A charging pile control system and method based on integrated energy

Abstract

The application provides a charging pile control system and method based on comprehensive energy. The method comprises the following steps: determining a new energy power generation amount prediction curve of the day based on daily meteorological forecast data and real-time observation meteorological data, respectively, determining a daily power consumption prediction curve based on the power consumption data of users in each day in a historical period; generating a control strategy according to the daily power consumption prediction curve, the daily new energy power generation amount prediction curve, a daily city power price curve and the real-time capacity of an energy storage subsystem; and controlling the first electric energy storage or the supply of the charging pile, the charging and discharging of the energy storage subsystem and the access of the grid electric energy based on the control strategy. The technical scheme provided by the application analyzes the electric energy demand end data and the electric energy supply end data, combines the price curve of the electric energy, selects the most economical power combination mode on the basis of meeting the electric energy demand and improves the overall economy.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of electric vehicle charging, in particular to a charging pile control system and method based on comprehensive energy. BACKGROUND

[0002] The current energy industry is in the process of deep reform, mainly from the aspects of energy structure adjustment, supply side reform, renewable energy development, energy marketization and energy environmental protection policy. It is reflected in the vigorous promotion of the transformation of fossil energy to clean energy, and at the same time, the existing power facilities using fossil fuels are transformed into electric power. Especially in the transportation and transportation industry, it is more obvious, that is, to vigorously promote the development of electric vehicles. At the same time, new energy projects are vigorously developed to cope with the rapid increase in electricity demand and provide sufficient energy support. The electric power market transaction is a more prominent representative of energy market reform, which aims to improve resource allocation efficiency, reduce costs, improve service quality, promote clean energy development, and improve the transparency of the electric power industry, so as to promote the healthy development of the electric power industry and improve the overall efficiency.

[0003] Electric vehicles play an important role in the implementation of energy structure adjustment in the transportation industry, and have developed rapidly in recent years, with an average annual growth rate of more than 30%. As a "fuel" supply station for electric vehicles, the construction of charging piles also maintains a high speed. As of now, the number of charging piles in the country has reached 500,000, which has greatly provided power source guarantee for the use and promotion of electric vehicles. At present, the electric energy of most charging piles is obtained from the power grid and then converted into electric energy input into the vehicle. Therefore, the current charging column mainly absorbs electric energy from the power grid in real time and converts it to charge the electric vehicle, which cannot be based on electricity price period and new energy for charging control, and the economy and intelligence are poor. SUMMARY

[0004] The present application provides a charging pile control system and method based on comprehensive energy to at least solve the technical problem of being unable to control charging based on electricity price period and new energy, poor economy and intelligence.

[0005] The first aspect embodiment of the present application provides a charging pile control system based on comprehensive energy, which comprises: a new energy power generation subsystem, a weather observation station, an energy storage subsystem, a strategy scheduling subsystem and a charging pile;

[0006] The new energy power generation subsystem is configured to generate first electric energy and send the first electric energy to the strategy scheduling subsystem;

[0007] The weather observation station is configured to obtain daily weather forecast data and real-time observation weather data, and send the daily weather forecast data and real-time observation weather data to the strategy scheduling subsystem;

[0008] the energy storage subsystem, configured to store or release electric energy;

[0009] the charging pile, configured to provide electric energy for an electric vehicle;

[0010] the strategy scheduling subsystem, configured to acquire a daily electricity consumption prediction curve, a daily new energy power generation prediction curve, a daily grid electricity price curve, and a real-time capacity of the energy storage subsystem;

[0011] the strategy scheduling subsystem, further configured to determine whether a to-be-controlled time belongs to a peak electricity price period, generate a control strategy based on a determination result, the daily electricity consumption prediction curve, the daily new energy power generation prediction curve, the daily grid electricity price curve, and the real-time capacity of the energy storage subsystem, and control the first electric energy storage or supply to the charging pile, the charging and discharging of the energy storage subsystem, and the access of grid electric energy based on the control strategy.

[0012] Preferably, the strategy scheduling subsystem comprises a prediction module, a scheduling module, and a control module.

[0013] the prediction module, configured to determine the daily new energy power generation prediction curve based on the daily weather forecast data and real-time observation weather data;

[0014] the prediction module, further configured to determine the daily electricity consumption prediction curve based on the daily user electricity consumption data in the historical period;

[0015] the scheduling module, configured to determine whether a to-be-controlled time belongs to a peak electricity price period, and generate a control strategy based on a determination result, the daily electricity consumption prediction curve, the daily new energy power generation prediction curve, the daily grid electricity price curve, and the real-time capacity of the energy storage subsystem;

[0016] the control module, configured to control the first electric energy storage or supply to the charging pile, the charging and discharging of the energy storage subsystem, and the access of grid electric energy based on the control strategy.

