Distributed photovoltaic energy storage system optimization configuration method and system

Abstract

The invention provides a distributed photovoltaic energy storage system optimization configuration method and system, and belongs to the technical field of power distribution, and the method comprises the steps: monitoring the real-time load power and photovoltaic real-time output of an access point; if the photovoltaic real-time output is smaller than the real-time load power, predicting future load data and future photovoltaic output data; drawing a difference curve according to the future load data and the future photovoltaic output data; according to the difference curve, the initial energy storage data and the electricity price table, determining an energy storage power generation intervention opportunity; and if the photovoltaic real-time output is greater than or equal to the real-time load power, the redundant photovoltaic generating capacity is transmitted to the distributed photovoltaic energy storage battery. According to the distributed photovoltaic energy storage system optimization configuration method and system, when the photovoltaic real-time output is smaller than the real-time load power, electricity is not taken from the energy storage battery immediately, the difference curve is calculated, the energy storage power generation intervention opportunity is determined according to the difference curve, the initial energy storage data and the electricity price table, and energy storage power generation intervention is more suitable.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution, and particularly to an optimization configuration method and system for a distributed photovoltaic energy storage system. Background Art

[0002] At present, distributed photovoltaic energy storage systems are widely put into use. Users can generate electricity by themselves through photovoltaic power generation and use it. At the same time, the excess generated electricity is stored in the energy storage system, reducing the electricity purchased from the power grid and the electricity bill expenditure. In addition, in terms of emergency power supply, the distributed photovoltaic energy storage system can provide emergency power supply when the power grid is out of power, avoiding unnecessary losses.

[0003] However, in the prior art, when the load end detects that the photovoltaic power generation is not enough for immediate use, it often gives priority to taking power from the energy storage system. When the power in the energy storage battery is exhausted, it takes power from the power grid. In the face of a large future power gap and a higher future electricity price than the current price, it will increase the electricity cost. Therefore, in this situation, the intervention timing of energy storage power generation in the prior art is not appropriate.

[0004] In view of this, there is an urgent need for an optimization configuration method and system for a distributed photovoltaic energy storage system to at least solve the above deficiencies. Summary of the Invention

[0005] One of the purposes of the present invention is to provide an optimization configuration method and system for a distributed photovoltaic energy storage system, which monitors the real-time load power and the real-time photovoltaic output. When the real-time photovoltaic output is less than the real-time load power, it does not immediately take power from the energy storage battery, but predicts the future load data and the future photovoltaic output data to calculate the difference curve, and determines the intervention timing of energy storage power generation according to the difference curve, the initial energy storage data and the electricity price table, and the intervention of energy storage power generation is more appropriate.

[0006] The optimization configuration method for a distributed photovoltaic energy storage system provided by an embodiment of the present invention includes: Monitoring the real-time load power and the real-time photovoltaic output of the access point; If the real-time photovoltaic output is less than the real-time load power, predicting the future load data and the future photovoltaic output data; Drawing a difference curve according to the future load data and the future photovoltaic output data; Determining the intervention timing of energy storage power generation according to the difference curve, the initial energy storage data and the electricity price table; If the real-time photovoltaic output is greater than or equal to the real-time load power, delivering the redundant photovoltaic power generation to the distributed photovoltaic energy storage battery.

[0007] Preferably, if the real-time photovoltaic output is less than the real-time load power, predicting the future load data and the future photovoltaic output data includes: Input the historical power consumption pattern data of the access point and the prediction time information into a preset LSTM neural network model to obtain future load data; Analyze the parameters of the photovoltaic modules and the meteorological information within the prediction time to obtain future photovoltaic power output data.

[0008] Preferably, determine the intervention timing of energy storage power generation according to the difference curve, initial energy storage data, and electricity price table, including: Determine the simulated intervention time point; According to the first curve enclosed area below the horizontal axis before the simulated intervention time point in the curve coordinate system and the initial energy storage data, determine the estimated energy storage data at the simulated intervention time point; According to the estimated energy storage data and the enclosure situation between the difference curve and the horizontal axis after the simulated intervention time point in the curve coordinate system, obtain the target time point; the target time point is: the energy storage exhaustion time point or the difference curve end time point; Obtain the second curve enclosed area above the horizontal axis before the simulated intervention time point and the third curve enclosed area above the horizontal axis after the target time point; Calculate the grid electricity fee according to the second curve enclosed area, the third curve enclosed area, and the electricity price table; Take the simulated intervention time point corresponding to the minimum grid electricity fee as the intervention timing of energy storage power generation.

