Electric energy metering system and method based on adjustable resource scene analysis

Abstract

The invention relates to the technical field of electric power metering, in particular to an electric energy metering system and method based on adjustable resource scene analysis, and the method comprises the steps: dividing electric equipment into fixed load equipment and elastic load equipment based on the working characteristics of the electric equipment, and obtaining the equipment operation data of the elastic load equipment; performing adjustable potential evaluation on the elastic load equipment based on the equipment operation data; estimating a power grid load fluctuation condition of the elastic load equipment in an adjustment participation process based on an adjustable potential evaluation result; estimating an electric energy metering precision loss risk of the elastic load equipment in an adjustment participation process based on a power grid load fluctuation condition; and optimizing an electric energy metering strategy based on the electric energy metering precision loss risk. According to the invention, the power grid load fluctuation and the electric energy metering precision loss risk of the elastic load equipment in the adjustment participation process are quantified, so that the electric energy metering accuracy and metering efficiency are improved.

Description

Technical Field

[0001] The present application relates to the technical field of power metering, and particularly to an electric energy metering system and method based on adjustable resource scenario analysis. Background Art

[0002] With the increasing access of more and more flexible resources such as distributed power sources, electric vehicles, and smart home appliances to the distribution network, the power consumption facilities and power consumption methods are becoming increasingly diverse. The distribution network no longer only undertakes the task of power distribution, but serves as a power distribution system integrating power production, transportation, storage, and distribution.

[0003] Residential load is an important part of the distribution system, and the peak period of residential load coincides highly with the peak period of the power grid. Therefore, the adjustable potential of residential load is huge. At the same time, with the wide installation of digital electric energy meters in residential households and the popularization of high-power smart appliances with communication functions, residential users can more conveniently respond to incentive mechanisms, participate in grid interaction, and relieve the power consumption pressure.

[0004] During the process of residential users responding to grid regulation, the frequent start and stop of electrical appliances will cause random and frequent fluctuations in the grid voltage and frequency, which will impact the accuracy of electric energy metering equipment. Currently, the steady-state electric energy metering method is usually adopted in the distribution management system, which is only applicable to steady-state systems with stable voltage and current. For a power supply system with obvious dynamic fluctuations and random changes, if the steady-state electric energy metering method is also adopted, metering errors will occur, and the existing electric energy metering system cannot dynamically adjust the electric energy metering method according to the grid fluctuation situation, resulting in insufficient accuracy and reliability of electric energy metering. Summary of the Invention

[0005] In order to overcome the defects and deficiencies existing in the prior art, the present application provides an electric energy metering system and method based on adjustable resource scenario analysis, which improves the accuracy and metering efficiency of electric energy metering by quantifying the grid load fluctuation and the risk of loss of electric energy metering accuracy during the participation of elastic load equipment in regulation.

[0006] In order to achieve the above object, the present application adopts the following technical solutions:

[0007] In a first aspect, the present application provides an electric energy metering method based on adjustable resource scenario analysis, including the following steps:

[0008] Classify the electrical equipment into fixed load equipment and elastic load equipment based on the working characteristics of the electrical equipment and obtain the equipment operation data of the elastic load equipment;

[0009] Evaluate the adjustable potential of the elastic load equipment based on the equipment operation data;

[0010] Estimate the grid load fluctuation situation of the flexible load equipment during the participation in the regulation process based on the adjustable potential evaluation result;

[0011] Estimate the risk of power metering accuracy loss of the flexible load equipment during the participation in the regulation process based on the grid load fluctuation situation;

[0012] Optimize the power metering strategy based on the risk of power metering accuracy loss.

[0013] Optionally, the evaluation of the adjustable potential of the flexible load equipment based on the equipment operation data includes:

[0014] Obtain the equipment operation data of the flexible load equipment, where the equipment operation data includes the operation period of the flexible load equipment and the rated power of the flexible load equipment;

[0015] Calculate the adjustable potential coefficient of the operation period and the adjustable potential coefficient of the operation power of the flexible load equipment respectively through the equipment operation data;

[0016] Perform weighted summation on the adjustable potential coefficient of the operation period and the adjustable potential coefficient of the operation power to obtain the adjustable potential index of the flexible load equipment, and the adjustable potential index is used to quantify the adjustable potential of the flexible load equipment during the participation in the regulation process.

