equal to a first difference threshold as similar charging events, obtaining the number of records of similar charging events m2, and calculating the second preference index Z of each user vehicle at the corresponding charging station using the formula: Z=m2 / m1;

[0099] Step S330: Obtain the first preference index X and the second preference index Z corresponding to the same charging station for each user vehicle, and use the formula: F=r1*X+r2*Z to calculate the score selection parameter F for each user vehicle facing the corresponding charging station, where r1 represents the reference coefficient corresponding to the first preference index, and r2 represents the reference coefficient corresponding to the second preference index.

[0100] As shown in the examples:

[0101] Three charging stations are recorded in the monitoring area, and all three charging stations are integrated photovoltaic and energy storage charging stations, and three charging modes are implemented in sequence;

[0102] Charging station 1 records charging events as follows: user vehicle 1 first charging, user vehicle 2 first charging, user vehicle 1 second charging;

[0103] Charging station 2 records the charging events as follows: user vehicle 2 is charged for the first time, user vehicle 3 is charged for the first time, user vehicle 1 is charged for the first time, user vehicle 3 is charged for the second time,

[0104] Charging station 3 records the charging events as follows: user vehicle 3 is charged for the first time, user vehicle 4 is charged for the first time, user vehicle 2 is charged for the first time, and user vehicle 4 is charged for the second time;

[0105] Based on the above records, it can be seen that charging station 1 records two user vehicles, and the charging characteristic value of each user vehicle corresponding to each charging event needs to be calculated respectively. Since user vehicle 2 is only recorded once, the optimal charging event corresponding to user vehicle 2 recorded at charging station 1 is the first charging event; and after comparing the data of the two event records of user vehicle 1, it can be obtained that the second charging is the optimal charging event of user vehicle 1; similarly, the optimal charging events of all users recorded by charging station 2 and charging station 3 and the optimal charging event set formed can be obtained;

[0106] It can be seen from the above that the number of optimal charging event sets in the records of user vehicle 1, user vehicle 2, and user vehicle 3 is not 1; the optimal charging event sets of multiple locations of user vehicle 4 only exist in the corresponding record of charging station 3, so the number is 1, and the first preference index X corresponding to user vehicle 4 is output as 1. If the calculation results show that X corresponding to user vehicle 1 is 0.45, X corresponding to user vehicle 2 is 0.73, and X corresponding to user vehicle 3 is 0.51;

[0107] Based on this, the second preference index is further analyzed. If the number of similar charging events recorded by the user's vehicle at charging station 1 is 1, then the second preference index of charging station 1 is 1 / 2, and so on. Calculate all user vehicles recorded at each charging station;

[0108] The scoring selection parameters are based on the user vehicle facing the charging station. For example, based on the historical records, the scoring selection parameters that can be calculated in charging station 3 are user vehicle 2, user vehicle 3, and user vehicle 4.

[0109] Step S400 includes the following specific steps:

[0110] Step S410: The responsive area refers to the maximum area that the user vehicle system can search and respond to for the user vehicle based on the charging pile cloud energy storage system when interconnected; if the user queries the vehicle system and finds that the remaining power can reach all charging stations within the distance, then the maximum area consisting of user vehicles corresponding to the same charging station is the responsive area, and the responsive area can be changed based on the specific situation of the real-time user vehicle;

[0111] Step S420: Taking each charging station as a monitoring site, when the real-time user vehicles responded within the response area of the monitoring site during the unit time period are not the historical record user vehicles, extract all the charging station distances recorded by the vehicle systems corresponding to the real-time user vehicles. When the distance between the monitoring site and the real-time user vehicle is less than or equal to the average distance of all charging stations, mark the real-time user vehicle as a real-time target vehicle; otherwise, mark the real-time user vehicle as a real-time exiled vehicle.

[0112] When the real-time user vehicle is a historical record vehicle, extract the scoring selection parameter F of the corresponding monitoring site of the real-time user vehicle. When the number of recorded scoring selection parameters of the real-time user vehicle is one, mark the real-time user vehicle as a real-time target vehicle; when the number of recorded scoring selection parameters of the real-time user vehicle is greater than one, calculate the average scoring selection parameter of all charging stations recorded by the real-time user vehicle. When the scoring selection parameter corresponding to the monitoring site is greater than or equal to the average scoring selection parameter, mark the real-time user vehicle as a real-time target vehicle; otherwise, mark the real-time user vehicle as a real-time exiled vehicle.