[0009] Preferably, calculate the grid electricity fee according to the second curve enclosed area, the third curve enclosed area, and the electricity price table, including: Read the electricity prices at the time points corresponding to the second curve enclosed area and the third curve enclosed area from the electricity price table; Enlarge the vertical axis values of the local difference curve points corresponding to the second curve enclosed area and the third curve enclosed area and reconnect them to obtain the target curve. The enlargement process includes: multiplying the vertical axis value of the local difference curve point by the electricity price at the time point corresponding to the local difference curve point; Take the sum of the enclosed areas between the target curve and the horizontal axis as the grid electricity fee.

[0010] Preferably, determine the simulated intervention time point, including: Based on a preset time interval, determine pre-simulation points within the time interval corresponding to the difference curve; Obtain the number of intersections between the difference curve and the horizontal axis before the pre-simulation point; If the number of intersections is even, take the first intersection between the difference curve and the horizontal axis after the pre-simulation point as the simulated intervention time point; If the number of intersections is odd, obtain the area relationship between the nth curve enclosed area and the (n + 1)th curve enclosed area after the corresponding pre-simulation point, where n is a positive odd number; Obtain the last curve-enclosed area among the curve-enclosed areas that meet the standard area relationship with continuous area relationships in the region. If the last curve-enclosed area is closed, update the intersection point with the horizontal axis that is farther from the coordinate origin as the simulated intervention time point.

[0011] The optimized configuration system for a distributed photovoltaic energy storage system provided by an embodiment of the present invention includes: A monitoring module for monitoring the real-time load power and real-time photovoltaic output at the access point; A prediction module for predicting future load data and future photovoltaic output data if the real-time photovoltaic output is less than the real-time load power; A difference curve determination module for drawing a difference curve based on the future load data and future photovoltaic output data; An intervention timing determination module for determining the energy storage power generation intervention timing according to the difference curve, initial energy storage data, and electricity price table; An energy storage module for delivering redundant photovoltaic power generation to the distributed photovoltaic energy storage battery if the real-time photovoltaic output is greater than or equal to the real-time load power.

[0012] Preferably, when the real-time photovoltaic output is less than the real-time load power, the prediction module predicts future load data and future photovoltaic output data, including: Input the historical electricity consumption pattern data and prediction time information of the access point into a preset LSTM neural network model to obtain future load data; Analyze the photovoltaic module parameters and meteorological information within the prediction time to obtain future photovoltaic output data.

[0013] Preferably, the intervention timing determination module determines the energy storage power generation intervention timing according to the difference curve, initial energy storage data, and electricity price table, including: Determine the simulated intervention time point; According to the first curve-enclosed area below the horizontal axis before the simulated intervention time point in the curve coordinate system and the initial energy storage data, determine the estimated energy storage data at the simulated intervention time point; According to the estimated energy storage data and the enclosed situation of the difference curve and the horizontal axis after the simulated intervention time point in the curve coordinate system, obtain the target time point; the target time point is: the energy storage exhaustion time point or the difference curve end time point; Obtain the second curve-enclosed area above the horizontal axis before the simulated intervention time point and the third curve-enclosed area above the horizontal axis after the target time point; Calculate the grid electricity fee according to the second curve-enclosed area, the third curve-enclosed area, and the electricity price table; Take the simulated intervention time point corresponding to the minimum grid electricity fee as the energy storage power generation intervention timing.

[0014] Preferably, the intervention timing determination module calculates the grid electricity charge according to the second curve enclosed area, the third curve enclosed area, and the electricity price list, including: Read the electricity prices at the time points corresponding to the second curve enclosed area and the third curve enclosed area from the electricity price list; Enlarge the vertical axis values of the local difference curve points corresponding to the second curve enclosed area and the third curve enclosed area and reconnect them to obtain the target curve. The enlargement process includes: multiplying the vertical axis value of the local difference curve point by the electricity price at the time point corresponding to the local difference curve point; Take the sum of the enclosed areas of the target curve and the horizontal axis as the grid electricity charge.