[0017] Optionally, the calculation of the adjustable potential coefficient of the operation period and the adjustable potential coefficient of the operation power of the flexible load equipment respectively through the equipment operation data includes:

[0018] Obtain the peak period of the grid load, and perform an intersection operation on the operation period of the flexible load equipment and the peak period of the grid load to obtain the overlapping duration between the operation period of the flexible load equipment and the peak period of the grid load;

[0019] Take the ratio of the overlapping duration to the total operation duration of the flexible load equipment as the adjustable potential coefficient of the operation period of the flexible load equipment;

[0020] Take the difference between the rated power of the flexible load equipment and the standby power of the flexible load equipment as the adjustable power of the flexible load equipment;

[0021] Take the ratio of the adjustable power of the flexible load equipment to the average value of the rated powers of all flexible load equipment as the adjustable potential coefficient of the operation power of the flexible load equipment.

[0022] Optionally, the estimation of the grid load fluctuation situation of the flexible load equipment during the participation in the regulation process based on the adjustable potential evaluation result includes:

[0023] Obtain the adjustable potential index of the flexible load equipment and the equipment operation data, where the equipment operation data includes the operation period of the flexible load equipment and the rated power of the flexible load equipment;

[0024] Multiply the adjustable potential index of the flexible load device by the rated power of the flexible load device to obtain the load fluctuation contribution value of the flexible load device;

[0025] Accumulate the load fluctuation contribution values of all flexible load devices in the power grid to obtain the total load fluctuation value of the power grid;

[0026] Take the ratio of the total load fluctuation value of the power grid to the reference load value of the power grid as the power grid load fluctuation index, and the power grid load fluctuation index is used to quantify the load fluctuation situation of all flexible load devices in the power grid during the participation in regulation.

[0027] Optionally, predicting the risk of loss of power metering accuracy of the flexible load device during the participation in regulation based on the power grid load fluctuation situation includes:

[0028] Obtain the adjustable power of the flexible load device and the power grid load fluctuation index. The adjustable power of the flexible load device is the difference between the rated power of the flexible load device and the standby power of the flexible load device;

[0029] Take the ratio of the average value of the adjustable power of all flexible load devices in the power grid to the average value of the rated power of the flexible load device as the instantaneous power fluctuation index of the flexible load device;

[0030] Perform weighted summation on the instantaneous power fluctuation index and the power grid load fluctuation index to obtain the power metering accuracy loss risk index, and the power metering accuracy loss risk index is used to quantify the risk of loss of power metering accuracy of the flexible load device during the participation in regulation.

[0031] Optionally, optimizing the power metering strategy based on the risk of loss of power metering accuracy includes:

[0032] Obtain the power metering accuracy loss risk index. When the power metering accuracy loss risk index is less than the preset power metering accuracy loss risk threshold, use the steady-state power metering algorithm for power metering. The steady-state power metering algorithm includes the dot product and power metering algorithm and the fast Fourier transform power metering algorithm;

[0033] When the power metering accuracy loss risk index is greater than or equal to the preset power metering accuracy loss risk threshold, use the dynamic power metering algorithm for power metering. The dynamic power metering algorithm includes the estimated fundamental frequency power metering algorithm, the improved fast Fourier transform power metering algorithm, and the time-frequency analysis power metering algorithm.

[0034] In a second aspect, the present application provides a power metering system based on adjustable resource scenario analysis, including:

[0035] A data acquisition module, configured to divide the electrical equipment into fixed load devices and flexible load devices based on the working characteristics of the electrical equipment and obtain the device operation data of the flexible load devices;

[0036] An adjustable potential evaluation module for evaluating the adjustable potential of flexible load devices based on device operation data;

[0037] A load fluctuation prediction module for predicting the grid load fluctuation of flexible load devices during the regulation process based on the adjustable potential evaluation results;

[0038] An accuracy loss prediction module for predicting the risk of power metering accuracy loss of flexible load devices during the regulation process based on the grid load fluctuation;

[0039] A metering strategy optimization module for optimizing the power metering strategy based on the risk of power metering accuracy loss.