[0113] Step S430: Extract all the real-time target vehicles marked by the monitoring site during the unit time period, obtain the total remaining electric energy E0 of the real-time photovoltaic power generation system and energy storage system recorded by the monitoring site, and the average power consumption e0 historically recorded by the real-time target vehicles; calculate the estimated total power consumption E of all real-time target vehicles, E = ∑e0; the analysis of the total remaining electric energy includes the estimated amount of power supply to the real-time existing vehicles by the real-time photovoltaic power generation system and energy storage system.

[0114] Step S440: When E0 ≥ E, the charging pile cloud energy storage system continues to monitor; when E0 < E, calculate the maximum grid demand E1 required by the monitoring site during the unit time period, E1 = E - E0; and at the end of the unit time period, the charging pile cloud energy storage system sends a power purchase reminder to the grid with the maximum grid demand. The unit time period refers to the monitoring duration corresponding to the minimum number of real-time target vehicles that is greater than the complete use of the total remaining electric energy and less than the estimated arrival time of the nearest real-time target vehicle at the monitoring site.

[0115] As shown in the embodiment: if the total remaining power can supply 3 real-time target vehicles for charging, and the 3 real-time target vehicles use a total monitoring time of 5 minutes, then the unit time period can be set to be greater than 5 minutes. During the continuous monitoring process, the nearest target vehicle is selected in real time to estimate the time it takes to reach the monitoring site. If the minimum time is less than or equal to 5 minutes, the unit time period is set to 5 minutes + continuous monitoring time; if the minimum time is 15 minutes, the unit time period can be set to an interval of (5, 15); generally, the longer the interval is set, the better, such as setting 14 minutes, and responding to the maximum grid demand at the end of 14 minutes. The monitoring site performs polling judgment and analysis in real time in a unit time period cycle. After each unit time period ends, the monitoring starting point of the next unit time period is monitored at the end point of the previous unit time period.

[0116] The purpose of analyzing the maximum grid demand that can be responded to per unit time period is to weaken the peak charging situation of the grid, estimate in advance the user vehicles that may go to the corresponding charging station and combine the actual remaining power in the station to purchase electricity from the grid in advance when it is necessary to store electricity, thereby reducing the risk of concentrated electricity purchases causing pressure on the grid energy.

