Distributed photovoltaic energy storage system optimization configuration method and system

Abstract

The present invention provides a distributed photovoltaic energy storage system optimization configuration method and system, belonging to the field of power distribution technology, the method includes: monitoring the real-time load power and photovoltaic real-time output of the access point; if the photovoltaic real-time output is less than the real-time load power, predicting future load data and future photovoltaic output data; drawing a difference curve based on the future load data and future photovoltaic output data; determining the time for energy storage power generation intervention based on the difference curve, initial energy storage data and electricity price list; if the photovoltaic real-time output is greater than or equal to the real-time load power, delivering redundant photovoltaic power generation to the distributed photovoltaic energy storage battery. The distributed photovoltaic energy storage system optimization configuration method and system of the present invention, when the photovoltaic real-time output is less than the real-time load power, does not immediately draw power from the energy storage battery, but instead calculates the difference curve, and determines the time for energy storage power generation intervention based on the difference curve, initial energy storage data and electricity price list, so that energy storage power generation intervention is more appropriate.

Description

Technical Field

[0001] The present invention relates to the field of power distribution technology, and in particular to a method and system for optimizing configuration of a distributed photovoltaic energy storage system. Background Art

[0002] Distributed photovoltaic energy storage systems are now widely used. Users can generate their own electricity through photovoltaic power generation and store excess power in the energy storage system, reducing the amount of electricity purchased by the grid and lowering electricity bills. Furthermore, distributed photovoltaic energy storage systems can provide emergency power during grid outages, avoiding unnecessary losses.

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

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

[0005] One of the purposes of the present invention is to provide a distributed photovoltaic energy storage system optimization configuration method and 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, power is not immediately drawn from the energy storage battery. Instead, the future load data and future photovoltaic output data are predicted to calculate the difference curve. Based on the difference curve, the initial energy storage data and the electricity price table, the timing of energy storage power generation intervention is determined, and the energy storage power generation intervention is more appropriate.

[0006] The embodiment of the present invention provides a method for optimizing the configuration of a distributed photovoltaic energy storage system, including:

[0007] Monitor the real-time load power and photovoltaic output of access points;

[0008] If the real-time photovoltaic output is less than the real-time load power, predict future load data and future photovoltaic output data;

[0009] Draw a difference curve based on future load data and future photovoltaic output data;

[0010] Determine the timing of energy storage intervention based on the difference curve, initial energy storage data, and electricity price list;

[0011] If the real-time photovoltaic output is greater than or equal to the real-time load power, the redundant photovoltaic power generation will be transmitted to the distributed photovoltaic energy storage battery.

[0012] Preferably, if the real-time photovoltaic output is less than the real-time load power, predicting future load data and future photovoltaic output data includes:

[0013] Input the access point's historical power consumption data and predicted time information into the preset LSTM neural network model to obtain future load data;

[0014] Analyze PV module parameters and meteorological information within the forecast period to obtain future PV output data.

[0015] Preferably, determining the time for energy storage power generation intervention based on the difference curve, initial energy storage data, and electricity price list includes:

[0016] Determine the time point for simulation intervention;

[0017] Determining 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;

[0018] 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 time point when the energy storage is exhausted or the end point of the difference curve;

[0019] Acquire a second curve enclosed area on the horizontal axis before the simulated intervention time point and a third curve enclosed area on the horizontal axis after the target time point;

[0020] Calculate the power charges of the power grid according to the area enclosed by the second curve, the area enclosed by the third curve and the power price list;

[0021] The simulated intervention time point corresponding to the minimum grid electricity price is used as the intervention opportunity for energy storage power generation.

[0022] Preferably, calculating the power grid electricity fee based on the area enclosed by the second curve, the area enclosed by the third curve, and the electricity price table includes:

[0023] Read the electricity prices at the time points corresponding to the areas enclosed by the second curve and the areas enclosed by the third curve from the electricity price table;

[0024] Expanding the vertical axis values ​​of the local difference curve points corresponding to the area enclosed by the second curve and the area enclosed by the third curve and reconnecting them to obtain the target curve, the expansion process comprising: 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;

[0025] The sum of the area enclosed by the target curve and the horizontal axis is taken as the grid electricity fee.

