Energy management and control method of power system based on photovoltaic charging pile

Abstract

The present invention discloses a method for energy management and regulation of an electric power system based on photovoltaic charging piles, comprising: collecting data of photovoltaic charging piles and battery components, setting a control strategy and a corresponding smooth transition period, tracking the charging demand of electric vehicles, establishing an energy collaborative regulation cycle in combination with the state of an energy storage converter, and dynamically adjusting the working state of the photovoltaic charging piles. This method solves the technical problem that the instability of photovoltaic power generation affects the power supply quality of the photovoltaic charging piles and causes unstable charging services of the power system. This method maximizes the utilization rate of photovoltaic energy by dynamically adjusting the working state of the photovoltaic charging piles, sets a control strategy guided by the stability of charging services, ensures that stable charging services can still be provided when photovoltaic energy is insufficient, effectively alleviates the volatility of photovoltaic power generation, establishes an energy collaborative regulation cycle, optimizes the charging process of electric vehicles, and improves the technical effect of improving the stability of charging services.

Description

Technical Field

[0001] The present invention relates to the technical field related to power system management, and specifically to a power system energy management and regulation method based on photovoltaic charging piles. Background Art

[0002] With the rapid growth of electric vehicles, efficient and stable management of photovoltaic charging piles to ensure the reliability and sustainability of charging services has become an urgent issue that needs to be addressed. The management of photovoltaic charging piles faces many challenges, including uncertainty in lighting conditions and dynamic changes in charging demand. Existing charging pile management systems are often unable to effectively adapt to these changes, resulting in low charging efficiency and even affecting the user's charging experience. In addition, many traditional systems lack intelligent control functions and are unable to adjust charging strategies in real time to cope with different lighting and load conditions.

[0003] In summary, the existing technology has the technical problem of instability of photovoltaic power generation, which affects the power supply quality of photovoltaic charging piles and leads to unstable charging services of the power system. Summary of the Invention

[0004] The present invention provides an energy management and control method for an electric power system based on a photovoltaic charging pile, aiming to solve the technical problem in the prior art that photovoltaic power generation is unstable, affecting the power supply quality of the photovoltaic charging pile and causing unstable charging service of the electric power system.

[0005] In view of the above problems, the technical solution to achieve the present invention is: a method for power system energy management and regulation based on photovoltaic charging piles, which includes: connecting multiple photovoltaic charging piles and collecting photovoltaic energy capture data of photovoltaic modules corresponding to the multiple photovoltaic charging piles;

[0006] Introducing photovoltaic energy storage data of the battery assembly, combined with photovoltaic energy capture data of the photovoltaic assembly, to set a first control strategy oriented towards stability of the charging service, the first control strategy corresponding to a first smooth transition period set;

[0007] Based on the target charging station marked by the multiple photovoltaic charging piles, synchronize the electric vehicles entering the target charging station and track the charging demand information of the electric vehicles entering the station;

[0008] Introducing the operating status data of the energy storage converter and combining it with the charging demand information of the electric vehicles entering the site, setting a second control strategy oriented towards maximizing the utilization rate of photovoltaic energy, wherein the second control strategy corresponds to a second smooth transition period set;

[0009] Establishing an energy collaborative control cycle based on the first control strategy and the first smooth transition period set, and the second control strategy and the second smooth transition period set;

[0010] Through the energy coordinated control cycle, the photovoltaic charging piles in working state among the multiple photovoltaic charging piles are dynamically adjusted in parallel to obtain the energy management strategy of the power system of the multiple photovoltaic charging piles in the current control cycle.

