Energy optimization use method for state monitoring of mobile energy storage equipment

Abstract

The invention discloses an energy optimization use method for state monitoring of mobile energy storage equipment, which relates to the field of energy optimization of energy storage equipment, and comprises the following steps: constructing a mobile energy storage equipment access point net structure; performing core point screening based on a mobile energy storage equipment access point website structure; setting a monitoring station area based on the trend distribution of the network structure of the access points of the mobile energy storage equipment and a core access point; the STM32 optimizes and adjusts the power supply quantity of the core access point based on the load rate of the access point of the monitoring station area equipment; and based on the energy input model of the mobile energy storage and various functional requirements of the mobile energy storage access equipment, solving optimal configuration of the mobile energy storage and the fixed energy storage by utilizing linear programming, and carrying out secondary fine tuning on the power supply quantity of the core access point. The method has the advantages that the operation condition of the mobile energy storage equipment is monitored in real time, and proper optimization adjustment is made, so that the purposes of optimizing energy output and supply structures and saving energy are achieved.

Description

Technical Field

[0001] The present invention relates to the field of energy optimization of energy storage devices, and specifically to an energy optimization usage method for state monitoring of mobile energy storage devices. Background Art

[0002] With the large-scale increase of 5G base stations, higher requirements are put forward for the power backup capacity of energy storage devices in tower base stations. In order to ensure the stability and reliability of the power supply of base station energy storage devices, it is necessary to monitor the operating status of base station energy storage devices in a timely manner and optimize the energy supply.

[0003] However, in reality, there is a problem of energy loss in the process of energy transmission and supply of mobile energy storage devices. If the device status cannot be monitored in real time, the device aging caused by energy loss will also increase the maintenance cost. Summary of the Invention

[0004] To solve the above technical problems, an energy optimization usage method for state monitoring of mobile energy storage devices is provided. This technical solution solves the problems of energy loss in the process of supplying power to mobile energy storage devices, device aging caused by energy loss, and increased device maintenance costs caused by device aging as mentioned in the above background art.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] An energy optimization usage method for state monitoring of mobile energy storage devices, including:

[0007] Obtain the access point situation of the mobile energy storage device and construct a mesh structure of the access points of the mobile energy storage device;

[0008] Based on the website structure of the access points of the mobile energy storage device, screen the core points;

[0009] Based on the trend distribution and core access points of the mesh structure of the access points of the mobile energy storage device, set up monitoring areas;

[0010] Based on the load change information of the device access points in the monitoring area collected by STM32, obtain the load rate of the device access points in the monitoring area;

[0011] Based on the load rate of the device access points in the monitoring area, STM32 optimizes and adjusts the power supply of the core access points;

[0012] Obtain the real-time predicted weather information from the meteorological bureau and establish an energy input model for the mobile energy storage;

[0013] Based on the energy input model of the mobile energy storage and the various functional requirements of the mobile energy storage access devices, use linear programming to solve the optimal configuration of the mobile energy storage and the fixed energy storage, and perform secondary fine-tuning of the power supply of the core access points.

[0014] Preferably, the steps for constructing the mesh structure of the access points of the mobile energy storage device include the following:

[0015] Obtain the mesh structure of the access points of the mobile energy storage device under different scenarios to form a mesh structure template of the access points of the mobile energy storage device classified by application scenarios;

[0016] Judge the application scenario of the mobile energy storage device and match the corresponding mesh structure template of the access points of the mobile energy storage device;

[0017] Obtain the access point situation of the mobile energy storage device, count the number of access points, the current flow direction of the access points, and the power distribution situation of the access points, and record them as access point details;

[0018] Based on the mesh structure template of the access points of the mobile energy storage device and the access point details, supplement and improve the mesh structure template of the access points of the mobile energy storage device to obtain the mesh structure of the access points of the mobile energy storage device.

[0019] Preferably, the screening of the core points based on the mesh structure of the access points of the mobile energy storage device includes:

[0020] Based on the mesh structure of the access points of the mobile energy storage device, record the load and power distribution situation of the mobile energy storage device accessing other devices at different time periods, draw a chart of the load and power distribution situation of the mobile energy storage device accessing other devices changing with time, and fit the curve equation of the chart;

[0021] Judge the function type to which the fitting equation belongs, and classify the other devices with the fitting equations of the same function type into the same category;

[0022] Statistically analyze the power distribution situation of the access point devices within the same category. Based on the power distribution ratio of the access point devices within the same category, record the access point devices with a power distribution ratio higher than the preset value as quasi-core access points;

[0023] Statistically analyze the power consumed or provided by the quasi-core access point devices, calculate the ratio of the useful work to the useless work, and record it as the work ratio. The access point device with the largest work ratio among the quasi-core access point devices is the core access point;

[0024] The obtaining of the preset value includes the following steps:

[0025] Summarize the power distribution ratio of the access point devices within the same category, divide the ratio size into several interval segments, count the number of access point devices in each interval segment, select the interval segment with the largest number of access point devices in the interval segment as the target interval, and the upper bound of the target interval is the preset value. If the upper bound of this interval segment is 100%, then take the lower bound of the target interval segment as the preset value.

