Power system energy management and regulation method based on photovoltaic charging pile

Abstract

The invention discloses an electric power system energy management and regulation method based on a photovoltaic charging pile, and the method comprises the steps: collecting the data of the photovoltaic charging pile and a battery assembly, setting a control strategy and a corresponding smooth transition time period, tracking the charging demands of an electric vehicle, combining the state of an energy storage converter, and building an energy cooperation regulation cycle. The working state of the photovoltaic charging pile is dynamically adjusted, the technical problem that the charging service of a power system is unstable due to the fact that photovoltaic power generation is unstable and the power supply quality of the photovoltaic charging pile is affected is solved, and the purpose of maximizing the utilization rate of photovoltaic energy by dynamically adjusting the working state of the photovoltaic charging pile is achieved. According to the technical scheme, a control strategy oriented to the charging service stability is set, and the technical effects that when photovoltaic energy is insufficient, the stable charging service can still be provided, the fluctuation of photovoltaic power generation is effectively relieved, an energy cooperative regulation and control cycle is established, the charging process of the electric vehicle is optimized, and the charging service stability is improved are ensured.

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 problem to be solved. The management of photovoltaic charging piles faces many challenges, including the uncertainty of lighting conditions and the dynamic changes in charging demand. The existing charging pile management system often cannot 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 cannot adjust charging strategies in real time to cope with different lighting and load conditions.

[0003] In summary, the prior art has the technical problem that photovoltaic power generation is unstable, 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 the instability of photovoltaic power generation affects the power supply quality of the photovoltaic charging pile and causes the unstable charging service of the electric power system.

[0005] In view of the above problems, the technical solution to realize the present invention is: a method for energy management and regulation of a power system based on a photovoltaic charging pile, which comprises: connecting a plurality of photovoltaic charging piles, and collecting photovoltaic energy capture data of photovoltaic components corresponding to the plurality of photovoltaic charging piles; Introducing photovoltaic energy storage data of the battery assembly, combined with photovoltaic energy capture data of the photovoltaic assembly, setting a first control strategy oriented towards stability of the charging service, wherein the first control strategy corresponds 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, combined 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 coordinated regulation 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 regulation cycle, the photovoltaic charging piles in working state among the multiple photovoltaic charging piles are dynamically adjusted in parallel synchronously to obtain the energy management strategy of the power system of the multiple photovoltaic charging piles in the current regulation cycle.

[0006] In summary, 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 achieves the technical effect of maximizing the utilization rate of photovoltaic energy by dynamically adjusting the working state 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 regulation cycle, optimizing the charging process of electric vehicles, and improving the stability of charging services. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 It is a flow chart of a method for energy management and control of a power system based on a photovoltaic charging pile according to the present invention; Figure 2 It is a flow chart of setting a first control strategy in a method for energy management and regulation of a power system based on a photovoltaic charging pile according to the present invention. DETAILED DESCRIPTION

[0008] The present invention will be described in detail below in conjunction with 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 comprises: 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, 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, and 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.

[0009] 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.

[0010] 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, and then introduce the photovoltaic energy storage data of the battery component. 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, set the first control strategy oriented towards the stability of the charging service.

[0011] The first control strategy is designed to ensure that charging services can continue when the light changes or the charging demand of electric vehicles fluctuates. For example, if the light suddenly weakens during a certain period, the charging power can be adjusted according to the previously set first smooth transition period set to avoid interruption of electric vehicle charging. Finally, based on the target charging station marked with multiple photovoltaic charging piles, the electric vehicles entering the target charging station are synchronized in real time to track the charging demand information of the electric vehicles entering the station. By using the intelligent scheduling system, the power demand of the electric vehicles entering the station in real time is analyzed and the configuration of charging resources is optimized, thereby improving the charging efficiency and ensuring that the charging demand of all electric vehicles can be met during peak hours.

[0012] 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 to maximize the utilization rate of photovoltaic energy. The second control strategy corresponds to a 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 dynamically adjusted in synchronous parallel to obtain the energy management strategy of the power system of the multiple photovoltaic charging piles in the current regulation cycle.

[0013] 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 regulate the charging and discharging status of the battery.

