A micro-grid cost calculation method and device based on a renewable energy storage system

Abstract

The application discloses a micro-grid cost calculation method and device based on a renewable energy storage system, which determines whether the computing power value is mutated, combines a voltage signal segment and an electricity consumption period, obtains a constraint function and a corresponding target voltage signal according to a wave crest set when it is determined that the electricity consumption peak period is reached, and obtains the power cost of the energy storage system in the electricity consumption period according to the target voltage signal, so as to effectively improve the accuracy of the energy storage system in calculating the power cost, and further guarantee the power cost calculation efficiency of the micro-grid as a whole.

Description

Technical Field

[0001] This application belongs to the technical field of power generation and energy storage systems, and specifically relates to a method and device for calculating the cost of microgrids based on renewable energy storage systems. Background Technology

[0002] As renewable energy systems, wind power and solar photovoltaic (PV) power generation systems can directly connect their generated electricity to the microgrid bus via converters. Energy storage systems can also be connected to the microgrid bus via rectifiers and inverters. Specifically, the wind-solar hybrid power generation system, composed of wind power and solar PV systems, can utilize grid dispatch and power control systems to store energy from or supply power to the microgrid, as well as provide reactive power compensation. Here, the power control system can receive grid dispatch instructions and reactive power compensation information based on the predicted power output of the wind turbines and solar PV systems, and control the operating modes of the rectifiers and inverters in conjunction with the energy status of the energy storage system.

[0003] Understandably, energy storage systems play a crucial role in the stable operation of wind-solar hybrid power generation systems. Since both wind and solar energy are intermittent and highly susceptible to weather conditions, energy storage systems are needed to store excess energy when wind and sunshine are abundant, and to continuously supply power to the system when wind and solar energy are insufficient to meet load demands.

[0004] In actual operation, microgrid power systems use energy storage systems for peak shaving and valley filling. However, when calculating electricity costs, the impact of energy storage systems on cost calculation is not taken into account. Furthermore, the power supply side of energy storage systems is susceptible to voltage fluctuations, resulting in low accuracy of electricity cost calculation and thus affecting the overall efficiency of electricity cost calculation for microgrids. Summary of the Invention

[0005] To address the aforementioned technical shortcomings in calculating electricity costs—namely, the failure to consider the impact of energy storage systems on cost calculations, and the low accuracy of electricity cost calculations due to the susceptibility of energy storage systems to voltage fluctuations at the power source—this application proposes a microgrid cost calculation method and apparatus based on renewable energy storage systems. The technical solution is as follows: In a first aspect, embodiments of this application provide a method for calculating the cost of a microgrid based on a renewable energy storage system, including: Multiple sets of computing power values ​​from the power supply end are acquired at preset time intervals. When a sudden change in computing power value is detected in any set of computing power values, the voltage signal segment and power consumption period corresponding to the set of computing power values ​​are acquired. When the electricity consumption period is detected to be during the peak electricity consumption cycle, the set of peaks in the voltage signal segment is determined; The minimum and maximum values ​​of the peaks in the peak set are removed, and the constraints of all peaks in the peak set after the removal process are solved to obtain the first constraint function. The first target voltage signal is calculated based on the first constraint function and the electricity consumption period, and the electricity cost of the energy storage system during the electricity consumption period is obtained based on the first target voltage signal.

[0006] In one alternative embodiment of the first aspect, after acquiring multiple sets of computing power values ​​from the power supply at preset time intervals, the method further includes: Calculate the difference between the nth computing power value in each set of computing power values ​​and the mean of the previous n-1 computing power values; where n is a positive integer greater than 1. When the ratio between the detected difference and the mean of the first n-1 computing power values ​​exceeds a preset threshold, it is determined that a sudden change has occurred in the computing power value set. When the ratio between the detected difference and the mean of the first n-1 computing power values ​​does not exceed a preset threshold, it is determined that there has been no sudden change in the computing power values ​​in the computing power value set; or When the product of the ratio between the detected difference and the mean of the first n-1 computing power values ​​and a preset constant exceeds a preset threshold, it is determined that a sudden change has occurred in the computing power value set. When the product of the ratio between the detected difference and the mean of the first n-1 computing power values ​​and a preset constant does not exceed a preset threshold, it is determined that the computing power values ​​in the computing power value set have not undergone a sudden change.

[0007] In another alternative to the first aspect, constraint solving is performed on all peaks in the peak set after the removal process to obtain a first constraint function, including: The electricity consumption time corresponding to each peak in the peak set after the removal process is determined, and a peak coordinate set is established based on each peak and the electricity consumption time corresponding to each peak; wherein, the peak coordinate set includes the coordinates corresponding to each peak in the peak set after the removal process. The distances between the set of peak coordinates and multiple preset linear functions are calculated respectively, and the preset linear function corresponding to the shortest distance is used as the first constraint function.