[0026] Preferably, the setting of the monitoring substation area based on the trend distribution and core access points of the mesh structure of the access points of the mobile energy storage device includes:

[0027] Monitor the change of electrical parameters of the access point device, and calculate the electrical parameter sensitivity of the access point device by using the calculation formula of the electrical parameter sensitivity of the access point device;

[0028] In each category, select the access points with electrical parameter sensitivity higher than that of the core access point in the same category, and record them as high-core access points. Among the high-core access points, select the access point with the electrical sensitive parameter closest to the core access point, and record it as the monitoring access point;

[0029] Summarize the monitoring access points in each category to obtain the monitoring substation area;

[0030] The calculation formula for the electrical parameter sensitivity of the access point device is:

[0031]

[0032] In the formula, S is the electrical parameter sensitivity of the access point device, V i is the access point voltage when the access point current is i, F i is the access point frequency deviation when the access point current is i, P i is the access point power factor when the access point current is i, and Δi is the magnitude of the current change.

[0033] Preferably, the STM32 optimizes and adjusts the power supply of the core access point based on the load rate of the access point devices in the monitoring substation area, including:

[0034] Record the device connection speed in the monitoring substation area in real time, draw an image of the device connection speed versus time, regularly fit the curve equation to obtain the real-time fitted curve equation, and calculate the integral of the fitted curve equation;

[0035] Mark the access point device whose integral of the real-time fitted curve equation exceeds the load identification value as the switching access point, and judge whether the integral of the real-time fitted curve equation of the target access point is greater than the integral of the real-time fitted curve equation of the switching access point device. If so, reduce half of the power supply of the target access point device and supply the reduced power to the switching access point device;

[0036] If not, reduce half of the power supply of the switching access point device and supply the reduced power to the target access point device;

[0037] Record the sum of the load rates of the devices used in the monitoring substation area within one cycle, mark the maximum load rate within one cycle as the maximum load rate, mark the time period where the maximum load rate is located as the maximum load time period, mark the minimum load rate within one cycle as the minimum load rate, and mark the time period where the minimum load rate is located as the minimum load time period;

[0038] Determine whether the difference between the equipment load rate during the maximum load period and the equipment load rate during the minimum load period in the monitored substation area is greater than the difference between the maximum load rate and the minimum load rate;

[0039] If so, reduce the power supply during the maximum load period and increase the power supply during the minimum load period;

[0040] The acquisition of the load identification value includes the following steps:

[0041] Determine the load reduction value of the access point device when the power of the access point device is adjusted to half of itself, and record it as the reduction value;

[0042] The load identification value is the sum of the difference between the maximum load rate and the minimum load rate and the reduction value.

[0043] Preferably, the acquisition of the real-time predicted weather information of the meteorological bureau and the establishment of the energy input model of the mobile energy storage include:

[0044] Based on the real-time prediction information sent by the meteorological bureau, obtain the solar radiation intensity at different time periods;

[0045] Calculate the output power of the photovoltaic panel, and the calculation formula for the output power of the photovoltaic panel is:

[0046] PW=sS t cos A t

[0047] In the formula, PW is the output power of the photovoltaic panel, S t is the solar radiation intensity at time period t, A t is the angle between the sun and the photovoltaic panel at time period t (0 < A << 90), and s is the solar radiation intensity coefficient;

[0048] Obtain the peak period and the off-peak period of the power grid;

[0049] Determine whether the target time period belongs to the peak period. If so, calculate the sum of the output power of the photovoltaic panel in the target time period and the peak preset value to obtain the energy output value;

[0050] If not, calculate the difference between the output power of the photovoltaic panel in the target time period and the off-peak preset value to obtain the energy output value;

[0051] Summarize the energy output values of different time periods to obtain the energy input model of the mobile energy storage;

[0052] The acquisition of the peak preset value and the off-peak preset value includes the following steps:

[0053] The peak preset value is the difference between the average price of the power grid electricity bill and the price during the peak period of the power grid, and the off-peak preset value is the difference between the average price of the power grid electricity bill and the price during the off-peak period of the power grid.