[0014] Execution steps: Introduce the operating status data of the energy storage inverter, including parameters such as the power output, charging efficiency and operating temperature of the equipment. Through these data, the health status and performance of the energy storage equipment 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 period of time, 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 under different lighting conditions, the energy storage inverter can effectively regulate the flow of electric energy. For example, if photovoltaic power generation exceeds demand, the excess electric energy can be stored in the battery for use in subsequent high-demand periods.

[0015] 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 coordinated regulation cycle is established. The energy coordinated 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 coordinated regulation cycle. For example, during the period when photovoltaic power generation is high and the charging demand of electric vehicles is low, the energy storage inverter can be instructed to increase the charging power to store more electrical energy in the battery; conversely, during the high demand period, the stored electrical energy is released first to ensure the continuity of the charging service; 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 demand of electric vehicles can be met in a timely manner.

[0016] 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: 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 the first difference factor and the second difference factor to determine the photovoltaic energy supply index, and the photovoltaic energy supply index is used to constrain multiple smooth transition period intervals in the first smooth transition period set.

[0017] Specifically, PV energy storage data is used to characterize the amount of PV energy stored in the battery module and its charging and discharging status, and PV energy capture data is used to characterize the electrical energy generated by the PV module in a specific time period.

[0018] 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 the 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 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.

[0019] 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.

[0020] Furthermore, according to the length of sunshine time associated with seasonal changes, a first difference factor is set, specifically including: Obtain weather forecast information, the weather forecast information including sunshine time information and cloud cover information within a forecast period; calculate a sunshine time start point and a sunshine time end point within the forecast period based on the sunshine time information within the forecast period; use the sunshine time start point and the sunshine time end point within the forecast period to mark multiple valid segments within the sunshine time length, and set a first difference factor.

[0021] Specifically, the first difference factor is a key parameter used to measure the impact of seasonal changes on the length of sunshine time.

[0022] Execution steps: Get weather forecast information, which includes sunshine time information and cloud cover information within the forecast period. Sunshine time information refers to the length of time that sunlight can be received within a specific number of days, while cloud cover information reflects the degree of cloud coverage and affects direct sunlight.

[0023] Based on this sunshine time information, the starting point and ending point of the 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 point and ending point of the sunshine time on this day are 6 am and 6 pm respectively.

[0024] 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.

[0025] The first difference factor is set based on multiple valid segments. The first difference factor will reflect the change in sunshine intensity and the potential power generation capacity of the photovoltaic components in each segment. For example, in a period of time with less cloud cover, the sunshine intensity is higher, and the corresponding first difference factor will be larger, while in a period of time with more cloud cover, the sunshine intensity is lower, and the first difference factor will be reduced accordingly. The above steps ensure that the control strategy of the charging service can be flexibly adjusted to maximize the utilization efficiency of photovoltaic power generation, more effectively manage power resources, optimize charging services, and ensure the charging stability of electric vehicles.

[0026] Furthermore, the sunshine time starting point and sunshine time ending point within the prediction period are used to mark multiple valid segments within the sunshine time length, and the first difference factor is set, specifically including: According to the starting point and the end point of the sunshine time within the predicted time period, mark multiple valid segments within the sunshine time length; for the multiple valid segments within the sunshine time length, calculate the segmented sunshine duration; according to the segmented sunshine duration, sort the multiple valid segments within the sunshine time length to obtain a sunshine duration sequence; select the first N items in the sunshine duration sequence, and calculate the sunshine duration ratio of the sum of the first N items as the first difference factor.

[0027] Specifically, the length of sunshine time refers to the time during which sunlight can effectively illuminate the photovoltaic modules within a certain period of time; execution steps: using the sunshine time start point and sunshine time end point within the prediction period, the system divides this period of time 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.

[0028] For each segment, calculate its corresponding segment sunshine duration, that is, the actual sunshine time in the time period, which may be affected by factors such as clouds 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.

[0029] Select the first N items in the sunshine duration sequence, calculate their sum, and get the sunshine duration ratio of the sum of the first N items, which will be used as the first difference factor. The first difference factor is a representative indicator used to measure the optimal sunshine conditions in a specific time period, which can help adjust the control strategy of photovoltaic components to better utilize photovoltaic resources and improve charging efficiency. For example, if on a certain day, the sunshine duration from 7 to 8 in the morning is 2 hours, and from 8 to 9 is 1 hour, in this way, the sum of the first two items is calculated and confirmed to be 3 hours, and further evaluated. The proportion of the total sunshine duration. The above steps improve the utilization efficiency of electric energy in a dynamic calculation manner, while ensuring the charging stability of electric vehicles under different climatic conditions and optimizing the overall energy management strategy.