[0008] In another alternative to the first aspect, the first target voltage signal is calculated based on the first constraint function and the electricity consumption period, including: Multiple reference times with consistent time intervals are selected during the electricity consumption period, and the reference voltage signal corresponding to each reference time is calculated based on the first constraint function. The first target voltage signal is obtained by weighted summation based on all reference voltage signals and a preset first weight value.

[0009] In another alternative to the first aspect, after obtaining the voltage signal segment and electricity consumption period corresponding to the computing power value set, the method further includes: When the electricity consumption period is detected to be in a low electricity consumption cycle, the set of troughs in the voltage signal segment is determined; The minimum and maximum values ​​of the valleys in the valley set are removed, and the constraints of all the valleys in the valley set after the removal process are solved to obtain the second constraint function. The second target voltage signal is calculated based on the second constraint function and the electricity consumption period, and the electricity cost of the energy storage system during the electricity consumption period is obtained based on the second target voltage signal.

[0010] In another alternative to the first aspect, after obtaining the voltage signal segment and electricity consumption period corresponding to the computing power value set, the method further includes: When the power consumption period is detected to be within the power consumption period cycle, the set of peaks and troughs in the voltage signal segment are determined. Calculate the mean value of the peaks corresponding to the set of peaks and the mean value of the troughs corresponding to the set of troughs respectively; The third target voltage signal is obtained by weighted summation of the peak average, trough average, and preset second weight value, and the electricity cost of the energy storage system during the electricity consumption period is obtained based on the third target voltage signal.

[0011] In another alternative to the first aspect, after acquiring multiple sets of computing power values ​​from the power supply at preset time intervals, the method further includes: When it is detected that no sudden change has occurred in the computing power value in any set of computing power values, the average computing power value corresponding to each set of computing power values ​​is calculated. Based on the average computing power and the preset hardware computing power, the electricity cost of the energy storage system in each preset time interval is obtained.

[0012] Secondly, embodiments of this application provide a microgrid cost calculation device based on a renewable energy storage system, comprising: The data acquisition module is used to acquire multiple sets of computing power values ​​from the power supply end at preset time intervals, and when a sudden change in the computing power value in any set of computing power values ​​is detected, acquire the voltage signal segment and power consumption period corresponding to the set of computing power values. The data processing module is used to determine the set of peaks in the voltage signal segment when the electricity consumption period is detected to be in the peak electricity consumption cycle. The data analysis module is used to remove the minimum and maximum values ​​of the peaks in the peak set, and to perform constraint solving on all the peaks in the peak set after the removal process to obtain the first constraint function. The cost calculation module is used to calculate the first target voltage signal based on the first constraint function and the electricity consumption period, and to obtain the power cost of the energy storage system during the electricity consumption period based on the first target voltage signal.

[0013] Thirdly, this application also provides a microgrid cost calculation device based on a renewable energy storage system, including a processor and a memory; The processor is connected to the memory; Memory, used to store executable program code; The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to implement the microgrid cost calculation method based on renewable energy storage system provided by the first aspect or any implementation of the first aspect of the embodiments of this application.

[0014] Fourthly, embodiments of this application provide a computer storage medium storing a computer program, which includes program instructions. When executed by a processor, the program instructions can implement the microgrid cost calculation method based on a renewable energy storage system provided in the first aspect or any implementation of the first aspect of this application.

[0015] In this embodiment, when calculating the power cost of the energy storage system of a microgrid, multiple sets of computing power values ​​from the power source are obtained at preset time intervals. When a sudden change in the computing power value in any set of computing power values ​​is detected, the voltage signal segment and the electricity consumption period corresponding to the computing power value set are obtained. When the electricity consumption period is detected to be in a peak electricity consumption cycle, the set of peaks in the voltage signal segment is determined. The minimum and maximum values ​​of the peaks in the peak set are removed, and all peaks in the peak set after removal are subjected to constraint solving to obtain a first constraint function. Based on the first constraint function and the electricity consumption period, a first target voltage signal is calculated, and the power cost of the energy storage system during the electricity consumption period is obtained based on the first target voltage signal. By judging whether the computing power value changes abruptly, and combining the voltage signal segment and the electricity consumption cycle at the time of electricity consumption, when it is determined that it is in the peak electricity consumption cycle, the constraint function and the corresponding target voltage signal are obtained based on the peak set. The electricity cost of the energy storage system during the electricity consumption period can be obtained based on the target voltage signal, so as to effectively improve the accuracy of the energy storage system in calculating the electricity cost, thereby ensuring the overall electricity cost calculation efficiency of the microgrid. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A flowchart illustrating the overall process of a microgrid cost calculation method based on a renewable energy storage system, provided in this application embodiment; Figure 2 A schematic diagram of the structure of a microgrid cost calculation device based on a renewable energy storage system provided in this application embodiment; Figure 3 This is a schematic diagram of the structure of another microgrid cost calculation device based on a renewable energy storage system provided in this application embodiment. Detailed Implementation

[0018] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0019] In the following description, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance. The following description provides multiple embodiments of this application, which can be substituted or combined with each other. Therefore, this application can also be considered to include all possible combinations of the same and / or different embodiments described. Thus, if one embodiment includes features A, B, and C, and another embodiment includes features B and D, then this application should also be considered to include embodiments containing one or more other possible combinations of A, B, C, and D, even if such embodiments are not explicitly described in the following text.