[0017] Further, the prediction module comprises a correction unit and a prediction unit.

[0018] the correction unit, configured to correct the daily weather forecast data based on the real-time observation weather data to obtain daily predicted weather data;

[0019] the prediction unit, configured to substitute the daily predicted weather data into a pre-established power generation prediction model to obtain the daily new energy power generation prediction curve;

[0020] The prediction unit is further configured to input the user power consumption data of each day in the historical period into a pre-established power consumption prediction model to obtain a power consumption prediction curve of the day.

[0021] Further, the control strategy comprises:

[0022] When the to-be-controlled time belongs to the peak electricity price period, it is determined whether the first electric energy meets the power consumption prediction value corresponding to the to-be-controlled time, if yes, the first electric energy is used to supply power to the charging pile, and the surplus first electric energy is used to charge the energy storage subsystem, otherwise, a first difference between the first electric energy and the power consumption prediction value is determined, and the first electric energy and the electric energy provided by the power grid are used to supply power to the charging pile.

[0023] When the to-be-controlled time belongs to the non-peak electricity price period, the electric energy provided by the power grid is used to supply power to the charging pile, and it is determined whether the first electric energy generated from the to-be-controlled time to the front of the peak electricity price period is less than the real-time capacity of the energy storage subsystem based on the real-time capacity of the energy storage subsystem, if no, the first electric energy is used to charge the energy storage subsystem, if yes, the electric energy provided by the power grid is used to charge the energy storage subsystem at the time corresponding to the low electricity price in the daily power price curve.

[0024] The second aspect embodiment of the application provides a charging pile control method based on comprehensive energy, comprising:

[0025] obtaining daily weather forecast data, real-time observation weather data, user power consumption data of each day in a historical period, a daily power price curve and a real-time capacity of an energy storage subsystem;

[0026] determining a daily new energy power generation prediction curve based on the daily weather forecast data and the real-time observation weather data respectively, and determining a daily power consumption prediction curve based on the user power consumption data of each day in the historical period;

[0027] generating a control strategy according to the daily power consumption prediction curve, the daily new energy power generation prediction curve, the daily power price curve and the real-time capacity of the energy storage subsystem;

[0028] controlling the first electric energy to be stored or supplied to the charging pile, the charging and discharging of the energy storage subsystem and the access of the power grid electric energy based on the control strategy.

[0029] Preferably, the determination of the daily new energy power generation prediction curve based on the daily weather forecast data and the real-time observation weather data comprises:

[0030] correcting the daily weather forecast data according to the real-time observation weather data to obtain daily predicted weather data;

[0031] The day prediction weather data is substituted into a pre-established power generation prediction model to obtain a day new energy power generation prediction curve.

[0032] Further, the day power consumption prediction curve is determined based on the power consumption data of the user in each day in the historical period, and the day new energy power generation prediction curve is determined based on the day prediction weather data.

[0033] The power consumption data of the user in each day in the historical period is input into a pre-established power consumption prediction model to obtain the day power consumption prediction curve.

[0034] The historical period is from the day before the day to the nth day before the day.

[0035] Further, the control strategy is generated according to the day power consumption prediction curve, the day new energy power generation prediction curve, the day electricity price curve and the real-time capacity of the energy storage subsystem.

[0036] When the to-be-controlled time belongs to the peak electricity price period, it is determined whether the first electric energy meets the power consumption prediction value corresponding to the to-be-controlled time, if yes, the first electric energy is used to supply power to the charging pile, and the excess first electric energy is used to charge the energy storage subsystem, otherwise, a first difference between the first electric energy and the power consumption prediction value is determined, and the first electric energy and the electric energy provided by the power grid are used to supply power to the charging pile, wherein the electric energy provided by the power grid is equal to the first difference.

[0037] When the to-be-controlled time belongs to the non-peak electricity price period, the electric energy provided by the power grid is used to supply power to the charging pile, and it is determined whether the first electric energy generated from the to-be-controlled time to the front of the peak electricity price period is less than the real-time capacity of the energy storage subsystem based on the real-time capacity of the energy storage subsystem, if no, the first electric energy is used to charge the energy storage subsystem, if yes, the electric energy provided by the power grid is used to charge the energy storage subsystem at the time corresponding to the low electricity price in the day electricity price curve.

[0038] The third aspect of the application provides an electronic device, which comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor executes the program to implement the method of the second aspect.

[0039] The fourth aspect of the application provides a computer readable storage medium, which stores a computer program, and the program is executed by a processor to implement the method of the second aspect.