[0040] Thirdly, the present application provides an electronic device, including: a processor and a memory. Among them, a computer program that can be called by the processor is stored in the memory, and the processor executes a power metering method based on adjustable resource scenario analysis by calling the computer program stored in the memory.

[0041] Fourthly, the present application provides a computer-readable storage medium storing instructions, which when run on a computer, cause the computer to execute a power metering method based on adjustable resource scenario analysis.

[0042] Compared with the prior art, the present application has the following advantages and beneficial effects:

[0043] By evaluating the adjustable potential of flexible load devices, the present application further predicts the grid load fluctuation and the risk of power metering accuracy loss of flexible load devices during the regulation process, and optimizes the power metering strategy based on the risk of power metering accuracy loss, effectively improving the accuracy and metering efficiency of power metering. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] By reading the detailed description of the non-limiting embodiments with reference to the following drawings, other features, purposes and advantages of the present application will become more obvious:

[0045] Figure 1 is the overall flow schematic diagram of the power metering method based on adjustable resource scenario analysis provided by the embodiment of the present application;

[0046] Figure 2 is the structural schematic diagram of the digital watt-hour meter provided by the embodiment of the present application;

[0047] Figure 3 is the structural schematic diagram of the power metering system based on adjustable resource scenario analysis provided by the embodiment of the present application;

[0048] Figure 4It is a schematic structural diagram of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0049] The technical solution of the present application will be described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations on the technical solution of the present application. Without conflict, the technical features in the embodiments of the present application and the embodiments can be combined with each other.

[0050] Please refer to Figure 1 , Figure 1 It is a schematic overall flow diagram of an electric energy metering method based on adjustable resource scenario analysis provided by an embodiment of the present application, specifically including the following steps:

[0051] S110: Classify electrical equipment into fixed load equipment and flexible load equipment based on the working characteristics of the electrical equipment and obtain the equipment operation data of the flexible load equipment. Among them, the operation period and operation power of the fixed load equipment cannot be adjusted, and the operation period or operation power of the flexible load equipment can be flexibly adjusted according to the grid demand;

[0052] S120: Evaluate the adjustable potential of the flexible load equipment based on the equipment operation data;

[0053] The adjustable potential refers to the ability of the flexible load equipment to adjust its operation period and operation power according to the grid demand during the process of participating in grid regulation. The adjustable potential index quantifies the magnitude of the adjustment ability that the flexible load equipment can contribute during the process of participating in load regulation. The adjustable potential index can not only effectively evaluate the adaptability of the flexible load equipment in different scenarios, but also provide a basis for grid load fluctuation evaluation and electric energy metering strategy optimization. The specific steps for evaluating the adjustable potential of the flexible load equipment based on the equipment operation data include:

[0054] Obtain the equipment operation data of the flexible load equipment. The equipment operation data includes the operation period of the flexible load equipment and the rated power of the flexible load equipment;

[0055] Calculate the operation period adjustable potential coefficient and the operation power adjustable potential coefficient of the flexible load equipment respectively through the equipment operation data;

[0056] Perform weighted summation on the operation period adjustable potential coefficient and the operation power adjustable potential coefficient to obtain the adjustable potential index of the flexible load equipment. The adjustable potential index is used to quantify the adjustable potential of the flexible load equipment during the process of participating in regulation;

[0057] The operation period adjustable potential coefficient and the operation power adjustable potential coefficient are key indicators used to quantify the adjustment ability of flexible load equipment in power grid regulation. Among them, the operation period adjustable potential coefficient reflects the flexibility of the equipment's operation time during the power grid peak period by calculating the proportion of the overlapping duration between the operation period of the flexible load equipment and the power grid load peak period in the total operation duration of the equipment. The higher the operation period adjustable potential coefficient, the greater the proportion of the equipment's operation time during the peak period, and the greater the adjustment potential that can be provided for the power grid by adjusting the operation period, thus alleviating the load pressure during the peak period. The operation power adjustable potential coefficient reflects the potential of the equipment in power adjustment by calculating the proportion of the difference between the rated power and the standby power of the equipment in the average value of the rated powers of all equipment. The specific steps for calculating the operation period adjustable potential coefficient and the operation power adjustable potential coefficient of the flexible load equipment through the equipment operation data are as follows:

[0058] Obtain the power grid load peak period, and perform an intersection operation between the operation period of the flexible load equipment and the power grid load peak period to obtain the overlapping duration between the operation period of the flexible load equipment and the power grid load peak period;

[0059] Take the ratio of the overlapping duration to the total operation duration of the flexible load equipment as the operation period adjustable potential coefficient of the flexible load equipment. The calculation formula for the operation period adjustable potential coefficient can be:

[0060]

[0061] In the formula, T overlap is the overlapping duration between the operation period of the flexible load equipment and the power grid load peak period, T overlap = ||T run ∩T high ||, T run is the time set of the operation period of the flexible load equipment, T high is the time set of the power grid load peak period, ||T run ∩T high || is the number of elements in the intersection set, T total is the total operation duration of the flexible load equipment, K T is the operation period adjustable potential coefficient of the flexible load equipment;

[0062] Take the difference between the rated power of the flexible load equipment and the standby power of the flexible load equipment as the adjustable power of the flexible load equipment;

[0063] Take the ratio of the adjustable power of the flexible load equipment to the average value of the rated powers of all flexible load equipment as the operation power adjustable potential coefficient of the flexible load equipment. The calculation formula for the operation power adjustable potential coefficient can be:

[0064]

[0065] Where P rated is the rated power of the flexible load device, and P standby is the standby power of the flexible load device, is the average value of the rated power of the flexible load device, P rated,i is the rated power of the i-th flexible load device in the power grid, n is the number of flexible load devices in the power grid, and K P is the adjustable power potential coefficient.

[0066] S130: Estimate the power grid load fluctuation situation of the flexible load device during the participation in the regulation process based on the adjustable potential evaluation result;

[0067] Quantify the overall influence degree of the flexible load device on the power grid load fluctuation during the participation in the power grid regulation process through the power grid load fluctuation index, that is, the influence of the adjustment effect of the adjustable potential of the flexible load device on the power grid load stability during actual operation. The power grid load fluctuation index can evaluate the synergy effect of the flexible load device during the load regulation process, so as to provide decision support for the optimization of the electric energy metering strategy. The specific steps for estimating the power grid load fluctuation situation of the flexible load device during the participation in the regulation process based on the adjustable potential evaluation result include:

[0068] Obtain the adjustable potential index and device operation data of the flexible load device. The device operation data includes the operation time period of the flexible load device and the rated power of the flexible load device;

[0069] Take the product of the adjustable potential index of the flexible load device and the rated power of the flexible load device as the load fluctuation contribution value of the flexible load device;

[0070] Accumulate the load fluctuation contribution values of all flexible load devices in the power grid to obtain the total power grid load fluctuation value;

[0071] Take the ratio of the total power grid load fluctuation value to the power grid reference load value as the power grid load fluctuation index. The power grid load fluctuation index is used to quantify the power grid load fluctuation situation of all flexible load devices in the power grid during the participation in the regulation process.

[0072] S140: Estimate the risk of loss of electric energy metering accuracy of the flexible load device during the participation in the regulation process based on the power grid load fluctuation situation;

[0073] The risk index of power metering accuracy loss quantifies the risk of power metering accuracy loss caused by power fluctuations and load fluctuations during the participation of flexible load devices in grid regulation. Specifically, the instantaneous power fluctuation index measures the fluctuation amplitude of the adjustable power of the device, while the grid load fluctuation index quantifies the overall load fluctuation of the grid. The risk index of power metering accuracy loss provides a basis for risk assessment for grid operators, helps optimize power metering strategies, reduce power metering errors, and ensure the accuracy and fairness of power metering. The specific steps for estimating the risk of power metering accuracy loss of flexible load devices during the participation in regulation based on the grid load fluctuation situation include:

[0074] Obtain the adjustable power of the flexible load device and the grid load fluctuation index. The adjustable power of the flexible load device is the difference between the rated power and the standby power of the flexible load device;

[0075] Take the ratio of the average adjustable power of all flexible load devices in the grid to the average rated power of the flexible load devices as the instantaneous power fluctuation index of the flexible load device;

[0076] Perform a weighted sum of the instantaneous power fluctuation index and the grid load fluctuation index to obtain the risk index of power metering accuracy loss, which is used to quantify the risk of power metering accuracy loss of flexible load devices during the participation in regulation.