[0020] The following description provides examples and does not limit the scope, applicability, or examples set forth in the claims. Changes may be made to the function and arrangement of the described elements without departing from the scope of this application. Various processes or components may be appropriately omitted, substituted, or added to the examples. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Furthermore, features described with respect to some examples may be combined into other examples.

[0021] Please see Figure 1 , Figure 1 The diagram shows an overall flowchart of a microgrid cost calculation method based on a renewable energy storage system provided in an embodiment of this application.

[0022] like Figure 1As shown, the method for calculating the cost of a microgrid based on a renewable energy storage system may include at least the following steps: Step 102: Obtain multiple sets of computing power values ​​from the power supply end according to a preset time interval, and when a sudden change in the computing power value in any set of computing power values ​​is detected, obtain the voltage signal segment and power consumption period corresponding to the set of computing power values.

[0023] In this embodiment, the microgrid cost calculation method based on renewable energy storage system can be applied, but is not limited to, to a microgrid terminal that communicates with wind farms, photovoltaic power plants, energy storage systems, and substations. This microgrid terminal can calculate the cost of the electricity output by the substation and the energy storage system. Renewable energy sources such as wind farms and photovoltaic power plants can output the converted electricity through the energy storage system to cooperate with the substation in ensuring the normal power supply for users during peak shaving and valley filling.

[0024] Specifically, when calculating the electricity cost of a microgrid's energy storage system, a set of computing power values ​​corresponding to the power supply terminals of multiple energy storage systems can be obtained at preset time intervals. For example, taking a preset time interval of 12 hours, the set of computing power values ​​corresponding to the power supply terminals of each energy storage system may include, but is not limited to, the computing power values ​​corresponding to each hour. Here, computing power value can be understood as the actual computing power of the energy storage system (or the processor of the energy storage system) in its working state, which may be calculated, but is not limited to, through a pre-built computing power quantitative model. It is understood that in the embodiments of this application, the computing power quantitative model may be an artificial intelligence model, which is trained through historical data of the microgrid. Its input features may include, but are not limited to, the current signal, voltage signal, or operating power of the energy storage system in its working state, which will not be elaborated further here.

[0025] Furthermore, when a sudden change in the computing power value is detected in any set of computing power values, it indicates that the voltage signal at the power supply end of the energy storage system corresponding to that set of computing power values ​​fluctuates significantly during operation. In order to ensure the accuracy of electricity cost calculation, the voltage signal segment and electricity consumption period corresponding to that set of computing power values ​​can be obtained.

[0026] As an optional embodiment of this application, after acquiring multiple sets of computing power values ​​from the power supply at preset time intervals, the method further includes: Calculate the difference between the nth computing power value in each set of computing power values ​​and the mean of the previous n-1 computing power values; where n is a positive integer greater than 1. When the ratio between the detected difference and the mean of the first n-1 computing power values ​​exceeds a preset threshold, it is determined that a sudden change has occurred in the computing power value set. When the ratio between the detected difference and the mean of the first n-1 computing power values ​​does not exceed a preset threshold, it is determined that there has been no sudden change in the computing power values ​​in the computing power value set; or When the product of the ratio between the detected difference and the mean of the first n-1 computing power values ​​and a preset constant exceeds a preset threshold, it is determined that a sudden change has occurred in the computing power value set. When the product of the ratio between the detected difference and the mean of the first n-1 computing power values ​​and a preset constant does not exceed a preset threshold, it is determined that the computing power values ​​in the computing power value set have not undergone a sudden change.

[0027] To more accurately determine whether a sudden change has occurred in the computing power values ​​within a set of computing power values, after obtaining each set of computing power values, when it is necessary to determine whether the nth computing power value in the set has experienced a sudden change, the mean of the first n-1 computing power values ​​in that set can be calculated first. Then, the difference between the nth computing power value and the mean can be calculated, along with the ratio between the nth computing power value and the mean. If the ratio exceeds a preset threshold, it indicates that the nth computing power value has experienced a sudden change, thus indicating a significant fluctuation in the voltage signal at the power supply end. It should be noted that the preset threshold here is a value greater than 0, meaning that the nth computing power value is greater than the mean of the first n-1 computing power values, representing a sudden increase. If the ratio does not exceed the preset threshold, it indicates that the nth computing power value has not experienced a sudden change, thus indicating that the voltage signal at the power supply end has not yet shown significant fluctuations. Here, n can be a positive integer greater than 1, and the maximum value of n does not exceed the total number of computing power values ​​in the set.