[0011] In summary, the one or more technical solutions provided in the present invention solve the technical problem that the instability of photovoltaic power generation affects the power supply quality of photovoltaic charging piles and causes unstable charging services of the power system. It realizes the technical effect of maximizing the utilization rate of photovoltaic energy by dynamically adjusting the working status of photovoltaic charging piles, setting a control strategy oriented towards charging service stability, ensuring that stable charging services can still be provided when photovoltaic energy is insufficient, effectively alleviating the volatility of photovoltaic power generation, establishing an energy coordinated control cycle, optimizing the charging process of electric vehicles, and improving the stability of charging services. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 This is a flow chart of a method for managing and regulating power system energy based on photovoltaic charging piles according to the present invention;

[0013] Figure 2 This is a flow chart of setting a first control strategy in a method for managing and regulating power system energy based on photovoltaic charging piles according to the present invention. DETAILED DESCRIPTION

[0014] The present invention will be described in detail below with reference to the accompanying drawings. Figure 1 As shown, the present invention provides a method for energy management and control of a power system based on a photovoltaic charging pile, wherein the method includes:

[0015] S1: Connect multiple photovoltaic charging piles and collect photovoltaic energy capture data of photovoltaic modules corresponding to the multiple photovoltaic charging piles; S2: Introduce the photovoltaic energy storage data of the battery module, combine it with the photovoltaic energy capture data of the photovoltaic module, and set a first control strategy oriented towards the stability of the charging service. The first control strategy corresponds to a first smooth transition period set; S3: Based on the target charging station marked by the multiple photovoltaic charging piles, synchronize the electric vehicles entering the target charging station and track the charging demand information of the electric vehicles entering the station.

[0016] Specifically, a photovoltaic charging pile refers to a facility that can use solar energy to provide charging services for electric vehicles; the photovoltaic components equipped with the photovoltaic charging pile are responsible for capturing solar energy and converting it into electrical energy; photovoltaic energy capture data refers to the energy generation data collected in real time by these components, such as light intensity and power output.

[0017] Execution steps: By connecting multiple photovoltaic charging piles, the photovoltaic energy capture data of the photovoltaic components of each charging pile is automatically collected. This is usually achieved using a data acquisition system, which can monitor the operating status of each photovoltaic charging pile in real time. For example, a charging pile generates 50kW of electricity when the sunshine is good. The system will record and analyze this data. Then, the photovoltaic energy storage data of the battery component is introduced. The photovoltaic energy storage data reflects the charging status and current storage capacity of the battery; combined with the photovoltaic energy capture data of the photovoltaic component, the first control strategy oriented towards the stability of the charging service is set.

[0018] The first control strategy aims to ensure continuous charging service despite changes in sunlight or fluctuations in EV charging demand. For example, if sunlight suddenly decreases during a certain period, the charging power can be adjusted based on a previously set first smooth transition period to avoid EV charging interruptions. Finally, based on the target charging station marked by multiple photovoltaic charging piles, the system synchronizes the incoming EVs in real time and tracks their charging demand information. Utilizing an intelligent scheduling system, the system analyzes the real-time power demand of incoming EVs and optimizes the allocation of charging resources, thereby improving charging efficiency and ensuring that the charging needs of all EVs can be met even during peak hours.

[0019] S4: Introduce the operating status data of the energy storage inverter, combine it with the charging demand information of the electric vehicles entering the site, and set a second control strategy oriented towards maximizing the utilization rate of photovoltaic energy. The second control strategy corresponds to the second smooth transition period set; S5: Based on the first control strategy and the first smooth transition period set, and the second control strategy and the second smooth transition period set, establish an energy collaborative regulation cycle; S6: Through the energy collaborative regulation cycle, the photovoltaic charging piles in the working state among the multiple photovoltaic charging piles are synchronously and dynamically adjusted in parallel to obtain the energy management strategy of the power system of the multiple photovoltaic charging piles in the current regulation cycle.

[0020] Specifically, the energy storage inverter is a key device connecting the photovoltaic system and the battery system. Its function is to convert the direct current captured by photovoltaics into alternating current suitable for charging the power grid or electric vehicles, and it can also regulate the charge and discharge status of the battery.