[0040] The technical scheme provided by the embodiments of the application at least brings the following beneficial effects:

[0041] The application provides a charging pile control system and method based on comprehensive energy. The system comprises a new energy power generation subsystem, a weather observation station, an energy storage subsystem, a strategy scheduling subsystem and a charging pile. The new energy power generation subsystem is configured to generate first electric energy and send the first electric energy to the strategy scheduling subsystem. The weather observation station is configured to acquire daily weather forecast data and real-time observation weather data and send the daily weather forecast data and the real-time observation weather data to the strategy scheduling subsystem. The energy storage subsystem is configured to store or release electric energy. The charging pile is configured to provide electric energy for an electric vehicle. The strategy scheduling subsystem is configured to acquire a daily power consumption prediction curve, a daily new energy power generation prediction curve, a daily power grid price curve and real-time capacity of the energy storage subsystem. The strategy scheduling subsystem is further configured to determine whether a to-be-controlled time belongs to a peak electricity price period, generate a control strategy based on a determination result, the daily power consumption prediction curve, the daily new energy power generation prediction curve, the daily power grid price curve and the real-time capacity of the energy storage subsystem, and control the first electric energy to be stored or supplied to the charging pile, the energy storage subsystem to charge or discharge and the power grid electric energy to be connected based on the control strategy. The technical scheme provided by the application can analyze electric energy demand end data and electric energy supply end data, combine a price curve of electric energy, select the most economical power combination mode on the basis of meeting the electric energy demand and improve overall economy.

[0042] Additional aspects and advantages of the application will be made apparent by the following description and the appended claims. BRIEF DESCRIPTION OF DRAWINGS

[0043] The above and / or additional aspects and advantages of the application will become apparent and be made clear to those skilled in the art from the following description and the appended claims, taken in conjunction with the accompanying drawings.

[0044] Figure 1 A first structural diagram of a charging pile control system based on comprehensive energy according to an embodiment of the application;

[0045] Figure 2 A second structural diagram of a charging pile control system based on comprehensive energy according to an embodiment of the application;

[0046] Figure 3 A structural diagram of a prediction module according to an embodiment of the application;

[0047] Figure 4 A flowchart of a charging pile control method based on comprehensive energy according to an embodiment of the application;

[0048] Figure 5A single-day time period preset train number column chart according to an embodiment of the present application is provided;

[0049] Figure 6 A single-day power consumption curve diagram of charging piles using city power according to an embodiment of the present application is provided;

[0050] Reference signs

[0051] The new energy power generation subsystem 1, the weather observation station 2, the energy storage subsystem 3, the strategy scheduling subsystem 4, the charging pile 5, the solar panel 1-1, the wind turbine 1-2, the prediction module 4-1, the scheduling module 4-2, the control module 4-3, the correction unit 4-1-1, and the prediction unit 4-1-2. DETAILED DESCRIPTION

[0052] Embodiments of the present application are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference signs represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary and are intended to explain the present application, and cannot be understood as a limitation of the present application.

[0053] The present application provides a charging pile control system and method based on comprehensive energy, wherein the system comprises: a new energy power generation subsystem, a weather observation station, an energy storage subsystem, a strategy scheduling subsystem, and a charging pile; the new energy power generation subsystem is configured to generate first electric energy and send the first electric energy to the strategy scheduling subsystem; the weather observation station is configured to obtain daily weather forecast data and real-time observation weather data, and send the daily weather forecast data and real-time observation weather data to the strategy scheduling subsystem; the energy storage subsystem is configured to store or release electric energy; the charging pile is configured to provide electric energy to an electric vehicle; the strategy scheduling subsystem is configured to obtain a daily electric energy consumption prediction curve, a daily new energy power generation prediction curve, a daily city power price curve, and a real-time capacity of the energy storage subsystem; the strategy scheduling subsystem is further configured to determine whether a to-be-controlled time belongs to a peak electricity price period, generate a control strategy based on a determination result, the daily electric energy consumption prediction curve, the daily new energy power generation prediction curve, the daily city power price curve, and the real-time capacity of the energy storage subsystem, and control the first electric energy to be stored or supplied to the charging pile, the charging and discharging of the energy storage subsystem, and the access of grid electric energy based on the control strategy. The technical solution provided by the present application analyzes electric energy demand end data and electric energy supply end data, and combines the price curve of electric energy, so as to select the most economical power combination mode on the basis of meeting the electric energy demand, thereby improving the overall economy.

[0054] A charging pile control system and method based on comprehensive energy are described below with reference to the accompanying drawings.