[0028] Of course, there might be cases where the nth computing power value is less than the average of the previous n-1 computing power values, meaning that the nth computing power value represents a sudden decrease. It's possible that when the product of the determined ratio and a preset constant exceeds a preset threshold, it indicates a sudden change in the nth computing power value, thus confirming a significant fluctuation in the voltage signal at the power supply end. Here, the preset constant can be understood as -1. Conversely, if the product of the determined ratio and the preset constant does not exceed the preset threshold, it indicates that the nth computing power value has not experienced a sudden change, thus confirming that the voltage signal at the power supply end has not yet shown significant fluctuations.

[0029] Step 104: When the electricity consumption period is detected to be in the peak electricity consumption cycle, determine the set of peaks in the voltage signal segment.

[0030] Specifically, after obtaining the voltage signal segment and electricity consumption period corresponding to the set of computing power values ​​that have undergone a sudden change, it is possible to determine which electricity consumption cycle the electricity consumption period belongs to. In this embodiment of the application, the electricity consumption cycle can be specifically divided into peak electricity consumption cycle, off-peak electricity consumption cycle and low electricity consumption period cycle, and each electricity consumption cycle corresponds to a different time period of the day.

[0031] When the electricity consumption period is detected to be during a peak electricity consumption cycle, it indicates that the energy storage system is outputting a large amount of power at the power source. To ensure the accuracy of electricity cost calculation, the target voltage signal can be determined based on the peaks in the voltage signal segment. In other words, all peaks in the voltage signal segment can be extracted to form a peak set.

[0032] Step 106: Remove the minimum and maximum values ​​of the peaks in the peak set, and then perform constraint solving on all peaks in the peak set after the removal process to obtain the first constraint function.

[0033] Specifically, after extracting the peak set, in order to avoid the peaks that are greatly affected by fluctuations from causing errors in the power calculation, the maximum and minimum values ​​of the peaks can be removed from the peak set. Of course, only the maximum or minimum values ​​of the peaks can be removed here, and it is not limited to this. Then, all the peaks in the peak set after the removal process are constrained and solved to obtain the first constraint function.

[0034] As another optional embodiment of this application, constraint solving is performed on all peaks in the peak set after the peak removal process to obtain a first constraint function, including: The electricity consumption time corresponding to each peak in the peak set after the removal process is determined, and a peak coordinate set is established based on each peak and the electricity consumption time corresponding to each peak; wherein, the peak coordinate set includes the coordinates corresponding to each peak in the peak set after the removal process. The distances between the set of peak coordinates and multiple preset linear functions are calculated respectively, and the preset linear function corresponding to the shortest distance is used as the first constraint function.

[0035] Specifically, in the process of calculating the first constraint function, it is possible, but not limited to, to use the Euclidean distance calculation method. First, the power consumption time corresponding to each peak in the peak set is determined, and a peak coordinate set is established with the power consumption time as the abscissa and the corresponding peak as the ordinate. Any peak coordinate in this peak coordinate set can be represented as (X, Y), where X can correspond to the power consumption time corresponding to the peak and Y can correspond to the peak value.

[0036] Next, any linear function can be selected from multiple preset linear functions, and the sum of the distances from all coordinates in the peak coordinate set to that linear function can be calculated. It can be understood that the smaller the sum of the distances, the stronger the correlation between the corresponding linear function and all coordinates in the peak coordinate set, and the more stable the fluctuations are compared to the fluctuations of all peaks in the peak coordinate set. Therefore, this linear function can be used as the first constraint function. The expression of any linear function here can be, but is not limited to, Y=A*X+B, and is not restricted to this.

[0037] Step 108: Calculate the first target voltage signal based on the first constraint function and the electricity consumption period, and obtain the power cost of the energy storage system during the electricity consumption period based on the first target voltage signal.

[0038] Specifically, after determining the first constraint function, in order to further ensure the effectiveness and reliability of the target voltage signal, multiple reference times with consistent time intervals can be selected during the power consumption period. Each reference time can be substituted into the first constraint function to obtain the corresponding reference voltage signal.

[0039] Furthermore, a first target voltage signal can be obtained by weighted summation based on all reference voltage signals and a preset first weight value. The preset first weight can be understood as the number of weight values ​​being the same as the number of all reference voltage signals, and each weight value being consistent.

[0040] Furthermore, after obtaining the first target voltage signal, the corresponding charge power can be obtained based on the first target voltage signal, but is not limited to this. The power cost of the energy storage system during the power consumption period can be calculated in combination with the power consumption period, thereby ensuring the stability and accuracy of the power cost calculation under the influence of fluctuations.

