Charging pile energy complementary method, system and charging pile based on renewable energy

Abstract

The present invention provides a charging pile energy complementation method, system and charging pile based on renewable energy, the method comprising the following steps: determining the future charging time interval in which the charging pile needs to provide charging services intermittently or continuously in the future according to all reserved charging demand information, and converting the reserved charging demand information into future load power time series data based on the future charging time interval; obtaining weather forecast information of the area where the new energy charging station is located in the future charging time interval; predicting the future power generation time series data of the photovoltaic wind power generation module in the future charging time interval based on the weather forecast information; detecting the remaining power data of the energy storage device; combining the remaining power data, the future load power time series data and the future power generation time series data, to generate an energy complementation strategy for the energy storage device in the future charging time interval. The present invention has the effect of efficiently utilizing renewable energy to charge electric vehicles.

Description

Technical Field

[0001] The present invention belongs to the technical field of new energy charging, and specifically relates to a charging pile energy complementation method, system and charging pile based on renewable energy. Background Art

[0002] With the popularity of electric vehicles and the rapid development of renewable energy, the demand and construction of new energy charging stations are increasing. As an environmentally friendly and energy-saving means of transportation, electric vehicles are favored by a large number of consumers, and the market size continues to expand. However, the charging problem of electric vehicles has become one of the bottlenecks restricting its further development. Traditional charging stations mainly rely on power supply from the power grid, which makes it difficult to make full use of renewable energy and easily causes excessive load on the power grid during peak hours.

[0003] At the same time, the demand and investment for renewable energy in the electric vehicle sector are also increasing. As the main forms of renewable energy, photovoltaic and wind power generation have seen rapid growth in installed capacity and power generation. However, the charging demand for electric vehicles is highly random and diverse. The charging time and demand for different users vary. In addition, photovoltaic power generation depends on the intensity of solar radiation, while wind power generation depends on wind speed and direction. These factors are affected by weather and environmental conditions and are difficult to accurately predict and stably control. This results in large fluctuations in power generation in different time periods, making it impossible to ensure a continuous and stable power supply. Therefore, in order to reduce dependence on the power grid, how to efficiently use renewable energy to charge electric vehicles has become an important issue that needs to be urgently addressed in the new energy field. Summary of the invention

[0004] The present invention provides a charging pile energy complementary method, system and charging pile based on renewable energy, so as to solve the problem that it is difficult to efficiently use renewable energy to charge electric vehicles.

[0005] In a first aspect, the present invention provides a charging pile energy complementation method based on renewable energy, which is applied to a new energy charging station, wherein the new energy charging station comprises a charging pile, an energy storage device and a photovoltaic wind power generation module, wherein the energy storage device is used to store electric energy, the photovoltaic wind power generation module is connected to a DC input end of the energy storage device, the DC output end of the energy storage device is connected to the charging pile, and the AC input end and the AC output end of the energy storage device are both connected to a municipal power grid;

[0006] The method comprises the following steps:

[0007] Acquiring the reservation charging demand information of multiple vehicles to be charged through the charging reservation system of the new energy charging station;

[0008] Determine a future charging time interval during which the charging pile needs to provide charging services intermittently or continuously in the future according to all the reserved charging demand information, and convert the reserved charging demand information into future load power time series data based on the future charging time interval;

[0009] Obtaining weather forecast information for the area where the new energy charging station is located within the future charging time interval;

[0010] Predicting the future power generation time series data of the photovoltaic and wind power generation modules within the future charging time interval based on the weather forecast information;

[0011] Detecting the remaining power data of the energy storage device;

[0012] The remaining electric energy data, the future load power time series data and the future power generation power time series data are combined to generate an energy complementary strategy for the energy storage device in the future charging time interval.

[0013] Optionally, the combining of the remaining electric energy data, the future load power time series data and the future power generation power time series data to generate an energy complementary strategy for the energy storage device in the future charging time interval comprises the following steps:

[0014] Fitting the future load power time series data and the future power generation time series data into a future load power curve and a future power generation curve respectively;

[0015] If, in any continuous time period of the future charging time interval, all future load power values ​​in the future load power curve are higher than all future power generation values ​​in the future power generation curve, the corresponding continuous time period is marked as a high load time period;

[0016] If, in any continuous time period of the future charging time interval, all future load power values ​​in the future load power curve are lower than all future power generation values ​​in the future power generation curve, the corresponding continuous time period is marked as a low-load time period;

[0017] If, in any continuous time period of the future charging time interval, all future load power values ​​in the future load power curve are 0, the corresponding continuous time period is marked as a power generation time period;

[0018] Counting the total value of future load power within the future charging time interval based on the future load power time series data;

[0019] Counting the total value of future power generation within the future charging time interval based on the future power generation time series data;

[0020] If the future total load power value is less than or equal to the future total power generation value, then the future surplus power generation of the photovoltaic wind power generation module in the future charging time interval is calculated by combining the future total load power value and the future total power generation value;

[0021] Calculate the remaining storage capacity of the energy storage device by combining the remaining power data and the upper limit of the power storage device;

[0022] If the remaining storage capacity is greater than or equal to the future surplus power generation, a first energy complementary strategy for the energy storage device in the future charging time interval is generated, and the first energy complementary strategy includes the following steps:

[0023] Control the energy storage device to provide direct current to the charging pile using its own stored electric energy and the photovoltaic and wind power generation modules during all the high-load time periods;

[0024] Control the energy storage device to use the photovoltaic and wind power generation modules to provide direct current to the charging pile and replenish its own stored electric energy during all low-load time periods;

[0025] The energy storage device is controlled to utilize the photovoltaic and wind power generation modules to supplement its own stored electric energy during all power generation time periods.

[0026] Optionally, the method further comprises the following steps:

[0027] If the remaining storage capacity is less than the future surplus power generation, a second energy complementary strategy for the energy storage device in the future charging time interval is generated, and the second energy complementary strategy includes the following steps:

[0028] Control the energy storage device to provide direct current to the charging pile using its own stored electric energy and the photovoltaic and wind power generation modules during all the high-load time periods;

[0029] Control the energy storage device to use the photovoltaic wind power generation module to provide direct current to the charging pile and replenish its own stored electric energy during all the low-load time periods, and control the energy storage device to use the photovoltaic wind power generation module to replenish its own stored electric energy during all the power generation time periods until the remaining storage amount is reduced to 0;

[0030] When the remaining storage capacity is reduced to 0, the energy storage device is controlled to use the photovoltaic wind power generation module to provide direct current to the charging pile and to provide electrical energy to the municipal power grid during all the low-load time periods, and the energy storage device is controlled to use the photovoltaic wind power generation module to provide electrical energy to the municipal power grid during all the power generation time periods.

[0031] Optionally, the method further comprises the following steps:

[0032] If the total value of the future load power is greater than the total value of the future generated power, obtaining a power grid power consumption prediction curve of the city power grid within the future charging time interval through the power grid dispatching system of the city power grid;

[0033] The future shortfall power generation of the photovoltaic and wind power generation modules in the future charging time interval is calculated by combining the future total load power value and the future total power generation value;

[0034] If the remaining storage capacity is greater than or equal to the future shortfall in power generation, generating the first energy complementary strategy for the energy storage device within the future charging time interval;

[0035] If the remaining storage capacity is less than the future power generation gap, the difference between the remaining storage capacity and the future power generation gap is calculated as the amount of electricity to be supplemented;

[0036] According to the time axis of the future charging time interval, the total differences between all the future load power values ​​and all the future power generation values ​​in each high-load time period are calculated in sequence, and the total differences are accumulated to obtain the total value of the gap power generation in the time period, until the total value of the gap power generation in the time period is greater than or equal to the remaining storage amount, and the high-load time period calculated when the total value of the gap power generation in the time period is greater than or equal to the remaining storage amount is marked as the target high-load time period;

[0037] The power grid power consumption prediction curve is used to calibrate the power replenishment time node between the start time of the future charging time interval and the start time of the target high-load time period, and the power grid power consumption prediction curve at the power replenishment time node is the lowest power grid power consumption between the start time of the future charging time interval and the start time of the target high-load time period;

[0038] Generate a third energy complementary strategy for the energy storage device in the future charging time interval, the third energy complementary strategy comprising the following steps:

[0039] Control the energy storage device to provide direct current to the charging pile using its own stored electric energy and the photovoltaic and wind power generation modules during all the high-load time periods;

[0040] Control the energy storage device to use the photovoltaic and wind power generation modules to provide direct current to the charging pile and replenish its own stored electric energy during all low-load time periods;

[0041] Control the energy storage device to use the photovoltaic and wind power generation modules to supplement the stored electric energy in the energy storage device during all power generation time periods;

[0042] The energy storage device is controlled to use the commercial power grid to supplement the stored electric energy at the electric energy supplementation time node.