[0055] Embodiment one

[0056] Figure 1 A structure diagram of a charging pile control system based on integrated energy according to an embodiment of the present application is shown in Figure 1 The system comprises a new energy electronic system 1, a weather observation station 2, an energy storage subsystem 3, a strategy scheduling subsystem 4 and a charging pile 5.

[0057] The new energy electronic system 1 is configured to generate first electric energy and send the first electric energy to the strategy scheduling subsystem.

[0058] The weather observation station 2 is configured to acquire daily weather forecast data and real-time observation weather data, and send the daily weather forecast data and real-time observation weather data to the strategy scheduling subsystem.

[0059] The energy storage subsystem 3 is configured to store or release electric energy.

[0060] The charging pile 5 is configured to provide electric energy to an electric vehicle.

[0061] The strategy scheduling subsystem 4 is configured to acquire a daily electricity consumption prediction curve, a daily new energy generation prediction curve, a daily grid electricity price curve and a real-time capacity of the energy storage subsystem.

[0062] The strategy scheduling subsystem 4 is further configured to determine whether a to-be-controlled time belongs to a peak electricity price period, generate a control strategy based on a determination result, the daily electricity consumption prediction curve, the daily new energy generation prediction curve, the daily grid electricity price curve and the real-time capacity of the energy storage subsystem, and control storage or supply of the first electric energy to the charging pile, charging and discharging of the energy storage subsystem and access of grid electric energy based on the control strategy.

[0063] It should be noted that, as shown in Figure 2 The new energy electronic system 1 comprises a solar panel 1-1 and a wind turbine 1-2, wherein the solar panel 1-1 and the wind turbine 1-2 are connected to the strategy scheduling subsystem 4 through respective interfaces.

[0064] In the embodiments of the present disclosure, as shown in Figure 2 The strategy scheduling subsystem 4 comprises a prediction module 4-1, a scheduling module 4-2 and a control module 4-3.

[0065] The prediction module 4-1 is configured to determine a daily new energy generation prediction curve based on the daily weather forecast data and the real-time observation weather data.

[0066] The prediction module 4-1 is further configured to determine a daily power consumption prediction curve based on the daily user power consumption data in the historical period.

[0067] The scheduling module 4-2 is configured to determine whether the to-be-controlled time belongs to a peak electricity price period, and generate a control strategy based on a determination result, the daily power consumption prediction curve, the daily new energy power generation prediction curve, the daily grid electricity price curve, and the real-time capacity of the energy storage subsystem.

[0068] The control module 4-3 is configured to control the first electric energy storage or supply, the charging and discharging of the energy storage subsystem, and the access of grid electric energy based on the control strategy, wherein the control module 4-3 is connected with the grid electric energy through an interface to control the access of grid electric energy.

[0069] It should be noted that the control strategy includes:

[0070] When the to-be-controlled time belongs to the peak electricity price period, it is determined whether the first electric energy at the to-be-controlled time meets the predicted power consumption value corresponding to the to-be-controlled time, if yes, the first electric energy is used to supply power to the charging pile, and the excess first electric energy is used to charge the energy storage subsystem 3, otherwise, a first difference between the first electric energy and the predicted power consumption value is determined, and the first electric energy and the grid electric energy are used to supply power to the charging pile 5.

[0071] When the to-be-controlled time belongs to the non-peak electricity price period, the grid electric energy is used to supply power to the charging pile, and it is determined whether the first electric energy generated from the to-be-controlled time to the peak electricity price period is less than the real-time capacity of the energy storage subsystem based on the real-time capacity of the energy storage subsystem, if no, the first electric energy is used to charge the energy storage subsystem 3, if yes, the grid electric energy is used to charge the energy storage subsystem 3 at a time corresponding to a low electricity price in the daily grid electricity price curve.

[0072] Further, as shown in the first aspect, Figure 3 The prediction module 4-1 includes a correction unit 4-1-1 and a prediction unit 4-1-2.

[0073] The correction unit 4-1-1 is configured to correct the daily weather forecast data according to the real-time observation meteorological data to obtain daily predicted weather data.

[0074] The prediction unit 4-1-2 is configured to substitute the daily predicted weather data into a pre-established power generation prediction model to obtain a daily new energy power generation prediction curve.

[0075] The prediction unit 4-1-2 is further configured to input the user's power consumption data of each day in the historical period into a pre-established power consumption prediction model to obtain a power consumption prediction curve of the day.

[0076] To sum up, the charging pile control system based on comprehensive energy provided in the embodiment can select the most economical power combination mode on the basis of meeting the power demand by analyzing the power demand end data and the power supply end data and combining the price curve of the power, thereby improving the overall economy.

