Dynamic scene perception based self-adaptive load balancing method for optical storage and charging system

Abstract

The application discloses a dynamic scene perception-based self-adaptive load balancing method for a light storage and charging system, relates to the technical field of load balancing of a light storage and charging system, and comprises the following steps: calculating photovoltaic power generation in different time periods based on sunshine time periods; analyzing single-time power load of a power grid every hour within a day based on historical power consumption data of a region; analyzing overload time periods and normal time periods, and calculating required power consumption in the overload time periods; analyzing required energy storage consumption of the light storage and charging system in the overload time periods; analyzing a storage and discharge balancing scheme of the light storage and charging system in the normal time periods based on the energy storage consumption; and the application is used to solve the problems that the existing load balancing technology of the light storage and charging system only considers the influence of an economic aspect and is difficult to accurately predict power consumption of residents, so that the light storage and charging system cannot fully play a role and the proportion of charging and discharging cannot be accurately planned in advance.

Description

Technical Field

[0001] This invention relates to the field of load balancing technology for photovoltaic energy storage and charging systems, specifically an adaptive load balancing method for photovoltaic energy storage and charging systems based on dynamic scene perception. Background Technology

[0002] The load balancing technology of photovoltaic-storage-charging system refers to a core technology that uses an advanced energy management system to monitor, intelligently schedule, and dynamically distribute the power flow between photovoltaics, energy storage batteries, the power grid, and charging loads in real time, so as to achieve optimal system operation under multiple constraints.

[0003] Existing load balancing technologies for photovoltaic (PV) energy storage and charging systems typically only consider the optimal economic solution. However, the greater role of PV energy storage and charging systems is to provide electricity to residents, reducing grid load and preventing power outages in some areas due to insufficient grid supply. In areas with scarce power resources, power shortages may occur, requiring the intervention of PV energy storage and charging systems to provide daily electricity for residents. However, when supplying electricity to the grid, the charging and discharging ratio needs to be adjusted, and multiple adjustments within a short period are not advisable. That is, the charging and discharging ratio of the PV energy storage and charging system needs to be stable within a certain period. To achieve a stable charging and discharging ratio, it is necessary to predict residents' electricity consumption and plan the charging and discharging ratio in advance. However, existing PV energy storage and charging system load balancing technologies... The existing load balancing technology for photovoltaic energy storage and charging systems has difficulty accurately predicting residents' electricity consumption, resulting in significant deviations. This makes it impossible to accurately plan the charging and discharging ratio in advance. For example, in the patent application with publication number CN119109019A, a method, device, system and electronic equipment for managing photovoltaic energy storage and charging systems are disclosed. This solution only considers the economic aspect and does not conduct a detailed analysis of the role of photovoltaic energy storage and charging systems in reducing grid load. In other words, it does not fully utilize the role of photovoltaic energy storage and charging systems. The existing load balancing technology for photovoltaic energy storage and charging systems still has the problems of only considering the economic impact and difficulty in accurately predicting residents' electricity consumption, which leads to the failure to fully utilize the role of photovoltaic energy storage and charging systems and the inability to accurately plan the charging and discharging ratio in advance. Summary of the Invention

[0004] This invention aims to at least partially solve one of the technical problems in the prior art. It obtains the distribution of electricity prices during different time periods in the region where the photovoltaic-storage-charging system is located, along with sunshine hours. Based on sunshine hours, it calculates the photovoltaic power generation during different electricity price periods. Then, based on historical electricity consumption data of the region, it extracts hourly high-load and low-load variation points. Furthermore, based on these hourly high-load and low-load variation points, and combined with temperature analysis, it obtains the grid's hourly load limit. Based on the load limit and hourly load, it analyzes overload and normal periods, calculating the electricity demand during overload periods. Then, it analyzes the energy storage consumption required by the photovoltaic-storage-charging system during overload periods using the photovoltaic power generation and demand data. Next, it analyzes the energy storage scheme of the photovoltaic-storage-charging system based on its storage capacity. Finally, based on the energy storage scheme and energy consumption, it analyzes the energy release scheme of the photovoltaic-storage-charging system during peak electricity periods. This addresses the problems of existing photovoltaic-storage-charging system load balancing technologies, which only consider economic factors and struggle to accurately predict residential electricity consumption, leading to underutilization of the system's capabilities and the inability to accurately plan the charging and discharging ratio in advance.

[0005] To achieve the above objectives, this application provides an adaptive load balancing method for a photoelectric storage and charging system based on dynamic scene awareness, comprising the following steps:

[0006] The distribution of electricity prices during different time periods in the region where the photovoltaic-storage-charging system is located is obtained, along with the sunshine duration. Based on the sunshine duration, the photovoltaic power generation during different electricity price periods is calculated.

[0007] Analyze the hourly power load of the power grid within a day based on the region's historical electricity consumption data;

[0008] Obtain the load limit of the power grid, analyze the overload period and normal period based on the load limit and hourly power load, and calculate the power demand during the overload period;

[0009] The analysis of photovoltaic power generation and demand during different time periods reveals the energy storage consumption required by the photovoltaic-storage-charging system during periods of overload.

[0010] Based on the analysis of energy storage consumption, a balanced storage and charging scheme for a photovoltaic-storage-charging system during normal periods is proposed.

[0011] Furthermore, the distribution of electricity prices during different time periods in the region where the photovoltaic-storage-charging system is located is obtained, along with the sunshine duration. The calculation of photovoltaic power generation during different electricity price periods based on the sunshine duration includes the following sub-steps:

[0012] The region where the photovoltaic-storage-charging system is located is named an electricity zone. The distribution of electricity price periods within the electricity zone is obtained, and these periods are numbered chronologically, using the symbol TD. nThis indicates that n is a non-zero natural number and n is the index of TD;

[0013] Statistics TD n The number of hours elapsed within, denoted as TH. n The photovoltaic panel area, photovoltaic panel conversion efficiency, and system total efficiency within the photovoltaic power station are obtained and labeled as S, η1, and η, respectively. At the same time, the daily solar radiation in the region is obtained and labeled as Q.