[0043] Optionally, determining a future charging time interval in which the charging pile needs to provide charging services intermittently or continuously in the future according to all the reserved charging demand information, and converting the reserved charging demand information into future load power time series data based on the future charging time interval includes the following steps:

[0044] Extracting the vehicle model information, scheduled charging start time, current vehicle power and scheduled charging power of each vehicle to be charged from the scheduled charging demand information;

[0045] For each of the vehicles to be charged, an estimated charging duration is calculated by combining the vehicle model information, the current power of the vehicle and the scheduled charging power, a future charging time period of the vehicle to be charged is determined according to the scheduled charging start time and the estimated charging time period, and an SOC charging curve of the vehicle to be charged in the future charging time period is generated;

[0046] Merging the future charging time periods of all the vehicles to be charged into the same time axis to obtain the future charging time intervals in which the charging piles need to provide charging services intermittently or continuously in the future;

[0047] The scheduled charging demand information is converted into future load power timing data based on the future charging time interval and according to the SOC charging curve.

[0048] Optionally, the converting the scheduled charging demand information into future load power time series data based on the future charging time interval and according to the SOC charging curve comprises the following steps:

[0049] Determine the segment step length according to the interval length of the future charging time interval;

[0050] Taking the interval start time of the future charging time interval as the starting point, setting a load power time node in the future charging time interval every other segment step;

[0051] For each of the load power time nodes, if the load power time node is not in any of the future charging time periods, the future load power of the load power time node is set to 0;

[0052] If the load power time node is within only one of the future charging time periods, the future charging time period in which the load power time node is located is used as the first future charging time period;

[0053] determining a first instantaneous charging power from the SOC charging curve in the first future charging time period according to the load power time node, wherein a timestamp of the first instantaneous charging power in the first future charging time period is the same as a timestamp of the load power time node;

[0054] Setting the future load power at the load power time node to the first instantaneous charging power;

[0055] If the load power time node is within a plurality of the future charging time periods, all of the plurality of the future charging time periods in which the load power time node is located are used as the second future charging time period;

[0056] Determine the second instantaneous charging power from the SOC charging curves in each of the second future charging time periods according to the load power time nodes, wherein the timestamps of all the second instantaneous charging powers in the corresponding second future charging time periods are the same as the timestamps of the load power time nodes;

[0057] Based on the SOC charging stage in which each second instantaneous charging power is located in the corresponding SOC charging curve, assigning a preset stage power adjustment weight to each second instantaneous charging power;

[0058] Performing weighted calculation in combination with all the second instantaneous charging powers and the stage power adjustment weights to obtain the second instantaneous charging power sum, and setting the future load power at the load power time node to the second instantaneous charging power sum;

[0059] The future load powers of all the load power time nodes are integrated to obtain future load power time series data.

[0060] Optionally, before predicting the future power generation time series data of the photovoltaic and wind power generation modules in the future charging time interval based on the weather forecast information, the following steps are also included:

[0061] Acquire historical meteorological information of the area where the new energy charging station is located and historical photovoltaic and wind power generation information of the photovoltaic and wind power generation module within the same historical time interval;

[0062] The grey correlation algorithm is used to calculate the data correlation between the historical meteorological data of each different meteorological type in the historical meteorological information and the historical photovoltaic and wind power generation information;

[0063] Screening out all the historical meteorological data whose data relevance is less than a preset relevance threshold, and integrating all the retained historical meteorological data into historical meteorological sample data;

[0064] Construct photovoltaic and wind power generation prediction model based on support vector regression algorithm;

[0065] The photovoltaic wind power generation prediction model is iteratively trained using the historical meteorological sample data. During the iterative training process of the photovoltaic wind power generation prediction model, the model parameters of the photovoltaic wind power generation prediction model are optimized based on the historical photovoltaic wind power generation information and through a genetic algorithm.

[0066] Optionally, the predicting of the future power generation time series data of the photovoltaic and wind power generation modules in the future charging time interval based on the weather forecast information comprises the following steps:

[0067] Setting a plurality of power generation time nodes within the future charging time interval according to the information timestamp of the weather forecast information;

[0068] For each of the power generation time nodes, filtering out invalid weather forecast information whose weather type in the weather forecast information is different from the historical weather sample data;

[0069] Input all the remaining weather forecast information into the photovoltaic wind power generation prediction model, and use the photovoltaic wind power generation prediction model to output the predicted power generation corresponding to the power generation time node;

[0070] The predicted power generation at all power generation time nodes is integrated to obtain future power generation time series data.

[0071] In a second aspect, the present invention also provides a charging pile energy complementarity system based on renewable energy, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the charging pile energy complementarity method based on renewable energy as described in the first aspect is implemented.

[0072] In a third aspect, the present invention further provides a charging pile, comprising:

[0073] A DC input module, connected to the DC output terminal of the energy storage device as described in the first aspect, and used to obtain the DC power output by the energy storage device;

[0074] The DC output module is used to output the DC power.

[0075] The beneficial effects of the present invention are:

[0076] The technical solution of the present invention can accurately predict future charging needs by systematically collecting and analyzing the reserved charging demand information of multiple vehicles to be charged. This prediction not only takes into account the specific needs of each vehicle, but also reasonably arranges the use time of the charging pile, thereby avoiding resource shortages during the peak charging period and resource waste during the trough period, greatly improving the operating efficiency of the charging station. By converting future charging demand information into load power time series data, the charging station can have a clear understanding and planning of future power demand. This data-driven management method enables the charging station to prepare in advance to ensure that the user's charging needs can be met at any time, improving user satisfaction and charging experience. By obtaining and analyzing weather forecast information, the future power generation of photovoltaic and wind power generation modules can be predicted. This prediction not only takes into account the impact of weather on power generation, but also combines specific time intervals, making the prediction results more accurate and reliable. Finally, by detecting the remaining power data of the energy storage device, combined with the future load power time series data and power generation power time series data, an optimized energy complementary strategy is generated. This can maximize the use of the electricity from the energy storage device, reduce dependence on the power grid, and provide necessary power support when power generation is insufficient or demand peaks, ensuring the continuity and stability of charging services. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 This is a schematic diagram of the site layout of a new energy charging station in one embodiment of the present application.

[0078] Figure 2 This is a schematic diagram of the system structure of a new energy charging station in one embodiment of the present application.

[0079] Figure 3 This is a flow chart of a charging pile energy complementation method based on renewable energy in one embodiment of the present application.

[0080] Description of reference numerals:

[0081] 1. Charging pile; 2. Energy storage device; 3. Photovoltaic wind power generation module; 31. Photovoltaic power generation unit; 32. Wind power generation unit. DETAILED DESCRIPTION

[0082] The following will be combined with the drawings in the embodiments of the present application to clearly describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments in the present application belong to the scope of protection of this application.

[0083] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0084] Reference Figure 1 and Figure 2 The new energy charging station is equipped with a sunshade, which is equipped with multiple charging piles for charging new energy vehicles. A photovoltaic wind power generation module is installed on the top of the sunshade. The photovoltaic wind power generation module includes a photovoltaic power generation unit composed of multiple solar panels and a wind power generation unit composed of multiple wind turbines. The wind turbine uses breeze power generation and can operate safely at a relatively low wind speed of more than 3m / S. The fan blades swing horizontally, which can continuously cool the sunshade while generating electricity.

[0085] Considering the continuous power generation of photovoltaic and wind power generation and reducing the impact on the power grid, a certain capacity of energy storage device is installed in the awning. The energy storage device includes an AC port and a DC port. The photovoltaic wind power generation module is connected to the DC input end of the energy storage device, and the DC output end of the energy storage device is connected to the charging pile. The AC input end and the AC output end of the energy storage device are both connected to the mains power grid. The energy storage device can store the electricity of renewable energy. And because the DC charging pile has a greater impact on the power grid at the moment of charging, the setting of the energy storage device can effectively solve the problem of the instantaneous increase of the power load of the DC charging pile. On the other hand, the photovoltaic power generation process usually requires the setting of an inverter to convert the DC power obtained by photovoltaic power generation into AC power. The present invention, through the setting of the energy storage device, does not need to use an inverter for direct-alternating-direct conversion between photovoltaic wind power generation and charging pile power supply. The power grid uses AC power. The AC port of the energy storage device is used to supply the power grid to the energy storage device as a supplement to the power used by the charging pile. When the power of renewable energy is insufficient, the energy storage device is out of power or the power grid is in a low period, the power of the power grid can charge the energy storage device.

[0086] In one of the embodiments, the energy storage device can also be used to detect grid abnormalities in the mains power grid (such as large fluctuations in the mains voltage, current and other data, or the mains voltage and current do not meet the charging pile voltage and current input parameters). When a grid abnormality occurs, the energy storage device is controlled to switch from the grid-connected operation mode to the island (off-grid) operation mode.

[0087] Figure 3 FIG. 1 is a flow chart of a charging pile energy complementary method based on renewable energy in one embodiment. It should be understood that although Figure 3 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 3 At least part of the steps in the above method may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps. Figure 3 As shown, the energy complementation method of a charging pile based on renewable energy disclosed in the present invention specifically includes the following steps:

[0088] S101. Obtaining reservation charging demand information of multiple vehicles to be charged through a charging reservation system of a new energy charging station.