[0054] Preferably, based on the energy input model of mobile energy storage and the various functional requirements of mobile energy storage access devices, the optimization configuration of mobile energy storage and fixed energy storage solved by linear programming includes:

[0055] Taking the reduction of the energy loss of mobile energy storage as the objective function, setting constraint conditions, which are composed of constraint condition one, constraint condition two, and constraint condition three;

[0056] Constraint condition one is that the energy input value minus the energy consumption value of the access device plus the existing power of the mobile energy storage device is less than or equal to the capacity of the mobile energy storage device;

[0057] Constraint condition two is that the energy consumption value of the mobile energy storage access device is greater than the energy input value of the mobile energy storage device;

[0058] Constraint condition three is that the power loss directly output by the mobile energy storage device is lower than the loss power indirectly output;

[0059] Under the condition of satisfying the above constraint conditions, use linear programming to solve the minimum value of the sum of the power loss directly output by the mobile energy storage device and the indirect output loss;

[0060] Obtain the mobile energy storage and fixed energy storage values of the mobile energy storage device.

[0061] Preferably, after the power supply of the monitoring substation area is adjusted based on the core access point, the secondary fine-tuning of the power supply of the core access point includes:

[0062] Based on the energy input model, judge whether the energy output value is less than the power consumption of the device at the access point of the mobile energy storage device;

[0063] If so, judge whether the stock of the mobile energy storage device is greater than the power consumption of the device at the access point of the mobile energy storage device;

[0064] If not, reduce the power consumption of the device at the access point of the mobile energy storage device until the power consumption of the device at the access point of the mobile energy storage device is less than the sum of the stock of the mobile energy storage device and the energy output value, and the direct power consumption of the device at the access point of the mobile energy storage device is the fixed energy storage value of the mobile energy storage device, and the indirect power consumption of the device at the access point of the mobile energy storage device is the mobile energy storage value of the mobile energy storage device.

[0065] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0066] The present invention provides an energy optimization method for state monitoring of mobile energy storage devices. This method monitors the state of mobile energy storage devices in real time and adjusts the power supply in real time based on the state of the energy storage devices, so as to achieve the purpose of optimizing the energy structure. Through this method, the energy loss during the energy transmission process of mobile energy storage devices can be minimized, resources can be saved, and the aging of devices caused by energy loss can be reduced, thereby reducing the maintenance cost. In addition, the energy loss of mobile energy storage devices can improve the energy transmission efficiency of the devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 It is a schematic flow chart of the energy optimization method for state monitoring of the mobile energy storage device of the present invention;

[0068] Figure 2 It is a schematic flow chart of constructing a mesh structure of access points of mobile energy storage devices of the present invention;

[0069] Figure 3 It is a schematic flow chart of screening core points based on the website structure of access points of mobile energy storage devices of the present invention;

[0070] Figure 4 It is a schematic flow chart of setting monitoring areas based on the distribution trend and core access points of the mesh structure of access points of mobile energy storage devices of the present invention;

[0071] Figure 5 It is a schematic flow chart of STM32 optimizing and adjusting the power supply of core access points based on the load rate of device access points in the monitoring area of the present invention;

[0072] Figure 6 It is a schematic flow chart of obtaining real-time predicted weather information from the meteorological bureau and establishing an energy input model for mobile energy storage of the present invention;

[0073] Figure 7 It is a schematic flow chart of using linear programming to solve the optimal configuration of mobile energy storage and fixed energy storage based on the energy input model of mobile energy storage and the various functional requirements of mobile energy storage access devices of the present invention;

[0074] Figure 8 It is a schematic flow chart of secondary fine-tuning of the power supply of core access points based on the load change of device access points in the monitoring area after the power supply of core access points is adjusted of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0075] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art can think of other obvious variations.

[0076] Refer to Figure 1As shown in the figure, an energy optimization usage method for monitoring the state of a mobile energy storage device includes:

[0077] Obtain the access point situation of the mobile energy storage device and construct a mesh structure of the access points of the mobile energy storage device;

[0078] Based on the website structure of the access points of the mobile energy storage device, conduct core point screening;

[0079] Based on the trend distribution and core access points of the mesh structure of the access points of the mobile energy storage device, set up monitoring regions;

[0080] Based on the load change information of the device access points in the monitoring regions collected by STM32, obtain the load rate of the device access points in the monitoring regions;

[0081] Based on the load rate of the device access points in the monitoring regions, STM32 optimally adjusts the power supply of the core access points;

[0082] Obtain the real-time predicted weather information from the meteorological bureau and establish an energy input model for the mobile energy storage;

[0083] Based on the energy input model of the mobile energy storage and the various functional requirements of the mobile energy storage access devices, use linear programming to solve the optimal configuration of the mobile energy storage and the fixed energy storage, and conduct secondary fine-tuning of the power supply of the core access points.