[0015] Preferably, the intervention timing determination module determines the simulated intervention time point, including: Based on a preset time interval, determine pre-simulation points within the time interval corresponding to the difference curve; Obtain the number of intersections between the difference curve and the horizontal axis before the pre-simulation point; If the number of intersections is even, take the first intersection between the difference curve and the horizontal axis after the pre-simulation point as the simulated intervention time point; If the number of intersections is odd, obtain the area relationship between the nth curve enclosed area and the (n + 1)th curve enclosed area after the corresponding pre-simulation point, where n is a positive odd number; Obtain the last curve enclosed area in the curve enclosed areas with consecutive area relationships that meet the standard area relationship. If the last curve enclosed area is closed, update the intersection with the horizontal axis that is farther from the coordinate origin as the simulated intervention time point.

[0016] The beneficial effects of the present invention are as follows: The present invention monitors the real-time load power and the real-time photovoltaic output. When the real-time photovoltaic output is less than the real-time load power, it does not immediately draw power from the energy storage battery. Instead, it predicts the future load data and future photovoltaic output data to calculate the difference curve, and determines the energy storage power generation intervention timing according to the difference curve, the initial energy storage data, and the electricity price list, making the energy storage power generation intervention more appropriate.

[0017] Other features and advantages of the present invention will be described in the subsequent specification, and part of them will become obvious from the specification or be understood by implementing the present invention. The objectives and other advantages of the present invention can be achieved and obtained through the structures specifically pointed out in this application document.

[0018] The technical solutions of the present invention will be further described in detail below through the drawings and embodiments. Description of the Drawings

[0019] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation to the present invention. In the accompanying drawings: Figure 1 It is a schematic diagram of the optimization configuration method for a distributed photovoltaic energy storage system in an embodiment of the present invention; Figure 2 It is a schematic diagram of a difference curve in an embodiment of the present invention; Figure 3 It is a schematic diagram of a standard curve in an embodiment of the present invention; Figure 4 It is a schematic diagram of the optimization configuration system for a distributed photovoltaic energy storage system in an embodiment of the present invention. Detailed implementation manners

[0020] The following describes the preferred embodiments of the present invention with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention and are not used to limit the present invention.

[0021] The embodiment of the present invention provides an optimization configuration method for a distributed photovoltaic energy storage system, as Figure 1 shown, including: Step 1: Monitor the real-time load power and real-time photovoltaic output of the access point; Among them, the access point is: the electrical interface where the distributed photovoltaic energy storage system is connected to the user load; the real-time load power is: the electric power consumed by the user load in real time; the real-time photovoltaic output is: the electric power output by the photovoltaic module in real time; Step 2: If the real-time photovoltaic output is less than the real-time load power, predict the future load data and future photovoltaic output data; When predicting, input the historical electricity consumption pattern data of the access point and the prediction time information into a preset LSTM neural network model to obtain the future load data; Analyze the photovoltaic module parameters and meteorological information during the prediction time to obtain the future photovoltaic output data; Among them, the future load data is: the electric power consumed by the predicted user load within a future period of time (prediction time). When predicting, use the LSTM neural network to analyze the historical electricity consumption pattern for prediction; the future photovoltaic output data is: the electric power output by the predicted photovoltaic module within a future period of time. When predicting, analyze in combination with the meteorological information (such as: irradiance, cloud cover) and photovoltaic module parameters (such as: module efficiency, inverter characteristics, shading loss) during this future period of time; the future period of time is a period of time from the current moment to the future, such as: within 2 hours from the current moment to the future; Step 3: Draw a difference curve according to the future load data and future photovoltaic output data; Among them, the difference curve is a curve plotted in a two-dimensional rectangular coordinate system, where the difference between the future load power and the future photovoltaic output in a future period of time is used as the y-axis value, and the moments in the future period of time are used as the x-axis value. Step 4: Determine the intervention timing of energy storage power generation according to the difference curve, initial energy storage data, and electricity price table. Among them, the initial energy storage data is the electricity stored in the energy storage battery when the real-time photovoltaic output is less than the real-time load power; the electricity price table stores the corresponding relationship between the grid electricity price and time; the intervention timing of energy storage power generation is the timing when the electricity in the energy storage battery is delivered to the user load. Step 5: If the real-time photovoltaic output is greater than or equal to the real-time load power, deliver the redundant photovoltaic power generation to the distributed photovoltaic energy storage battery. Among them, the redundant photovoltaic power generation is the power generation of the photovoltaic module that is not consumed by the user load when the real-time photovoltaic output is greater than or equal to the real-time load power.