[0089] Among them, the charging station's reservation system is used to collect and store users' charging demand information. Users usually make reservations through mobile applications, websites or other online platforms, and submit detailed information including estimated arrival time, required charging amount, vehicle type, etc. The system will store this information in the database to form a detailed record. For example, suppose a user makes an appointment to charge his electric car from 9 am to 11 am tomorrow. The system will record this time period and its corresponding charging demand. In order to ensure the accuracy and completeness of the data, the reservation system usually sets multiple fields to capture the user's detailed needs, such as user ID, vehicle ID, reservation time period, required charging amount, charging pile type, etc. This information can be processed and analyzed through database query and analysis tools to provide basic data for subsequent steps. In this way, the charging station can understand the future charging demand in advance, avoid uneven resource allocation, ensure that there are enough charging piles available during peak hours, and not waste resources during low periods. In addition, the system can also set a reminder function to send a reminder notification when the user's appointment time is approaching to ensure that the user arrives on time for charging, further improving the operating efficiency and user satisfaction of the charging station.

[0090] S102. Determine the future charging time interval during which the charging pile needs to provide charging services intermittently or continuously based on all the reserved charging demand information, and convert the reserved charging demand information into future load power time series data based on the future charging time interval.

[0091] Among them, a systematic analysis is performed on the collected reservation information to determine the specific charging time period of each charging pile in the future and its corresponding charging demand. The specific implementation method can be realized by writing an algorithm, which generates a future charging time interval table based on the reservation information. For example, assuming that in the reservation information of a certain day, there are 10 vehicles that need to be charged from 9 am to 11 am, and the charging demand of each vehicle is 20kWh, then the total charging demand in this time period is 200kWh. Then, these time periods and power requirements are converted into time series data to form a future load power time series diagram. This time series diagram can intuitively show the load situation at each time point, helping the charging station to reasonably arrange the use of charging piles, ensuring that there are enough charging piles available during peak hours and not wasting resources during trough hours.

[0092] S103. Obtain weather forecast information for the area where the new energy charging station is located during the future charging time period.

[0093] Among them, weather forecast information usually includes data such as sunshine intensity, wind speed, wind direction, temperature, etc. in the next few days, which are crucial for predicting photovoltaic and wind power generation. Specific implementation methods can obtain real-time weather forecast data from weather data providers through API interfaces. For example, you can write a script and call the API interface of the weather data provider to obtain weather data for the next few days. Assume that the weather forecast shows that the sunshine intensity in the next three days will be 500W / m 2 、600W / m 2 , 700W / m 2 The wind speeds are 5m / s, 6m / s and 7m / s respectively. These data will serve as an important basis for the subsequent prediction of photovoltaic and wind power generation.

[0094] S104. Predict the future power generation time series data of the photovoltaic and wind power generation modules within the future charging time interval based on the weather forecast information.

[0095] Among them, the future power generation is calculated by using the obtained weather forecast information and combining the characteristics of photovoltaic and wind power generation modules. For example, the output power of the photovoltaic power generation module is proportional to the sunshine intensity, which can be calculated by the formula P pv =η pv ·A·I is calculated, where P pv is the photovoltaic power generation, η pv is the efficiency of the photovoltaic module, A is the area of ​​the photovoltaic module, and I is the intensity of sunlight. Similarly, the wind power generation power can be calculated by the formula P w =0.5ρ·M·v 3 Calculate, where P wis the wind power, ρ is the air density, M is the rotor swept area, and v is the wind speed. Combining these formulas with weather forecast data, a future power generation time series diagram can be generated to show the power generation at each time point.

[0096] S105. Detect the remaining power data of the energy storage device.

[0097] Among them, the energy storage device usually includes a battery pack or other forms of energy storage equipment, and its remaining power can be determined by measuring voltage, current and remaining capacity. These data will be transmitted to the management system of the charging station in real time to facilitate subsequent energy scheduling and management. In order to ensure the accuracy and real-time nature of the data, the energy storage device is usually equipped with high-precision sensors and data transmission modules to ensure that the data can be transmitted and processed in real time. Through this real-time monitoring, the status of the energy storage device can be understood at any time to ensure that sufficient power support can be provided when needed. In addition, the remaining power data of the energy storage device can also help optimize the energy management strategy, maximize the use of the power of the energy storage device, reduce dependence on the power grid, and improve energy utilization efficiency.

[0098] S106. Combining the remaining electric energy data, the future load power time series data and the future power generation power time series data, generate an energy complementary strategy for the energy storage device in the future charging time interval.

[0099] Among them, a comprehensive analysis of the various data obtained above is used to formulate an optimized energy management strategy. For example, in a certain period of time, if the power of photovoltaic and wind power generation is not enough to meet the charging demand, the energy storage device can be mobilized to provide power support. Through this comprehensive scheduling, the charging station can ensure that stable charging services can be provided at any time, improve user satisfaction, and effectively utilize renewable energy and reduce dependence on the power grid.

[0100] In one embodiment, combining the remaining electric energy data, the future load power time series data and the future power generation time series data to generate an energy complementary strategy for the energy storage device in the future charging time interval includes the following steps:

[0101] Fitting the future load power time series data and the future power generation time series data into a future load power curve and a future power generation curve respectively;

[0102] If, in any continuous time period of the future charging time interval, all future load power values ​​in the future load power curve are higher than all future generation power values ​​in the future generation power curve, the corresponding continuous time period is marked as a high load time period;

[0103] If, in any continuous time period of the future charging time interval, all future load power values ​​in the future load power curve are lower than all future generation power values ​​in the future generation power curve, the corresponding continuous time period is marked as a low load time period;

[0104] If all future load power values ​​in the future load power curve are 0 in any continuous time period of the future charging time interval, the corresponding continuous time period is marked as a power generation time period;

[0105] The total value of future load power within the future charging time interval is calculated based on the future load power time series data;

[0106] Based on the future power generation time series data, the total value of future power generation within the future charging time interval is calculated;

[0107] If the total value of future load power is less than or equal to the total value of future power generation, the future surplus power generation of the photovoltaic and wind power generation modules in the future charging time interval is calculated by combining the total value of future load power and the total value of future power generation;

[0108] The remaining storage capacity of the energy storage device is calculated by combining the remaining power data and the upper limit of the power storage device;

[0109] If the remaining storage capacity is greater than or equal to the future surplus power generation, a first energy complementary strategy for the energy storage device in the future charging time interval is generated. The first energy complementary strategy includes the following steps:

[0110] Control the energy storage device to use its own stored energy and photovoltaic and wind power generation modules to provide DC power to the charging pile during all high-load periods;

[0111] Control the energy storage device to use the photovoltaic and wind power generation modules to provide direct current to the charging piles during all low-load periods and replenish its own stored energy at the same time;

[0112] The energy storage device is controlled to utilize the photovoltaic and wind power generation modules to supplement its own stored electrical energy during all power generation time periods.

[0113] In this embodiment, the step of fitting the future load power time series data and the future power generation time series data into the future load power curve and the future power generation curve first requires the use of data fitting technology. Data fitting is a mathematical method that analyzes existing data points and finds a mathematical function to approximate these data points. Polynomial fitting, spline interpolation or other suitable fitting methods can be used to fit the future load power time series data P load (t) and future power generation time series data P gen Fitting into two continuous function curves. These curves can more intuitively show the load and power generation conditions at different time points in the future.

[0114] In any continuous time period of the future charging time interval, if all future load power values ​​in the future load power curve are higher than all future power values ​​in the future power generation curve, the corresponding continuous time period is marked as a high load time period. The specific implementation method is to compare the fitted power curve point by point. For example, set a time period [t1, t2], in this time period, if P load (t)>P gen (t) For all t∈[t1, t2], this condition is established, then the time period is marked as a high-load time period. Traverse all time periods, compare the load power and the generated power point by point, and find all time periods that meet the conditions.

[0115] In any continuous time period of the future charging time interval, if all future load power values ​​in the future load power curve are lower than all future power generation values ​​in the future power generation curve, the corresponding continuous time period is marked as a low load time period. The specific implementation method is similar to the marking of the high load time period. A time period [t3, t4] is set. In this time period, if P load (t)<P gen (t) For all t∈[t3, t4], this condition is established, then the time period is marked as a low-load time period. Traverse all time periods, compare the load power and the generated power point by point, and find all time periods that meet the conditions.

[0116] In any continuous time period of the future charging time interval, if all future load power values ​​in the future load power curve are 0, the corresponding continuous time period is marked as the power generation time period. The specific implementation method is to check the fitted load power curve point by point. For example, set a time period [t5, t6], in this time period, if P load (t) = 0 is true for all t∈[t5, t6], then mark this time period as the power generation time period. Traverse all time periods, check the load power point by point, and find all time periods that meet the conditions.

[0117] The step of calculating the total value of future load power within the future charging time interval based on the future load power time series data first requires integrating the future load power time series data. Assume that the future load power time series data is expressed by P load (t) indicates that the future charging time interval is [t0, tf], then the total value of future load power can be obtained by integral calculation, that is, The specific implementation method can use numerical integration technology, such as trapezoidal integration method or Simpson integration method, to integrate the discrete load power time series data. In this way, the total load power value in the future charging time interval can be obtained, which provides a basis for subsequent energy scheduling and management. The step of calculating the total value of future power generation in the future charging time interval based on the future power generation time series data is similar to the calculation of the total load power value, which can also be obtained by integral calculation.