[0030] Furthermore, according to the light intensity level associated with seasonal changes, a second difference factor is set, specifically 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; match the strong light intensity information and the weak light intensity information within the prediction period according to the cloud cover information within the prediction period and the light intensity level standard; use the strong light intensity information and the weak light intensity information within the prediction period to mark multiple valid segments within the light intensity level and set a second difference factor.

[0031] 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).

[0032] Implementation steps: Get historical light intensity information, including light intensity data over the past few days or months. This data is usually collected through light sensors or weather stations. Set light intensity level standards and divide 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.

[0033] 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 period accordingly.

[0034] Using the light intensity information of strong light and weak light, these data are marked into multiple valid segments, which can represent the operating status under different lighting conditions. The second difference factor will reflect the changes in light intensity in different time periods, which will help optimize the operation strategy of the charging pile. For example, assuming that in a summer forecast period, the light intensity in the morning is 900W / m² (strong light), and in the afternoon due to cloud cover, the light intensity is reduced to 150W / m² (weak light). These segments are automatically marked and the charging strategy is adjusted based on this information to maximize the utilization of photovoltaic energy, ensure the stability and efficiency of electric vehicle charging, improve the accuracy of energy management, and optimize the sustainability of charging services.

[0035] Furthermore, the first control strategy corresponds to a first smooth transition period set, specifically including: Connect multiple photovoltaic charging piles in the 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 the initialization photovoltaic energy supply average index, and determine the first control strategy corresponding to the first smooth transition period set by a sliding mechanism.

[0036] Specifically, the target charging site refers to a place dedicated to providing charging services for electric vehicles, which includes multiple photovoltaic charging piles.

[0037] 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 difference factor and the second difference factor set previously are used to configure a sliding window radius W, which reflects the impact of light changes on photovoltaic energy capture and supply.

[0038] The sliding window radius W is a dynamic range. The 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.

[0039] The sliding mechanism is adopted to continuously update the average index according to the data collected in real time, so as to continuously adjust and optimize the first smooth transition period set corresponding to the first control strategy. Specifically, if the light conditions suddenly change within a certain time period, the average index of photovoltaic energy supply is recalculated according to the data in the sliding window, and the working mode of the charging pile is adjusted accordingly, such as increasing the charging power when the light intensity is high, and reducing the charging load when the light intensity is weakened, so as to achieve the balance of power supply and the stability of charging service. In the above steps, this flexible control method ensures that the photovoltaic charging pile can efficiently utilize renewable energy while meeting the charging needs of electric vehicles.

[0040] Furthermore, based on the sliding window radius W, the average index of the initial photovoltaic energy supply is calculated, including: Based on the sliding window radius W, set the smoothing coefficient , and the initialized photovoltaic energy supply average index is taken 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))); where I avg (t) is the average PV 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.

[0041] Specifically, the PV energy supply index is used to measure the energy output capacity of PV 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 index of photovoltaic energy supply.

[0042] 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, and 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 impact of temperature and dirt level on photovoltaic energy supply are respectively calculated by combining historical data and real-time environmental factors through the photovoltaic energy supply average index calculation formula, so as to more accurately reflect 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, a more accurate photovoltaic energy supply index can be obtained through correction, which is used for subsequent energy management and regulation decisions to ensure the stability and efficiency of photovoltaic charging piles in charging services, thereby meeting the charging needs of electric vehicles.

[0043] Furthermore, through the energy coordinated 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, which also includes: 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 working state, and the charging adjustment parameters include charging power; at the same time, the updated first control strategy and the second control strategy are evaluated with the charging efficiency index, and feedback optimization is performed on the next control cycle according to the charging efficiency evaluation result.

[0044] Execution steps: The energy coordinated control cycle 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 will be automatically updated to ensure that the strategy always reflects the latest environmental conditions and needs. Specifically, the control strategy is adjusted as necessary based on real-time data and previous charging demand information.

[0045] After the first control strategy and the second control strategy 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 and the charging demand of the electric vehicle 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.

[0046] Based on the results of the charging efficiency evaluation, feedback optimization is performed for the next regulation cycle. This means that if a strategy does not perform as expected in actual operation, the problem can be analyzed based on the collected data, so that 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 modes 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.