[0084] The present invention proposes an energy optimization usage method for monitoring the state of a mobile energy storage device. This method monitors the state of the mobile energy storage device in real time, and based on the state of the energy storage device, adjusts the power supply in real time to achieve the purpose of optimizing the energy structure. This method adopts the methods of core point screening and setting up monitoring regions. The load rate of the entire energy storage device is reflected through the load rate of the devices in the monitoring regions, and the power supply of the entire mobile energy storage device is controlled by adjusting the power supply of the core access point devices, thereby realizing energy optimization. Through this method, the energy loss during the energy transmission process of the mobile energy storage device can be minimized, saving resources while reducing the device aging caused by energy loss, thereby reducing the maintenance cost. In addition, the energy loss of the mobile energy storage device can improve the energy transmission efficiency of the device.

[0085] Refer to Figure 2 As shown in the figure, constructing a mesh structure of the access points of the mobile energy storage device includes the following steps:

[0086] Obtain the mesh structure of the access points of the mobile energy storage device under different scenarios to form a mesh structure template of the access points of the mobile energy storage device classified by application scenarios;

[0087] Judge the application scenario of the mobile energy storage device and match the corresponding mesh structure template of the access points of the mobile energy storage device;

[0088] Obtain the access point situation of the mobile energy storage device, count the number of access points, the current flow direction of the access points, and the power distribution situation of the access points, which is recorded as the access point details;

[0089] Based on the mobile energy storage device access point network structure template and the access point details, supplement and improve the mobile energy storage device access point network structure template to obtain the mobile energy storage device access point network structure.

[0090] It can be explained that classifying the mobile energy storage device according to the application scenario and then applying the corresponding template helps to quickly build the mobile energy storage device access point network structure on the pre-constructed template. The strong similarity between some application scenarios of the mobile energy storage device provides the feasibility for summarizing and inducing the template. In addition, supplementing the details of the template is beneficial to constructing a more detailed and complete mobile energy storage device access point network structure.

[0091] Refer to Figure 3 As shown, based on the mobile energy storage device access point website structure, the core point screening includes:

[0092] Based on the mobile energy storage device access point network structure, record the load and power distribution situation of the mobile energy storage device accessing other devices at different time periods, draw a chart of the load and power distribution situation of the mobile energy storage device accessing other devices changing with time, and fit the curve equation of the chart;

[0093] Judge the function type to which the fitting equation belongs, and divide the other devices with the fitting equations of the same function type into the same category;

[0094] Statistically analyze the power distribution situation of the access point devices within the same category. Based on the power distribution proportion of the access point devices within the same category, record the access point devices with a power distribution proportion higher than the preset value as quasi-core access points;

[0095] Statistically analyze the power consumed or provided by the quasi-core access point devices, calculate the ratio of the useful work to the useless work, which is recorded as the work ratio. The access point device with the largest work ratio among the quasi-core access point devices is the core access point;

[0096] The acquisition of the preset value includes the following steps:

[0097] Summarize the power distribution proportion of the access point devices within the same category, divide the proportion size into several interval segments, count the number of access point devices in each interval segment, select the interval segment with the largest number of access point devices in the interval segment as the target interval, and the upper bound of the target interval is the preset value. If the upper bound of this interval segment is 100%, then take the lower bound of the target interval segment as the preset value.

[0098] It can be explained that classifying devices based on the load and power distribution of mobile energy storage devices changing over time is beneficial for unified management of the same type of devices. Selecting a core access point from each device of the same category enables each type of device to have a representative, facilitating unified regulation. First, select devices with a power distribution ratio higher than that of most devices based on power distribution as quasi-core access points, meeting the requirement that the power distribution ratio of the core access point is large. Adjusting the power of the core access point can play a role in regulating devices of the same category. Then, select the core access point based on the ratio of useful work to useless work. The large useful work and useless work of the core access point can play a role in maximizing the useful work power after adjusting the power of the core access point device.

[0099] Referring to Figure 4 as shown, based on the trend distribution of the access point mesh structure of the mobile energy storage device and the core access point, the monitoring sub-region is set to include:

[0100] Monitoring the change of electrical parameters of the access point device, and using the calculation formula for the electrical parameter sensitivity of the access point device to calculate the electrical parameter sensitivity of the access point device;

[0101] Select from each device of the same category the access points with electrical parameter sensitivity higher than that of the core access point, denoted as high-core access points, and select from the high-core access points the access points with the electrical sensitive parameters closest to the core access point, denoted as monitoring access points;

[0102] Summarize the monitoring access points in each device of the same category to obtain the monitoring sub-region;

[0103] The calculation formula for the electrical parameter sensitivity of the access point device is:

[0104]

[0105] In the formula, S is the electrical parameter sensitivity of the access point device, V i is the access point voltage when the access point current is i, F i is the access point frequency deviation when the access point current is i, P i is the access point power factor when the access point current is i, and Δi is the magnitude of the current change.