[0077] Based on the charging pile control system provided in the embodiment, the application further provides a charging pile control method based on comprehensive energy, as shown in Figure 4 As shown in the flowchart of the charging pile control method based on comprehensive energy provided in the embodiment, the method comprises the following steps. Figure 4

[0078] Step 1: obtaining the meteorological forecast data of the day, the real-time observed meteorological data, the user's power consumption data of each day in the historical period, the power price curve of the day, and the real-time capacity of the energy storage subsystem.

[0079] It should be noted that the power price curve can be obtained from the power market trading service center to obtain the power price curve data of the power market in a certain future time.

[0080] Step 2: determining the new energy power generation prediction curve of the day based on the meteorological forecast data of the day and the real-time observed meteorological data, and determining the power consumption prediction curve of the day based on the user's power consumption data of each day in the historical period.

[0081] In the embodiment of the present disclosure, the determination of the new energy power generation prediction curve of the day based on the meteorological forecast data of the day and the real-time observed meteorological data comprises:

[0082] The meteorological forecast data of the day is corrected according to the real-time observed meteorological data to obtain predicted meteorological data of the day;

[0083] The predicted meteorological data of the day is substituted into a pre-established power generation prediction model to obtain the new energy power generation prediction curve of the day.

[0084] It should be noted that the machine learning technology can learn the historical measured meteorological data and the historical power generation data to establish a meteorological-power generation relationship model. After the future meteorological data, i.e., the meteorological forecast data of the day, is corrected by the real-time observed meteorological data, more accurate predicted meteorological data is obtained. Then, after the corrected predicted meteorological data is substituted into the meteorological-power generation relationship model, i.e., the pre-established power generation prediction model, the new energy power generation prediction curve of the day is obtained. In this way, power dispatching can be better arranged, and energy utilization efficiency can be improved. ​

[0085] It should be noted that the new energy power generation includes wind power generation and solar power generation. The diversity of natural resources can be utilized to achieve diversified energy supply. For example, during the day when the solar energy resource is abundant, the solar panel can provide a large amount of electric energy; and at night or when the solar energy resource is insufficient, the wind turbine generator can provide stable electric energy. This diversified energy supply mode can reduce the dependence on a single energy source and enhance stability and reliability.

[0086] In the embodiments of the present disclosure, the determination of the daily power consumption prediction curve based on the power consumption data of the user in each day of the historical period includes:

[0087] The power consumption data of the user in each day of the historical period is input into a pre-established power consumption prediction model to obtain the daily power consumption prediction curve.

[0088] The historical period is one day to n days before the current day.

[0089] It should be noted that the machine learning technology can utilize the power consumption data of the user in each day of the historical period and the real-time power consumption data to predict the electric energy demand curve, i.e., the power consumption prediction curve, in a certain future time. By analyzing the user's power consumption behavior, weather conditions, seasonal changes and other factors, a power consumption prediction model can be established to accurately predict future electric energy demand. This can help better arrange power dispatching to meet the user's electric energy demand while avoiding energy waste.

[0090] It should be noted that the daily new energy power generation prediction curve and the daily power consumption prediction curve can be updated and predicted in real time based on the real-time data corresponding thereto to ensure the accuracy of the prediction curve.

[0091] Step 3: generating a control strategy according to the daily power consumption prediction curve, the daily new energy power generation prediction curve, the daily grid electricity price curve and the real-time capacity of the energy storage subsystem.

[0092] In the embodiments of the present disclosure, the step 3 specifically includes:

[0093] When the to-be-controlled time belongs to the peak electricity price period, it is determined whether the first electric energy meets the power consumption prediction value corresponding to the to-be-controlled time. If yes, the first electric energy is used to supply power to the charging pile, and the excess first electric energy is used to charge the energy storage subsystem. Otherwise, a first difference between the first electric energy and the power consumption prediction value is determined, and the first electric energy and the electric energy provided by the power grid are used to supply power to the charging pile.

[0094] The electric energy provided by the power grid is equal to the first difference.

[0095] When the to-be-controlled time belongs to the off-peak electricity price period, the power supply provided by the power grid is used to supply power to the charging pile, and whether the first electric energy generated from the to-be-controlled time to the front of the peak electricity price period is less than the real-time capacity of the energy storage subsystem is judged based on the real-time capacity of the energy storage subsystem, if not, the first electric energy is used to charge the energy storage subsystem, if yes, the power supply provided by the power grid is used to charge the energy storage subsystem at the low electricity price corresponding time in the daily commercial power price curve.

[0096] It should be noted that, in combination with machine learning technology and real-time data, real-time adjustment of power dispatching strategy can be realized. According to new power prediction, electric energy demand prediction and commercial electric energy price and other factors, the power dispatching strategy can be adjusted in real time to make it more flexible and adaptive to changes in actual situation, further improving energy utilization efficiency.