[0014] The daily power generation of a photovoltaic power station is calculated using the formula L=Q×S×η1×η, where L is the daily power generation of the photovoltaic power station.

[0015] Obtain the sunshine duration of the power area and calculate TD. n The duration of the intersection with the sunshine duration is denoted as TG. n Through formula Calculate TD n Photovoltaic power generation during the period, of which TL n For TD n Photovoltaic power generation during certain periods.

[0016] Furthermore, analyzing the hourly power load of the power grid within a day based on the region's historical electricity consumption data includes the following sub-steps:

[0017] Extract hourly high load and hourly low load variation points based on historical electricity consumption data of the region;

[0018] Based on the hourly high load change points and hourly low load change points, and combined with temperature analysis, the hourly power load of the power grid is analyzed within a day.

[0019] Furthermore, extracting hourly high load variation points and hourly low load variation points based on historical electricity consumption data for a region includes the following sub-steps:

[0020] The historical electricity consumption data records the electricity consumption date, hourly load, and maximum temperature. Based on dividing the historical electricity consumption data into weekday data and holiday data, if the electricity consumption date is a weekday, the hourly load corresponding to the electricity consumption date is included in the weekday data; if the electricity consumption date is a holiday, the hourly load corresponding to the electricity consumption date is included in the holiday data.

[0021] The maximum temperature is the highest temperature recorded on the day of electricity consumption. The hourly load record shows the average hourly load over a 24-hour period, and these average hourly loads are numbered chronologically and denoted by the symbol HL. m It is represented as follows, where m is a non-zero natural number and m is the index of HL, 1≤m≤24;

[0022] With m as the X-axis, HL mEstablish a two-dimensional coordinate system for the Y-axis, named "Hourly Load Variation Chart," and set HL... m Enter the hourly load change chart according to m, and name the coordinate points in the hourly load change chart as hourly load change points;

[0023] For each value of m, name the highest and lowest points of the hourly load change points as the hourly high load change point and the hourly low load change point, respectively. Keep the hourly high load change point and the hourly low load change point, and remove all other hourly load change points.

[0024] Furthermore, based on hourly high load variation points and hourly low load variation points, and combined with temperature analysis, the hourly power load of the power grid within a day includes the following sub-steps:

[0025] A two-dimensional coordinate system is established with the maximum temperature as the horizontal axis and the hourly load as the vertical axis, named the Temperature Load Influence Diagram. The hourly load is entered into the Temperature Load Influence Diagram according to the maximum temperature.

[0026] Discrete regression analysis was performed on the temperature load influence diagram. The curve obtained from the regression was named the temperature load influence curve. The minimum and maximum values ​​of the vertical axis on the temperature load influence curve were obtained and labeled as EI and EA, respectively.

[0027] Obtain the highest temperature value of the day and name it the real-time maximum temperature. Find the coordinate point in the temperature load influence curve where the horizontal axis is equal to the real-time maximum temperature and name it the real-time temperature influence point. Mark the vertical axis corresponding to the real-time temperature influence point as WE.

[0028] The Y-axis values ​​of the hourly low load change point and the hourly high load change point corresponding to the m-th hour are respectively labeled as HI. m and HA m Let the hourly power load in the m-th hour be SHP. m There is a relationship as well as Solve for two SHPs m And calculate the average value to obtain the actual hourly power load in the m-th hour, using the symbol SPL. m express.

[0029] Furthermore, the load ceiling of the power grid is obtained, and based on the load ceiling and hourly power load analysis, overload periods and normal periods are divided. The power demand during overload periods is calculated, including the following sub-steps:

[0030] Obtain the upper limit of the power grid load and calculate the SPL (Size and Performance) that exceeds the upper limit of the load. m Marked as SUL m ;

[0031] SUL mThe period from point m-1 to point m is designated as the overload period, and the hour between two points that do not belong to the overload period is designated as the normal period.

[0032] Calculate SUL m The difference between the load limit and the demand is named the electricity demand, denoted by the symbol NEL. m express.

[0033] Furthermore, analyzing the energy storage consumption required by the photovoltaic-storage-charging system during overload periods based on photovoltaic power generation and demand includes the following sub-steps:

[0034] calculate The calculation result is named the hourly average power generation and represented by the symbol PG;

[0035] The overload periods are numbered according to their chronological order, using the symbol CT. i This indicates that, where i is a non-zero natural number and i is the index of CT, CT is... i Corresponding NEL m Marked as CL i ;

[0036] Calculate CL i -PG, the calculation result is named energy storage consumption, using the symbol ESC. i If the energy storage consumption is negative, it is considered to be zero.

[0037] Furthermore, the energy storage and charging system's energy storage balance scheme during normal periods, based on energy storage consumption analysis, includes the following sub-steps:

[0038] Analyze the energy storage scheme of the photovoltaic energy storage and charging system based on its storage capacity.

[0039] Based on the analysis of energy storage schemes and energy consumption, the energy release scheme of the photovoltaic-storage-charging system during peak power periods is analyzed.