[0118] If the total value of future load power is less than or equal to the total value of future power generation, the future surplus power generation of the photovoltaic and wind power generation modules in the future charging time interval is calculated by combining the total value of future load power and the total value of future power generation. The specific implementation method is to simply calculate the difference between the two. This calculation method is very intuitive and can directly reflect the excess power that the photovoltaic and wind power generation modules can provide in the future charging time interval, providing a basis for subsequent energy storage and energy management. The remaining storage capacity of the energy storage device can be obtained by calculating the difference between the remaining power data and the upper limit of the power storage device.

[0119] If the remaining storage capacity is greater than or equal to the future surplus power generation, the first energy complementary strategy of the energy storage device in the future charging time interval is generated. The first energy complementary strategy includes the following steps: controlling the energy storage device to use its own stored electric energy and photovoltaic wind power generation modules to provide direct current to the charging pile in all high-load time periods; controlling the energy storage device to use photovoltaic wind power generation modules to provide direct current to the charging pile and supplement its own stored electric energy at the same time in all low-load time periods; controlling the energy storage device to use photovoltaic wind power generation modules to supplement its own stored electric energy in all power generation time periods. Thus, the charging and discharging strategy of the energy storage device is dynamically adjusted according to the high-load time period, low-load time period and power generation time period marked above. For example, in the high-load time period, electric energy is preferentially extracted from the energy storage device, and the power of the photovoltaic and wind power generation modules is combined to ensure that the charging demand is met. In the low-load time period, the power of the photovoltaic and wind power generation modules is preferentially used, and the excess power is stored in the energy storage device. In the power generation time period, all the power generation power is used to supplement the electric energy of the energy storage device. Through this comprehensive scheduling, it can be ensured that in the future charging time interval, the charging station can provide stable charging services, maximize the use of renewable energy, and improve energy utilization efficiency.

[0120] In one embodiment, if the remaining storage capacity is less than the future surplus power generation, a second energy complementary strategy for the energy storage device in the future charging time interval is generated, and the second energy complementary strategy includes the following steps:

[0121] Control the energy storage device to use its own stored energy and photovoltaic and wind power generation modules to provide DC power to the charging pile during all high-load periods;

[0122] Control the energy storage device to use the photovoltaic wind power generation module to provide direct current to the charging pile during all low-load periods and replenish its own stored energy at the same time, and control the energy storage device to use the photovoltaic wind power generation module to replenish its own stored energy during all power generation periods until the remaining storage capacity is reduced to 0;

[0123] When the remaining storage capacity is reduced to 0, the energy storage device is controlled to use the photovoltaic wind power generation module to provide DC power to the charging pile during all low-load time periods and to provide electrical energy to the municipal power grid at the same time, and the energy storage device is controlled to use the photovoltaic wind power generation module to provide electrical energy to the municipal power grid during all power generation time periods.

[0124] In this embodiment, during all high-load time periods, the energy storage device will be controlled to use its own stored electrical energy and the photovoltaic wind power generation module to jointly provide direct current to the charging pile. The specific implementation principle is that by real-time monitoring of the power of the energy storage device and the power generation of the photovoltaic wind power generation module, the system can determine the current power demand and supply. During high-load time periods, the power demand of the charging pile is high. At this time, the energy storage device will automatically release the stored electrical energy to make up for the part that the photovoltaic wind power generation module cannot fully meet. For example, assuming that during a high-load time period, the charging pile requires 100 kilowatts of electricity, and the photovoltaic wind power generation module can only provide 60 kilowatts, then the energy storage device will release 40 kilowatts of electricity to meet the demand. In this way, it is ensured that during high-load time periods, the charging pile can obtain a stable and sufficient power supply to avoid charging interruptions or delays due to insufficient power.

[0125] During all low-load time periods, the energy storage device will be controlled to use the photovoltaic wind power generation module to provide direct current to the charging pile, and at the same time replenish its own stored electrical energy. The specific implementation principle is that during low-load time periods, the power demand of the charging pile is relatively low, and the power generation of the photovoltaic wind power generation module can usually meet or even exceed the demand. At this time, the system will give priority to using the power of the photovoltaic wind power generation module to power the charging pile, and store the excess power in the energy storage device. For example, assuming that during a low-load time period, the charging pile requires 30 kilowatts of electricity, and the photovoltaic wind power generation module can provide 50 kilowatts, then the remaining 20 kilowatts of electricity will be stored by the energy storage device. In this way, not only can the power supply of the charging pile during the low-load time period be ensured, but also the excess renewable energy can be effectively utilized to increase the power reserve of the energy storage device, and prepare for future high-load time periods.

[0126] During all power generation time periods, the energy storage device will be controlled to use the photovoltaic wind power generation module to supplement its own stored electrical energy until the remaining storage capacity is reduced to 0. The specific implementation principle is that during the power generation time period, the system will detect that the load power is 0, at which time all the power generated by the photovoltaic wind power generation module can be used to charge the energy storage device. The system will continuously monitor the storage capacity of the energy storage device to ensure that the electrical energy of the photovoltaic wind power generation module is stored to the maximum extent during the power generation time period. For example, assuming that the photovoltaic wind power generation module can provide 40 kilowatts of electricity during the power generation time period, and the remaining storage capacity of the energy storage device is 100 kilowatts, then during this time period, the energy storage device will gradually store the 40 kilowatts of electricity until its storage capacity reaches 100 kilowatts. In this way, it is ensured that the electrical energy of the photovoltaic wind power generation module is fully utilized during the power generation time period, maximizing the electrical energy reserve of the energy storage device.

[0127] When the remaining storage capacity is reduced to 0, during all low-load time periods, the energy storage device will be controlled to use the photovoltaic wind power generation module to provide DC power to the charging pile, and at the same time provide power to the mains grid. The specific implementation principle is that when the storage capacity of the energy storage device reaches the upper limit, that is, the remaining storage capacity is 0, the excess power of the photovoltaic wind power generation module will no longer be stored. At this time, the system will transmit this excess power to the mains grid to avoid waste. For example, assuming that during a low-load period, the charging pile requires 20 kilowatts of electricity, and the photovoltaic wind power generation module can provide 50 kilowatts, and the energy storage device is full, then the remaining 30 kilowatts of electricity will be transmitted to the mains grid. In this way, not only can the power supply of the charging pile during the low-load period be ensured, but also the excess renewable energy can be transmitted to the power grid to support the stable operation of the power grid.

[0128] During all power generation time periods, when the remaining storage capacity is reduced to 0, the energy storage device will be controlled to use the photovoltaic wind power generation module to provide power to the city power grid. The specific implementation principle is that during the power generation time period, the system will detect that the load power is 0, and all the power generated by the photovoltaic wind power generation module can be used to charge the energy storage device. However, when the storage capacity of the energy storage device reaches the upper limit, that is, the remaining storage capacity is 0, the excess power of the photovoltaic wind power generation module will not be able to continue to be stored. At this time, the system will transmit this excess power to the city power grid to avoid waste. For example, assuming that the photovoltaic wind power generation module can provide 40 kilowatts of electricity during the power generation time period, and the energy storage device is full, then this 40 kilowatts of electricity will be transmitted to the city power grid. In this way, it is ensured that the power of the photovoltaic wind power generation module is fully utilized during the power generation time period, maximizing the support for the stable operation of the power grid while avoiding the waste of renewable energy.

[0129] In one embodiment, if the total value of future load power is greater than the total value of future power generation power, a grid power consumption prediction curve of the mains grid in the future charging time interval is obtained through the grid dispatching system of the mains grid;

[0130] The future shortfall power generation of the photovoltaic and wind power generation modules in the future charging time interval is calculated by combining the future total load power value and the future total power generation value;

[0131] If the remaining storage capacity is greater than or equal to the future shortfall in power generation, a first energy complementary strategy for the energy storage device in the future charging time interval is generated;

[0132] If the remaining storage capacity is less than the future shortfall in power generation, the difference between the remaining storage capacity and the future shortfall in power generation is calculated as the amount of electricity to be supplemented;

[0133] According to the time axis of the future charging time interval, the total difference between all future load power values ​​and all future power generation values ​​in each high-load time period is calculated in sequence, and the total difference is accumulated to obtain the total value of the gap power generation in the time period, until the total value of the gap power generation in the time period is greater than or equal to the remaining storage amount, and the high-load time period calculated when the total value of the gap power generation in the time period is greater than or equal to the remaining storage amount is marked as the target high-load time period;

[0134] The power replenishment time node is calibrated between the start time of the future charging time interval and the start time of the target high-load time period according to the power grid power consumption prediction curve. The power grid predicted power consumption at the power replenishment time node of the power grid power consumption prediction curve is the minimum power grid predicted power consumption between the start time of the future charging time interval and the start time of the target high-load time period.

[0135] Generate a third energy complementary strategy for the energy storage device in a future charging time interval, the third energy complementary strategy comprising the following steps:

[0136] Control the energy storage device to use its own stored energy and photovoltaic and wind power generation modules to provide DC power to the charging pile during all high-load periods;

[0137] Control the energy storage device to use the photovoltaic and wind power generation modules to provide direct current to the charging piles during all low-load periods and replenish its own stored energy at the same time;

[0138] Control the energy storage device to use the photovoltaic and wind power generation modules to supplement its own stored energy during all power generation periods;

[0139] The energy storage device is controlled to use the mains power grid to supplement its own stored energy at the energy supplement time node.