[0047] In summary, the embodiments of the present invention have the following beneficial effects: 1. Achieve stability in charging services to ensure that users’ charging needs can be met under different lighting conditions.

[0048] 2. By establishing an energy coordinated regulation cycle and dynamically adjusting the charging power, the optimal match between photovoltaic power generation and battery storage can be achieved, thereby maximizing the utilization of photovoltaic energy.

[0049] 3. Through data analysis, potential charging needs can be predicted and identified in advance, improving charging efficiency and response speed.

[0050] 4. Due to the adoption of energy-based coordinated control cycles, 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 in the working state of multiple photovoltaic charging piles, and the charging adjustment parameters include charging power; at the same time, the updated first control strategy and the second control strategy are evaluated with the charging efficiency index, and feedback optimization is performed for the next control cycle based on the charging efficiency evaluation results. Through this continuous iteration and optimization process, it can ensure that the photovoltaic power generation system and the charging service system associated with the photovoltaic charging piles always maintain the best performance under various working conditions, meet the charging needs of users, and improve the overall energy efficiency of the system.

[0051] 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, and no unnecessary restrictions are made here.

[0052] The above-mentioned technical scheme only reflects the preferred technical scheme of the technical scheme of the embodiment of the present invention. Some changes that may be made to some parts thereof by technicians in this technical field all reflect the new principles of the embodiment of the present invention. Obviously, technicians in this field can make various changes and modifications to the present invention without departing from the scope of the present invention.

Claims

1. A method for energy management and control of a power system based on photovoltaic charging piles, characterized in that: include: Connecting multiple photovoltaic charging piles and collecting photovoltaic energy capture data of photovoltaic components 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, setting a first control strategy oriented towards stability of the charging service, wherein the first control strategy corresponds 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, combined 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 coordinated regulation 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 regulation cycle, the photovoltaic charging piles in working state among the multiple photovoltaic charging piles are dynamically adjusted in parallel synchronously to obtain the energy management strategy of the power system of the multiple photovoltaic charging piles in the current regulation cycle.

2. The method for energy management and control of a power system based on a photovoltaic charging pile according to claim 1, characterized in that: Setting a first control strategy oriented towards stability of charging services, wherein the first control strategy corresponds to a first smooth transition period set, further comprising: A first difference factor is set according to the length of sunshine time associated with seasonal changes; Setting a second difference factor according to 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, and the photovoltaic energy supply index is used to constrain multiple smooth transition period intervals in the first smooth transition period set.

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

4. The method for energy management and control of a power system based on a photovoltaic charging pile according to claim 3, characterized in that: Using the sunshine time start point and sunshine time end point within the prediction period, marking multiple valid segments within the sunshine time length, and setting the first difference factor, including: According to the sunshine time starting point and sunshine time ending point within the predicted time period, marking multiple valid segments within the sunshine time length; For multiple valid segments within the sunshine duration, calculating the sunshine duration 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.

5. The method for energy management and control of a power system based on a photovoltaic charging pile according to claim 4, 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, the strong light intensity information and the weak light intensity information within the predicted time period are matched; 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.

6. The method for energy management and control of a power system based on a photovoltaic charging pile according to claim 5, 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 average index of the initialized photovoltaic energy supply is calculated, and the first control strategy is determined to correspond to the first smooth transition period set by a sliding mechanism.

7. The method for energy management and control of a power system based on a photovoltaic charging pile according to claim 6, characterized in that: Based on the sliding window radius W, the average index of the initial photovoltaic energy supply is calculated, including: Based on the sliding window radius W, set the smoothing coefficient , and the initialized photovoltaic energy supply average index is taken 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: avg (t)= I avg (t-1)+(1- ) (I(t) exp(1+ T(t) (1- D(t)); Among them, I avg (t) is the average PV 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.

8. The method for energy management and control of a power system based on a photovoltaic charging pile according to claim 7, characterized in that: Through the energy coordinated regulation cycle, the photovoltaic charging piles in the working state among the plurality of photovoltaic charging piles 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 regulation cycle, and further comprising: Based on the energy cooperative control cycle, at the beginning of each control cycle, the first control strategy and the second control strategy are updated; 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 with the charging efficiency index, and feedback optimization is performed on the next regulation cycle according to the charging efficiency evaluation results.

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