[0040] Furthermore, analyzing the energy storage scheme of a photovoltaic energy storage and charging system based on its storage capacity includes the following sub-steps:

[0041] The periods of low and flat prices are collectively referred to as energy storage periods, while the periods of peak prices are designated as energy release periods. These energy storage and energy release periods are numbered using the symbol SF. j Let be an expression, where j is a non-zero natural number and j is the index of SF. If j is odd, then SF is... j For the energy storage period, if j is even, then SF j This is the energy release period;

[0042] Obtain the storage capacity of the optical storage and charging system, and obtain the SF6. i The photovoltaic power generation during the specified period is labeled GE.j ;

[0043] Starting with j=1, determine GE. j Check if the storage capacity is greater than or equal to the storage capacity. If so, mark the storage capacity as VE. j If not, then GE j Marked as VE j Add j+2 and repeat the analysis until all odd values ​​of j have been analyzed.

[0044] The VE j The index j only exists in odd values, if VE j For GE j , then SF j All the electricity generated by the internal photovoltaic power station is stored in the photovoltaic-storage-charging system;

[0045] If VE j For storage capacity, the storage capacity is labeled SV, and (GE) is calculated. j -SV) / GE j The calculation result is labeled as CF. j , will SF j CF generated by internal photovoltaic power station j A portion of the electrical energy is stored in a photovoltaic-storage-charging system, and the remaining electrical energy is uploaded to the power grid, ultimately resulting in an energy storage solution.

[0046] Furthermore, based on the energy storage scheme and energy consumption analysis, the energy release scheme of the photovoltaic-energy storage-charging system during peak power periods includes the following sub-steps:

[0047] SF when j is even j The corresponding energy storage consumption and duration are respectively labeled RN j and RT j At the same time, obtain SF when j is odd. j Corresponding VE j ;

[0048] Starting with j=1, calculate (VE) j -RN j+1 ) / RT j+1 The calculation result is labeled as KF. j+1 In SF j+1 During this period, all the electricity generated by the photovoltaic power station is uploaded to the grid, and KF is simultaneously called from the photovoltaic-storage-charging system. j+1 The electrical energy is uploaded to the power grid, and in SF j+1 CT scan i During this period, an additional ESC call is made from the optical storage and charging system. i The electrical energy is uploaded to the power grid, and the analysis is repeated for j+2 to finally obtain the energy release scheme.

[0049] The beneficial effects of this invention are as follows: This invention obtains the distribution of electricity prices during different time periods in the region where the photovoltaic-storage-charging system is located, and simultaneously obtains the sunshine duration. Based on the sunshine duration, it calculates the photovoltaic power generation during different electricity price periods. Then, based on the region's historical electricity consumption data, it extracts hourly high load change points and hourly low load change points. Based on these hourly high load change points and hourly low load change points, and combined with temperature analysis, it predicts the hourly single-hour power load of the power grid within a day. The advantage is that it considers the factors that have the greatest impact on electricity consumption, thereby predicting the hourly single-hour power load of the power grid within a day, which improves the accuracy and effectiveness of the load balancing of the photovoltaic-storage-charging system.

[0050] This invention obtains the upper limit of the power grid load, analyzes overload and normal periods based on the upper limit and hourly power load, and calculates the power demand during overload periods. Then, it analyzes the energy storage consumption required by the photovoltaic energy storage and charging system during overload periods based on the photovoltaic power generation and power demand during each period. Next, it analyzes the energy storage scheme of the photovoltaic energy storage and charging system based on its storage capacity. Finally, it analyzes the energy release scheme of the photovoltaic energy storage and charging system during peak power periods based on the energy storage scheme and energy storage consumption. The advantage is that it fully considers the role of the photovoltaic energy storage and charging system in maintaining grid stability, preventing power shortages that could lead to power curtailment or instability in some areas. At the same time, it provides stable energy storage and energy release schemes for each time period, avoiding frequent adjustments to the charge-discharge ratio, and further improving the accuracy and effectiveness of the load balancing of the photovoltaic energy storage and charging system. Attached Figure Description

[0051] Figure 1 This is a flowchart illustrating the steps of the method of the present invention;

[0052] Figure 2 This is a graph showing the hourly load variation of the workday data in this invention.

[0053] Figure 3 This is a schematic diagram of the hourly high load variation points and the hourly low load variation points of the present invention.

[0054] Figure 4 This is a diagram illustrating the influence of temperature load on the invention. Detailed Implementation

[0055] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] Example 1, please refer to Figure 1As shown, this application provides an adaptive load balancing method for a photoelectric storage and charging system based on dynamic scene awareness, including the following steps:

[0057] Step S1 involves obtaining the distribution of electricity prices during different time periods in the region where the photovoltaic-storage-charging system is located, as well as the sunshine duration, and calculating the photovoltaic power generation during different electricity price periods based on the sunshine duration. Step S1 includes the following sub-steps:

[0058] Step S101: Name the region where the photovoltaic-storage-charging system is located as an electricity zone, obtain the distribution of electricity price periods in the electricity zone, number the electricity price periods according to the chronological order, and use the symbol TD. n This indicates that n is a non-zero natural number and n is the index of TD;

[0059] In practice, the electricity price periods vary in different regions, and the specific period is based on the planning of the power area. For example, the distribution of electricity price periods in the power area in this embodiment is as follows: 23:00-7:00 is the off-peak period, 7:00-8:30 is the parity period, 8:30-12:00 is the peak period, 12:00-17:00 is the parity period, and 17:00-23:00 is the peak period, which are TD1 to TD5 in sequence, that is, arranged in the order of off-peak period, parity period, peak period, parity period and peak period. At the same time, since the off-peak period 23:00-7:00 spans from the previous night to the next morning, which is two days, but the period from 23:00 to 00:00 on the previous night does not require the intervention of the photovoltaic-storage-charging system and the photovoltaic power station cannot generate electricity, it does not affect the specific analysis of this embodiment and can be regarded as being within the same day.

[0060] Step S102, Calculate TD n The number of hours elapsed within, denoted as TH. n The photovoltaic panel area, photovoltaic panel conversion efficiency, and system total efficiency within the photovoltaic power station are obtained and labeled as S, η1, and η, respectively. At the same time, the daily solar radiation in the region is obtained and labeled as Q.