[0140] In this embodiment, when the total value of future load power is greater than the total value of future power generation power, the grid power consumption forecast curve of the mains power grid in the future charging time interval is obtained through the grid dispatching system of the mains power grid. The specific implementation principle is that the grid dispatching system usually predicts the future grid power consumption based on historical data, weather conditions, seasonal changes and other factors, and generates corresponding prediction curves. These prediction curves can reflect the power demand of the power grid in various time periods in the future. For example, assuming that the future charging time interval is from 8:00 to 18:00 on a certain day, the grid dispatching system will provide hourly power consumption forecast data in this time period. These data will be displayed in the form of curves to help judge the grid load at various time points in the future.

[0141] The future gap in power generation of the photovoltaic wind power generation module in the future charging time interval is calculated by combining the total value of future load power and the total value of future power generation. The specific implementation principle is to calculate the power gap that cannot be met by the photovoltaic wind power generation module by comparing the total load power with the total power generation power in the future charging time interval. For example, assuming that the total load power in the future charging time interval is 1000 kilowatts and the total power generation power is 700 kilowatts, the future gap in power generation will be 300 kilowatts. This gap reflects the part of the charging pile demand that the photovoltaic wind power generation module cannot fully meet in the future charging time interval. By calculating the future gap in power generation, it is possible to clearly determine the amount of electricity that needs to be supplemented by other means (such as energy storage devices or municipal power grids) to ensure that the charging pile can obtain a stable power supply in the future charging time interval.

[0142] If the remaining storage capacity is greater than or equal to the future power generation gap, the first energy complementary strategy of the energy storage device in the future charging time interval is generated. The specific implementation principle is to judge whether the energy storage device can completely make up for the shortcomings of the photovoltaic wind power generation module in the future charging time interval by comparing the current remaining storage capacity of the energy storage device with the future power generation gap. For example, assuming that the current remaining storage capacity of the energy storage device is 400 kilowatts and the future power generation gap is 300 kilowatts, the energy storage device has enough electricity to make up for the future power gap. In this case, the energy storage device will release the stored electric energy in the future charging time interval, and cooperate with the photovoltaic wind power generation module to provide direct current to the charging pile. In this way, it is ensured that the charging pile can obtain a stable and sufficient power supply in the future charging time interval, avoiding charging interruptions or delays caused by insufficient power.

[0143] If the remaining storage capacity is less than the future power generation gap, the difference between the remaining storage capacity and the future power generation gap is calculated as the amount of electricity to be supplemented. The specific implementation principle is to calculate the part of the power gap that the energy storage device cannot meet by comparing the future power generation gap with the remaining storage capacity. For example, assuming that the current remaining storage capacity of the energy storage device is 200 kilowatts, and the future power generation gap is 300 kilowatts, then the amount of electricity to be supplemented is 100 kilowatts. This amount of electricity to be supplemented reflects the part of the charging pile demand that the energy storage device and the photovoltaic wind power generation module cannot fully meet in the future charging time interval. By calculating the amount of electricity to be supplemented, it is possible to clearly determine the amount of electricity that needs to be supplemented through other means (such as the municipal power grid) to ensure that the charging pile can obtain a stable power supply in the future charging time interval.

[0144] According to the time axis of the future charging time interval, the total difference between all future load power values ​​and all future power generation values ​​in each high-load time period is calculated in sequence, and the total difference is accumulated to obtain the total value of the gap power generation in the time period, until the total value of the gap power generation in the time period is greater than or equal to the remaining storage capacity, and the high-load time period calculated when the total value of the gap power generation in the time period is greater than or equal to the remaining storage capacity is marked as the target high-load time period. The specific implementation principle is to compare the future load power and the power generation power in each time period, and accumulate and calculate the power gap in each time period until the accumulated total power gap is greater than or equal to the remaining storage capacity of the energy storage device. For example, assuming that there are multiple high-load time periods in the future charging time interval, and the power gap in each time period is 50 kilowatts, 70 kilowatts, and 60 kilowatts respectively, when accumulated to the third time period, the total gap reaches 180 kilowatts, and the remaining storage capacity of the energy storage device is 170 kilowatts. At this time, the third time period will be marked as the target high-load time period.

[0145] According to the power grid power consumption prediction curve, the power supply time node is calibrated between the start time of the future charging time interval and the start time of the target high load time period. The power grid power consumption prediction curve at the power supply time node is the lowest power grid predicted power consumption between the start time of the future charging time interval and the start time of the target high load time period. The specific implementation principle is to determine the lowest power grid load time point in the future charging time interval as the best time node for power supply by analyzing the power grid power consumption prediction curve. For example, assuming that the future charging time interval is 8:00 to 18:00, the start time of the target high load time period is 14:00, and between 8:00 and 14:00, the power grid power consumption prediction curve shows that the lowest load time point is 10:00, and the power grid predicted power consumption at this time is the lowest value. By selecting the lowest load time point for power supply, the impact on the power grid can be effectively reduced, the use of power resources can be optimized, and the charging pile can obtain a stable power supply in the future charging time interval. In addition, the lowest power grid load time point usually has a lower electricity price, and the economic benefits can be maximized when power supply is performed at this time.

[0146] Generate a third energy complementary strategy for the energy storage device in the future charging time interval, the third energy complementary strategy includes the following steps: control the energy storage device to use its own stored electric energy and photovoltaic wind power generation modules to provide direct current to the charging pile in all high-load time periods; control the energy storage device to use photovoltaic wind power generation modules to provide direct current to the charging pile in all low-load time periods and supplement its own stored electric energy at the same time; control the energy storage device to use photovoltaic wind power generation modules to supplement its own stored electric energy in all power generation time periods; control the energy storage device to use the city power grid to supplement its own stored electric energy at the power replenishment time node. The specific implementation principle is to formulate a detailed energy complementary strategy by comprehensively considering the future load power, power generation power, remaining storage capacity of the energy storage device and the power grid power consumption forecast curve to ensure that the charging pile can obtain a stable and sufficient power supply in the future charging time interval. For example, during high-load periods, the energy storage device will release stored electric energy and cooperate with the photovoltaic wind power generation module to supply power to the charging pile; during low-load periods, the photovoltaic wind power generation module will give priority to supplying power to the charging pile, and the excess electric energy will be stored in the energy storage device; during the power generation period, the electric energy of the photovoltaic wind power generation module will give priority to supplementing the energy storage device; at the time of electric energy supplementation, the energy storage device will use the city power grid for electric energy supplementation. In this way, it is ensured that the charging pile can obtain a stable and sufficient power supply during the future charging time interval, while optimizing the use of power resources, reducing the impact on the power grid, and improving the overall energy utilization efficiency.

[0147] In one embodiment, determining a future charging time interval in which a charging pile needs to provide charging services intermittently or continuously in the future according to all scheduled charging demand information, and converting the scheduled charging demand information into future load power time series data based on the future charging time interval includes the following steps:

[0148] Extract the model information, scheduled charging start time, current power of the vehicle and scheduled charging power of each vehicle to be charged from the scheduled charging demand information;

[0149] For each vehicle to be charged, the estimated charging time is calculated based on the vehicle model information, the current power of the vehicle and the scheduled charging power. The future charging time period of the vehicle to be charged is determined according to the scheduled charging start time and the estimated charging time, and the SOC charging curve of the vehicle to be charged in the future charging time period is generated;

[0150] Merge the future charging time periods of all vehicles to be charged into the same timeline to obtain the future charging time intervals where the charging piles need to provide charging services intermittently or continuously in the future;

[0151] Based on the future charging time interval and according to the SOC charging curve, the scheduled charging demand information is converted into future load power timing data.

[0152] In this embodiment, first, it is necessary to parse out relevant data from the scheduled charging demand information submitted by the user. The scheduled charging demand information usually contains multiple fields, including basic information of the vehicle, the scheduled charging time, the current battery power, and the power the user wants to achieve. For example, regular expressions or specific markers can be used to identify and extract this information. The extracted data should include vehicle model information (such as brand and model), scheduled charging start time (specific date and time), vehicle current power (expressed as a percentage), and scheduled charging power (target power percentage).

[0153] According to the battery capacity and charging characteristics of different models, combined with the current power of the vehicle and the target charging power, the time required to complete the charging can be calculated. First, it is necessary to find the corresponding charging parameters, such as battery capacity and maximum charging power, based on the vehicle model information. These parameters can usually be obtained from the technical documents or database of the vehicle manufacturer. Then, based on the current power of the vehicle and the target power, the difference in power required to be charged is calculated. Next, using the charging power and battery capacity, the estimated charging time can be calculated. Add the scheduled charging start time to the estimated charging time to determine the end time of the vehicle's charging, thereby determining the future charging time period. Next, based on the charging time period and charging power, generate the SOC (battery state) charging curve of the vehicle during the charging process. The SOC charging curve reflects the change in the battery power during the charging process, usually with time as the horizontal axis and the percentage of power as the vertical axis.