[0022] The working principle and beneficial effects of the above technical solution are as follows: Specifically, for example: the access point is the electrical interface connected to the load of a company's office. At 17:00, it is detected that the real-time photovoltaic output is less than the real-time load power. Generally, the access point of a configured photovoltaic energy storage system will immediately give priority to power transmission through the energy storage battery. However, in the present invention, the intervention of energy storage power generation is not immediately triggered. Instead, the data gaps of the load power and photovoltaic output during the period from 17:00 to 19:00 are predicted. For example, the load gaps corresponding to 17:00 - 17:30 and 18:00 - 18:30 are 1 kWh and 2 kWh respectively. In addition, the initial energy storage data is: 1.3 kWh, the energy storage battery generates 0.2 kWh during the period from 17:30 to 18:00, the electricity price from 17:00 to 18:00 is 1 yuan / kWh, and the electricity price from 18:00 to 19:00 is 1.5 yuan / kWh.

[0023] If the system calculates and immediately transmits power through the energy storage battery at 17:00, then during the period from 17:00 to 19:00, 1 kWh will be transmitted through the energy storage battery from 17:00 to 17:30, 0.5 kWh will be transmitted through the energy storage battery and 1.5 kWh will be transmitted through the grid at 18:00 - 18:30, and the electricity price is 2.25 yuan. If power is not immediately transmitted through the energy storage battery at 17:00, for example: 1 kWh is transmitted through the grid from 17:00 to 17:30, 1.5 kWh is transmitted through the energy storage battery and 0.5 kWh is transmitted through the grid at 18:00, and the electricity price is 1.75 yuan.

[0024] By comparison, 18:00 is a more preferable intervention time for energy storage power generation compared to the moment when the real-time PV output is less than the real-time load power (i.e., 17:00). Through repeated calculations and comparisons, the optimal intervention time for energy storage power generation is determined. When the current time reaches the intervention time for energy storage power generation, the power transmission task of the energy storage battery is triggered.

[0025] The present invention monitors the real-time load power and the real-time PV output. When the real-time PV output is less than the real-time load power, instead of immediately drawing power from the energy storage battery, it predicts future load data and future PV output data to calculate the difference curve. Based on the difference curve, the initial energy storage data, and the electricity price table, the intervention time for energy storage power generation is determined, and the intervention of energy storage power generation is more appropriate.

[0026] In one embodiment, determining the intervention time for energy storage power generation according to the difference curve, the initial energy storage data, and the electricity price table includes: Determine the simulated intervention time point; According to the first curve enclosed area below the horizontal axis before the simulated intervention time point in the curve coordinate system and the initial energy storage data, determine the estimated energy storage data at the simulated intervention time point; Among them, the curve coordinate system is a two-dimensional rectangular coordinate system constructed with the difference between the future load power and the future PV output within the prediction time as the y-axis and the prediction time as the x-axis; the estimated energy storage data is the sum value of the area of the first curve enclosed area and the initial energy storage data. When calculating the area of the curve enclosed area, if the difference curve forms a closed figure with the coordinate axes, directly calculate the area of this closed figure; if it is not closed, use the corresponding truncated side of the difference curve as the enclosed boundary to determine the closed figure and calculate the area, and the truncated side of the curve is perpendicular to the horizontal axis; According to the estimated energy storage data and the enclosure situation of the difference curve and the horizontal axis after the simulated intervention time point in the curve coordinate system, obtain the target time point; the target time point is: the energy storage exhaustion time point or the difference curve end time point; Among them, the enclosure situation is: how much is the enclosed area of the difference curve above the horizontal axis and how much is the enclosed area below the horizontal axis between the simulated intervention time point and each time point after the simulated intervention time point; the energy storage exhaustion time point is: the time point after the simulated intervention time point corresponding to the enclosure situation where the enclosed area above the horizontal axis minus the enclosed area below the horizontal axis in the enclosure situation is equal to the estimated energy storage data; Obtain the second curve enclosed area above the horizontal axis before the simulated intervention time point and the third curve enclosed area above the horizontal axis after the target time point; Calculate the grid electricity charge according to the second curve enclosed area, the third curve enclosed area, and the electricity price table; Among them, when calculating the grid electricity fee based on the second curve enclosed area, the third curve enclosed area, and the electricity price list, read the electricity price at the time points corresponding to the second curve enclosed area and the third curve enclosed area from the electricity price list, multiply the vertical axis value of the local difference curve points corresponding to the second curve enclosed area and the third curve enclosed area by the electricity price at the corresponding time points of the local difference curve points to expand the vertical axis value of the local difference curve points, reconnect the expanded coordinate points to obtain the target curve, and use the sum of the enclosed areas of the target curve and the horizontal axis as the grid electricity fee; Take the simulated intervention time point corresponding to the minimum grid electricity fee as the energy storage power generation intervention timing.