[0097] At the same time, the combination of wind power generation, solar power generation, energy storage subsystem and power grid generation has multiple advantages in actual scene, environment and cost, etc., which can better meet the energy demand, improve the energy utilization efficiency, and also help to reduce the impact on the environment and reduce the energy cost.

[0098] It should be noted that the electric quantity and the electricity price are integrated, the discharge / charge curve data of different power sources are calculated, the difference between the final electric quantity purchase and sale price is comprehensively selected, and the optimal scheme is executed, and the formulation process of the control strategy includes:

[0099] According to the analysis of the electric quantity and the electric source of the charging pile in this place every day, it can be known that:

[0100]

[0101] Among them, is the daily required electric quantity of the charging pile in this area, is the electric energy supplied by the commercial power, is the electric energy generated by new energy.

[0102] The electric quantity of the charging pile at any time within a day can be obtained by the following formula:

[0103]

[0104] Among them, is the electric quantity discharged by the energy storage subsystem in each period, is the electric quantity charged into the energy storage subsystem in each period; the above formula is applicable to each period within the day.

[0105] Among them, the commercial electric energy used by each period of storage is as follows:

[0106]

[0107] The electricity price of each period can be obtained from the electricity price of each period:

[0108]

[0109] From the above information, we can know And The charging and discharging logic of peak and off-peak periods is different, and the key strategy is to determine the number of each period And The allocation of P value makes the daily electricity P value minimum.

[0110] Step 4: Based on the control strategy, control the charging and discharging of the first electric energy storage or charging pile, energy storage subsystem and the access of grid power.

[0111] The following is an empirical test according to the above scheme in Yumen area of Gansu Province, which involves the following calculation data acquisition channels:

[0112] Local wind and light resource data: downloaded from the authoritative global new energy resource library, and the corresponding power generation data is obtained through professional power generation calculation process.

[0113] Grid electricity price data: downloaded from the Internet authoritative website in 2019 Gansu area national grid price standard.

[0114] Single charging pile vehicle flow information: preset according to the distribution of resident activity period.

[0115] Single vehicle charging capacity data: preset according to the average battery capacity of 80% of electric vehicle at present.

[0116] The cost standard of each period in this area is shown in table 1.

[0117] Table 1

[0118]

[0119] As Figure 5 The column chart shows the preset vehicle times of each period in a day, assuming that each vehicle can be fully charged by charging 50kwh in the charging pile.

[0120] Combined with the above electricity cost of each period, the cost of grid electricity access of charging pile in a single day can be calculated as:

[0121]

[0122]

[0123] The cost of grid electricity access of charging pile in a single year can also be obtained as:

[0124]

[0125] The following example selects a point in Yumen City, Gansu Province, at 40.328172, 96.88981. The charging pile is equipped with a 200kwh energy storage device, two small wind turbines with a single machine installed capacity of 10kw, and a solar panel with an installed capacity of 20kw.

[0126] The scheme adopts the "peak clipping" measure to fill the valley of electricity, and avoid the peak period of power grid electricity. The energy of the energy storage subsystem is preferentially supplied to the charging of vehicles in the peak period. When the charging pile charges the vehicles in the current time period, it preferentially uses the new energy supplied electricity, then uses the valley period of electricity, and finally uses the electricity in the flat period, completely avoiding the peak period of electricity supply.

[0127] The new energy electricity produced in the valley period from 23:00 to 7:00 is directly supplied to the energy storage subsystem. All vehicles use the electricity in this period to charge, and the remaining capacity of the energy storage subsystem is supplemented by the electricity in this period and the new energy electricity in the next period; from 7:00 to 10:00, the electricity in the flat period is used to charge the vehicles in this period, and 100kwh of electricity is used. The new energy produced in this period is directly supplied to the energy storage subsystem to ensure the charging demand of the next peak period; from 10:00 to 15:00, it is in the peak period, in order to avoid the peak period of electricity charging standard, the electricity in this period is completely used to charge the vehicles in this period, and the electricity in this period is 0kwh; from 15:00 to 18:00, the electricity in the flat period is used to charge the vehicles in this period, and the energy storage device of the charging pile is stored. The energy storage subsystem requires electricity from this period and the new energy produced in the last period, as well as the electricity in this period, which can cover the electricity demand in the next peak period; from 18:00 to 21:00, it is in the peak period, in order to avoid the peak period of electricity charging standard, the electricity in this period is used to charge the vehicles in this period, and the electricity in this period is 0kwh; from 21:00 to 23:00, the electricity in this period and the new energy produced in this period and the last peak period is used to charge the vehicles in this period.