[0041] Understandably, for periods of electricity consumption without significant fluctuations, the average computing power corresponding to each set of computing power values ​​can be calculated. Based on this average computing power value and the preset hardware computing power value, the electricity cost of the energy storage system in each preset time interval can be obtained. In other words, the electricity cost can also be obtained through the calculation of computing power values.

[0042] As another optional embodiment of this application, after obtaining the voltage signal segment and electricity consumption period corresponding to the computing power value set, the method further includes: When the electricity consumption period is detected to be in a low electricity consumption cycle, the set of troughs in the voltage signal segment is determined; The minimum and maximum values ​​of the valleys in the valley set are removed, and the constraints of all the valleys in the valley set after the removal process are solved to obtain the second constraint function. The second target voltage signal is calculated based on the second constraint function and the electricity consumption period, and the electricity cost of the energy storage system during the electricity consumption period is obtained based on the second target voltage signal.

[0043] When the power consumption period is detected to be during a low-consumption cycle, it indicates that the energy storage system's power output at the power source is relatively low. To ensure the accuracy of power cost calculation, the target voltage signal can be determined based on the trough in the voltage signal segment. It is understood that the calculation process here can be found in the aforementioned embodiments and will not be elaborated further.

[0044] As another optional embodiment of this application, after obtaining the voltage signal segment and electricity consumption period corresponding to the computing power value set, the method further includes: When the power consumption period is detected to be within the power consumption period cycle, the set of peaks and the set of troughs in the voltage signal segment are determined; Calculate the mean value of the peaks corresponding to the set of peaks and the mean value of the troughs corresponding to the set of troughs respectively; The third target voltage signal is obtained by weighted summation of the peak average, the trough average, and the preset second weight value, and the power cost of the energy storage system during the power consumption period is obtained based on the third target voltage signal.

[0045] When the detected electricity consumption period falls within a normal consumption cycle, it indicates that the energy storage system's power output is normal. To ensure the accuracy of electricity cost calculation, the target voltage signal can be determined based on the peaks and troughs in the voltage signal segment. It is understood that the calculation process here can be found in the aforementioned embodiments and will not be elaborated further. In this embodiment, the preset second weight value can be set to a weight value corresponding to the average peak value that is greater than the weight value corresponding to the average trough value, but is not limited to this.

[0046] Please see Figure 2 , Figure 2 A schematic diagram of a microgrid cost calculation device based on a renewable energy storage system provided in an embodiment of this application is shown.

[0047] like Figure 2 As shown, the microgrid cost calculation device based on a renewable energy storage system may include at least a data acquisition module 201, a data processing module 202, a data analysis module 203, and a cost calculation module 204, wherein: The data acquisition module 201 is used to acquire multiple sets of computing power values ​​from the power supply end at preset time intervals, and when a sudden change in the computing power value in any set of computing power values ​​is detected, acquire the voltage signal segment and power consumption period corresponding to the set of computing power values. Data processing module 202 is used to determine the set of peaks in the voltage signal segment when the power consumption period is detected to be in the peak power consumption cycle; The data analysis module 203 is used to remove the minimum and maximum values ​​of the peaks in the peak set, and to perform constraint solving on all the peaks in the peak set after the removal process to obtain the first constraint function. The cost calculation module 204 is used to calculate the first target voltage signal based on the first constraint function and the electricity consumption period, and to obtain the power cost of the energy storage system during the electricity consumption period based on the first target voltage signal.

[0048] In some possible embodiments, after acquiring multiple sets of computing power values ​​from the power supply at preset time intervals, the method further includes: Calculate the difference between the nth computing power value in each set of computing power values ​​and the mean of the previous n-1 computing power values; where n is a positive integer greater than 1. When the ratio between the detected difference and the mean of the first n-1 computing power values ​​exceeds a preset threshold, it is determined that a sudden change has occurred in the computing power value set. When the ratio between the detected difference and the mean of the first n-1 computing power values ​​does not exceed a preset threshold, it is determined that there has been no sudden change in the computing power values ​​in the computing power value set; or When the product of the ratio between the detected difference and the mean of the first n-1 computing power values ​​and a preset constant exceeds a preset threshold, it is determined that a sudden change has occurred in the computing power value set. When the product of the ratio between the detected difference and the mean of the first n-1 computing power values ​​and a preset constant does not exceed a preset threshold, it is determined that the computing power values ​​in the computing power value set have not undergone a sudden change.

[0049] In some possible embodiments, constraint solving is performed on all peaks in the peak set after the peak removal process to obtain a first constraint function, including: The electricity consumption time corresponding to each peak in the peak set after the removal process is determined, and a peak coordinate set is established based on each peak and the electricity consumption time corresponding to each peak; wherein, the peak coordinate set includes the coordinates corresponding to each peak in the peak set after the removal process. The distances between the set of peak coordinates and multiple preset linear functions are calculated respectively, and the preset linear function corresponding to the shortest distance is used as the first constraint function.