[0106] It can be explained that based on the rate of change of the sensitivity of the electrical parameters of the access point device with respect to the rate of change of the access point current, the sensitivity of the electrical parameters of the access point device can be obtained. The electrical parameters of the access point device include the voltage, frequency deviation, and power factor of the access point device. These data can all reflect the performance of the access point device circuit. Select a device with an electrical parameter sensitivity close to that of the core access point device as the monitoring substation area. The change in the load rate of the monitoring substation area device can more accurately reflect the change in the load rate of the core access point device. Selecting a device with an electrical parameter sensitivity higher than that of the core access point as the monitoring substation area can regulate the power supply of the core access point before a relatively obvious load rate fluctuation occurs in the core access point load rate.

[0107] Referring to Figure 5 As shown, based on the access point load rate of the monitoring substation area device, the STM32 optimizes and adjusts the power supply of the core access point, including:

[0108] Record the device connection speed in the monitoring substation area in real time, draw an image of the device connection speed versus time, regularly fit the curve equation to obtain the real-time fitted curve equation, and calculate the integral of the fitted curve equation;

[0109] Mark the access point device whose integral of the real-time fitted curve equation with the target access point exceeds the load identification value as the exchange access point. Determine whether the integral of the real-time fitted curve equation of the target access point is greater than the integral of the real-time fitted curve equation of the exchange access point device. If so, reduce half of the power supply of the target access point device and supply the reduced power to the exchange access point device;

[0110] If not, reduce half of the power supply of the exchange access point device and supply the reduced power to the target access point device;

[0111] Record the sum of the load rates of all devices used in the monitoring substation area within one cycle. Denote the maximum load rate within one cycle as the maximum load rate, and denote the time period corresponding to the maximum load rate as the maximum load time period. Denote the minimum load rate within one cycle as the minimum load rate, and denote the time period corresponding to the minimum load rate as the minimum load time period;

[0112] Determine whether the difference between the device load rate in the maximum load time period and the device load rate in the minimum load time period of the monitoring substation area is greater than the difference between the maximum load rate and the minimum load rate;

[0113] If so, reduce the power supply in the maximum load time period and increase the power supply in the minimum load time period;

[0114] The acquisition of the load identification value includes the following steps:

[0115] Judge the load reduction value of the access point device when the power supply of the access point device is adjusted to half of its own, and denote it as the reduction value;

[0116] The load identification value is the sum of the difference between the maximum load rate and the minimum load rate and the reduction value.

[0117] It can be explained that a fast connection speed can reflect a low load rate of the device. By fitting the equation of the device connection speed with respect to time, calculating the integral, and determining whether the integral of the device connection speed over a period of time exceeds the load identification value compared to the integral of the connection speed of any device. If so, corresponding regulation is carried out to avoid a too large difference in load rate between access point devices, resulting in a reduction in device efficiency. The load identification value is related to the difference between the maximum load rate and the minimum load rate. The difference between the maximum load rate and the minimum load rate can reflect the maximum value of the load rate difference. The reduction value is the reduction value of the load when the power is adjusted to half of itself, which can quantify the change in the load rate after power adjustment.

[0118] Refer to Figure 6 As shown, obtaining the real-time predicted weather information from the meteorological bureau, the energy input model of the mobile energy storage includes:

[0119] Based on the real-time predicted information sent by the meteorological bureau, obtaining the solar radiation intensity at different time periods;

[0120] Calculating the output power of the photovoltaic panel, and the calculation formula for the output power of the photovoltaic panel is:

[0121] PW = sS t cos A t

[0122] In the formula, PW is the output power of the photovoltaic panel, S t is the solar radiation intensity at time period t, A t is the angle between the sun and the photovoltaic panel at time period t (0 < A << 90), and s is the solar radiation intensity coefficient;

[0123] Obtaining the peak time period and the low peak time period of the power grid;

[0124] Judging whether the target time period belongs to the peak time period. If so, calculating the sum of the output power of the photovoltaic panel in the target time period and the peak preset value to obtain the energy output value;

[0125] If not, calculating the difference between the output power of the photovoltaic panel in the target time period and the low peak preset value to obtain the energy output value;

[0126] Summarizing the energy output values of different time periods to obtain the energy input model of the mobile energy storage;

[0127] The obtaining of the peak preset value and the low peak preset value includes the following steps:

[0128] The peak preset value is taken as the difference between the average price of the grid electricity bill and the price during the peak period of the grid, and the off-peak preset value is taken as the difference between the average price of the grid electricity bill and the price during the off-peak period of the grid.