[0128] As Figure 6 shown is the electricity curve of the charging pile using the electricity in a single day. Because two small wind turbines and solar panels are connected, each small wind turbine has a single machine installed capacity of 10kw, and the connected solar panel has a single machine installed capacity of 20kw, they can also produce electricity to supply power to the charging pile. The following calculates the electricity capacity they produce.

[0129] After site selection, the point is set at 40.328172, 96.88981. A single 10kw small wind turbine installed at this site can generate 14800kwh per year. Since two are connected, the annual wind energy can produce 29600kwh of electricity;

[0130] Query this point data, the single year full hours of solar panels can be calculated:

[0131]

[0132] The single year power generation of a 20kw solar panel installed at this site can be calculated as follows:

[0133]

[0134] From the above, the annual power generation of two wind turbines and solar panels is:

[0135]

[0136] There is a certain space-time mismatch between wind power generation and solar power generation power curve and actual power demand curve, which leads to energy efficiency loss, and the loss rate is set to 20%. Thus, their annual actual power generation is:

[0137]

[0138] The average hourly power generation is:

[0139] 49328÷365÷24=5.63 (kwh)

[0140] From the above calculation, Table 2 can be obtained

[0141] Table 2

[0142]

[0143] From the above table, the daily electricity cost of the system is 277 yuan, and the annual electricity cost is 101105 yuan.

[0144] Through the above cost calculation, the system can save 82428 yuan in annual electricity cost.

[0145] Next, the investment recovery period is calculated. The cost of the system is concentrated in the energy storage subsystem, two small wind turbines and solar panels. The price of a 10kw wind turbine is 34900 yuan, the price of a 20kw solar panel is 60000 yuan, and the price of the energy storage subsystem is 170000 yuan. The total cost is about 299800 yuan.

[0146] From the above data, the payback period can be calculated as:

[0147] 299800 ÷ 82428 = 3.64 (years)

[0148] The energy storage subsystem can be charged and discharged about 6000 times, and can be charged and discharged twice a day. The life of the energy storage subsystem can be calculated to be at least 8 years. That is, the system can recover all investment funds in 3.64 years of operation, and will be in a profitable state thereafter. Therefore, this scheme is feasible.

[0149] In summary, the charging pile control method based on comprehensive energy proposed in the embodiment has the following advantages:

[0150] 1. Improve energy utilization efficiency: Through intelligent scheduling management and integrated use of multiple energy sources, it can more effectively manage and utilize electric energy resources, improve energy utilization efficiency, and flexibly adjust the use and storage of different energy sources according to real-time energy supply and demand conditions, avoiding waste of new energy and excessive use of high-priced grid electricity.

[0151] 2. Reduce energy costs: By selecting the optimal power combination method and optimal electric energy control strategy, energy costs can be minimized, and the most economical power combination method can be selected according to the electricity market price curve and the cost of different power sources, thereby reducing energy procurement costs.

[0152] 3. Reduce dependence on traditional energy: By integrating new energy generation and energy storage technology, the dependence on traditional energy is reduced, especially in the case of connecting small wind turbines, solar panels and other new energy generation devices, the system can make more use of clean energy, reduce dependence on traditional energy, and reduce environmental impact.

[0153] 4. Promote sustainable energy development and utilization: The implementation of this technical solution promotes the development and utilization of sustainable energy. By integrating new energy generation and energy storage technology, renewable energy can be more effectively utilized, promoting the development and utilization of sustainable energy, reducing environmental impact, and promoting the transformation and upgrading of energy structure.

[0154] 5. Improve the stability and reliability of the power system: Through intelligent scheduling management and integrated use of multiple energy sources, the stability and reliability of the power system can be improved, and the use and storage of energy can be flexibly adjusted according to real-time energy supply and demand conditions, ensuring the stable operation and power supply reliability of the power system.

[0155] Embodiment Two

[0156] To achieve the above-mentioned embodiments, the present disclosure further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the control method according to the first embodiment.

[0157] Embodiment three

[0158] To achieve the above-mentioned embodiments, the present disclosure further provides a computer readable storage medium, which stores a computer program, wherein the program is executed by a processor to implement the control method according to the first embodiment.

[0159] In the description of the specification, the description of the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples. In addition, the skilled in the art can combine and combine the different embodiments or examples described in the specification and the features of the different embodiments or examples without contradiction.

[0160] Any process or method descriptions in flow charts or described elsewhere herein can be understood as representing code modules, segments, or portions of code which include one or more executable instructions for implementing specific logic functions (or steps) in the process, and that the various embodiments of preferred implementations of the application can include additional or fewer steps or processes or a different arrangement of steps or processes as will occur to those skilled in the art. The various embodiments of the application can be further understood from the following examples.