[0061] Step S103: Calculate the daily power generation of the photovoltaic power station using the formula L=Q×S×η1×η, where L is the daily power generation of the photovoltaic power station.

[0062] Step S104: Obtain the sunshine duration of the power area and calculate TD. n The duration of the intersection with the sunshine duration is denoted as TG. n Through formula Calculate TD n Photovoltaic power generation during the period, of which TL n For TD n Photovoltaic power generation during the specified time period;

[0063] In the specific implementation, the statistics for TH1 to TH5 are 8, 1.5, 3.5, 5 and 6 respectively. L=Q×S×η1×η is the existing formula for calculating power generation, which will not be described in detail in this embodiment. By obtaining Q, S, η1 and η, the daily power generation L is calculated to be 4000kWh. The sunshine period is obtained as 7:30-17:30, a total of 10 hours. The intersection is obtained to obtain TG1 to TG5 as 0, 1, 3.5, 5 and 0.5 respectively. Taking TD2 as an example, the calculation is obtained as TL2=(TG2 / TG1+TG2+TG3+TG4+TG5)×L=400kWh. Similarly, TL1 to TL5 are calculated as 0kWh, 400kWh, 1400kWh, 2000kWh and 200kWh respectively.

[0064] Step S2 involves analyzing the hourly power load of the power grid within a day based on the region's historical electricity consumption data. Step S2 includes the following sub-steps:

[0065] Step S201: Extract hourly high load change points and hourly low load change points based on the region's historical electricity consumption data;

[0066] Step S201 includes the following sub-steps:

[0067] Step S2011: The historical electricity consumption data records the electricity consumption date, hourly load, and maximum temperature. Based on dividing the historical electricity consumption data into weekday data and holiday data, if the electricity consumption date is a weekday, the hourly load corresponding to the electricity consumption date is included in the weekday data; if the electricity consumption date is a holiday, the hourly load corresponding to the electricity consumption date is included in the holiday data.

[0068] Step S2012: The maximum temperature is the highest temperature recorded on the day of electricity consumption. The hourly load record shows the average hourly load over the 24 hours of the day. The average hourly loads are numbered in chronological order and labeled with the symbol HL. m It is represented as follows, where m is a non-zero natural number and m is the index of HL, 1≤m≤24;

[0069] In practice, historical electricity consumption data can be provided by the power resources department. Some of the historical electricity consumption data is shown in Table 1 below:

[0070] Table 1. Partial Data of Historical Electricity Consumption

[0071]

[0072] The storage format of historical electricity consumption data is shown in Table 1. All of this data can be obtained by consulting the power sector's database. The maximum temperature is the highest temperature reached on that day. It is worth noting that factors affecting electricity consumption in a region include economic, climatic, social, and industrial factors. Among these, economic, social, and industrial factors within a region are usually fixed and do not change significantly in the short term. Significant changes typically take several years. Therefore, economic, social, and industrial factors can be considered as variables affecting electricity consumption. Furthermore, holidays are also an important factor affecting electricity consumption because residents' behavior changes between holidays and weekdays, which leads to… This has led to changes in residents' electricity consumption patterns, resulting in significant fluctuations in electricity consumption. Therefore, historical electricity consumption data was divided into weekday and holiday data. Furthermore, climate is also a major factor influencing electricity consumption. When temperatures are low, residents require more electricity for heating, while when temperatures are high, they require more for cooling. This demonstrates that electricity consumption varies significantly with temperature. It's important to note that even at the same temperature, electricity consumption may not be exactly the same, as changes in an individual's behavior can cause subtle variations in electricity consumption. However, these minor variations are usually negligible and will not affect the analysis results. (HL1 to HL) 24 This refers to the power load of the power grid per hour within 24 hours. However, since the power load fluctuates every moment, the average value of the power load within one hour is taken, which is the hourly average load.

[0073] Please see Figure 2 As shown, in step S2013, with m as the X-axis, HL m Establish a two-dimensional coordinate system for the Y-axis, named "Hourly Load Variation Chart," and set HL... m Enter the hourly load change chart according to m, and name the coordinate points in the hourly load change chart as hourly load change points;

[0074] Please see Figure 3 As shown in step S2014, the highest and lowest points of the hourly load change points in each value of m are named the hourly high load change point and the hourly low load change point, respectively. The hourly high load change point and the hourly low load change point are retained, and all other hourly load change points are removed.

[0075] In practice, weekday data and holiday data are analyzed independently. This embodiment takes weekday data as an example and constructs an hourly load variation chart for weekday data as shown below. Figure 2 As shown, by Figure 2It can be seen that although there are significant fluctuations in electricity consumption within the same time period, such as between 0:00 and 1:00 on each workday, which is usually affected by temperature, there are also smaller fluctuations within the hourly load change points in the same column. These smaller fluctuations are usually negligible, but including all hourly load change points in the reference range during specific calculations would lead to the superposition of these smaller fluctuations, affecting the calculation results. Therefore, only the hourly high load change points and hourly low load change points are used as the basis for subsequent analysis. After removing these, the hourly high load change points and hourly low load change points are obtained as follows: Figure 3 As shown, Figure 3 In the diagram, the black coordinates represent the hourly high load variation points, and the gray coordinates represent the hourly low load variation points.

[0076] Step S202: Based on the hourly high load change points and hourly low load change points, and combined with temperature analysis, analyze the hourly power load of the power grid within a day.

[0077] Step S202 includes the following sub-steps:

[0078] Please see Figure 4 As shown, in step S2021, a two-dimensional coordinate system is established with the maximum temperature as the horizontal axis and the hourly load as the vertical axis, named the Temperature Load Influence Diagram, and the hourly load is entered into the Temperature Load Influence Diagram according to the maximum temperature.