[0021] Execution steps: Introduce the operating status data of the energy storage inverter, including parameters such as the device's power output, charging efficiency, and operating temperature. Through this data, the health status and performance of the energy storage device can be understood in real time. Combined with the charging demand information of the electric vehicles entering the site, that is, the actual demand for electricity of the electric vehicles in a specific time period, set a second control strategy guided by the maximum utilization rate of photovoltaic energy. The second control strategy corresponds to the second smooth transition period set to ensure that the energy storage inverter can effectively regulate the flow of electricity under different lighting conditions. For example, if photovoltaic power generation exceeds demand, the excess electricity can be stored in the battery for use in subsequent high-demand periods.

[0022] Based on the first control strategy and the first smooth transition period set, as well as the second control strategy and the second smooth transition period set, an energy collaborative regulation cycle is established. The energy collaborative regulation cycle continuously monitors the working status of the photovoltaic charging piles and the charging status of the battery, optimizes the overall energy management, and performs synchronous parallel dynamic adjustment on multiple photovoltaic charging piles in working state through the energy collaborative regulation cycle. For example, during periods when photovoltaic power generation is high and electric vehicle charging demand is low, the energy storage converter can be instructed to increase the charging power to store more electrical energy in the battery; conversely, during periods of high demand, the stored electrical energy is released first to ensure the continuity of charging services; the power system energy management strategy of multiple photovoltaic charging piles is obtained within the current regulation cycle. The energy management strategy not only improves the utilization efficiency of photovoltaic energy, but also ensures that the charging needs of electric vehicles can be met in a timely manner.

[0023] Furthermore, if Figure 2 As shown, a first control strategy oriented towards the stability of the charging service is set, the first control strategy corresponds to a first smooth transition period set, and further includes:

[0024] A first difference factor is set according to the length of sunshine time associated with seasonal changes; a second difference factor is set according to the light intensity level associated with seasonal changes; the photovoltaic energy capture data of the photovoltaic component is corrected by using the first difference factor and the second difference factor to determine the photovoltaic energy supply index, which is used to constrain multiple smooth transition period intervals in the first smooth transition period set.

[0025] Specifically, photovoltaic energy storage data is used to characterize the amount of photovoltaic energy stored in the battery module and its charging and discharging status, and photovoltaic energy capture data is used to characterize the electrical energy generated by the photovoltaic module in a specific time period.

[0026] Execution steps: Introduce the photovoltaic energy storage data of the battery module and combine it with the photovoltaic energy capture data of the photovoltaic module to set a first control strategy oriented towards the stability of the charging service. In order to better reflect the impact of different seasons on photovoltaic power generation, set a first difference factor according to the length of sunshine time associated with seasonal changes. The first difference factor can quantify the impact of changes in sunshine time on the photovoltaic power generation potential. For example, the sunshine time is longer in summer, so the first difference factor will be relatively high to reflect the power generation advantage during this period.

[0027] A second difference factor is set according to the light intensity level associated with seasonal changes. The second difference factor is used to evaluate the impact of light intensity on the performance of photovoltaic modules. For example, the light intensity is weak in winter, so the corresponding second difference factor will be reduced; next, the photovoltaic energy capture data of the photovoltaic module is corrected by the first difference factor and the second difference factor to determine the photovoltaic energy supply index. The photovoltaic energy supply index is a comprehensive indicator that reflects the power generation capacity of the photovoltaic system under specific conditions. The corrected data can more accurately reflect the actual power generation situation; finally, the photovoltaic energy supply index is used to constrain multiple smooth transition period intervals in the first smooth transition period set to ensure that the power supply capacity and stability in different time periods are taken into account when scheduling charging services, so as to optimize the charging experience of electric vehicles and improve the utilization efficiency of photovoltaic energy.