[0050] In some possible embodiments, the first target voltage signal is calculated based on the first constraint function and the electricity consumption period, including: Multiple reference times with consistent time intervals are selected during the electricity consumption period, and the reference voltage signal corresponding to each reference time is calculated based on the first constraint function. The first target voltage signal is obtained by weighted summation based on all reference voltage signals and a preset first weight value.

[0051] In some possible embodiments, after obtaining the voltage signal segment and electricity consumption period corresponding to the computing power value set, the method further includes: When the electricity consumption period is detected to be in a low electricity consumption cycle, the set of troughs in the voltage signal segment is determined; The minimum and maximum values ​​of the valleys in the valley set are removed, and the constraints of all the valleys in the valley set after the removal process are solved to obtain the second constraint function. The second target voltage signal is calculated based on the second constraint function and the electricity consumption period, and the electricity cost of the energy storage system during the electricity consumption period is obtained based on the second target voltage signal.

[0052] In some possible embodiments, after obtaining the voltage signal segment and electricity consumption period corresponding to the computing power value set, the method further includes: When the power consumption period is detected to be within the power consumption period cycle, the set of peaks and troughs in the voltage signal segment are determined. Calculate the mean value of the peaks corresponding to the set of peaks and the mean value of the troughs corresponding to the set of troughs respectively; The third target voltage signal is obtained by weighted summation of the peak average, trough average, and preset second weight value, and the electricity cost of the energy storage system during the electricity consumption period is obtained based on the third target voltage signal.

[0053] In some possible embodiments, after acquiring multiple sets of computing power values ​​from the power supply at preset time intervals, the method further includes: When it is detected that no sudden change has occurred in the computing power value in any set of computing power values, the average computing power value corresponding to each set of computing power values ​​is calculated. Based on the average computing power and the preset hardware computing power, the electricity cost of the energy storage system in each preset time interval is obtained.

[0054] Those skilled in the art will clearly understand that the technical solutions of the embodiments of this application can be implemented by means of software and / or hardware. In this specification, "unit" and "module" refer to software and / or hardware that can independently complete or cooperate with other components to complete a specific function, wherein the hardware may be, for example, a field-programmable gate array (FPGA), an integrated circuit (IC), etc.

[0055] Please see Figure 3 , Figure 3 This paper presents a schematic diagram of the structure of another microgrid cost calculation device based on a renewable energy storage system provided in an embodiment of this application.

[0056] like Figure 3 As shown, the microgrid cost calculation device 300 based on a renewable energy storage system may include at least one processor 301, at least one network interface 304, a user interface 303, a memory 305, and at least one communication bus 302.

[0057] The communication bus 302 can be used to realize the connection and communication of the above components.

[0058] The user interface 303 may include buttons, and the optional user interface may also include a standard wired interface or a wireless interface.

[0059] The network interface 304 may include, but is not limited to, Bluetooth modules, NFC modules, Wi-Fi modules, etc.

[0060] The processor 301 may include one or more processing cores. The processor 301 connects to various parts within the microgrid cost calculation device 300 based on the renewable energy storage system using various interfaces and lines. It executes or runs instructions, programs, code sets, or instruction sets stored in the memory 305, and calls data stored in the memory 305 to perform various functions and process data within the microgrid cost calculation device 300 based on the renewable energy storage system. Optionally, the processor 301 may be implemented using at least one hardware form of DSP, FPGA, or PLA. The processor 301 may integrate one or more of the following: CPU, GPU, and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 301 and may be implemented as a separate chip.

[0061] The memory 305 may include RAM or ROM. Optionally, the memory 305 may include a non-transitory computer-readable medium. The memory 305 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 305 may also be at least one storage device located remotely from the aforementioned processor 301. Figure 3 As shown, the memory 305, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and a microgrid cost calculation application based on a renewable energy storage system.

[0062] Specifically, the processor 301 can be used to call the microgrid cost calculation application based on renewable energy storage system stored in the memory 305, and specifically perform the following operations: Multiple sets of computing power values ​​from the power supply end are acquired at preset time intervals. When a sudden change in computing power value is detected in any set of computing power values, the voltage signal segment and power consumption period corresponding to the set of computing power values ​​are acquired. When the electricity consumption period is detected to be during the peak electricity consumption cycle, the set of peaks in the voltage signal segment is determined; The minimum and maximum values ​​of the peaks in the peak set are removed, and the constraints of all peaks in the peak set after the removal process are solved to obtain the first constraint function. The first target voltage signal is calculated based on the first constraint function and the electricity consumption period, and the electricity cost of the energy storage system during the electricity consumption period is obtained based on the first target voltage signal.