[0129] It can be explained that the output power of the photovoltaic panel is calculated using the solar radiation intensity and the angle between the sun and the photovoltaic panel as one of the reference values for energy output. The supply of the power grid does not change with time, so the electricity bill of the power grid reflects the energy output. The higher the electricity bill of the power grid, the lower the corresponding energy output. When adjusting the power consumption for energy output later, restricting the less power consumption means completing the power distribution within a certain electricity bill budget, which can play a role in saving expenses.

[0130] Refer to Figure 7 As shown, based on the energy input model of the mobile energy storage and the various functional requirements of the mobile energy storage access device, using linear programming to solve the optimal configuration of the mobile energy storage and the fixed energy storage includes:

[0131] Taking the reduction of the energy loss of the mobile energy storage as the objective function, setting constraint conditions, which are composed of constraint condition one, constraint condition two, and constraint condition three;

[0132] Constraint condition one is that the energy input value minus the energy consumption value of the access device plus the existing power of the mobile energy storage device is less than or equal to the capacity of the mobile energy storage device;

[0133] Constraint condition two is that the energy consumption value of the mobile energy storage access device is greater than the energy input value of the mobile energy storage device;

[0134] Constraint condition three is that the power loss directly output by the mobile energy storage device is lower than the loss power of the indirect output;

[0135] Under the condition of satisfying the above constraint conditions, using linear programming to solve the minimum value of the sum of the power loss directly output by the mobile energy storage device and the loss of the indirect output;

[0136] Obtain the values of the mobile energy storage and the fixed energy storage of the mobile energy storage device.

[0137] It can be explained that by setting three constraint conditions and using linear programming to find the optimal solution, the first constraint condition is to prevent the lack of available power storage space after planning, resulting in the problem of no available storage space. The second constraint condition and the third constraint condition are both to avoid invalid operations. If the energy consumption value of the mobile energy storage device is less than the energy input value, the energy input value can be directly used for device supply. If the direct output power loss is higher than the loss power of the indirect output, all indirect outputs can be adopted, that is, the energy input value is used for device supply.

[0138] Refer to Figure 8As shown in the figure, after adjusting the power supply of the core access point and monitoring the load change of the device access point in the monitoring area, the secondary fine-tuning of the power supply of the core access point includes:

[0139] Based on the energy input model, determine whether the energy output value is less than the power consumption of the device at the mobile energy storage device access point;

[0140] If so, determine whether the stock of the mobile energy storage device is greater than the power consumption of the device at the mobile energy storage device access point;

[0141] If not, lower the power consumption of the device at the mobile energy storage device access point until the power consumption of the device at the mobile energy storage device access point is less than the sum of the stock of the mobile energy storage device and the energy output value, and the direct power consumption of the device at the mobile energy storage device access point is the fixed energy storage value of the mobile energy storage device, and the indirect power consumption of the device at the mobile energy storage device access point is the mobile energy storage value of the mobile energy storage device.

[0142] It can be explained that the purpose of the secondary fine-tuning is to reasonably allocate the power supply source of the mobile energy storage device according to the calculated fixed energy storage and mobile energy storage values of the mobile energy storage device on the premise of the first adjustment of the load rate between the average devices, so as to achieve the lowest loss in the energy transmission process. Judging that the energy output value is less than the power consumption of the device at the mobile energy storage device access point and judging that the stock of the mobile energy storage device is less than the power consumption of the device at the mobile energy storage device access point are both to ensure Figure 7 the feasibility of the algorithm in the above. If the above conditions are not met, the power supply device in the energy output value can be directly used.

[0143] Furthermore, this solution also proposes a storage medium, on which a computer-readable program is stored. When the computer-readable program is called, it executes the above energy optimization use method for monitoring the state of the mobile energy storage device.

[0144] It can be understood that the storage medium can be a magnetic medium, such as a floppy disk, a hard disk, a magnetic tape; an optical medium, such as a DVD; or a semiconductor medium, such as a solid-state disk (SSD).

[0145] In summary, the advantages of the present invention are as follows: The present invention proposes an energy optimization use method for monitoring the state of a mobile energy storage device. This method monitors the state of the mobile energy storage device in real time and adjusts the power supply in real time based on the state of the energy storage device to achieve the purpose of optimizing the energy structure. Through this method, the energy loss in the energy transmission process of the mobile energy storage device can be minimized, the equipment aging caused by energy loss can be reduced while saving resources, thereby reducing the maintenance cost. In addition, the energy loss of the mobile energy storage device can improve the energy transmission efficiency of the device.