[0117] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0118] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for supervising the operation status of a distributed charging pile cloud energy storage system, characterized in that: Including the following analysis steps: Step S100: Obtain the historical charging events recorded by each charging station in the monitoring area. The charging station is a photovoltaic-storage-charging-discharging integrated charging station. Based on the historical charging events, screen out the optimal charging events of each user vehicle recorded by each charging station in the monitoring area and form an optimal charging event set; The step S100 includes the following specific steps: Step S110: The historical charging event records the charging mode implemented by the user's vehicle based on the charging station. The charging mode includes charging by using the power of the photovoltaic power generation system, the power of the energy storage system and the power purchased from the power grid. The power of the photovoltaic power generation system has a higher priority than the power of the energy storage system and the power purchased from the power grid. The historical charging event also records the endpoint power Q and charging time T of the user's vehicle. The endpoint power refers to the initial power of the user's vehicle and the power at the end. Using the formula: Y i =a1*(Q 2i -Q 1i / T i )+a2*P i Calculate the charging characteristic value Y of the i-th user vehicle in each charging station i , where Q 2i represents the power of the i-th user's vehicle at the end of charging, Q 1i represents the initial charging capacity of the i-th user's vehicle, T i represents the charging time of the i-th user’s vehicle, P i represents the power consumption pressure characteristic value of the i-th user vehicle corresponding to the charging stage, and a1 represents the unit charging capacity (Q 2i -Q 1i / T i ) corresponding to the reference coefficient, a2 represents the corresponding reference coefficient of the voltage characteristic value; Step S120: When the power source of the corresponding charging stage of the user vehicle is only the electric energy of the photovoltaic-storage power generation system, the power consumption pressure characteristic value is K1. When the power source of the corresponding charging stage of the user vehicle is only the electric energy of the energy storage system, the power consumption pressure characteristic value is K2. When the power source of the corresponding charging stage of the user vehicle is only the electric energy purchased from the power grid, the power consumption pressure characteristic value is K3, and 1≥K1>K2>K3>0; When the power source of the corresponding charging stage of the user vehicle is the electric energy of the photovoltaic-storage power generation system and the electric energy of the energy storage system, the power consumption pressure characteristic value is K1 - b1, and K2 < K1 - b1 < K1. When the power source of the corresponding charging stage of the user vehicle is the electric energy of the energy storage system and the electric energy purchased from the power grid, the power consumption pressure characteristic value is K2 - b1, and K3 < K2 - b1 < K2. When the power source of the corresponding charging stage of the user vehicle is the electric energy of the photovoltaic-storage power generation system, the electric energy of the energy storage system, and the electric energy purchased from the power grid, the power consumption pressure characteristic value is K2 - b2, and K3 < K2 - b2 < K2 - b1; P = {K1, K2, K3, K1 - b1, K2 - b1, K2 - b2}; Step S130: When the number of times that the same user's vehicle is recorded in the same charging station is not one, extract the maximum value Y of the user's charging characteristic imax The corresponding charging event is taken as the optimal charging event, and the optimal charging events of all users recorded in the same charging station constitute an optimal charging event set; Step S200: Based on the optimal charging event sets of each charging station in the monitoring area, when the number of records of the same user vehicle in the optimal charging event set is greater than one, mark the user vehicle as a multi-source user vehicle, and the optimal charging event set recorded by the multi-source user vehicle as a multi-source set; analyze the first preference index of the multi-source user vehicle at each charging station corresponding to the multi-source set; The step S200 includes the following specific steps: Step S210: When the number of optimal charging event sets where the record of the same user vehicle is located is one, output the first preference index X = 1; Step S220: Extract the unit charging capacity W recorded by the charging station corresponding to the j-th multi-set where the multi-user vehicle is located j and the power pressure characteristic value P j , and mark the maximum unit charge capacity W in the multivariate set corresponding to each multivariate user vehicle max and the maximum value of the characteristic value of power consumption pressure P max , using the formula: X j =(W j / W max )*(P j / P max ) Calculate the first preference index X of the corresponding charging station when each multivariate user vehicle is in the jth multivariate set j ; Step S300: Extract the historical charging times of the user vehicle recorded at the charging station and combine with the optimal charging event to analyze the second preference index of each user vehicle at the corresponding charging station; generate a scoring selection parameter for the same user vehicle facing each charging station based on the second preference index and the first preference index; The step S300 includes the following specific process: Step S310: Take the optimal charging event set recorded by each charging station in the monitoring area as the analysis object, extract the number of charging event records m1 of each user vehicle recorded by the analysis object at the corresponding charging station, and the charging characteristic value Y of each historical charging event; Excluding the optimal charging event, compare the charging characteristic values of the historical charging events recorded by the corresponding user vehicle at the same charging station with the charging characteristic value of the optimal charging event one by one to obtain the first difference C, C = Ymax - Y; Step S320: Extract historical charging events where the first difference is less than or equal to the first difference threshold as similar charging events, obtain the record count m2 of the similar charging events, and use the formula: Z = m2 / m1 to calculate the second preference index Z of each user vehicle at the corresponding charging station; Step S330: Obtain the first preference index X and the second preference index Z of each user vehicle corresponding to the same charging station, and use the formula: F = r1 * X + r2 * Z to calculate the scoring selection parameter F of each user vehicle for the corresponding charging station, where r1 represents the reference coefficient corresponding to the first preference index, and r2 represents the reference coefficient corresponding to the second preference index; Step S400: Real-time monitor the real-time user vehicles in the responsive area of each charging station within a unit time period, determine whether the real-time user vehicles are historical record user vehicles, and based on the judgment result and combined with the scoring selection parameter, extract the power supply situation of the real-time charging station to analyze the maximum grid demand required by the charging station within a unit time period; The step S400 includes the following specific steps: Step S410: The responsive area refers to the maximum area that the user vehicle system can search and respond to for user vehicles based on the charging pile cloud energy storage system in an interconnected situation; Step S420: Take each charging station as a monitoring site. When the real-time user vehicle responded within the responsive area of the monitoring site within a unit time period is not a historical record user vehicle, extract all the charging station distances recorded by the vehicle system corresponding to the real-time user vehicle. When the distance between the monitoring site and the real-time user vehicle is less than or equal to the average distance of all charging stations, mark the real-time user vehicle as a real-time target vehicle; otherwise, mark the real-time user vehicle as a real-time exile vehicle; When the real-time user vehicle is a historical record vehicle, extract the scoring selection parameter F of the real-time user vehicle corresponding to the monitoring site. When the number of recorded scoring selection parameters of the real-time user vehicle is one, mark the real-time user vehicle as a real-time target vehicle; When the number of recorded scoring selection parameters of the real-time user vehicle is greater than one, calculate the average scoring selection parameter of all charging stations recorded by the real-time user vehicle. When the scoring selection parameter corresponding to the monitoring site is greater than or equal to the average scoring selection parameter, mark the real-time user vehicle as a real-time target vehicle; otherwise, mark the real-time user vehicle as a real-time exile vehicle; Step S430: Extract all the real-time target vehicles marked by the monitoring site within a unit time period, obtain the total remaining electric energy E0 of the real-time energy storage power generation system and the energy storage system recorded by the monitoring site, and the average power consumption e0 historically recorded by the real-time target vehicles; calculate the estimated total power consumption E of all real-time target vehicles, E = ∑e0; Step S440: When E0 ≥ E, the charging pile cloud energy storage system continues to monitor; when E0 < E, calculate the maximum grid demand E1 required by the monitoring site within a unit time period, E1 = E - E0; And at the end of the unit time period, the charging pile cloud energy storage system sends a power purchase reminder to the grid with the maximum grid demand.