[0079] Step S2022: Perform discrete regression analysis on the temperature load influence diagram, name the curve obtained from the regression as the temperature load influence curve, and obtain the minimum and maximum values ​​of the vertical axis on the temperature load influence curve, which are marked as EI and EA respectively.

[0080] Step S2023: Obtain the highest temperature value of the day and name it the real-time maximum temperature. Find the coordinate point in the temperature load influence curve where the horizontal axis is equal to the real-time maximum temperature and name it the real-time temperature influence point. Mark the vertical axis corresponding to the real-time temperature influence point as WE.

[0081] In practice, the temperature load impact diagram is constructed as follows: Figure 4 As shown, the temperature load impact diagram reflects the trend of residential electricity consumption with the maximum temperature. By analyzing the temperature load impact diagram using a discrete regression model, the temperature load impact curve is obtained. The temperature load impact curve is... Figure 4 The curve in Figure 4Due to the large amount of data, only a small amount of data is displayed to illustrate the relationship between electricity consumption and temperature. The EI and EA values ​​are 78 and 120, respectively. For example, if the current date is July 13, 2025, and the highest temperature of the day is 28℃, the coordinate point on the horizontal axis of the temperature load influence curve that equals the real-time highest temperature is found to obtain the real-time temperature influence point. The real-time temperature influence point is the intersection of the temperature load influence curve and the straight line X=28. The WE value is extracted to be 100.0824, which is approximately 100.1 after rounding to one decimal place.

[0082] Step S2024: Mark the Y-axis values ​​of the hourly low load change point and the hourly high load change point corresponding to the m-th hour as HI respectively. m and HA m Let the hourly power load in the m-th hour be SHP. m There is a relationship as well as Solve for two SHPs m And calculate the average value to obtain the actual hourly power load in the m-th hour, using the symbol SPL. m express;

[0083] In practice, assuming we want to predict residential electricity consumption between 0:00 and 1:00 on July 13, 2025, we will assume the hourly electricity load in the first hour is SHP1, and mark the Y-axis values ​​of the corresponding hourly low load change point and hourly high load change point in the first hour as HI1 and HA1, respectively. Figure 3 The Y-axis values ​​of the hourly low load change point and the hourly high load change point at X=1 are used to obtain HI1 and HA1, which are 3585 and 6195 respectively. These values ​​are rounded to integers. HI1 and HA1 represent the minimum and maximum residential electricity consumption during the period from 0:00 to 1:00 in historical electricity consumption data. Since this example uses weekday data, all listed data are weekday data and will not be affected by holiday data. The only factor with a significant impact on electricity consumption during the same time period within a day is temperature. Therefore, HI1 can be considered as EI, and HA1 as EA. This leads to the relationship... as well as WE / EI is the ratio of residential electricity consumption at the highest temperature of 28℃ to the lowest residential electricity consumption, but it does not introduce the variable of time period. HI1, on the other hand, is the lowest residential electricity consumption during the period from 0:00 to 1:00, which is equivalent to EI. Therefore, SHP1 / HI1 is equivalent to WE / EI. Similarly, but ultimately the calculation results are not the same because HI1 and HA1 themselves have slight deviations, for example... The calculated SHP1 is 4600.75, while The calculated SHP1 is 5167.66, rounded to two decimal places. Therefore, the final result is the average of the two values, resulting in SPL1 of 4884, rounded to the nearest integer. Similarly, each SPL is calculated... m .

[0084] Step S3: Obtain the upper limit of the power grid load, analyze the overload period and normal period based on the upper limit and hourly power load, and calculate the power demand during the overload period; Step S3 includes the following sub-steps:

[0085] Step S301: Obtain the upper limit of the power grid load and calculate the SPL that exceeds the upper limit of the load. m Marked as SUL m ;

[0086] Step S302, SUL m The period from point m-1 to point m is designated as the overload period, and the hour between two points that do not belong to the overload period is designated as the normal period.

[0087] Step S303, calculate SUL m The difference between the load limit and the demand is named the electricity demand, denoted by the symbol NEL. m express;

[0088] In practice, the load limit was determined to be 14,500 kWh, and the SUL was calculated. m SUL only 20 That is, the hour from 19:00 to 20:00 is considered the overload period, and the rest of the time is the normal period, and SUL is obtained. 20 The calculated energy demand is 15428 kWh, NEL. 20 It is 928 kWh.

[0089] Step S4 involves analyzing the energy storage consumption required by the photovoltaic power generation and demand during periods of overload for the photovoltaic-storage-charging system. Step S4 includes the following sub-steps:

[0090] Step S401, calculate The calculation result is named the hourly average power generation and represented by the symbol PG;

[0091] Step S402: Number the overload periods according to their chronological order, using the symbol CT. i This indicates that, where i is a non-zero natural number and i is the index of CT, CT is... i Corresponding NEL m Marked as CL i ;

[0092] Step S403, calculate CLi -PG, the calculation result is named energy storage consumption, using the symbol ESC. i If the energy storage consumption is negative, it is considered to be zero.

[0093] In practice, the hourly average power generation PG is calculated to be 400 kWh. CT1 and CL1 are obtained by numbering, with CL1 being 928 kWh and ESC1 being 528 kWh. This means that in addition to the basic photovoltaic power generation, 528 kWh of electricity needs to be drawn from the photovoltaic-storage-charging system to ensure that the power load of the grid does not exceed the load limit.