[0154] The future charging time periods of all vehicles to be charged are merged into the same time axis to obtain the future charging time intervals in which the charging pile needs to provide charging services intermittently or continuously in the future. First, by merging the charging time periods of all vehicles to be charged on the time axis, the overall charging demand of the charging pile in the future is determined. Specifically, it is necessary to arrange the charging time periods of each vehicle in chronological order, and find the overlapping and continuous parts of these time periods to determine one or more continuous time intervals. In this time interval, the charging pile needs to provide charging services intermittently or continuously to meet the charging needs of different vehicles. By analyzing the SOC charging curve in the future charging time interval, the charging power demand at each time point is calculated to generate the future load power time series data. The SOC charging curve reflects the changes in the battery power during the charging process. By converting these changes into charging power, the power demand of the charging pile at each time point can be obtained. Next, the load power time series data of all vehicles are superimposed to obtain the total load power time series data of the charging pile in the future charging time interval.

[0155] In one embodiment, converting the scheduled charging demand information into future load power time series data based on the future charging time interval and according to the SOC charging curve includes the following steps:

[0156] Determine the segment step length according to the interval duration of the future charging time interval;

[0157] Taking the start time of the future charging time interval as the starting point, a load power time node is set in the future charging time interval every segment step;

[0158] For each load power time node, if the load power time node is not in any future charging time period, the future load power of the load power time node is set to 0;

[0159] If the load power time node is in only one future charging time period, the future charging time period in which the load power time node is located is taken as the first future charging time period;

[0160] Determine a first instantaneous charging power from an SOC charging curve in a first future charging time period according to a load power time node, wherein a timestamp of the first instantaneous charging power in the first future charging time period is the same as a timestamp of the load power time node;

[0161] Setting the future load power at the load power time node to the first instantaneous charging power;

[0162] If the load power time node is within multiple future charging time periods, all of the multiple future charging time periods in which the load power time node is located are used as the second future charging time period;

[0163] Determine the second instantaneous charging power from the SOC charging curves in each second future charging time period according to the load power time node, and the timestamps of all the second instantaneous charging powers in the corresponding second future charging time period are the same as the timestamps of the load power time node;

[0164] Based on the SOC charging stage in which each second instantaneous charging power is located in the corresponding SOC charging curve, a preset stage power adjustment weight is assigned to each second instantaneous charging power;

[0165] Combining all second instantaneous charging powers and stage power adjustment weights for weighted calculation, the sum of the second instantaneous charging powers is obtained, and the future load power at the load power time node is set to the sum of the second instantaneous charging powers;

[0166] The future load power of all load power time nodes is integrated to obtain the future load power time series data.

[0167] In this embodiment, it is first necessary to clarify the start time and end time of the future charging time interval, and then calculate the total duration of this time interval. Next, the segmentation step size needs to be determined based on this total duration. The selection of the segmentation step size should take into account the length of the charging time interval and the accuracy requirements of the charging demand. Usually the segmentation step size can be a fixed time interval, for example, a time node is set every 15 minutes or 30 minutes. This ensures that there are enough time nodes in the entire future charging time interval to accurately reflect the changes in charging demand.

[0168] Taking the start time of the future charging time interval as the starting point, a load power time node is set every segment step in the future charging time interval. The purpose of this step is to evenly distribute the load power time nodes throughout the future charging time interval, and these nodes will be used to record and predict future charging power requirements. In specific implementation, starting from the start time of the future charging time interval, a time node is set every segment step until the end time of the future charging time interval is reached or exceeded. In this way, a series of load power time nodes can be formed in the future charging time interval, and these nodes will be used for subsequent charging power calculations.

[0169] For each load power time node, it is necessary to check whether the time node is in any future charging time period. If the load power time node is not in any future charging time period, the future load power of the load power time node is set to 0. The purpose of this step is to ensure that the charging power is calculated only in the time period when charging is actually required. If a load power time node is not in any future charging time period, it means that charging is not required at this time node, so the future load power of the time node is set to 0, indicating that there is no charging demand at this time node.

[0170] If the load power time node is only in one future charging time period, the future charging time period in which the load power time node is located is taken as the first future charging time period. The purpose of this step is to clarify the charging time period to which the load power time node belongs, so as to calculate the charging power later. In specific implementation, check whether the load power time node is only in one future charging time period. If so, mark the future charging time period as the first future charging time period.

[0171] The first instantaneous charging power is determined from the SOC charging curve in the first future charging time period according to the load power time node. The SOC charging curve refers to the charging power change curve of the electric vehicle battery under different charging states. In specific implementation, by finding the SOC charging curve with the timestamp corresponding to the load power time node in the first future charging time period, the instantaneous charging power corresponding to the timestamp is determined, that is, the first instantaneous charging power. The timestamp of the first instantaneous charging power in the first future charging time period is the same as the timestamp of the load power time node.

[0172] Set the future load power of the load power time node to the first instantaneous charging power. The purpose of this step is to assign the calculated first instantaneous charging power to the future load power of the load power time node. In specific implementation, the first instantaneous charging power determined in the previous step is directly assigned to the future load power of the load power time node to ensure that the charging power at this time node accurately reflects the actual demand.

[0173] If the load power time node is in multiple future charging time periods, the multiple future charging time periods in which the load power time node is located are all used as the second future charging time period. The purpose of this step is to handle the situation where the load power time node is in multiple charging time periods at the same time. In specific implementation, check whether the load power time node is in multiple future charging time periods. If so, mark these future charging time periods as second future charging time periods. Determine the second instantaneous charging power from the SOC charging curves in each second future charging time period according to the load power time node. In specific implementation, by finding the SOC charging curve corresponding to the timestamp of the load power time node in each second future charging time period, determine the instantaneous charging power corresponding to the timestamp, that is, the second instantaneous charging power. The timestamps of all second instantaneous charging powers in the corresponding second future charging time period are the same as the timestamp of the load power time node.

[0174] Based on the SOC charging stage in which each second instantaneous charging power is located in the corresponding SOC charging curve, a preset stage power adjustment weight is assigned to each second instantaneous charging power. The SOC charging stage refers to the charging stage of the electric vehicle battery under different charging states, such as the constant current charging stage, the constant voltage charging stage, and the trickle charging stage. Different charging stages correspond to different charging power requirements. In specific implementation, when the battery voltage reaches the set maximum value (usually the rated voltage of the battery), the constant voltage charging stage is entered. In this stage, the voltage remains constant and the current gradually decreases. Due to differences in the internal resistance and charging state of the battery, changes in current may cause power fluctuations. Therefore, it is necessary to assign corresponding stage power adjustment weights according to the SOC charging stage in which each second instantaneous charging power is located to reflect the power adjustment requirements of different charging stages.

[0175] The sum of the second instantaneous charging powers is obtained by combining all the second instantaneous charging powers and the stage power adjustment weights for weighted calculation. In specific implementation, each second instantaneous charging power is multiplied by its corresponding stage power adjustment weight, and then all weighted charging powers are added together to obtain the sum of the second instantaneous charging powers. In this way, the power requirements of different charging stages can be comprehensively considered to obtain a more accurate charging power prediction.

[0176] Set the future load power of the load power time node to the sum of the second instantaneous charging power. The purpose of this step is to assign the sum of the second instantaneous charging power obtained by weighted calculation to the future load power of the load power time node. In specific implementation, the sum of the second instantaneous charging power calculated in the previous step is directly assigned to the future load power of the load power time node to ensure that the charging power at this time node accurately reflects the actual demand.

[0177] Integrate the future load power of all load power time nodes to obtain the future load power time series data. The purpose of this step is to summarize the future load power of all load power time nodes to form a complete future load power time series data. In specific implementation, the future load power of all load power time nodes is arranged in chronological order to form a time series that reflects the changes in charging power demand within the future charging time interval. These time series data can be used for the formulation of charging plans and the optimal configuration of charging facilities.

[0178] In one embodiment, before predicting the future power generation time series data of the photovoltaic and wind power generation modules in the future charging time interval based on the weather forecast information, the following steps are also included:

[0179] Obtain historical meteorological information of the area where the new energy charging station is located and historical photovoltaic and wind power generation information of the photovoltaic and wind power generation modules within the same historical time interval;

[0180] The grey correlation algorithm is used to calculate the data correlation between the historical meteorological data of each different meteorological type in the historical meteorological information and the historical photovoltaic and wind power generation information;

[0181] Screen out all historical meteorological data with data correlation less than a preset correlation threshold, and integrate all retained historical meteorological data into historical meteorological sample data;

[0182] Construct photovoltaic and wind power generation prediction model based on support vector regression algorithm;

[0183] The photovoltaic and wind power generation prediction model is iteratively trained using historical meteorological sample data. During the iterative training process of the photovoltaic and wind power generation prediction model, the model parameters of the photovoltaic and wind power generation prediction model are optimized through genetic algorithms based on historical photovoltaic and wind power generation information.

[0184] In this embodiment, obtaining the historical meteorological information of the area where the new energy charging station is located and the historical photovoltaic wind power generation information of the photovoltaic wind power generation module within the same historical time interval is a basic step for photovoltaic wind power generation prediction. First, it is necessary to clarify the start and end time of the historical time interval, and then obtain detailed meteorological information within the time interval from the meteorological data provider or meteorological database. These meteorological information usually include data of multiple meteorological types such as temperature, humidity, wind speed, wind direction, rainfall, solar radiation intensity, etc. At the same time, the actual power generation data within the same time interval is obtained from the monitoring system or database of the photovoltaic and wind power generation modules, including photovoltaic power generation and wind power generation. The acquisition of these data can be carried out through API interface, database query or data file import. By collecting these historical data, sufficient data basis can be provided for subsequent correlation analysis and training of prediction models.