[0063] In some possible embodiments, after acquiring multiple sets of computing power values ​​from the power supply at preset time intervals, the method further includes: Calculate the difference between the nth computing power value in each set of computing power values ​​and the mean of the previous n-1 computing power values; where n is a positive integer greater than 1. When the ratio between the detected difference and the mean of the first n-1 computing power values ​​exceeds a preset threshold, it is determined that a sudden change has occurred in the computing power value set. When the ratio between the detected difference and the mean of the first n-1 computing power values ​​does not exceed a preset threshold, it is determined that there has been no sudden change in the computing power values ​​in the computing power value set; or When the product of the ratio between the detected difference and the mean of the first n-1 computing power values ​​and a preset constant exceeds a preset threshold, it is determined that a sudden change has occurred in the computing power value set. When the product of the ratio between the detected difference and the mean of the first n-1 computing power values ​​and a preset constant does not exceed a preset threshold, it is determined that the computing power values ​​in the computing power value set have not undergone a sudden change.

[0064] In some possible embodiments, constraint solving is performed on all peaks in the peak set after the peak removal process to obtain a first constraint function, including: The electricity consumption time corresponding to each peak in the peak set after the removal process is determined, and a peak coordinate set is established based on each peak and the electricity consumption time corresponding to each peak; wherein, the peak coordinate set includes the coordinates corresponding to each peak in the peak set after the removal process. The distances between the set of peak coordinates and multiple preset linear functions are calculated respectively, and the preset linear function corresponding to the shortest distance is used as the first constraint function.

[0065] In some possible embodiments, the first target voltage signal is calculated based on the first constraint function and the electricity consumption period, including: Multiple reference times with consistent time intervals are selected during the electricity consumption period, and the reference voltage signal corresponding to each reference time is calculated based on the first constraint function. The first target voltage signal is obtained by weighted summation based on all reference voltage signals and a preset first weight value.

[0066] In some possible embodiments, after obtaining the voltage signal segment and electricity consumption period corresponding to the computing power value set, the method further includes: When the electricity consumption period is detected to be in a low electricity consumption cycle, the set of troughs in the voltage signal segment is determined; The minimum and maximum values ​​of the valleys in the valley set are removed, and the constraints of all the valleys in the valley set after the removal process are solved to obtain the second constraint function. The second target voltage signal is calculated based on the second constraint function and the electricity consumption period, and the electricity cost of the energy storage system during the electricity consumption period is obtained based on the second target voltage signal.

[0067] In some possible embodiments, after obtaining the voltage signal segment and electricity consumption period corresponding to the computing power value set, the method further includes: When the power consumption period is detected to be within the power consumption period cycle, the set of peaks and troughs in the voltage signal segment are determined. Calculate the mean value of the peaks corresponding to the set of peaks and the mean value of the troughs corresponding to the set of troughs respectively; The third target voltage signal is obtained by weighted summation of the peak average, trough average, and preset second weight value, and the electricity cost of the energy storage system during the electricity consumption period is obtained based on the third target voltage signal.

[0068] In some possible embodiments, after acquiring multiple sets of computing power values ​​from the power supply at preset time intervals, the method further includes: When it is detected that no sudden change has occurred in the computing power value in any set of computing power values, the average computing power value corresponding to each set of computing power values ​​is calculated. Based on the average computing power and the preset hardware computing power, the electricity cost of the energy storage system in each preset time interval is obtained.

[0069] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method. The computer-readable storage medium may include, but is not limited to, any type of disk, including floppy disks, optical disks, DVDs, CD-ROMs, microdrives, as well as magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic cards or optical cards, nanosystems (including molecular memory ICs), or any type of medium or device suitable for storing instructions and / or data.

[0070] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0071] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0072] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between apparatuses or units may be electrical or other forms.