[0026] Preferably, determining the simulated intervention time point includes:

[0027] Based on a preset time interval, a pre-simulation point is determined within a time interval corresponding to the difference curve;

[0028] Get the number of intersections between the difference curve and the horizontal axis before the pre-simulation point;

[0029] If the number of intersection points is even, the intersection point of the first difference curve and the horizontal axis after the pre-simulation point is taken as the simulation intervention time point;

[0030] If the number of intersection points is odd, obtain the area relationship between the area enclosed by the nth curve and the area enclosed by the n+1th curve after the corresponding pre-simulation point, where n is a positive odd number;

[0031] The last curve-enclosed area in the curve-enclosed areas that are continuous and meet the standard area relationship is obtained. If the last curve-enclosed area is closed, its intersection with the horizontal axis that is farther from the coordinate origin is updated as the simulation intervention time point.

[0032] The distributed photovoltaic energy storage system optimization configuration system provided by the embodiment of the present invention includes:

[0033] Monitoring module, used to monitor the real-time load power and photovoltaic output of the access point;

[0034] The prediction module is 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;

[0035] A difference curve determination module is used to draw a difference curve based on future load data and future photovoltaic output data;

[0036] An intervention timing determination module is used to determine the energy storage power generation intervention timing based on the difference curve, initial energy storage data, and electricity price table;

[0037] The energy storage module is used to transmit 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.

[0038] Preferably, 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:

[0039] Input the access point's historical power consumption data and predicted time information into the preset LSTM neural network model to obtain future load data;

[0040] Analyze PV module parameters and meteorological information within the forecast period to obtain future PV output data.

[0041] Preferably, the intervention timing determination module determines the energy storage power generation intervention timing based on the difference curve, the initial energy storage data and the electricity price table, including:

[0042] Determine the time point for simulation intervention;

[0043] Determining 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;

[0044] 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 time point when the energy storage is exhausted or the end point of the difference curve;

[0045] Acquire a second curve enclosed area on the horizontal axis before the simulated intervention time point and a third curve enclosed area on the horizontal axis after the target time point;

[0046] Calculate the power charges of the power grid according to the area enclosed by the second curve, the area enclosed by the third curve and the power price list;

[0047] The simulated intervention time point corresponding to the minimum grid electricity price is used as the intervention opportunity for energy storage power generation.

[0048] Preferably, the intervention timing determination module calculates the grid electricity fee based on the area enclosed by the second curve, the area enclosed by the third curve, and the electricity price table, including:

[0049] Read the electricity prices at the time points corresponding to the areas enclosed by the second curve and the areas enclosed by the third curve from the electricity price table;

[0050] Expanding the vertical axis values ​​of the local difference curve points corresponding to the area enclosed by the second curve and the area enclosed by the third curve and reconnecting them to obtain the target curve, the expansion process comprising: 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;

[0051] The sum of the area enclosed by the target curve and the horizontal axis is taken as the grid electricity fee.

[0052] Preferably, the intervention timing determination module determines the simulated intervention time point, including:

[0053] Based on a preset time interval, a pre-simulation point is determined within a time interval corresponding to the difference curve;

[0054] Get the number of intersections between the difference curve and the horizontal axis before the pre-simulation point;

[0055] If the number of intersection points is even, the intersection point of the first difference curve and the horizontal axis after the pre-simulation point is taken as the simulation intervention time point;

[0056] If the number of intersection points is odd, obtain the area relationship between the area enclosed by the nth curve and the area enclosed by the n+1th curve after the corresponding pre-simulation point, where n is a positive odd number;

[0057] The last curve-enclosed area in the curve-enclosed areas that are continuous and meet the standard area relationship is obtained. If the last curve-enclosed area is closed, its intersection with the horizontal axis that is farther from the coordinate origin is updated as the simulation intervention time point.

[0058] The beneficial effects of the present invention are:

[0059] 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, power is not immediately drawn from the energy storage battery. Instead, the future load data and future photovoltaic output data are predicted to calculate the difference curve. Based on the difference curve, the initial energy storage data and the electricity price list, the timing of energy storage power generation intervention is determined, and the energy storage power generation intervention is more appropriate.