[0185] The grey correlation algorithm is used to calculate the data correlation between the historical meteorological data of each different meteorological type in the historical meteorological information and the historical photovoltaic and wind power generation information in order to determine which meteorological factors have a significant impact on photovoltaic and wind power generation. The grey correlation algorithm is a mathematical method for analyzing the correlation between system factors, which is suitable for situations where the sample size is small and the data is incomplete. In the specific implementation, the historical meteorological data and the historical photovoltaic and wind power generation data are standardized to eliminate the influence of the dimension. Then, the correlation between the data sequence of each meteorological type and the power generation data sequence is calculated. The higher the correlation, the greater the impact of the meteorological factor on the power generation. In this way, the meteorological factors that have a significant impact on photovoltaic and wind power generation can be identified, providing a basis for subsequent data screening and model training.

[0186] Screening out all historical meteorological data with a data correlation less than the preset correlation threshold and integrating all retained historical meteorological data into historical meteorological sample data is to improve the effectiveness and accuracy of the prediction model. The preset correlation threshold is set based on actual needs and experience, and is used to screen out meteorological factors that have a significant impact on power generation. In specific implementation, the correlation calculated by the gray correlation algorithm is compared with the preset threshold, and all meteorological data with a correlation less than the threshold are screened out. The retained meteorological data is integrated into historical meteorological sample data, which will be used for subsequent model training. Through this screening, the interference of irrelevant or less influential data can be reduced, and the training efficiency and prediction accuracy of the prediction model can be improved.

[0187] The purpose of building a photovoltaic and wind power generation prediction model based on the support vector regression algorithm is to establish a mathematical model that can accurately predict future power generation. The support vector regression algorithm is a machine learning algorithm that is suitable for regression problems, can handle high-dimensional data and has good generalization ability. In specific implementation, it is necessary to first select appropriate kernel functions (such as linear kernels, radial basis kernels, etc.) and hyperparameters, and then input the screened historical meteorological sample data and the corresponding historical power generation data into the support vector regression algorithm for training. Through training, the algorithm will learn the nonlinear relationship between meteorological factors and power generation, and build a model that can predict power generation based on future meteorological data. The construction process of this model requires multiple iterations to continuously optimize the model parameters and improve the prediction accuracy.

[0188] The photovoltaic and wind power generation prediction model is iteratively trained using historical meteorological sample data. During the iterative training process of the photovoltaic and wind power generation prediction model, the model parameters of the photovoltaic and wind power generation prediction model are optimized based on historical photovoltaic and wind power generation information and through genetic algorithms in order to further improve the accuracy and robustness of the prediction model. Iterative training refers to repeatedly adjusting model parameters and conducting multiple trainings during the model training process to gradually optimize model performance. Genetic algorithm is an optimization algorithm that simulates natural selection and genetic mechanisms and is suitable for solving complex optimization problems. In specific implementation, historical meteorological sample data and historical power generation data are input into the support vector regression model for initial training, and then the model parameters are optimized using genetic algorithms. The genetic algorithm generates a set of new model parameters through operations such as selection, crossover, and mutation, and selects a parameter combination with better performance in each generation of iteration. Through multiple iterations and parameter optimization, the prediction accuracy of the model can be gradually improved, enabling it to more accurately predict future photovoltaic and wind power generation. Ultimately, the fully trained and optimized prediction model will be able to provide high-precision power generation predictions based on future meteorological data.

[0189] In one embodiment, predicting the future power generation time series data of the photovoltaic and wind power generation modules in the future charging time interval based on the weather forecast information includes the following steps:

[0190] Setting multiple power generation time nodes within the future charging time interval according to the information timestamp of the weather forecast information;

[0191] For each power generation time node, filter out invalid weather forecast information whose weather type is different from historical weather sample data;

[0192] Input all remaining weather forecast information into the photovoltaic wind power generation prediction model, and use the photovoltaic wind power generation prediction model to output the predicted power generation corresponding to the power generation time node;

[0193] Integrate the predicted power generation at all power generation time nodes to obtain future power generation time series data.

[0194] In this embodiment, it is first necessary to determine the start and end time of the future charging time interval, for example, from 8 am to 6 pm on a certain day. Then, multiple time nodes are set within this time interval according to the time accuracy of the weather forecast information (such as every hour, every half hour). Each time node represents a specific moment, such as 8 am, 9 am, 10 am, etc. The interval of the time nodes can be determined according to actual needs and the time accuracy of the weather forecast information. By setting these time nodes, the future charging time interval can be subdivided into multiple time periods, providing a basis for predicting the power generation power for each time period.

[0195] For each power generation time node, the invalid weather forecast information in the weather forecast information whose weather type is different from the historical weather sample data is screened out to ensure that the weather data input into the prediction model is consistent with the data type used during model training. In specific implementation, it is first necessary to check the weather forecast information corresponding to each time node, including data of multiple weather types such as temperature, humidity, wind speed, wind direction, rainfall, solar radiation intensity, etc. Then, compare these weather forecast information with the historical weather sample data to screen out invalid weather forecast information with inconsistent weather types or data formats. For example, if there is no rainfall item in the historical weather sample data, and the weather forecast information contains rainfall data, the rainfall data needs to be screened out. Through this screening, it can be ensured that the weather data input into the prediction model is consistent with the data type used during model training, thereby improving the accuracy of the prediction results.

[0196] All the remaining weather forecast information is input into the photovoltaic wind power generation prediction model, and the photovoltaic wind power generation prediction model is used to output the predicted power generation corresponding to the power generation time node in order to generate the predicted power generation value at each time node. In the specific implementation, the filtered weather forecast information is input into the previously trained photovoltaic wind power generation prediction model one by one according to the time node. The prediction model calculates the predicted power generation value corresponding to each time node based on the input meteorological data and its internal mathematical relationships and parameters. The output result of the model is the predicted power generation value at each time node. Integrating the predicted power generation of all power generation time nodes to obtain the future power generation time series data is the last step in predicting future power generation. In the specific implementation, the predicted power generation value of each time node is arranged in chronological order to form a complete power generation time series data. These time series data can be expressed as a time series, and each time point corresponds to a predicted power generation value. By integrating these data, the power generation change in the future charging time interval can be obtained.

[0197] The present invention also discloses a charging pile energy complementation system based on renewable energy, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the charging pile energy complementation method based on renewable energy described in any one of the above-mentioned embodiments is implemented.

[0198] Among them, the processor can adopt a central processing unit (CPU). Of course, according to actual usage, other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. can also be adopted. The general-purpose processor can adopt a microprocessor or any conventional processor, etc., and this application does not impose any restrictions on this.

[0199] Among them, the memory can be an internal storage unit of a computer device, such as a hard disk or memory of a computer device, or an external storage device of a computer device, such as a plug-in hard disk, a smart memory card (SMC), a secure digital card (SD) or a flash memory card (FC) equipped on the computer device, etc., and the memory can also be a combination of an internal storage unit and an external storage device of a computer device. The memory is used to store computer programs and other programs and data required by the computer device. The memory can also be used to temporarily store data that has been output or is to be output, and this application does not impose any restrictions on this.

[0200] The present invention also discloses a charging pile, comprising:

[0201] A DC input module, connected to the DC output terminal of the energy storage device described in any one of the above embodiments, for obtaining the DC power output by the energy storage device;

[0202] DC output module, used to output direct current.

[0203] A person skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of protection of the present application is limited to these examples. In line with the concept of the present application, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of different aspects of one or more embodiments of the present application as above, which are not provided in detail for the sake of simplicity.

[0204] One or more embodiments of the present application are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the present application. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of one or more embodiments of the present application should be included in the protection scope of the present application.