[0027] The working principle and beneficial effects of the above technical solution are as follows: The difference curve is a curve showing the future load data and the future photovoltaic output changing with time within the prediction time (for example: within two hours after the moment when the real-time photovoltaic output is less than the real-time load power). The horizontal axis of its reference coordinate system is time, and the vertical axis is the difference. When the difference curve is above the horizontal axis, it means that the load is higher than the photovoltaic output, and it is necessary to consider whether to draw electricity from the grid or from the photovoltaic energy storage battery.

[0028] First, determine the simulated intervention time point. When determining, it can be selected randomly or according to a preset time interval among the interval points in the time interval corresponding to the difference curve. For example, Figure 2 is a schematic diagram of the difference curve, is the moment when the real-time photovoltaic output is less than the real-time load power, is the prediction time interval, is 2 hours, is the simulated intervention time point, and Q is the target time point.

[0029] Determine the electricity price at the corresponding time points according to the second curve enclosed area, the third curve enclosed area, and the electricity price list. For example, it is 1 yuan per degree and is 1.5 yuan per degree. According to the electricity price at the corresponding time points and the vertical axis value of the local difference curve points corresponding to the second curve enclosed area and the third curve enclosed area, determine the target curve. The schematic diagram of the target curve is as Figure 3 shown. Use the sum of the enclosed areas of the target curve and the horizontal axis as the grid electricity fee. Figure 3 The area in is also called the curve enclosed area. When calculating the area of such curve enclosed areas, use the side of the corresponding truncated curve as the enclosed boundary to calculate the area, and the side of the truncated curve is perpendicular to the horizontal axis.

[0030] The present invention introduces a difference curve, introduces a simulated intervention time point on the curve, analyzes the intervention situation at the simulated intervention time point within the prediction time according to the curve enclosure situation before and after the simulated intervention time point and the electricity price list, quantifies the electricity consumption process and electricity price consumption of the energy storage battery, and determines the energy storage power generation intervention timing according to the simulated intervention time point corresponding to the minimum grid electricity fee calculated by simulation. The determination of the energy storage power generation intervention timing is more reasonable and intuitive.

[0031] In one embodiment, determining the simulated intervention time point includes: Based on a preset time interval, determining pre-simulation points within the corresponding time interval of the difference curve; Among them, the preset time interval is set manually in advance, for example: 5 minutes; when determining the pre-simulation points, for example, starting from Figure 2 and determining a pre-simulation point every 5 minutes; Obtaining the number of intersections between the difference curve and the horizontal axis before the pre-simulation point; If the number of intersections is even, taking the first intersection between the difference curve and the horizontal axis after the pre-simulation point as the simulated intervention time point; Among them, if the number of intersections is even, it indicates that the pre-simulation point is below the horizontal axis, the photovoltaic output is greater than the load power, and there is no need to judge the energy storage intervention timing; If the number of intersections is odd, obtaining the area relationship between the nth curve-enclosed area and the (n + 1)th curve-enclosed area after the corresponding pre-simulation point, where n is a positive odd number; Among them, if the number of intersections is odd, it indicates that the pre-simulation point is above the horizontal axis, and the area relationship is: whether the area of the nth curve-enclosed area is greater than the area of the (n + 1)th curve-enclosed area; Obtaining the last curve-enclosed area that meets the standard area relationship with continuous area relationships among the curve-enclosed areas. If the last curve-enclosed area is closed, updating the intersection with the horizontal axis that is farther from the coordinate origin as the simulated intervention time point.