2. A distributed charging pile cloud energy storage system operation status monitoring system, such as a distributed charging pile cloud energy storage system operation status monitoring method described in claim 1, characterized in that: It includes a historical charging event acquisition module, an optimal charging event set construction module, a preference index analysis module, a scoring selection parameter analysis module, and a maximum grid demand warning module; The historical charging event acquisition module is used to acquire historical charging events recorded by each charging station in the monitoring area; The optimal charging event set building module is used to filter out the optimal charging events recorded by each charging station in the monitoring area for each user's vehicle and form an optimal charging event set; The preference index analysis module is used to analyze the first preference index and the second preference index of the multiple user vehicles at the charging station corresponding to each multiple set; The scoring selection parameter analysis module is used to generate scoring selection parameters for the same user vehicle facing each charging station; The maximum grid demand warning module is used to extract the real-time power supply situation of the charging station and analyze the maximum grid demand required by the charging station in a unit time period.

3. A distributed charging pile cloud energy storage system operation status monitoring system according to claim 2, characterized in that: The optimal charging event set construction module includes a historical charging event extraction unit, a charging characteristic value calculation unit, a power consumption pressure characteristic value analysis unit and an optimal charging event determination unit; The historical charging event extraction unit is used to record the charging mode implemented by the user's vehicle based on the charging station, as well as the endpoint power and charging duration of the user's vehicle; The charging characteristic value calculation unit is used to calculate the charging characteristic value of the user vehicle in each charging station based on the data in the historical charging event extraction unit; The power consumption pressure characteristic value analysis unit is used to analyze the characteristic value corresponding to the power source of the user's vehicle corresponding to the charging stage; The optimal charging event determination unit is used to extract the charging event corresponding to the maximum value of the user's charging characteristic as the optimal charging event, and to form an optimal charging event set by recording the optimal charging events of all users in the same charging station.

4. A distributed charging pile cloud energy storage system operation status monitoring system according to claim 3, characterized in that: The preference index analysis module includes a first preference index calculation unit and a second preference index calculation unit; The first preference index calculation unit is used to extract the unit charging capacity and power consumption pressure characteristic values ​​recorded by the charging station corresponding to the multi-user set where the multi-user vehicle is located, and calculate the first preference index of the charging station corresponding to each multi-user vehicle when the multi-user vehicle is located in the multi-user set; The second preference index calculation unit is used to take the optimal charging event set recorded by each charging station in the monitoring area as the analysis object, extract the number of charging event records of each user vehicle at the corresponding charging station recorded by the analysis object, and calculate the second preference index of each user vehicle at the corresponding charging station based on the analysis of the first difference.

5. A distributed charging pile cloud energy storage system operation status monitoring system according to claim 4, characterized in that: The maximum grid demand warning module includes a responsive area determination unit, a real-time target vehicle marking unit, a site power analysis unit and a maximum grid demand calculation unit; The responsive area determination unit is used to determine the maximum area that the user vehicle system can search and respond to for the user vehicle based on the charging pile cloud energy storage system when interconnected; The real-time target vehicle marking unit is used to analyze each charging station as a monitoring site, determine the relationship between the real-time user vehicle and the historical record user vehicle to determine the real-time target vehicle; The site power analysis unit is used to analyze the total remaining power of the real-time photovoltaic power generation system and the energy storage system recorded by the monitoring site, as well as the average power consumption of the real-time target vehicle historical records, to estimate the estimated total power consumption of the real-time target vehicle; The maximum grid demand calculation unit is used to calculate the maximum grid demand required by the monitoring site within a unit time period based on the data recorded in the site power analysis unit; At the end of the unit time period, the charging pile cloud energy storage system will remind the power grid to purchase electricity at the maximum power grid demand.

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