[0077] S150: Optimize the power metering strategy based on the risk of power metering accuracy loss;

[0078] By calculating the risk index of power metering accuracy loss, the digital power meter is further controlled to dynamically select the best power metering algorithm to reduce the impact on power metering accuracy during the regulation of flexible load devices. Please refer to Figure 2 , Figure 2 is a schematic structural diagram of the digital power meter provided by the embodiment of the present application. The digital power meter is composed of a microprocessor, a dual-channel A / D converter, a regulated power supply, an external interface, and a display. Voltage and current data are transmitted to the microprocessor for power calculation after passing through the dual-channel A / D converter. Steady-state power metering algorithms and dynamic power metering algorithms can run in the microprocessor. Power metering algorithm switching instructions are transmitted to the microprocessor through the external interface. When the risk index of power metering accuracy loss is low, the microprocessor runs a steady-state power metering algorithm with higher calculation efficiency to improve metering efficiency while ensuring accuracy. When the risk index of power metering accuracy loss is high, the microprocessor runs a more complex dynamic power metering algorithm to cope with the risk of metering accuracy loss caused by power fluctuations. The specific steps for optimizing the power metering strategy based on the risk of power metering accuracy loss include:

[0079] Obtain the risk index of power metering accuracy loss. When the risk index of power metering accuracy loss is less than the preset risk threshold of power metering accuracy loss, use the steady-state power metering algorithm for power metering. The steady-state power metering algorithm includes the dot product and power metering algorithm and the fast Fourier transform power metering algorithm;

[0080] When the risk index of power metering accuracy loss is greater than or equal to the preset risk threshold of power metering accuracy loss, use the dynamic power metering algorithm for power metering. The dynamic power metering algorithm includes the estimated fundamental frequency power metering algorithm, the improved fast Fourier transform power metering algorithm, and the time-frequency analysis power metering algorithm.

[0081] It should be noted that the setting parameters in the embodiments of the present application, such as the weighting weights and the value-taking method of the preset risk threshold of power metering accuracy loss, are as follows: construct a data set by obtaining the device operation data of the flexible load device, substitute it into the calculation of the risk index of power metering accuracy loss, and at the same time obtain the judgment result of the expert on the risk of power metering accuracy loss. Import the risk index of power metering accuracy loss and the judgment result into the fitting software, and output the weighting weights and the preset risk threshold of power metering accuracy loss that meet the maximum judgment accuracy rate.

[0082] Please refer to Figure 3 , Figure 3 FIG. is a schematic structural diagram of a power metering system based on adjustable resource scenario analysis provided by the embodiments of the present application. The embodiments of the present application provide a power metering system based on adjustable resource scenario analysis, including:

[0083] A data acquisition module 210, configured to divide the electrical equipment into fixed load equipment and flexible load equipment based on the working characteristics of the electrical equipment and obtain the device operation data of the flexible load equipment;

[0084] An adjustable potential evaluation module 220, configured to evaluate the adjustable potential of the flexible load equipment based on the device operation data;

[0085] A load fluctuation prediction module 230, configured to predict the grid load fluctuation situation of the flexible load equipment during the participation in the regulation process based on the adjustable potential evaluation result;

[0086] An accuracy loss prediction module 240, configured to predict the risk of power metering accuracy loss of the flexible load equipment during the participation in the regulation process based on the grid load fluctuation situation;

[0087] A metering strategy optimization module 250, configured to optimize the power metering strategy based on the risk of power metering accuracy loss.