[0060] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be achieved and obtained through the structures specifically pointed out in this application document.

[0061] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] 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 of the present invention. In the accompanying drawings:

[0063] Figure 1 Schematic diagram of a method for optimizing configuration of a distributed photovoltaic energy storage system according to an embodiment of the present invention;

[0064] Figure 2 Schematic diagram of a difference curve in an embodiment of the present invention;

[0065] Figure 3 This is a schematic diagram of a standard curve in an embodiment of the present invention;

[0066] Figure 4 Schematic diagram of a distributed photovoltaic energy storage system optimization configuration system in an embodiment of the present invention. DETAILED DESCRIPTION

[0067] The preferred embodiments of the present invention are described below 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.

[0068] The embodiment of the present invention provides a method for optimizing the configuration of a distributed photovoltaic energy storage system. Figure 1 Shown, including:

[0069] Step 1: Monitor the real-time load power and photovoltaic output of the access point;

[0070] The access point is the electrical interface that connects the distributed photovoltaic energy storage system to the user load; the real-time load power is the real-time power consumed by the user load; the real-time photovoltaic output is the real-time power output of the photovoltaic module;

[0071] Step 2: If the real-time PV output is less than the real-time load power, predict future load data and future PV output data;

[0072] During the prediction process, the historical power consumption data and prediction time information of the access point are input into the preset LSTM neural network model to obtain future load data;

[0073] Analyze PV module parameters and meteorological information within the forecast period to obtain future PV output data;

[0074] Among them, future load data is the predicted electric power consumed by user loads over a period of time in the future (prediction time). The prediction is made by analyzing historical electricity consumption patterns using an LSTM neural network. Future photovoltaic output data is the predicted electric power output of photovoltaic modules over a period of time in the future. The prediction is analyzed by combining meteorological information (such as irradiance and cloud cover) and photovoltaic module parameters (such as module efficiency, inverter characteristics, and shading loss) over the future period. The future period is the period from the current moment to the future, for example, from the current moment to the next two hours.

[0075] Step 3: Draw a difference curve based on future load data and future photovoltaic output data;

[0076] The difference curve is a curve drawn in a two-dimensional rectangular coordinate system, with the difference between the future load power and the future PV output over a period of time in the future as the y-axis value and the moment in the future period as the x-axis value.

[0077] Step 4: Determine the timing for energy storage to intervene based on the difference curve, initial energy storage data, and electricity price list.

[0078] The initial energy storage data is the amount of 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 correspondence between grid electricity prices and time. The energy storage power generation intervention timing is the time when the energy in the energy storage battery is delivered to the user load.

[0079] Step 5: If the real-time photovoltaic output is greater than or equal to the real-time load power, the redundant photovoltaic power generation is transmitted to the distributed photovoltaic energy storage battery;

[0080] Among them, redundant photovoltaic power generation is the power generation of photovoltaic components 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.

[0081] The working principle and beneficial effects of the above technical solution are:

[0082] For example, an access point, such as an electrical interface connected to a company's office load, detects at 5:00 PM that the real-time photovoltaic output is less than the real-time load power. Typically, access points equipped with a photovoltaic energy storage system will immediately prioritize power transmission through the energy storage battery. However, the present invention does not immediately trigger energy storage power generation intervention. Instead, it predicts the gaps in load power and photovoltaic output data between 5:00 PM and 7:00 PM. For example, the two load gaps between 5:00 PM and 5:30 PM and 6:00 PM are 1 kWh and 2 kWh, respectively. Furthermore, the initial energy storage data is 1.3 kWh, and the energy storage battery generates 0.2 kWh between 5:30 PM and 6:00 PM. The electricity price between 5:00 PM and 6:00 PM is 1 yuan / kWh, and 1.5 yuan / kWh between 6:00 PM and 7:00 PM.

[0083] The system calculates that if power is immediately supplied from the energy storage battery at 5:00 PM, then between 5:00 PM and 7:00 PM, 1 kWh of electricity will be supplied from the energy storage battery between 5:00 PM and 5:30 PM, and 0.5 kWh will be supplied from the energy storage battery and 1.5 kWh from the grid between 6:00 PM and 6:30 PM, with a price of 2.25 RMB. If power is not immediately supplied from the energy storage battery at 5:00 PM, for example, 1 kWh will be supplied from the grid between 5:00 PM and 5:30 PM, and 1.5 kWh will be supplied from the energy storage battery and 0.5 kWh from the grid between 6:00 PM and 6:00 PM, the price will be 1.75 RMB.