[0032] Among them, the standard area relationship is: the area of the nth curve-enclosed area is less than the area of the (n + 1)th curve-enclosed area; when the area of the continuous nth curve-enclosed area is less than the area of the (n + 1)th curve-enclosed area, it indicates that the spontaneous use of electricity can be realized within this continuous curve-enclosed area, and there is no need to consider the backward shift of the previously accumulated energy storage. Therefore, skip this area and take the intersection with the horizontal axis that is farther from the coordinate origin of the last closed curve-enclosed area as the simulated intervention time point. If the last curve-enclosed area is not closed, it indicates that the prediction time has ended and this process does not need to be simulated; closed means that the curve-enclosed area has two intersections with the horizontal axis, such as Figure 3 the area in.

[0033] The working principle and beneficial effects of the above technical solution are: When determining the simulation intervention time point, the present invention first determines the pre-simulation point based on the time interval. According to the number of intersections between the difference curve and the horizontal axis before the pre-simulation point, determine where the pre-simulation point is on the horizontal axis; next, further reduce the number of pre-simulation points; when reducing, compare the area relationship between the area enclosed by the nth curve and the area enclosed by the n+1th curve with the area relationship of the standard area, consider the self-generation and self-use capacity within the local time range, and make a judgment on the necessity of postponing the previous accumulated energy storage, and select the simulation intervention time point based on the judgment result, which greatly reduces the simulation workload and improves the efficiency of determining the timing of energy storage power generation intervention.

[0034] The embodiment of the present invention provides a distributed photovoltaic energy storage system optimization configuration system, such as Figure 4 As shown, including: Monitoring module 1, used to monitor the real-time load power and photovoltaic real-time output of the access point; Prediction module 2, used to predict future load data and future photovoltaic output data if the real-time photovoltaic output is less than the real-time load power; The difference curve determination module 3 is used to draw a difference curve according to future load data and future photovoltaic output data; An intervention timing determination module 4 is used to determine the energy storage power generation intervention timing according to the difference curve, the initial energy storage data and the electricity price table; The energy storage module 5 is used to transmit the redundant photovoltaic power generation to the distributed photovoltaic energy storage battery if the real-time photovoltaic output is greater than or equal to the real-time load power.

[0035] In one embodiment, if the real-time photovoltaic output is less than the real-time load power, the prediction module predicts future load data and future photovoltaic output data, including: Input the historical power consumption data and predicted time information of the access point into the preset LSTM neural network model to obtain future load data; Analyze PV module parameters and meteorological information within the forecast period to obtain future PV output data.

[0036] In one embodiment, the intervention timing determination module determines the energy storage power generation intervention timing according to the difference curve, the initial energy storage data and the electricity price table, including: Determine the time point for simulation intervention; Determine the estimated energy storage data at the simulated intervention time point based on the first curve enclosed area below the horizontal axis before the simulated intervention time point in the curve coordinate system and the initial energy storage data; According to the estimated energy storage data and the encirclement of the difference curve and the horizontal axis after the simulated intervention time point in the curve coordinate system, the target time point is obtained; the target time point is: the energy storage exhaustion time point or the end point of the difference curve; Obtain the second curve enclosed area above the horizontal axis before the simulated intervention time point and the third curve enclosed area above the horizontal axis after the target time point; Calculate the grid electricity fee according to the second curve enclosed area, the third curve enclosed area and the electricity price list; Take the simulated intervention time point corresponding to the minimum grid electricity fee as the energy storage power generation intervention timing.