[0028] Furthermore, according to the sunshine duration associated with seasonal changes, a first difference factor is set, specifically including:

[0029] Obtain weather forecast information, the weather forecast information including sunshine time information and cloud cover information within the forecast period; calculate the sunshine time starting point and sunshine time ending point within the forecast period based on the sunshine time information within the forecast period; use the sunshine time starting point and sunshine time ending point within the forecast period to mark multiple valid segments within the sunshine time length, and set a first difference factor.

[0030] Specifically, the first difference factor is a key parameter used to measure the impact of seasonal changes on the length of sunshine time.

[0031] Execution steps: Get weather forecast information, which includes sunshine time and cloud cover information within the forecast period. Sunshine time refers to the length of time sunlight can be received within a specific number of days, while cloud cover reflects the degree of cloud cover, which affects direct sunlight.

[0032] Based on this sunshine time information, the starting and ending points of sunshine time within the prediction period are calculated. For example, if the sunrise time on a certain day is 6 am and the sunset time is 6 pm, then the starting and ending points of sunshine time on that day are 6 am and 6 pm respectively.

[0033] Using the start and end points of sunshine time within the forecast period, the system marks multiple valid segments within this period. These valid segments can be in units of hours or half hours, so as to facilitate a more detailed analysis of the impact of sunshine duration on photovoltaic power generation.

[0034] The first difference factor is set based on multiple valid segments. This factor reflects the variation in sunlight intensity within each segment and the potential power generation capacity of the PV panels. For example, during periods of light cloud cover, sunlight intensity is high, resulting in a larger first difference factor. However, during periods of heavy cloud cover, sunlight intensity is low, resulting in a smaller first difference factor. These steps ensure that the charging service control strategy can be flexibly adjusted to maximize the utilization efficiency of photovoltaic power generation, more effectively manage power resources, optimize charging services, and ensure charging stability for electric vehicles.

[0035] Furthermore, the sunshine time starting point and sunshine time ending point within the forecast period are used to mark multiple valid segments within the sunshine time length, and a first difference factor is set, specifically including:

[0036] According to the starting point and the end point of the sunshine time within the predicted time period, multiple valid segments within the sunshine time length are marked; for the multiple valid segments within the sunshine time length, the segmented sunshine duration is calculated; according to the segmented sunshine duration, the multiple valid segments within the sunshine time length are sorted to obtain a sunshine duration sequence; the first N items in the sunshine duration sequence are selected, and the sunshine duration ratio of the sum of the first N items is calculated as the first difference factor.

[0037] Specifically, the length of sunshine time refers to the time during which sunlight can effectively illuminate the photovoltaic modules within a specific time period; execution steps: using the sunshine time start point and sunshine time end point within the prediction period, the system divides this period into multiple valid segments. For example, if the sunshine time on a certain day is from 6 am to 6 pm, these twelve hours can be divided into multiple valid segments, such as one segment per hour.

[0038] For each segment, calculate its corresponding segment sunshine duration, that is, the actual sunshine time in that time period, which may be affected by factors such as cloud cover and weather changes; based on the sunshine duration of each segment, the system sorts these valid segments to generate a sunshine duration sequence. The sunshine duration sequence reflects the order of sunshine duration in each segment, usually from longest to shortest.

[0039] The first N items in the sunshine duration sequence are selected and their sum is calculated to determine the proportion of sunshine duration to the sum of the first N items. This will serve as the first difference factor, a representative indicator of optimal sunshine conditions within a specific time period. This factor can help adjust the control strategy of photovoltaic modules to better utilize photovoltaic resources and improve charging efficiency. For example, if on a certain day, the sunshine duration from 7:00 to 8:00 in the morning is 2 hours, and from 8:00 to 9:00 it is 1 hour, this method is used to calculate and confirm that the sum of the first two items is 3 hours, and further evaluate its proportion of the total sunshine duration. The above steps improve the utilization efficiency of electricity through dynamic calculation, while ensuring the charging stability of electric vehicles under different climate conditions and optimizing the overall energy management strategy.