[0084] Comparison shows that 18:00 is a more preferable time for energy storage and power generation intervention than the moment when the real-time photovoltaic output is less than the real-time load power (i.e. 17:00). Repeated calculations and comparisons are performed to determine the optimal time for energy storage and power generation intervention. When the current time reaches the time for energy storage and power generation intervention, the energy storage battery's power transmission task is triggered.

[0085] 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, power is not immediately drawn from the energy storage battery. Instead, the future load data and future photovoltaic output data are predicted to calculate the difference curve. Based on the difference curve, the initial energy storage data and the electricity price list, the timing of energy storage power generation intervention is determined, and the energy storage power generation intervention is more appropriate.

[0086] In one embodiment, determining the timing for energy storage and power generation intervention based on the difference curve, initial energy storage data, and electricity price list includes:

[0087] Determine the time point for simulation intervention;

[0088] Determining 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;

[0089] The curve coordinate system is a two-dimensional rectangular coordinate system constructed with the difference between the future load power and the future photovoltaic 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 of the area of ​​the area enclosed by the first curve and the initial energy storage data. When calculating the area of ​​the area enclosed by the curve, if the difference curve and the coordinate axis form a closed figure, the area of ​​the closed figure can be directly calculated; if not, the edge of the corresponding truncated difference curve is used as the enclosing boundary to determine the closed figure and calculate the area, and the edge of the truncated curve is perpendicular to the horizontal axis.

[0090] 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 time point when the energy storage is exhausted or the end point of the difference curve;

[0091] The encirclement situation is defined as the area above and below the horizontal axis of the difference curve between the simulated intervention time point and the time points after each simulated intervention time point. The energy storage depletion time point is defined as the time point after the simulated intervention time point when the encirclement area above the horizontal axis minus the encirclement area below the horizontal axis in the encirclement situation equals the encirclement situation of the estimated energy storage data.

[0092] Acquire a second curve enclosed area on the horizontal axis before the simulated intervention time point and a third curve enclosed area on the horizontal axis after the target time point;

[0093] Calculate the power charges of the power grid according to the area enclosed by the second curve, the area enclosed by the third curve and the power price list;

[0094] When calculating the power grid electricity fee based on the area enclosed by the second curve, the area enclosed by the third curve, and the electricity price table, the electricity prices at the corresponding time points of the areas enclosed by the second curve and the area enclosed by the third curve are read from the electricity price table, the vertical axis values ​​of the local difference curve points corresponding to the areas enclosed by the second curve and the areas enclosed by the third curve and the electricity prices at the corresponding time points of the local difference curve points are multiplied to expand the vertical axis values ​​of the local difference curve points, the expanded coordinate points are reconnected to obtain the target curve, and the sum of the enclosed areas of the target curve and the horizontal axis is used as the power grid electricity fee;

[0095] The simulated intervention time point corresponding to the minimum grid electricity price is used as the intervention opportunity for energy storage power generation.

[0096] The working principle and beneficial effects of the above technical solution are:

[0097] The difference curve plots future load data and future PV output over time within a forecast period (for example, within two hours of the moment when the real-time PV output falls below the real-time load power). The horizontal axis of the reference coordinate system is time, and the vertical axis is the difference. When the difference curve is above the horizontal axis, it indicates that the load is higher than the PV output, and consideration should be given to whether power is being drawn from the grid or from the PV storage battery.

[0098] First, determine the simulation intervention time point. When determining, select the interval point in the time interval corresponding to the difference curve. It can be selected randomly or according to a preset time interval. For example, Figure 2 is a schematic diagram of the difference curve, is the moment when the real-time PV output is less than the real-time load power, is the prediction time interval, is 2 hours, is the simulation intervention time point, and Q is the target time point.