[0094] Step S5: Analyze the energy storage and charging system's energy storage balance scheme during normal periods based on energy storage consumption. Step S5 includes the following sub-steps:

[0095] Step S501: Analyze the energy storage scheme of the photovoltaic energy storage and charging system based on its storage capacity;

[0096] Step S501 includes the following sub-steps:

[0097] Step S5011: The valley price period and the parity price period are collectively referred to as the energy storage period, and the peak price period is named the energy release period. The energy storage period and the energy release period are numbered and symbolized by SF. j Let be an expression, where j is a non-zero natural number and j is the index of SF. If j is odd, then SF is... j For the energy storage period, if j is even, then SF j This is the energy release period;

[0098] Step S5012: Obtain the storage capacity of the optical storage and charging system, and obtain the SF6. i The photovoltaic power generation during the specified period is labeled GE. j ;

[0099] In specific implementation, 23:00-7:00 is the off-peak price period, 7:00-8:30 is the parity price period, 8:30-12:00 is the peak price period, 12:00-17:00 is the parity price period, and 17:00-23:00 is the peak price period. These periods are combined to form the following timeframes: 23:00-8:30 is the energy storage period, 8:30-12:00 is the energy release period, 12:00-17:00 is the energy storage period, and 17:00-23:00 is the energy release period, numbered SF1 to SF4 respectively. Among these, SF1 and SF3 are energy storage periods, and SF2 and SF4 are energy release periods. The resulting storage capacity is 1000kWh. Taking SF1 as an example, SF1 is obtained by merging TD1 and TD2, while TL1 and TL2 are 0kWh and 400kWh respectively. Therefore, GE1 is 0 + 400 = 400kWh.

[0100] Step S5013, starting with j=1, determine GE j Check if the storage capacity is greater than or equal to the storage capacity. If so, mark the storage capacity as VE. j If not, then GE j Marked as VE j Add j+2 and repeat the analysis until all odd values ​​of j have been analyzed.

[0101] Step S5014, VE j The index j only exists in odd values, if VE j For GE j , then SF j All the electricity generated by the internal photovoltaic power station is stored in the photovoltaic-storage-charging system;

[0102] Step S5015, if VE j For storage capacity, the storage capacity is labeled SV, and (GE) is calculated. j -SV) / GE j The calculation result is labeled as CF. j , will SF j CF generated by internal photovoltaic power station j A portion of the electrical energy is stored in a photovoltaic energy storage and charging system, and the remaining electrical energy is uploaded to the power grid, ultimately resulting in an energy storage solution.

[0103] In practice, starting with j=1, GE1 is less than the storage capacity, meaning that the photovoltaic power plant's output during SF1 cannot fully charge the photovoltaic-storage-charging system. Since electricity consumption and prices are lower during the storage period, all photovoltaic power is stored, resulting in VE1 of 400 kWh. When j=3, GE3 = 2000 kWh, which is greater than the storage capacity, indicating that the photovoltaic power plant's output during SF3 can fully charge the system with a surplus. Therefore, VE3 is equal to the storage capacity, i.e., 1000 kWh. Since VE1 is 400kWh, which is less than the storage capacity, all photovoltaic power needs to be stored in the photovoltaic-storage-charging system during SF1. VE3 is 1000kWh, and GE3 is greater than VE3. Therefore, it is necessary to calculate the proportion of surplus photovoltaic power that is uploaded to the grid. GE3 is 2000kWh, SV is 1000kWh, and CF3 is calculated to be 0.5. This means that during SF3, half of the photovoltaic power generated needs to be stored in the photovoltaic-storage-charging system, and the other half needs to be uploaded to the grid. This is the energy storage scheme.

[0104] Step S502: Analyze the energy release scheme of the photovoltaic-storage-charging system during peak power periods based on the energy storage scheme and energy storage consumption.

[0105] Step S502 includes the following sub-steps:

[0106] Step S5021, SF when j is even j The corresponding energy storage consumption and duration are respectively labeled RN j and RT j At the same time, obtain SF when j is odd. j Corresponding VE j ;

[0107] Step S5022, starting with j=1, calculate (VE) j -RN j+1 ) / RT j+1 The calculation result is labeled as KF. j+1 In SF j+1 During this period, all the electricity generated by the photovoltaic power station is uploaded to the grid, and KF is simultaneously called from the photovoltaic-storage-charging system. j+1 The electrical energy is uploaded to the power grid, and in SF j+1 CT scan i During this period, an additional ESC is called from the optical storage and charging system. i The electrical energy is uploaded to the power grid, and the analysis is repeated for j+2 to finally obtain the energy release scheme;

[0108] In specific implementation, taking SF4 as an example, RN4 and RT4 are marked as 528kWh and 6h respectively. SF4 is the energy release period, and the energy storage period before SF4 is SF3. VE3 is obtained as 1000kWh, which means that the photovoltaic-storage-charging system can store 1000kWh of photovoltaic power in SF3. When j=3, (VE3-RN4) / RT4 is calculated to obtain KF4 as 78.7kWh. The calculation result is rounded to one decimal place. During SF4, all the electricity generated by the photovoltaic power station per hour is uploaded to the grid, and 78.7kWh of photovoltaic power is called from the photovoltaic-storage-charging system per hour and uploaded to the grid. At the same time, CT1 is during SF4, and an additional 528kWh of photovoltaic power is called from the photovoltaic-storage-charging system and uploaded to the grid during CT1. This is the energy release scheme. The analysis of weekday data is now complete. The analysis process of holiday data is exactly the same as that of weekday data, and will not be described in detail in this embodiment.