[0073] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0074] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0075] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0076] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0077] The above are merely exemplary embodiments of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Those skilled in the art will readily conceive of embodiments of this disclosure upon considering the specification and practicing the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described herein. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A method for calculating the cost of a microgrid based on a renewable energy storage system, characterized in that, include: Multiple sets of computing power values ​​from the power supply side are acquired at preset time intervals. When a sudden change in the computing power value in any set of computing power values ​​is detected, the voltage signal segment and power consumption period corresponding to the set of computing power values ​​are acquired. The set of computing power values ​​includes the computing power value corresponding to each hour. The computing power value is the actual computing power of the energy storage system in the working state, which is calculated by a pre-constructed computing power quantitative model. When the electricity consumption period is detected to be in a peak electricity consumption cycle, the set of peaks in the voltage signal segment is determined; The minimum and maximum values ​​of the peaks in the peak set are removed, and the constraints of all peaks in the peak set after the removal process are solved to obtain the first constraint function. A first target voltage signal is calculated based on the first constraint function and the electricity consumption period, and the power cost of the energy storage system during the electricity consumption period is obtained based on the first target voltage signal. The process further includes, after obtaining the voltage signal segment and electricity consumption period corresponding to the computing power value set: When the electricity consumption period is detected to be in a low electricity consumption cycle, the set of troughs in the voltage signal segment is determined; The minimum and maximum values ​​of the valleys in the valley set are removed, and the constraints of all the valleys in the valley set after the removal process are solved to obtain the second constraint function. The second target voltage signal is calculated based on the second constraint function and the electricity consumption period, and the power cost of the energy storage system during the electricity consumption period is obtained based on the second target voltage signal. When the power consumption period is detected to be within the power consumption period cycle, the set of peaks and the set of troughs in the voltage signal segment are determined; Calculate the mean value of the peaks corresponding to the set of peaks and the mean value of the troughs corresponding to the set of troughs respectively; The third target voltage signal is obtained by weighted summation of the peak average, the trough average, and the preset second weight value, and the power cost of the energy storage system during the power consumption period is obtained based on the third target voltage signal.

2. The method according to claim 1, characterized in that, The constraint solution process is performed on all peaks in the peak set after the removal process to obtain the first constraint function, including: The electricity consumption time corresponding to each peak in the peak set after the removal process is determined, and a peak coordinate set is established based on each peak and the electricity consumption time corresponding to each peak; wherein, the peak coordinate set includes the coordinates corresponding to each peak in the peak set after the removal process. The distances between the set of peak coordinates and multiple preset linear functions are calculated respectively, and the preset linear function corresponding to the shortest distance is used as the first constraint function.

3. The method according to claim 1, characterized in that, The step of calculating the first target voltage signal based on the first constraint function and the electricity consumption period includes: Multiple reference times with consistent time intervals are selected within the power consumption period, and a reference voltage signal corresponding to each reference time is calculated based on the first constraint function. The first target voltage signal is obtained by performing a weighted summation calculation based on all the reference voltage signals and a preset first weight value.

4. The method according to claim 1, characterized in that, After acquiring multiple sets of computing power values ​​from the power supply at preset time intervals, the method further includes: When it is detected that no sudden change has occurred in the computing power value in any set of computing power values, the average computing power value corresponding to each set of computing power values ​​is calculated; Based on the average computing power and the preset hardware computing power value, the electricity cost of the energy storage system in each preset time interval is obtained.

5. A microgrid cost calculation device based on a renewable energy storage system, characterized in that, include: The data acquisition module is used to acquire multiple sets of computing power values ​​from the power supply end at preset time intervals, and when a sudden change in the computing power value in any set of the computing power values ​​is detected, acquire the voltage signal segment and power consumption period corresponding to the computing power value set; wherein, the computing power value set includes the computing power value corresponding to each hour; the computing power value is the actual computing power of the energy storage system in the working state, which is calculated through a pre-constructed computing power quantitative model; The data processing module is used to determine the set of peaks in the voltage signal segment when the electricity consumption period is detected to be in a peak electricity consumption cycle. The data analysis module is used to remove the minimum and maximum values ​​of the peaks in the peak set, and to perform constraint solving on all the peaks in the peak set after the removal process to obtain the first constraint function. The cost calculation module is used to calculate a first target voltage signal based on the first constraint function and the electricity consumption period, and to obtain the power cost of the energy storage system during the electricity consumption period based on the first target voltage signal. The process further includes, after obtaining the voltage signal segment and electricity consumption period corresponding to the computing power value set: When the electricity consumption period is detected to be in a low electricity consumption cycle, the set of troughs in the voltage signal segment is determined; The minimum and maximum values ​​of the valleys in the valley set are removed, and the constraints of all the valleys in the valley set after the removal process are solved to obtain the second constraint function. The second target voltage signal is calculated based on the second constraint function and the electricity consumption period, and the power cost of the energy storage system during the electricity consumption period is obtained based on the second target voltage signal. When the power consumption period is detected to be within the power consumption period cycle, the set of peaks and the set of troughs in the voltage signal segment are determined; Calculate the mean value of the peaks corresponding to the set of peaks and the mean value of the troughs corresponding to the set of troughs respectively; The third target voltage signal is obtained by weighted summation of the peak average, the trough average, and the preset second weight value, and the power cost of the energy storage system during the power consumption period is obtained based on the third target voltage signal.

6. A microgrid cost calculation device based on a renewable energy storage system, characterized in that, Including the processor and memory; The processor is connected to the memory; The memory is used to store executable program code; The processor runs a program corresponding to the executable program code stored in the memory to perform the steps of the method as described in any one of claims 1-4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer-readable storage medium stores instructions that, when executed on a computer or processor, cause the computer or processor to perform the steps of the method as described in any one of claims 1-4.

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