[0037] In one embodiment, the intervention timing determination module calculates the grid electricity fee according to the second curve enclosed area, the third curve enclosed area and the electricity price list, including: Read the electricity prices at the time points corresponding to the second curve enclosed area and the third curve enclosed area from the electricity price list; Enlarge the vertical axis values of the local difference curve points corresponding to the second curve enclosed area and the third curve enclosed area and reconnect them to obtain the target curve. The enlargement process includes: multiplying the vertical axis value of the local difference curve point by the electricity price at the time point corresponding to the local difference curve point; Take the sum of the enclosed areas of the target curve and the horizontal axis as the grid electricity fee.

[0038] In one embodiment, the intervention timing determination module determines the simulated intervention time point, including: Based on a preset time interval, determine pre-simulation points within the corresponding time interval of the difference curve; Obtain the number of intersections of the difference curve and the horizontal axis before the pre-simulation point; If the number of intersections is even, take the first intersection of the difference curve and the horizontal axis after the pre-simulation point as the simulated intervention time point; If the number of intersections is odd, obtain the area relationship between the nth curve enclosed area and the (n + 1)th curve enclosed area after the corresponding pre-simulation point, where n is a positive odd number; Obtain the last curve enclosed area among the curve enclosed areas with a continuous area relationship that meets the standard area relationship. If the last curve enclosed area is closed, update the intersection with the horizontal axis that is farther from the coordinate origin as the simulated intervention time point.

[0039] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these changes and modifications.

Claims

1. A method for optimizing the configuration of a distributed photovoltaic energy storage system, characterized in that Including: Monitoring the real-time load power of the access point and the real-time PV output; If the real-time PV output is less than the real-time load power, predicting future load data and future PV output data; Drawing a difference curve based on the future load data and future PV output data; Determining the intervention timing of energy storage power generation according to the difference curve, initial energy storage data and electricity price table; If the real-time PV output is greater than or equal to the real-time load power, sending the redundant PV power generation to the distributed PV energy storage battery.

2. The optimization configuration method of the distributed photovoltaic energy storage system according to claim 1, wherein If the real-time PV output is less than the real-time load power, predicting future load data and future PV output data, including: Inputting the historical electricity consumption rule data of the access point and the prediction time information into a preset LSTM neural network model to obtain future load data; Analyzing the PV module parameters and meteorological information within the prediction time to obtain future PV output data.

3. The optimization configuration method of the distributed photovoltaic energy storage system according to claim 1, wherein Determining the intervention timing of energy storage power generation according to the difference curve, initial energy storage data and electricity price table, including: Determining the simulated intervention time point; According to the first curve enclosing area below the horizontal axis before the simulated intervention time point in the curve coordinate system and the initial energy storage data, determining the estimated energy storage data at the simulated intervention time point; According to the estimated energy storage data and the enclosing situation of the difference curve and the horizontal axis after the simulated intervention time point in the curve coordinate system, obtaining the target time point; the target time point is: the energy storage exhaustion time point or the difference curve end time point; Obtaining the second curve enclosing area above the horizontal axis before the simulated intervention time point and the third curve enclosing area above the horizontal axis after the target time point; Calculating the grid electricity fee according to the second curve enclosing area, the third curve enclosing area and the electricity price table; Taking the simulated intervention time point corresponding to the minimum grid electricity fee as the intervention timing of energy storage power generation.

4. The optimization configuration method of the distributed photovoltaic energy storage system according to claim 3, wherein, Calculating the grid electricity fee according to the second curve enclosing area, the third curve enclosing area and the electricity price table, including: Reading the electricity prices at the time points corresponding to the second curve enclosing area and the third curve enclosing area from the electricity price table; Expanding the vertical axis values of the local difference curve points corresponding to the second curve enclosing area and the third curve enclosing area and reconnecting them to obtain the target curve. The expansion process includes: multiplying the vertical axis value of the local difference curve point by the electricity price at the time point corresponding to the local difference curve point; Taking the sum of the enclosing areas of the target curve and the horizontal axis as the grid electricity fee.