[0099] The electricity price at the corresponding time point is determined based on the area enclosed by the second curve, the area enclosed by the third curve, and the electricity price table. For example, it is 1 yuan per kWh and 1.5 yuan per kWh. The target curve is determined based on the electricity price at the corresponding time point and the vertical axis value of the local difference curve point corresponding to the area enclosed by the second curve and the area enclosed by the third curve. The schematic diagram of the target curve is shown in FIG. Figure 3 As shown. The sum of the target curve and the area enclosed by the horizontal axis is taken as the grid electricity fee. Figure 3 The area in is also called the curve-enclosed area. When calculating the area of ​​such a curve-enclosed area, the edge of the corresponding truncation curve is used as the enclosing boundary for calculation, and the edge of the truncation curve is perpendicular to the horizontal axis.

[0100] The present invention introduces a difference curve, and by introducing a simulated intervention time point on the curve, the intervention situation at the simulated intervention time point within the prediction time is analyzed according to the curve encirclement situation before and after the simulated intervention time point and the electricity price table, the energy storage battery power consumption process and electricity price consumption are quantified, and the energy storage power generation intervention timing is determined 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.

[0101] In one embodiment, determining the simulated intervention time point includes:

[0102] Based on a preset time interval, a pre-simulation point is determined within a time interval corresponding to the difference curve;

[0103] The preset time interval is set manually, for example, 5 minutes; when determining the pre-simulation point, for example, Figure 2 At the beginning of the simulation, a pre-simulation point is determined every 5 minutes;

[0104] Get the number of intersections between the difference curve and the horizontal axis before the pre-simulation point;

[0105] If the number of intersection points is even, the intersection point of the first difference curve and the horizontal axis after the pre-simulation point is taken as the simulation intervention time point;

[0106] If the number of intersections is even, it means that the pre-simulation point is below the horizontal axis, the PV output is greater than the load power, and there is no need to determine the timing of energy storage intervention.

[0107] If the number of intersection points is odd, obtain the area relationship between the area enclosed by the nth curve and the area enclosed by the n+1th curve after the corresponding pre-simulation point, where n is a positive odd number;

[0108] Among them, if the number of intersection points is odd, it means that the pre-simulation point is above the horizontal axis. The area relationship is: whether the area of ​​the area enclosed by the nth curve is greater than the area of ​​the area enclosed by the n+1th curve;

[0109] The last curve-enclosed area in the curve-enclosed areas that are continuous and meet the standard area relationship is obtained. If the last curve-enclosed area is closed, its intersection with the horizontal axis that is farther from the coordinate origin is updated as the simulation intervention time point.

[0110] Among them, the standard area relationship is: the area of ​​the area enclosed by the nth curve is smaller than the area of ​​the area enclosed by the n+1th curve; when the area of ​​the area enclosed by the continuous nth curve is smaller than the area of ​​the area enclosed by the n+1th curve, it means that the self-generation and self-use of electricity can be achieved in the continuous curve enclosed area, and there is no need to consider the backward shift of the previous accumulated energy storage. Therefore, skip this area, and take the intersection of the last closed curve enclosed area far from the coordinate origin with the horizontal axis as the simulation intervention time point. If the last curve enclosed area is not closed, it means that the prediction time is over 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.

[0111] The working principle and beneficial effects of the above technical solution are:

[0112] When determining the simulation intervention time point, the present invention first determines a pre-simulation point based on the time interval. The location of the pre-simulation point on the horizontal axis is determined based on the number of intersections between the difference curve and the horizontal axis before the pre-simulation point. Next, the number of pre-simulation points is further reduced. During this reduction, the area relationship between the area enclosed by the nth curve and the area enclosed by the (n+1)th curve is compared with the area relationship of the standard area. The self-generation and self-consumption capacity within the local time range is considered to determine the necessity of postponing the pre-accumulated energy storage. Based on this determination, the simulation intervention time point is selected, significantly reducing the simulation workload and improving the efficiency of determining the timing of energy storage and power generation intervention.

[0113] The embodiment of the present invention provides a distributed photovoltaic energy storage system optimization configuration system, such as Figure 4 Shown, including:

[0114] Monitoring module 1, used to monitor the real-time load power and photovoltaic real-time output of the access point;

[0115] Prediction module 2 is 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;

[0116] The difference curve determination module 3 is used to draw a difference curve based on future load data and future photovoltaic output data;

[0117] An intervention timing determination module 4 is used to determine the energy storage power generation intervention timing based on the difference curve, the initial energy storage data and the electricity price table;

[0118] 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.