[0088] In the embodiments of the present application, the adjustable potential evaluation module 220 is configured to evaluate the adjustable potential of the flexible load equipment based on the device operation data. Evaluating the adjustable potential of the flexible load equipment based on the device operation data includes:

[0089] Obtain the device operation data of the flexible load device, where the device operation data includes the operation period of the flexible load device and the rated power of the flexible load device;

[0090] Calculate the adjustable potential coefficient of the operation period and the adjustable potential coefficient of the operation power of the flexible load device respectively through the device operation data;

[0091] Perform a weighted sum of the adjustable potential coefficient of the operation period and the adjustable potential coefficient of the operation power to obtain the adjustable potential index of the flexible load device, and the adjustable potential index is used to quantify the adjustable potential of the flexible load device during the participation in the regulation process;

[0092] Calculate the adjustable potential coefficient of the operation period and the adjustable potential coefficient of the operation power of the flexible load device respectively through the device operation data, including:

[0093] Obtain the peak period of the grid load, and perform an intersection operation on the operation period of the flexible load device and the peak period of the grid load to obtain the overlapping duration between the operation period of the flexible load device and the peak period of the grid load;

[0094] Take the ratio of the overlapping duration to the total operation duration of the flexible load device as the adjustable potential coefficient of the operation period of the flexible load device. The calculation formula of the adjustable potential coefficient of the operation period can be:

[0095]

[0096] In the formula, T overlap is the overlapping duration between the operation period of the flexible load device and the peak period of the grid load, T overlap =||T run ∩T high ||, T run is the time set of the operation period of the flexible load device, T high is the time set of the peak period of the grid load, ||T run ∩T high || is the number of elements in the intersection set, T total is the total operation duration of the flexible load device, K T is the adjustable potential coefficient of the operation period of the flexible load device;

[0097] Take the difference between the rated power of the flexible load device and the standby power of the flexible load device as the adjustable power of the flexible load device;

[0098] Take the ratio of the adjustable power of the flexible load device to the average value of the rated powers of all flexible load devices as the adjustable potential coefficient of the operation power of the flexible load device. The calculation formula of the adjustable potential coefficient of the operation power can be:

[0099]

[0100] Where P rated is the rated power of the elastic load device, and P standby is the standby power of the elastic load device. is the average value of the rated power of the elastic load device. P rated,i is the rated power of the i-th elastic load device in the power grid, n is the number of elastic load devices in the power grid, and K P is the adjustable potential coefficient of the operating power.

[0101] In the embodiment of the present application, the load fluctuation prediction module 230 is used to predict the power grid load fluctuation situation of the elastic load device during the participation in the regulation process based on the adjustable potential evaluation result. Predicting the power grid load fluctuation situation of the elastic load device during the participation in the regulation process includes:

[0102] Obtain the adjustable potential index and device operation data of the elastic load device. The device operation data includes the operation time period of the elastic load device and the rated power of the elastic load device;

[0103] Take the product of the adjustable potential index of the elastic load device and the rated power of the elastic load device as the load fluctuation contribution value of the elastic load device;

[0104] Accumulate the load fluctuation contribution values of all elastic load devices in the power grid to obtain the total power grid load fluctuation value;

[0105] Take the ratio of the total power grid load fluctuation value to the power grid reference load value as the power grid load fluctuation index. The power grid load fluctuation index is used to quantify the power grid load fluctuation situation of all elastic load devices during the participation in the regulation process.

[0106] In the embodiment of the present application, the accuracy loss prediction module 240 is used to predict the risk of power metering accuracy loss of the elastic load device during the participation in the regulation process based on the power grid load fluctuation situation. Predicting the risk of power metering accuracy loss of the elastic load device during the participation in the regulation process includes:

[0107] Obtain the adjustable power of the elastic load device and the power grid load fluctuation index. The adjustable power of the elastic load device is the difference between the rated power of the elastic load device and the standby power of the elastic load device;

[0108] Take the ratio of the average value of the adjustable power of all elastic load devices in the power grid to the average value of the rated power of the elastic load device as the instantaneous power fluctuation index of the elastic load device;

[0109] The instantaneous power fluctuation index and the grid load fluctuation index are weighted and summed to obtain the power metering accuracy loss risk index, which is used to quantify the power metering accuracy loss risk of the flexible load device during the participation in the regulation process.