Claims

1. A charging pile energy complementation method based on renewable energy, characterized in that: Applied to a new energy charging station, the new energy charging station includes a charging pile, an energy storage device and a photovoltaic wind power generation module, the energy storage device is used to store electric energy, the photovoltaic wind power generation module is connected to the DC input end of the energy storage device, the DC output end of the energy storage device is connected to the charging pile, and the AC input end and AC output end of the energy storage device are both connected to the mains power grid; The method comprises the following steps: Acquiring the reservation charging demand information of multiple vehicles to be charged through the charging reservation system of the new energy charging station; Determine a future charging time interval during which the charging pile needs to provide charging services intermittently or continuously in the future according to all the reserved charging demand information, and convert the reserved charging demand information into future load power time series data based on the future charging time interval; Obtaining weather forecast information for the area where the new energy charging station is located within the future charging time interval; Predicting the future power generation time series data of the photovoltaic and wind power generation modules within the future charging time interval based on the weather forecast information; Detecting the remaining power data of the energy storage device; Fitting the future load power time series data and the future power generation time series data into a future load power curve and a future power generation curve respectively; If, in any continuous time period of the future charging time interval, all future load power values ​​in the future load power curve are higher than all future power generation values ​​in the future power generation curve, the corresponding continuous time period is marked as a high load time period; If, in any continuous time period of the future charging time interval, all future load power values ​​in the future load power curve are lower than all future power generation values ​​in the future power generation curve, the corresponding continuous time period is marked as a low-load time period; If, in any continuous time period of the future charging time interval, all future load power values ​​in the future load power curve are 0, the corresponding continuous time period is marked as a power generation time period; Counting the total value of future load power within the future charging time interval based on the future load power time series data; Counting the total value of future power generation within the future charging time interval based on the future power generation time series data; If the future total load power value is less than or equal to the future total power generation value, then the future surplus power generation of the photovoltaic wind power generation module in the future charging time interval is calculated by combining the future total load power value and the future total power generation value; Calculate the remaining storage capacity of the energy storage device by combining the remaining power data and the upper limit of the power storage device; If the remaining storage amount is greater than or equal to the future surplus power generation amount, generating a first energy complementary strategy for the energy storage device within the future charging time interval; If the remaining storage capacity is less than the future surplus power generation capacity, generating a second energy complementary strategy for the energy storage device within the future charging time interval; The first energy complementation strategy includes the following steps: Control the energy storage device to provide direct current to the charging pile using its own stored electric energy and the photovoltaic and wind power generation modules during all the high-load time periods; Control the energy storage device to use the photovoltaic and wind power generation modules to provide direct current to the charging pile and replenish its own stored electric energy during all low-load time periods; Control the energy storage device to use the photovoltaic and wind power generation modules to supplement its own stored electric energy during all power generation time periods; The second energy complementary strategy comprises the following steps: Control the energy storage device to provide direct current to the charging pile using its own stored electric energy and the photovoltaic and wind power generation modules during all the high-load time periods; Control the energy storage device to use the photovoltaic wind power generation module to provide direct current to the charging pile and replenish its own stored electric energy during all the low-load time periods, and control the energy storage device to use the photovoltaic wind power generation module to replenish its own stored electric energy during all the power generation time periods until the remaining storage amount is reduced to 0; When the remaining storage capacity is reduced to 0, the energy storage device is controlled to use the photovoltaic wind power generation module to provide direct current to the charging pile and to provide electrical energy to the municipal power grid during all the low-load time periods, and the energy storage device is controlled to use the photovoltaic wind power generation module to provide electrical energy to the municipal power grid during all the power generation time periods.

2. The energy complementation method of charging piles based on renewable energy according to claim 1 is characterized in that: The method further comprises the steps of: If the total value of the future load power is greater than the total value of the future generated power, obtaining a power grid power consumption prediction curve of the city power grid within the future charging time interval through the power grid dispatching system of the city power grid; The future shortfall power generation of the photovoltaic and wind power generation modules in the future charging time interval is calculated by combining the future total load power value and the future total power generation value; If the remaining storage capacity is greater than or equal to the future shortfall in power generation, generating the first energy complementary strategy for the energy storage device within the future charging time interval; If the remaining storage capacity is less than the future power generation gap, the difference between the remaining storage capacity and the future power generation gap is calculated as the amount of electricity to be supplemented; According to the time axis of the future charging time interval, the total differences between all the future load power values ​​and all the future power generation values ​​in each high-load time period are calculated in sequence, and the total differences are accumulated to obtain the total value of the gap power generation in the time period, until the total value of the gap power generation in the time period is greater than or equal to the remaining storage amount, and the high-load time period calculated when the total value of the gap power generation in the time period is greater than or equal to the remaining storage amount is marked as the target high-load time period; The power grid power consumption prediction curve is used to calibrate the power replenishment time node between the start time of the future charging time interval and the start time of the target high-load time period, and the power grid power consumption prediction curve at the power replenishment time node is the lowest power grid power consumption between the start time of the future charging time interval and the start time of the target high-load time period; Generate a third energy complementary strategy for the energy storage device in the future charging time interval, the third energy complementary strategy comprising the following steps: Control the energy storage device to provide direct current to the charging pile using its own stored electric energy and the photovoltaic and wind power generation modules during all the high-load time periods; Control the energy storage device to use the photovoltaic and wind power generation modules to provide direct current to the charging pile and replenish its own stored electric energy during all low-load time periods; Control the energy storage device to use the photovoltaic and wind power generation modules to supplement the stored electric energy in the energy storage device during all power generation time periods; The energy storage device is controlled to use the commercial power grid to supplement the stored electric energy at the electric energy supplementation time node.

3. The energy complementation method of charging piles based on renewable energy according to claim 1, characterized in that: The step of determining a future charging time interval in which the charging pile needs to provide charging services intermittently or continuously in the future according to all the reserved charging demand information, and converting the reserved charging demand information into future load power time series data based on the future charging time interval includes the following steps: Extracting the vehicle model information, scheduled charging start time, current vehicle power and scheduled charging power of each vehicle to be charged from the scheduled charging demand information; For each of the vehicles to be charged, an estimated charging duration is calculated by combining the vehicle model information, the current power of the vehicle and the scheduled charging power, a future charging time period of the vehicle to be charged is determined according to the scheduled charging start time and the estimated charging time period, and an SOC charging curve of the vehicle to be charged in the future charging time period is generated; Merging the future charging time periods of all the vehicles to be charged into the same time axis to obtain the future charging time intervals in which the charging piles need to provide charging services intermittently or continuously in the future; The scheduled charging demand information is converted into future load power timing data based on the future charging time interval and according to the SOC charging curve.

4. The energy complementary method of charging piles based on renewable energy according to claim 3 is characterized in that: The converting the scheduled charging demand information into future load power time series data based on the future charging time interval and according to the SOC charging curve comprises the following steps: Determine the segment step length according to the interval length of the future charging time interval; Taking the interval start time of the future charging time interval as the starting point, setting a load power time node in the future charging time interval every other segment step; For each of the load power time nodes, if the load power time node is not in any of the future charging time periods, the future load power of the load power time node is set to 0; If the load power time node is only within one of the future charging time periods, the future charging time period in which the load power time node is located is used as the first future charging time period; determining a first instantaneous charging power from the SOC charging curve in the first future charging time period according to the load power time node, wherein a timestamp of the first instantaneous charging power in the first future charging time period is the same as a timestamp of the load power time node; Setting the future load power at the load power time node to the first instantaneous charging power; If the load power time node is within a plurality of the future charging time periods, all of the plurality of the future charging time periods in which the load power time node is located are used as the second future charging time period; Determine the second instantaneous charging power from the SOC charging curves in each of the second future charging time periods according to the load power time nodes, wherein the timestamps of all the second instantaneous charging powers in the corresponding second future charging time periods are the same as the timestamps of the load power time nodes; Based on the SOC charging stage in which each second instantaneous charging power is located in the corresponding SOC charging curve, assigning a preset stage power adjustment weight to each second instantaneous charging power; Performing weighted calculation in combination with all the second instantaneous charging powers and the stage power adjustment weights to obtain the second instantaneous charging power sum, and setting the future load power at the load power time node to the second instantaneous charging power sum; The future load powers of all the load power time nodes are integrated to obtain future load power time series data.

5. The method for energy complementation of charging piles based on renewable energy according to claim 1, characterized in that: Before predicting the future power generation time series data of the photovoltaic and wind power generation modules in the future charging time interval based on the weather forecast information, the method further includes the following steps: Acquire historical meteorological information of the area where the new energy charging station is located and historical photovoltaic and wind power generation information of the photovoltaic and wind power generation module within the same historical time interval; The grey correlation algorithm is used to calculate the data correlation between the historical meteorological data of each different meteorological type in the historical meteorological information and the historical photovoltaic and wind power generation information; Screening out all the historical meteorological data whose data relevance is less than a preset relevance threshold, and integrating all the retained historical meteorological data into historical meteorological sample data; Construct photovoltaic and wind power generation prediction model based on support vector regression algorithm; The photovoltaic wind power generation prediction model is iteratively trained using the historical meteorological sample data. During the iterative training process of the photovoltaic wind power generation prediction model, the model parameters of the photovoltaic wind power generation prediction model are optimized based on the historical photovoltaic wind power generation information and through a genetic algorithm.

6. The energy complementation method of charging piles based on renewable energy according to claim 5 is characterized in that: The method of predicting the future power generation time series data of the photovoltaic and wind power generation modules in the future charging time interval based on the weather forecast information comprises the following steps: Setting a plurality of power generation time nodes within the future charging time interval according to the information timestamp of the weather forecast information; For each of the power generation time nodes, filtering out invalid weather forecast information whose weather type in the weather forecast information is different from the historical weather sample data; Input all the remaining weather forecast information into the photovoltaic wind power generation prediction model, and use the photovoltaic wind power generation prediction model to output the predicted power generation corresponding to the power generation time node; The predicted power generation at all power generation time nodes is integrated to obtain future power generation time series data.

7. A charging pile energy complementary system based on renewable energy, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the charging pile energy complementation method based on renewable energy as described in any one of claims 1 to 6 is implemented.

8. A charging pile, characterized in that: Deployed in a new energy charging station, the new energy charging station also includes an energy storage device and a photovoltaic wind power generation module, the new energy charging station is equipped with a charging pile energy complementary method based on renewable energy according to any one of claims 1 to 6, and the charging pile includes: A DC input module, connected to the DC output terminal of the energy storage device, for obtaining the DC power output by the energy storage device; The DC output module is used to output the DC power.

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