5. The optimization configuration method of the distributed photovoltaic energy storage system according to claim 3, wherein Determining the simulated intervention time point, including: Based on a preset time interval, determining pre-simulation points within the time interval corresponding to the difference curve; Obtaining the number of intersections between the difference curve and the horizontal axis before the pre-simulation point; If the number of intersections is even, taking the first intersection between the difference curve and the horizontal axis after the pre-simulation point as the simulated intervention time point; If the number of intersections is odd, obtaining the area relationship between the nth curve enclosing area and the (n + 1)th curve enclosing area after the corresponding pre-simulation point, where n is a positive odd number; Obtaining the last curve enclosing area among the curve enclosing areas with continuous area relationships that meet the standard area relationship. If the last curve enclosing area is closed, updating the intersection with the horizontal axis farther from the coordinate origin as the simulated intervention time point.

6. Distributed photovoltaic energy storage system optimization configuration system, characterized in that, Including: A monitoring module for monitoring the real-time load power of the access point and the real-time PV output; A prediction module, which is used to predict future load data and future PV output data if the real-time PV output is less than the real-time load power; A difference curve determination module, which is used to draw a difference curve according to the future load data and the future PV output data; An intervention timing determination module, which is used to determine the energy storage power generation intervention timing according to the difference curve, the initial energy storage data and the electricity price table; An energy storage module, which is used to deliver redundant PV power generation to the distributed PV energy storage battery if the real-time PV output is greater than or equal to the real-time load power.

7. The optimized configuration system for a distributed photovoltaic energy storage system according to claim 6, wherein If the real-time PV output is less than the real-time load power, the prediction module predicts future load data and future PV output data, including: Inputting the historical power consumption rule data of the access point and the prediction time information into a preset LSTM neural network model to obtain future load data; Analyzing the PV module parameters and the meteorological information within the prediction time to obtain future PV output data.

8. The optimized configuration system for a distributed photovoltaic energy storage system according to claim 6, characterized in that, The intervention timing determination module determines the energy storage power generation intervention timing according to the difference curve, the initial energy storage data and the electricity price table, including: Determining the simulated intervention time point; According to the first curve enclosing area below the horizontal axis before the simulated intervention time point in the curve coordinate system and the initial energy storage data, determining the estimated energy storage data at the simulated intervention time point; According to the estimated energy storage data and the enclosing situation of the difference curve and the horizontal axis after the simulated intervention time point in the curve coordinate system, obtaining the target time point; the target time point is: the energy storage exhaustion time point or the difference curve end time point; Obtaining the second curve enclosing area above the horizontal axis before the simulated intervention time point and the third curve enclosing area above the horizontal axis after the target time point; Calculating the grid electricity charge according to the second curve enclosing area, the third curve enclosing area and the electricity price table; Taking the simulated intervention time point corresponding to the minimum grid electricity charge as the energy storage power generation intervention timing.

9. The optimized configuration system for a distributed photovoltaic energy storage system according to claim 8, wherein The intervention timing determination module calculates the grid electricity charge according to the second curve enclosing area, the third curve enclosing area and the electricity price table, including: Reading the electricity prices at the time points corresponding to the second curve enclosing area and the third curve enclosing area from the electricity price table; Expanding the vertical axis values of the local difference curve points corresponding to the second curve enclosing area and the third curve enclosing area and reconnecting them to obtain the target curve. The expansion process includes: multiplying the vertical axis value of the local difference curve point by the electricity price at the time point corresponding to the local difference curve point; Taking the sum of the enclosing areas of the target curve and the horizontal axis as the grid electricity charge.

10. The optimized configuration system for a distributed photovoltaic energy storage system according to claim 8, characterized in that, The intervention timing determination module determines the simulated intervention time point, including: Based on a preset time interval, determining pre-simulation points within the time interval corresponding to the difference curve; Obtaining the number of intersections of the difference curve and the horizontal axis before the pre-simulation point; If the number of intersections is even, taking the first intersection of the difference curve and the horizontal axis after the pre-simulation point as the simulated intervention time point; If the number of intersections is odd, obtaining the area relationship between the nth curve enclosing area and the (n + 1)th curve enclosing area after the corresponding pre-simulation point, where n is a positive odd number; Obtaining the last curve enclosing area in the curve enclosing areas with a continuous area relationship that meets the standard area relationship. If the last curve enclosing area is closed, updating the intersection with the horizontal axis that is farther from the coordinate origin as the simulated intervention time point.

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