[0110] In the embodiment of the present application, the metering strategy optimization module 250 is used to optimize the power metering strategy based on the power metering accuracy loss risk. Optimizing the power metering strategy based on the power metering accuracy loss risk includes:

[0111] Obtain the power metering accuracy loss risk index. When the power metering accuracy loss risk index is less than the preset power metering accuracy loss risk threshold, the steady-state power metering algorithm is used for power metering. The steady-state power metering algorithm includes the dot product and power metering algorithm and the fast Fourier transform power metering algorithm;

[0112] When the power metering accuracy loss risk index is greater than or equal to the preset power metering accuracy loss risk threshold, the dynamic power metering algorithm is used for power metering. The dynamic power metering algorithm includes the estimated fundamental frequency power metering algorithm, the improved fast Fourier transform power metering algorithm, and the time-frequency analysis power metering algorithm.

[0113] For the steps of each parameter and each unit module in the power metering system based on the adjustable resource scenario analysis of the present application to implement the corresponding functions, reference can be made to the parameters and steps in the embodiment of the power metering method based on the adjustable resource scenario analysis in the above text, which will not be elaborated here.

[0114] Please refer to Figure 4 , an embodiment of the present invention also provides an electronic device 300, including a memory 310, a processor 320, and a communication bus 330; the memory 310 and the processor 320 are connected through the communication bus 330. The memory 310 stores instructions that can be loaded and executed by the processor 320 to perform the power metering method based on the adjustable resource scenario analysis provided in the above embodiment.

[0115] The memory 310 can be used to store instructions, programs, codes, code sets, or instruction sets. The memory 310 may include a program storage area and a data storage area. Among them, the program storage area can store instructions for implementing the operating system, instructions for at least one function, and instructions for implementing the power metering method based on the adjustable resource scenario analysis provided in the above embodiment, etc.; the data storage area can store data involved in the power metering method based on the adjustable resource scenario analysis provided in the above embodiment, etc.

[0116] The processor 320 may include one or more processing cores. By running or executing instructions, programs, code sets, or instruction sets stored in the memory 310, the processor 320 invokes the data stored in the memory 310 to perform various functions of this application and process data. The processor 320 may be at least one of an application specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a central processing unit (CPU), a controller, a microcontroller, and a microprocessor. It can be understood that for different devices, the electronic devices for implementing the functions of the above-mentioned processor 320 may also be others, and the embodiments of this application do not make specific limitations.

[0117] The communication bus 330 may include a path for transmitting information between the above components. The communication bus 330 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The communication bus 330 may be divided into an address bus, a data bus, a control bus, etc. For the sake of convenience of representation, Figure 4 only a double arrow is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0118] The embodiments of this application provide a computer-readable storage medium storing a computer program that can be loaded and executed by a processor to perform the power metering method based on adjustable resource scenario analysis provided in the above embodiments.

[0119] In the embodiments of the present application, a computer-readable storage medium may be a tangible device that holds and stores instructions used by an instruction execution device. The computer-readable storage medium may be, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination of the foregoing. Specifically, the computer-readable storage medium may be a portable computer disk, a hard disk, a USB flash drive, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a podium random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, an optical disk, a magnetic disk, a mechanical encoding device, and any combination of the foregoing.

[0120] The term "comprising", "including" or any other variation thereof is intended to cover a non-exclusive inclusion, such that a process, method, article or apparatus that comprises a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or apparatus.

[0121] The above description is only a preferred embodiment of the present application and an explanation of the applied technical principle. Those skilled in the art should understand that the scope of the application involved in the present application is not limited to the technical solution formed by the specific combination of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the foregoing application concept. For example, a technical solution formed by mutually replacing the above features with (but not limited to) technical features having similar functions in the present application.

Claims

1. The electric energy metering method based on adjustable resource scenario analysis is characterized in that: The steps include: Based on the working characteristics of the electrical equipment, the electrical equipment is divided into fixed load equipment and flexible load equipment, and the equipment operation data of the flexible load equipment is obtained; Evaluate the adjustable potential of elastic load equipment based on equipment operation data; Based on the adjustable potential assessment results, the grid load fluctuation of the elastic load equipment during the regulation process is estimated; Estimate the risk of loss of electric energy metering accuracy of flexible load equipment during the regulation process based on grid load fluctuations; Optimize electric energy metering strategy based on the risk of loss of electric energy metering accuracy.