[0119] 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:

[0120] Input the access point's historical power consumption data and predicted time information into the preset LSTM neural network model to obtain future load data;

[0121] Analyze PV module parameters and meteorological information within the forecast period to obtain future PV output data.

[0122] In one embodiment, the intervention timing determination module determines the energy storage power generation intervention timing based on the difference curve, the initial energy storage data, and the electricity price table, including:

[0123] Determine the time point for simulation intervention;

[0124] Determining 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;

[0125] 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 time point when the energy storage is exhausted or the end point of the difference curve;

[0126] Acquire a second curve enclosed area on the horizontal axis before the simulated intervention time point and a third curve enclosed area on the horizontal axis after the target time point;

[0127] Calculate the power charges of the power grid according to the area enclosed by the second curve, the area enclosed by the third curve and the power price list;

[0128] The simulated intervention time point corresponding to the minimum grid electricity price is used as the intervention opportunity for energy storage power generation.

[0129] In one embodiment, the intervention timing determination module calculates the grid electricity fee based on the area enclosed by the second curve, the area enclosed by the third curve, and the electricity price table, including:

[0130] Read the electricity prices at the time points corresponding to the areas enclosed by the second curve and the areas enclosed by the third curve from the electricity price table;

[0131] Expanding the vertical axis values ​​of the local difference curve points corresponding to the area enclosed by the second curve and the area enclosed by the third curve and reconnecting them to obtain the target curve, the expansion process comprising: 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;

[0132] The sum of the area enclosed by the target curve and the horizontal axis is taken as the grid electricity fee.

[0133] In one embodiment, the intervention timing determination module determines the simulated intervention time point, including:

[0134] Based on a preset time interval, a pre-simulation point is determined within a time interval corresponding to the difference curve;

[0135] Get the number of intersections between the difference curve and the horizontal axis before the pre-simulation point;

[0136] If the number of intersection points is even, the intersection point of the first difference curve and the horizontal axis after the pre-simulation point is taken as the simulation intervention time point;

[0137] If the number of intersection points is odd, obtain the area relationship between the area enclosed by the nth curve and the area enclosed by the n+1th curve after the corresponding pre-simulation point, where n is a positive odd number;

[0138] The last curve-enclosed area in the curve-enclosed areas that are continuous and meet the standard area relationship is obtained. If the last curve-enclosed area is closed, its intersection with the horizontal axis that is farther from the coordinate origin is updated as the simulation intervention time point.

[0139] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A distributed photovoltaic energy storage system optimization configuration method, characterized in that: include: Monitor the real-time load power and photovoltaic output of access points; If the real-time photovoltaic output is less than the real-time load power, predict future load data and future photovoltaic output data; Draw a difference curve based on future load data and future photovoltaic output data; Determine the timing of energy storage intervention based on the difference curve, initial energy storage data, and electricity price list; If the real-time photovoltaic output is greater than or equal to the real-time load power, the redundant photovoltaic power generation will be transmitted to the distributed photovoltaic energy storage battery; The timing of energy storage intervention is determined based on the difference curve, initial energy storage data, and electricity price list, including: Determine the time point for simulation intervention; Determining 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 time point when the energy storage is exhausted or the end point of the difference curve; Acquire a second curve enclosed area on the horizontal axis before the simulated intervention time point and a third curve enclosed area on the horizontal axis after the target time point; Calculate the power charges of the power grid according to the area enclosed by the second curve, the area enclosed by the third curve and the power price list; The simulated intervention time point corresponding to the minimum grid electricity price is used as the intervention opportunity for energy storage power generation.

2. The distributed photovoltaic energy storage system optimization configuration method according to claim 1, characterized in that: If the real-time PV output is less than the real-time load power, the future load data and future PV output data are predicted, including: Input the access point's historical power consumption data and predicted time information 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.