[0109] Example 2: This application provides an electronic device, which may include a processor, a communication interface, a memory, and a communication bus. The processor, communication interface, and memory communicate with each other via the communication bus. The memory stores computer-readable instructions, and the processor can call these instructions. When the processor executes a computer-readable instruction, it runs steps such as those in the adaptive load balancing method for a photovoltaic-storage-charging system based on dynamic scene perception, to achieve the following functions: calculating the photovoltaic power generation during different electricity price periods based on sunshine duration; analyzing the hourly power load of the power grid within a day based on historical electricity consumption data of the region; analyzing overload periods and normal periods, and calculating the power demand during overload periods; analyzing the energy storage consumption required by the photovoltaic-storage-charging system during overload periods; and analyzing the energy storage and release balance scheme of the photovoltaic-storage-charging system during normal periods based on the energy storage consumption.

[0110] Furthermore, when the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0111] Example 3: This application also provides a computer program product, which includes a computer program stored on a computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the adaptive load balancing method for a photovoltaic-storage-charging system based on dynamic scene perception provided by the above methods. This method includes: calculating the photovoltaic power generation during different electricity price periods based on sunshine hours; analyzing the hourly power load of the power grid within a day based on historical electricity consumption data of the region; analyzing overload periods and normal periods, and calculating the power demand during overload periods; analyzing the energy storage consumption required by the photovoltaic-storage-charging system during overload periods; and analyzing the energy storage and release balance scheme of the photovoltaic-storage-charging system during normal periods based on the energy storage consumption.

[0112] Example 4: This application also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it performs the steps in the above-mentioned adaptive load balancing method for a photovoltaic-storage-charging system based on dynamic scene perception, to achieve the following functions: calculating the photovoltaic power generation during different electricity price periods based on sunshine hours; analyzing the hourly power load of the power grid within a day based on historical electricity consumption data of the region; analyzing overload periods and normal periods, and calculating the power demand during overload periods; analyzing the energy storage consumption required by the photovoltaic-storage-charging system during overload periods; and analyzing the energy storage and release balance scheme of the photovoltaic-storage-charging system during normal periods based on the energy storage consumption.

[0113] Based on the above description of the embodiments, the embodiments of the present invention can be provided as methods, systems, or computer program products. Based on this understanding, the above technical solutions, in essence or in terms of their contribution to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or certain parts of the embodiments.

[0114] In the embodiments provided in this application, it should be understood that the disclosed system or method can be implemented in other ways. The embodiments described above are merely illustrative. For example, the division of modules or units is only a logical functional division, and there may be other division methods in actual implementation. Furthermore, multiple modules or units may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interfaces. The indirect coupling or communication connection between systems, modules, and units may be electrical, mechanical, or other forms.

[0115] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. An adaptive load balancing method for a photovoltaic energy storage and charging system based on dynamic scene perception, characterized in that, Includes the following steps: The distribution of electricity prices during different time periods in the region where the photovoltaic-storage-charging system is located is obtained, along with the sunshine duration. Based on the sunshine duration, the photovoltaic power generation during different electricity price periods is calculated. Analyze the hourly power load of the power grid within a day based on the region's historical electricity consumption data; Obtain the load limit of the power grid, analyze the overload period and normal period based on the load limit and hourly power load, and calculate the power demand during the overload period; The analysis of photovoltaic power generation and demand during different time periods reveals the energy storage consumption required by the photovoltaic-storage-charging system during periods of overload. Based on the analysis of energy storage consumption, the storage and release balance scheme of the photovoltaic-storage-charging system during normal periods is analyzed. Analyzing the hourly power load of the power grid within a day based on historical electricity consumption data of the region includes the following sub-steps: Extract hourly high load and hourly low load variation points based on historical electricity consumption data of the region; Based on the hourly high load variation points and hourly low load variation points, and combined with temperature analysis, the hourly power load of the power grid is analyzed within a day. Extracting hourly high load and hourly low load variation points from historical electricity consumption data for a region includes the following sub-steps: The historical electricity consumption data records the electricity consumption date, hourly load, and maximum temperature. Based on dividing the historical electricity consumption data into weekday data and holiday data, if the electricity consumption date is a weekday, the hourly load corresponding to the electricity consumption date is included in the weekday data; if the electricity consumption date is a holiday, the hourly load corresponding to the electricity consumption date is included in the holiday data. The maximum temperature is the highest temperature recorded on the day of electricity consumption. The hourly load record shows the average hourly load over a 24-hour period, and these average hourly loads are numbered chronologically and denoted by the symbol HL. m It is represented as follows, where m is a non-zero natural number and m is the index of HL, 1≤m≤24; With m as the X-axis, HL m Establish a two-dimensional coordinate system for the Y-axis, named "Hourly Load Variation Chart," and set HL... m Enter the hourly load change chart according to m, and name the coordinate points in the hourly load change chart as hourly load change points; Name the highest and lowest points of the hourly load change points for each value of m as the hourly high load change point and the hourly low load change point, respectively. Keep the hourly high load change point and the hourly low load change point, and remove all other hourly load change points. Based on hourly high load variation points and hourly low load variation points, and combined with temperature analysis, the hourly single-hour power load of the power grid within a day includes the following sub-steps: A two-dimensional coordinate system is established with the maximum temperature as the horizontal axis and the hourly load as the vertical axis, named the Temperature Load Influence Diagram. The hourly load is entered into the Temperature Load Influence Diagram according to the maximum temperature. Discrete regression analysis was performed on the temperature load influence diagram. The curve obtained from the regression was named the temperature load influence curve. The minimum and maximum values ​​of the vertical axis on the temperature load influence curve were obtained and labeled as EI and EA, respectively. Obtain the highest temperature value of the day and name it the real-time maximum temperature. Find the coordinate point in the temperature load influence curve where the horizontal axis is equal to the real-time maximum temperature and name it the real-time temperature influence point. Mark the vertical axis corresponding to the real-time temperature influence point as WE. The Y-axis values ​​of the hourly low load change point and the hourly high load change point corresponding to the m-th hour are respectively labeled as HI. m and HA m Let the hourly power load in the m-th hour be SHP. m There is a relationship as well as Solve for two SHPs m And calculate the average value to obtain the actual hourly power load in the m-th hour, using the symbol SPL. m express.