[0146] The foregoing has shown and described the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and what is described in the above embodiments and the specification is only the principle of the present invention. Without departing from the spirit and scope of the present invention, various changes and improvements will occur to the present invention, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. An energy optimization usage method for state monitoring of a mobile energy storage device, characterized in that Including: Obtain the access point situation of the mobile energy storage device and construct a mesh structure of the access points of the mobile energy storage device; Based on the website structure of the access points of the mobile energy storage device, conduct core point screening; Based on the trend distribution of the mesh structure of the access points of the mobile energy storage device and the core access points, set up monitoring substations; Based on the load change information of the device access points in the monitoring substation collected by STM32, obtain the load rate of the device access points in the monitoring substation; Based on the load rate of the device access points in the monitoring substation, STM32 optimally adjusts the power supply of the core access points; Obtain the real-time predicted weather information from the meteorological bureau and establish an energy input model for the mobile energy storage; Based on the energy input model of the mobile energy storage and the various functional requirements of the mobile energy storage access devices, use linear programming to solve the optimal configuration of the mobile energy storage and the fixed energy storage, and conduct secondary fine-tuning of the power supply of the core access points.

2. The energy optimization usage method for state monitoring of a mobile energy storage device according to claim 1, wherein The construction of the mesh structure of the access points of the mobile energy storage device includes the following steps: Obtain the mesh structure of the access points of the mobile energy storage device under different scenarios, and form a mesh structure template of the access points of the mobile energy storage device classified by application scenarios; Judge the application scenario of the mobile energy storage device and match the corresponding mesh structure template of the access points of the mobile energy storage device; Obtain the access point situation of the mobile energy storage device, count the number of access points, the current flow direction of the access points, and the power distribution situation of the access points, and record them as access point details; Based on the mesh structure template of the access points of the mobile energy storage device and the access point details, supplement and improve the mesh structure template of the access points of the mobile energy storage device to obtain the mesh structure of the access points of the mobile energy storage device.

3. An energy optimization usage method for state monitoring of a mobile energy storage device according to claim 2, characterized in that The core point screening based on the website structure of the access points of the mobile energy storage device includes: Based on the mesh structure of the access points of the mobile energy storage device, record the load and power distribution situation of the mobile energy storage device accessing other devices at different time periods, draw a chart of the load and power distribution situation of the mobile energy storage device accessing other devices changing with time, and fit the curve equation of the chart; Judge the function type to which the fitting equation belongs, and classify other devices with the same type of fitting equation into the same category; Statistically analyze the power distribution situation of the access point devices within the same category, and based on the proportion of the power distribution of the access point devices within the same category, record the access point devices with a power distribution proportion higher than the preset value as quasi-core access points; Statistically analyze the power consumed or provided by the quasi-core access point devices, calculate the ratio of the useful work to the useless work, and record it as the work ratio. The access point device with the largest work ratio among the quasi-core access point devices is the core access point; The acquisition of the preset value includes the following steps: Summarize the proportion of the power distribution of the access point devices within the same category, divide the proportion size into several interval segments, count the number of access point devices in each interval segment, select the interval segment with the largest number of access point devices within the interval segment as the target interval, and the upper bound of the target interval is the preset value. If the upper bound of this interval segment is 100%, then take the lower bound of the target interval segment as the preset value.

4. The energy optimization usage method for state monitoring of a mobile energy storage device according to claim 3, characterized in that, The setting of the monitoring substation based on the trend distribution of the mesh structure of the access points of the mobile energy storage device and the core access points includes: Monitor the change situation of the electrical parameters of the access point devices, and use the calculation formula of the electrical parameter sensitivity of the access point devices to calculate the electrical parameter sensitivity of the access point devices; Screen out the access points in each category whose electrical parameter sensitivity is higher than that of the core access point, and mark them as high-core access points. Then, screen out the access point whose electrical sensitive parameters are closest to the core access point from the high-core access points, and mark it as the monitoring access point; Summarize the monitoring access points in each category to obtain the monitoring substation area; The calculation formula for the electrical parameter sensitivity degree of the access point device is as follows: where S is the sensitivity of the electrical parameters of the access point device, V i is the access point voltage when the access point current is i, F i is the access point frequency deviation when the access point current is i, P i is the access point power factor when the access point current is i, and Δi is the magnitude of the current change.