[0040] Furthermore, a second difference factor is set according to the light intensity level associated with seasonal changes, specifically including:

[0041] Obtain historical light intensity information and set a light intensity level standard, wherein the light intensity level standard includes a light intensity unit and a light intensity classification; according to the cloud cover information within the forecast period, compare with the light intensity level standard, match the strong light intensity information and the weak light intensity information within the forecast period; use the strong light intensity information and the weak light intensity information within the forecast period to mark multiple valid segments within the light intensity level, and set a second difference factor.

[0042] Specifically, light intensity levels are standards used to classify and rate light intensity, usually expressed in units such as watts per square meter (W / m²).

[0043] Implementation steps: Obtain historical light intensity information, including light intensity data from the past few days or months. This data is usually collected by light sensors or weather stations. Set light intensity level standards and classify light intensity into multiple levels. For example, light intensity below 200W / m² is defined as weak light, while light intensity above 800W / m² is defined as strong light.

[0044] During the predicted time period, the cloud cover information (i.e., the cloud cover situation during the predicted time period) is compared with the light intensity level standard to determine the strong light intensity information and weak light intensity information during the time period. For example, if the predicted cloud cover information shows that a certain period is cloudy, which may cause the light intensity to decrease, the system will record the weak light information of the time period accordingly.

[0045] Using information about strong and weak light intensity, this data is labeled into multiple valid segments, representing operating states under different lighting conditions. A second difference factor reflects how light intensity varies over time, helping to optimize charging station operation strategies. For example, assume that during a summer forecast period, morning light intensity is 900W / m² (strong light), while afternoon cloud cover reduces light intensity to 150W / m² (weak light). These segments are automatically labeled and the charging strategy adjusted based on this information to maximize photovoltaic energy utilization, ensure stable and efficient EV charging, improve energy management accuracy, and optimize the sustainability of charging services.

[0046] Furthermore, the first control strategy corresponds to a first smooth transition period set, specifically including:

[0047] Connect multiple photovoltaic charging piles in a target charging field to obtain a charging station network; in the charging station network, use the first difference factor and the second difference factor to configure a sliding window radius W; based on the sliding window radius W, calculate an initialized photovoltaic energy supply average index, and determine a first smooth transition period set corresponding to the first control strategy using a sliding mechanism.

[0048] Specifically, the target charging site refers to a place dedicated to providing charging services for electric vehicles, which includes multiple photovoltaic charging piles.

[0049] Execution steps: By connecting these photovoltaic charging piles, the overall data structure of the charging station network will be obtained, so that the operating status and location distribution of each charging pile can be fully understood. On this basis, the first and second difference factors set previously are used to configure a sliding window radius W, which reflects the impact of light changes on photovoltaic energy capture and supply.

[0050] The sliding window radius W is a dynamic range. Average calculation and analysis of data are performed within the corresponding time window to better smooth out short-term fluctuations and improve the stability of photovoltaic energy supply. Based on the sliding window radius W, the initialized photovoltaic energy supply average index is calculated. The initialized photovoltaic energy supply average index can reflect the energy supply capacity of the charging station under current conditions.

[0051] A sliding mechanism continuously updates this average index based on real-time data, thereby continuously adjusting and optimizing the first set of smooth transition periods corresponding to the first control strategy. Specifically, if light conditions suddenly change within a certain time period, the average PV energy supply index is recalculated based on the data within the sliding window, and the charging pile's operating mode is adjusted accordingly. For example, charging power may be increased during periods of high light intensity, while charging load may be reduced during periods of low light intensity, thereby achieving a balanced power supply and stable charging service. This flexible control approach ensures that PV charging piles can efficiently utilize renewable energy while meeting the charging needs of electric vehicles.