2. The adaptive load balancing method for a photovoltaic energy storage and charging system based on dynamic scene perception according to claim 1, characterized in that, Obtain the distribution of electricity prices during different time periods in the region where the photovoltaic-storage-charging system is located, and simultaneously obtain the sunshine duration. Calculating the photovoltaic power generation during different electricity price periods based on the sunshine duration includes the following sub-steps: The region where the photovoltaic-storage-charging system is located is named an electricity zone. The distribution of electricity price periods within the electricity zone is obtained, and these periods are numbered chronologically, using the symbol TD. n This indicates that n is a non-zero natural number and n is the index of TD; Statistics TD n The number of hours elapsed within, denoted as TH. n The photovoltaic panel area, photovoltaic panel conversion efficiency, and system total efficiency within the photovoltaic power station are obtained and labeled as S, η1, and η, respectively. At the same time, the daily solar radiation in the region is obtained and labeled as Q. The daily power generation of a photovoltaic power station is calculated using the formula L=Q×S×η1×η, where L is the daily power generation of the photovoltaic power station. Obtain the sunshine duration of the power area and calculate TD. n The duration of the intersection with the sunshine duration is denoted as TG. n Through formula Calculate TD n Photovoltaic power generation during the period, of which TL n For TD n Photovoltaic power generation during certain periods.

3. The adaptive load balancing method for a photoelectric storage and charging system based on dynamic scene perception according to claim 2, characterized in that, Obtaining the power grid's load ceiling, analyzing overload and normal periods based on the load ceiling and hourly power load, and calculating the electricity demand during overload periods includes the following sub-steps: Obtain the upper limit of the power grid load and calculate the SPL (Size and Performance) that exceeds the upper limit of the load. m Marked as SUL m ; SUL m The period from point m-1 to point m is designated as the overload period, and the hour between two points that do not belong to the overload period is designated as the normal period. Calculate SUL m The difference between the load limit and the demand is named the electricity demand, denoted by the symbol NEL. m express.

4. The adaptive load balancing method for a photoelectric storage and charging system based on dynamic scene perception according to claim 3, characterized in that, Analyzing the energy storage consumption required by the photovoltaic-storage-charging system during overload periods based on photovoltaic power generation and demand includes the following sub-steps: calculate The calculation result is named the hourly average power generation and represented by the symbol PG; The overload periods are numbered according to chronological order and identified by the symbol CT. i This indicates that, where i is a non-zero natural number and i is the index of CT, CT is... i Corresponding NEL m Marked as CL i ; Calculate CL i -PG, the calculation result is named energy storage consumption, using the symbol ESC. i If the energy storage consumption is negative, it is considered to be zero.

5. The adaptive load balancing method for a photoelectric storage and charging system based on dynamic scene perception according to claim 4, characterized in that, The energy storage and charging system's energy balance scheme during normal periods, based on energy storage consumption analysis, includes the following sub-steps: Analyze the energy storage scheme of the photovoltaic energy storage and charging system based on its storage capacity. Based on the analysis of energy storage schemes and energy consumption, the energy release scheme of the photovoltaic-storage-charging system during peak power periods is analyzed.

6. The adaptive load balancing method for a photoelectric storage and charging system based on dynamic scene perception according to claim 5, characterized in that, Based on the storage capacity analysis of the photovoltaic-storage-charging system, the energy storage scheme of the photovoltaic-storage-charging system includes the following sub-steps: The periods of low and flat prices are collectively referred to as energy storage periods, while the periods of peak prices are designated as energy release periods. These energy storage and energy release periods are numbered using the symbol SF. j Let be an expression, where j is a non-zero natural number and j is the index of SF. If j is odd, then SF is... j For the energy storage period, if j is even, then SF j This is the energy release period; Obtain the storage capacity of the optical storage and charging system, and obtain the SF6. i The photovoltaic power generation during the specified period is labeled GE. j ; Starting with j=1, determine GE. j Check if the storage capacity is greater than or equal to the storage capacity. If so, mark the storage capacity as VE. j If not, then GE j Marked as VE j Add j+2 and repeat the analysis until all odd values ​​of j have been analyzed. The VE j The index j only exists in odd values, if VE j For GE j , then SF j All the electricity generated by the internal photovoltaic power station is stored in the photovoltaic-storage-charging system; If VE j For storage capacity, the storage capacity is labeled SV, and (GE) is calculated. j -SV) / GE j The calculation result is labeled as CF. j , will SF j CF generated by internal photovoltaic power station j A portion of the electrical energy is stored in a photovoltaic-storage-charging system, and the remaining electrical energy is uploaded to the power grid, ultimately resulting in an energy storage solution.

7. The adaptive load balancing method for a photoelectric storage and charging system based on dynamic scene perception according to claim 6, characterized in that, Based on the analysis of energy storage schemes and energy consumption, the energy release scheme of the photovoltaic-energy storage-charging system during peak power periods includes the following sub-steps: SF when j is even j The corresponding energy storage consumption and duration are respectively labeled RN j and RT j At the same time, obtain SF when j is odd. j Corresponding VE j ; Starting with j=1, calculate (VE) j -RN j+1 ) / RT j+1 The calculation result is labeled as KF. j+1 In SF j+1 During this period, all the electricity generated by the photovoltaic power station is uploaded to the grid, and KF is simultaneously called from the photovoltaic-storage-charging system. j+1 The electrical energy is uploaded to the power grid, and in SF j+1 CT scan i During this period, an additional ESC is called from the optical storage and charging system. i The electrical energy is uploaded to the power grid, and the analysis is repeated for j+2 to finally obtain the energy release scheme.

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