5. The energy optimization usage method for state monitoring of a mobile energy storage device according to claim 4, characterized in that, Based on the load rate of the device access point in the monitoring substation area, the STM32 optimizes and adjusts the power supply of the core access point, including: Record the device connection speed in the monitoring substation area in real time, draw an image of the device connection speed versus time, regularly fit the curve equation to obtain the real-time fitted curve equation, and calculate the integral of the fitted curve equation; Mark the access point device whose integral of the real-time fitted curve equation with the target access point exceeds the load identification value as the switching access point. Determine whether the integral of the real-time fitted curve equation of the target access point is greater than the integral of the real-time fitted curve equation of the switching access point device. If so, reduce half of the power supply of the target access point device and supply the reduced power to the switching access point device; If not, reduce half of the power supply of the switching access point device and supply the reduced power to the target access point device; Record the sum of the load rates of the devices used in the monitoring substation area within a period. Mark the maximum load rate within a period as the maximum load rate, and mark the time period where the maximum load rate is located as the maximum load time period. Mark the minimum load rate within a period as the minimum load rate, and mark the time period where the minimum load rate is located as the minimum load time period; Determine whether the difference between the device load rate in the maximum load time period and the device load rate in the minimum load time period of the monitoring substation area is greater than the difference between the maximum load rate and the minimum load rate; If so, reduce the power supply in the maximum load time period and increase the power supply in the minimum load time period; The acquisition of the load identification value includes the following steps: Judge the load reduction value of the access point device when the power supply of the access point device is adjusted to half of its own, and mark it as the reduction value; The load identification value is the sum of the difference between the maximum load rate and the minimum load rate and the reduction value.

6. The energy optimization usage method for state monitoring of a mobile energy storage device according to claim 5, characterized in that, The acquisition of the real-time predicted weather information from the meteorological bureau and the establishment of the energy input model of the mobile energy storage include: Based on the real-time prediction information sent by the meteorological bureau, obtain the solar radiation intensity at different time periods; Calculate the output power of the photovoltaic panel. The calculation formula for the output power of the photovoltaic panel is as follows: PW = sS t cosA t Wherein, PW is the output power of the photovoltaic panel, S t is the solar radiation intensity at time period t, A t is the angle between the sun and the photovoltaic panel at time period t (0 < A < 90), and s is the solar radiation intensity coefficient; Obtain the peak time period and low peak time period of the power grid; Determine whether the target time period belongs to the peak time period. If so, calculate the sum of the output power of the photovoltaic panel in the target time period and the peak preset value to obtain the energy output value; If not, calculate the difference between the output power of the photovoltaic panel in the target time period and the low peak preset value to obtain the energy output value; Summarize the energy output values at different time periods to obtain the energy input model of the mobile energy storage; The acquisition of the peak preset value and the low peak preset value includes the following steps: The peak preset value is the difference between the average price of the power grid electricity bill and the price during the peak period of the power grid. The low peak preset value is the difference between the average price of the power grid electricity bill and the price during the low peak period of the power grid.

7. An energy optimization usage method for state monitoring of a mobile energy storage device according to claim 6, characterized in that, Based on the energy input model of mobile energy storage and the various functional requirements of mobile energy storage access devices, the optimization configuration of mobile energy storage and fixed energy storage solved by linear programming includes: Taking the reduction of the energy loss of mobile energy storage as the objective function, setting constraint conditions, which are composed of constraint condition one, constraint condition two, and constraint condition three; Constraint condition one is that the energy input value minus the energy consumption value of the access device plus the existing power of the mobile energy storage device is less than or equal to the capacity of the mobile energy storage device; Constraint condition two is that the energy consumption value of the mobile energy storage access device is greater than the energy input value of the mobile energy storage device; Constraint condition three is that the power loss directly output by the mobile energy storage device is lower than the loss power indirectly output; Under the condition of satisfying the above constraint conditions, use linear programming to solve the minimum value of the sum of the power loss directly output and the loss indirectly output by the mobile energy storage device; Obtain the mobile energy storage and fixed energy storage values of the mobile energy storage device.

8. The energy optimization usage method for state monitoring of a mobile energy storage device according to claim 7, characterized in that, After the power supply of the monitoring substation area changes based on the power supply adjustment of the core access point, the secondary fine-tuning of the power supply of the core access point includes: Based on the energy input model, judge whether the energy output value is less than the power consumption of the device at the mobile energy storage device access point; If so, judge whether the stock of the mobile energy storage device is greater than the power consumption of the device at the mobile energy storage device access point; If not, reduce the power consumption of the device at the mobile energy storage device access point until the power consumption of the device at the mobile energy storage device access point is less than the sum of the stock of the mobile energy storage device and the energy output value, and the direct power consumption of the device at the mobile energy storage device access point is the fixed energy storage value of the mobile energy storage device, and the indirect power consumption of the device at the mobile energy storage device access point is the mobile energy storage value of the mobile energy storage device.

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