[0161] Although the embodiments of the present application have been shown and described above, it should be understood that the above-mentioned embodiments are exemplary and should not be construed as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above-mentioned embodiments within the scope of the present application.

Claims

1. A charging pile control system based on integrated energy, characterized in that, The system comprises a new energy power generation subsystem, a weather observation station, an energy storage subsystem, a strategy scheduling subsystem and a charging pile; The new energy power generation subsystem is configured to generate first electric energy and send the first electric energy to the strategy scheduling subsystem; the new energy power generation subsystem comprises a wind power generation subsystem and a solar power generation subsystem; The weather observation station is configured to send acquired daily weather forecast data and real-time observation weather data to the strategy scheduling subsystem; The energy storage subsystem is configured to store or release electric energy; The charging pile is configured to provide electric energy to an electric vehicle; The strategy scheduling subsystem is configured to acquire a daily electric energy consumption prediction curve, a daily new energy power generation prediction curve, a daily grid electricity price curve and a real-time capacity of the energy storage subsystem; the strategy scheduling subsystem comprises a prediction module, a scheduling module and a control module; The prediction module is configured to correct the daily weather forecast data based on the real-time observation weather data, input the corrected data into a pre-established power generation prediction model to obtain the daily new energy power generation prediction curve, and input electric energy consumption data of users in each day in a historical period into a pre-established electric energy consumption prediction model to obtain the daily electric energy consumption prediction curve, wherein the historical period is from one day before the current day to n days before the current day; The scheduling module is configured to determine whether a to-be-controlled time belongs to a peak electricity price period, and generate a control strategy based on a determination result, the daily electric energy consumption prediction curve, the daily new energy power generation prediction curve, the daily grid electricity price curve and the real-time capacity of the energy storage subsystem; the control strategy comprises: If the to-be-controlled time belongs to the peak electricity price period, it is determined whether the first electric energy at the to-be-controlled time meets a predicted electric energy consumption value corresponding to the to-be-controlled time; if yes, the first electric energy is used to supply power to the charging pile; otherwise, a first difference between the first electric energy and the predicted electric energy consumption value is determined, and the first electric energy and electric energy provided by a power grid are used to supply power to the charging pile, wherein the electric energy provided by the power grid is equal to the first difference; If the to-be-controlled time belongs to a non-peak electricity price period, electric energy provided by the power grid is used to supply power to the charging pile, and it is determined whether first electric energy generated from the to-be-controlled time to a time before the peak electricity price period based on the real-time capacity of the energy storage subsystem is less than the real-time capacity of the energy storage subsystem; if yes, the electric energy provided by the power grid is used to charge the energy storage subsystem at a time corresponding to a low electricity price in the daily grid electricity price curve; otherwise, the first electric energy is used to charge the energy storage subsystem; The control module is configured to control storage or supply of the first electric energy to the charging pile, charging and discharging of the energy storage subsystem and access of power grid electric energy based on the control strategy, so that daily electricity cost Pmin is minimized on the basis of meeting electric energy demand; wherein a calculation formula of the daily electricity cost Pmin is: ； Wherein, , is the electricity used by each time period, is the electricity required by each time period of the charging pile, is the electricity generated by each time period of new energy, is the electricity discharged by each time period of the energy storage subsystem, is the electricity charged into each time period of the energy storage subsystem.

2. The control method of the integrated energy-based charging pile control system according to claim 1, characterized in that, The method comprises: acquiring daily weather forecast data, real-time observation weather data, electric energy consumption data of users in each day in a historical period, a daily grid electricity price curve and a real-time capacity of an energy storage subsystem; The daily new energy power generation prediction curve is determined based on the daily weather forecast data and real-time observation weather data respectively, and the daily power consumption prediction curve is determined based on the power consumption data of users in each day in a historical period; The control strategy is generated according to the daily power consumption prediction curve, the daily new energy power generation prediction curve, the daily grid electricity price curve and the real-time capacity of the energy storage subsystem; The first electric energy storage or supply charging pile, the charging and discharging of the energy storage subsystem and the access of the grid electric energy are controlled based on the control strategy, so as to minimize the daily electricity bill P on the basis of meeting the electric energy demand, wherein the calculation formula of the daily electricity bill P is: ； Wherein, , is the electricity used by each time period, is the electricity required by each time period of the charging pile, is the electricity generated by each time period of new energy, is the electricity discharged by each time period of the energy storage subsystem, is the electricity charged into each time period of the energy storage subsystem.

3. An electronic device, comprising: The method comprises the following steps: The computer program is stored in the memory and executable on the processor, and the processor executes the program to realize the method of claim 2.

4. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to realize the method of claim 2.

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