[0052] Furthermore, based on the sliding window radius W, the initialization photovoltaic energy supply average index is calculated, including:

[0053] Based on the sliding window radius W, set the smoothing coefficient , and the initialized photovoltaic energy supply average index is used as the first observation value, where, = ,0< <1; For each photovoltaic energy capture point of a photovoltaic module, the average photovoltaic energy supply index is calculated as follows: I avg (t)= I avg (t-1)+(1- ) (I(t) exp(1+ T(t) (1- D(t))); where I avg (t) is the average photovoltaic energy supply index at time t, It is used to determine the impact of empirical data on the average photovoltaic energy supply index. I(t) is the photovoltaic energy supply index at time t. is the influence coefficient of temperature on photovoltaic energy supply, T(t) is the temperature at time t, is the impact coefficient of the dirt level of the PV panel on the PV energy supply, and D(t) is the dirt level at time t.

[0054] Specifically, the photovoltaic energy supply index is used to measure the energy output capacity of photovoltaic modules at a specific point in time; the smoothing coefficient The value range is between 0 and 1, and is used to balance the impact of historical data and real-time data on the average photovoltaic energy supply index.

[0055] Execution steps: Set the smoothing coefficient Initialize the average photovoltaic energy supply index as the first observation value, that is, record the photovoltaic energy supply situation at the beginning. Further, for each photovoltaic energy capture point of the photovoltaic module, use the calculation formula to update the average photovoltaic energy supply index. The formula is I avg (t)= I avg (t-1)+(1- ) (I(t) exp(1+ T(t) (1- D(t))); where I avg (t) is the average PV energy supply index at the current time t, I(t) is the real-time PV energy supply index, T(t) is the real-time temperature, D(t) represents the dirt level of the PV panel, and The coefficients corresponding to the effects of temperature and dirt level on photovoltaic energy supply are respectively calculated. The photovoltaic energy supply average index calculation formula can combine historical data and real-time environmental factors for dynamic calculation, thereby more accurately reflecting the actual performance of photovoltaic modules. For example, if the temperature of a photovoltaic module rises, causing its efficiency to decrease, then The value will affect the calculation results, making I avg Similarly, if there is dust on the photovoltaic panel, The value will also affect the output, thereby reducing the overall photovoltaic energy supply index. In the above steps, through correction, a more accurate photovoltaic energy supply index can be obtained, which is used for subsequent energy management and regulation decisions, ensuring the stability and efficiency of photovoltaic charging piles in charging services, thereby meeting the charging needs of electric vehicles.

[0056] Furthermore, through the energy coordinated control cycle, the photovoltaic charging piles in the plurality of photovoltaic charging piles in a working state are synchronously and dynamically adjusted in parallel to obtain the energy management strategy of the power system of the plurality of photovoltaic charging piles in the current control cycle, further comprising:

[0057] Based on the energy coordinated control cycle, at the beginning of each control cycle, the first control strategy and the second control strategy are updated; the updated first control strategy and the second control strategy are executed to configure the charging adjustment parameters of the multiple photovoltaic charging piles in a working state, and the charging adjustment parameters include the charging power; at the same time, the updated first control strategy and the second control strategy are evaluated based on the charging efficiency index, and feedback optimization is performed on the next control cycle based on the charging efficiency evaluation result.

[0058] Execution steps: The energy coordinated control loop can respond to the operating status of the photovoltaic charging pile and the charging demand of the electric vehicle in real time. Whenever a new control cycle begins, the first control strategy and the second control strategy are automatically updated to ensure that the strategies always reflect the latest environmental conditions and demands. Specifically, the control strategies are adjusted as necessary based on real-time data and previous charging demand information.

[0059] After the first and second control strategies are updated, a new control strategy is executed, which includes configuring the charging adjustment parameters of the photovoltaic charging piles in working state. Specifically, the most important charging adjustment parameter is the charging power. The adjustment of the charging power can be dynamically configured according to the current photovoltaic energy supply situation and the charging demand of electric vehicles to ensure the effective distribution and use of electric energy. In addition, the charging efficiency index is set to evaluate the actual effect of the updated control strategy.