3. The distributed photovoltaic energy storage system optimization configuration method according to claim 1, characterized in that: Calculate the grid electricity charges based on the area enclosed by the second curve, the area enclosed by the third curve, and the electricity price list, including: Read the electricity prices at the time points corresponding to the areas enclosed by the second curve and the areas enclosed by the third curve from the electricity price table; Expanding the vertical axis values ​​of the local difference curve points corresponding to the area enclosed by the second curve and the area enclosed by the third curve and reconnecting them to obtain the target curve, the expansion process comprising: 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; The sum of the area enclosed by the target curve and the horizontal axis is taken as the grid electricity fee.

4. The distributed photovoltaic energy storage system optimization configuration method according to claim 1, characterized in that: Determine the timing of the simulated intervention, including: Based on a preset time interval, a pre-simulation point is determined within a time interval corresponding to the difference curve; Get the number of intersections between the difference curve and the horizontal axis before the pre-simulation point; If the number of intersection points is even, the intersection point of the first difference curve and the horizontal axis after the pre-simulation point is taken as the simulation intervention time point; If the number of intersection points is odd, obtain the area relationship between the area enclosed by the nth curve and the area enclosed by the n+1th curve after the corresponding pre-simulation point, where n is a positive odd number; The last curve-enclosed area in the curve-enclosed areas that are continuous and meet the standard area relationship is obtained. If the last curve-enclosed area is closed, its intersection with the horizontal axis that is farther from the coordinate origin is updated as the simulation intervention time point.

5. Distributed photovoltaic energy storage system optimization configuration system, characterized by: include: Monitoring module, used to monitor the real-time load power and photovoltaic output of the access point; The prediction module is 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; A difference curve determination module is used to draw a difference curve based on future load data and future photovoltaic output data; An intervention timing determination module is used to determine the energy storage power generation intervention timing based on the difference curve, initial energy storage data, and electricity price table; Energy storage module, used to transmit redundant photovoltaic power generation to distributed photovoltaic energy storage batteries if the real-time photovoltaic output is greater than or equal to the real-time load power; The intervention timing determination module determines the energy storage power generation intervention timing based on the difference curve, initial energy storage data, and electricity price table, including: Determine the time point for simulation intervention; Determining 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 time point when the energy storage is exhausted or the end point of the difference curve; Acquire a second curve enclosed area on the horizontal axis before the simulated intervention time point and a third curve enclosed area on the horizontal axis after the target time point; Calculate the power charges of the power grid according to the area enclosed by the second curve, the area enclosed by the third curve and the power price list; The simulated intervention time point corresponding to the minimum grid electricity price is used as the intervention opportunity for energy storage power generation.

6. The distributed photovoltaic energy storage system optimization configuration system according to claim 5, characterized in that: 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: Input the access point's historical power consumption data and predicted time information 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.

7. The distributed photovoltaic energy storage system optimization configuration system according to claim 5, characterized in that: The intervention timing determination module calculates the grid electricity fee based on the area enclosed by the second curve, the area enclosed by the third curve, and the electricity price table, including: Read the electricity prices at the time points corresponding to the areas enclosed by the second curve and the areas enclosed by the third curve from the electricity price table; Expanding the vertical axis values ​​of the local difference curve points corresponding to the area enclosed by the second curve and the area enclosed by the third curve and reconnecting them to obtain the target curve, the expansion process comprising: 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; The sum of the area enclosed by the target curve and the horizontal axis is taken as the grid electricity fee.

8. The distributed photovoltaic energy storage system optimization configuration system according to claim 5, characterized in that: The intervention timing determination module determines the simulated intervention time point, including: Based on a preset time interval, a pre-simulation point is determined within a time interval corresponding to the difference curve; Get the number of intersections between the difference curve and the horizontal axis before the pre-simulation point; If the number of intersection points is even, the intersection point of the first difference curve and the horizontal axis after the pre-simulation point is taken as the simulation intervention time point; If the number of intersection points is odd, obtain the area relationship between the area enclosed by the nth curve and the area enclosed by the n+1th curve after the corresponding pre-simulation point, where n is a positive odd number; The last curve-enclosed area in the curve-enclosed areas that are continuous and meet the standard area relationship is obtained. If the last curve-enclosed area is closed, its intersection with the horizontal axis that is farther from the coordinate origin is updated as the simulation intervention time point.

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