[0060] Based on the results of the charging efficiency evaluation, feedback optimization is performed for the next regulation cycle. This means that if a certain strategy performs less than expected in actual operation, the problem can be analyzed based on the collected data, and the strategy can be further adjusted. For example, if it is found that some photovoltaic charging piles have low charging efficiency under specific conditions, the operating mode of these charging piles can be adjusted, or electric vehicles can be guided to choose other charging piles when necessary. Through this process of continuous iteration and optimization, it can ensure that the photovoltaic power generation system and charging service system associated with the photovoltaic charging piles always maintain optimal performance under various working conditions, meet the charging needs of users, and improve the overall energy efficiency of the system.

[0061] In summary, the embodiments of the present invention have the following beneficial effects:

[0062] 1. Achieve charging service stability to ensure that users' charging needs can be met under different lighting conditions.

[0063] 2. By establishing a coordinated energy control cycle and dynamically adjusting charging power, the optimal match between photovoltaic power generation and battery storage is achieved, thereby maximizing the utilization of photovoltaic energy.

[0064] 3. Through data analysis, potential charging needs can be predicted and identified in advance, improving charging efficiency and response speed.

[0065] 4. Utilizing an energy-coordinated control cycle, the first and second control strategies are updated at the beginning of each control cycle. These updated strategies are then executed to configure the charging adjustment parameters, including charging power, for multiple PV charging piles currently in operation. Simultaneously, these updated strategies are evaluated using charging efficiency metrics, and feedback optimization is performed for the next control cycle based on the efficiency evaluation results. This continuous iterative and optimization process ensures that the photovoltaic power generation system and charging service system associated with the PV charging piles maintain optimal performance under all operating conditions, meeting user charging needs and improving the overall energy efficiency of the system.

[0066] In summary, any step can be stored as a computer instruction or program in an unlimited computer memory and can be called and recognized by an unlimited computer processor, without any unnecessary restrictions.

[0067] The above technical solutions only reflect the preferred technical solutions of the technical solutions of the embodiments of the present invention. Any changes that may be made to certain parts thereof by those skilled in the art all reflect the novel principles of the embodiments of the present invention. Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the scope of the present invention.

Claims

1. A method for managing and regulating power system energy based on photovoltaic charging piles, characterized in that: include: Connecting multiple photovoltaic charging piles and collecting photovoltaic energy capture data of photovoltaic modules corresponding to the multiple photovoltaic charging piles; Introducing photovoltaic energy storage data of the battery assembly, combined with photovoltaic energy capture data of the photovoltaic assembly, to set a first control strategy oriented towards stability of the charging service, the first control strategy corresponding to a first smooth transition period set; Based on the target charging station marked by the multiple photovoltaic charging piles, synchronize the electric vehicles entering the target charging station and track the charging demand information of the electric vehicles entering the station; Introducing the operating status data of the energy storage converter and combining it with the charging demand information of the electric vehicles entering the site, setting a second control strategy oriented towards maximizing the utilization rate of photovoltaic energy, wherein the second control strategy corresponds to a second smooth transition period set; Establishing an energy collaborative control cycle based on the first control strategy and the first smooth transition period set, and the second control strategy and the second smooth transition period set; Through the energy coordinated control cycle, the photovoltaic charging piles in the plurality of photovoltaic charging piles in a working state are synchronously and dynamically adjusted in parallel to obtain the energy management strategy of the power system of the plurality of photovoltaic charging piles in the current control cycle, further comprising: Based on the energy cooperative control cycle, at the beginning of each control cycle, updating the first control strategy and the second control strategy; Executing the updated first control strategy and the second control strategy to configure charging adjustment parameters of the plurality of photovoltaic charging piles in working state, wherein the charging adjustment parameters include charging power; At the same time, the updated first control strategy and the second control strategy are evaluated based on the charging efficiency index and feedback optimization is performed on the next control cycle based on the charging efficiency evaluation results; Setting a first control strategy oriented towards the stability of the charging service, the first control strategy corresponding to a first smooth transition period set, further comprising: Setting a first difference factor based on the length of sunshine time associated with seasonal changes; Setting a second difference factor based on the light intensity level associated with seasonal changes; The photovoltaic energy capture data of the photovoltaic assembly is corrected by using the first difference factor and the second difference factor to determine a photovoltaic energy supply index, which is used to constrain multiple smooth transition period intervals in the first smooth transition period set.

2. The method for managing and controlling power system energy based on photovoltaic charging piles according to claim 1, characterized in that: According to the sunshine duration associated with seasonal changes, the first difference factor is set, including: Obtaining weather forecast information, including sunshine duration and cloud cover information within a forecast period; Calculate the sunshine time starting point and sunshine time ending point within the predicted period according to the sunshine time information within the predicted period; The sunshine time start point and sunshine time end point within the prediction period are used to mark multiple valid segments within the sunshine time length, and a first difference factor is set.

3. The method for managing and controlling power system energy based on photovoltaic charging piles according to claim 2, characterized in that: Using the sunshine time start point and sunshine time end point within the forecast period, marking multiple valid segments within the sunshine time length, and setting a first difference factor, including: Marking multiple valid segments within the sunshine time length according to the sunshine time starting point and sunshine time ending point within the predicted time period; For multiple valid segments within the sunshine time length, calculating the sunshine time length of each segment; According to the segmented sunshine duration, multiple valid segments within the sunshine time length are sorted to obtain a sunshine duration sequence; The first N items in the sunshine duration sequence are selected, and the sunshine duration ratio of the sum of the first N items is calculated as the first difference factor.

4. The method for managing and controlling power system energy based on photovoltaic charging piles according to claim 3, characterized in that: According to the light intensity level associated with seasonal changes, the second difference factor is set, including: Acquire historical light intensity information and set a light intensity level standard, wherein the light intensity level standard includes a light intensity unit and a light intensity classification; According to the cloud cover information within the predicted time period, and in comparison with the light intensity level standard, matching the strong light intensity information and the weak light intensity information within the predicted time period; The strong light intensity information and the weak light intensity information within the prediction time period are used to mark multiple valid segments within the light intensity level and set a second difference factor.

5. The method for managing and controlling power system energy based on photovoltaic charging piles according to claim 4, characterized in that: Determining that the first control strategy corresponds to a first smooth transition period set includes: Connect multiple photovoltaic charging piles in the target charging field to obtain a charging station network; In the charging station network, configuring a sliding window radius W using the first difference factor and the second difference factor; Based on the sliding window radius W, the initialized photovoltaic energy supply average index is calculated, and the first control strategy corresponding to the first smooth transition period set is determined by a sliding mechanism.

6. The method for managing and controlling power system energy based on photovoltaic charging piles according to claim 5, characterized in that: Based on the sliding window radius W, the initialization photovoltaic energy supply average index is calculated, including: Based on the sliding window radius W, set the smoothing coefficient , and the initialized photovoltaic energy supply average index is used as the first observation value, where, = ,0< <1; For each photovoltaic energy capture point of a photovoltaic module, the photovoltaic energy supply average index is calculated as follows: I avg (t)= I avg (t-1)+(1- ) (I(t) exp(1+ T(t) (1- D(t))); Among them, I avg (t) is the average photovoltaic energy supply index at time t, It is used to determine the impact of empirical data on the average photovoltaic energy supply index. I(t) is the photovoltaic energy supply index at time t. is the influence coefficient of temperature on photovoltaic energy supply, T(t) is the temperature at time t, is the impact coefficient of the dirt level of the PV panel on the PV energy supply, and D(t) is the dirt level at time t.

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