2. The electric energy metering method based on adjustable resource scenario analysis according to claim 1 is characterized in that: The adjustable potential assessment of the elastic load device based on the device operation data includes: Acquire equipment operation data of the elastic load equipment, where the equipment operation data includes an operation period of the elastic load equipment and a rated power of the elastic load equipment; Calculate the adjustable potential coefficient of the operation period and the adjustable potential coefficient of the operation power of the elastic load equipment respectively through the equipment operation data; The adjustable potential coefficient of the operating period and the adjustable potential coefficient of the operating power are weightedly summed to obtain the adjustable potential index of the elastic load equipment. The adjustable potential index is used to quantify the adjustable potential of the elastic load equipment during the adjustment process.

3. The electric energy metering method based on adjustable resource scenario analysis according to claim 2 is characterized in that: The step of calculating the adjustable potential coefficient of the operation period and the adjustable potential coefficient of the operation power of the elastic load equipment respectively through the equipment operation data includes: Obtain the peak load period of the power grid, and perform an intersection operation on the operation period of the elastic load device and the peak load period of the power grid to obtain the overlapping time length of the operation period of the elastic load device and the peak load period of the power grid; The ratio of the overlapping time to the total operation time of the elastic load equipment is used as the adjustable potential coefficient of the operation time of the elastic load equipment; The difference between the rated power of the elastic load equipment and the standby power of the elastic load equipment is used as the adjustable power of the elastic load equipment; The ratio of the adjustable power of the elastic load equipment to the average rated power of all elastic load equipment is taken as the adjustable operating power potential coefficient of the elastic load equipment.

4. The electric energy metering method based on adjustable resource scenario analysis according to claim 1 is characterized in that: The method of estimating the power grid load fluctuation of the elastic load device during the regulation process based on the adjustable potential assessment result includes: Obtaining an adjustable potential index and equipment operation data of the elastic load equipment, wherein the equipment operation data includes an operation period of the elastic load equipment and a rated power of the elastic load equipment; The product of the adjustable potential index of the elastic load equipment and the rated power of the elastic load equipment is taken as the load fluctuation contribution value of the elastic load equipment; The load fluctuation contribution values ​​of all elastic load devices in the power grid are accumulated to obtain the total load fluctuation value of the power grid; The ratio of the total grid load fluctuation value to the grid benchmark load value is taken as the grid load fluctuation index. The grid load fluctuation index is used to quantify the grid load fluctuation of all elastic load devices in the grid during the regulation process.

5. The electric energy metering method based on adjustable resource scenario analysis according to claim 3 is characterized in that: The method of estimating the risk of loss of electric energy metering accuracy of the elastic load equipment during the regulation process based on the load fluctuation of the power grid includes: Obtaining the adjustable power of the elastic load device and the grid load fluctuation index, wherein the adjustable power of the elastic load device is the difference between the rated power of the elastic load device and the standby power of the elastic load device; The ratio of the average adjustable power of all elastic load devices in the power grid to the average rated power of the elastic load devices is used as the instantaneous power fluctuation index of the elastic load devices; The instantaneous power fluctuation index and the grid load fluctuation index are weightedly summed to obtain the electric energy metering accuracy loss risk index, which is used to quantify the risk of electric energy metering accuracy loss of elastic load equipment during the regulation process.

6. The electric energy metering method based on adjustable resource scenario analysis according to claim 1 is characterized in that: The method for optimizing the electric energy metering strategy based on the risk of electric energy metering accuracy loss includes: Obtaining an electric energy metering accuracy loss risk index; when the electric energy metering accuracy loss risk index is less than a preset electric energy metering accuracy loss risk threshold, adopting a steady-state electric energy metering algorithm to perform electric energy metering; the steady-state electric energy metering algorithm includes a dot product sum electric energy metering algorithm and a fast Fourier transform electric energy metering algorithm; When the electric energy metering accuracy loss risk index is greater than or equal to the preset electric energy metering accuracy loss risk threshold, the dynamic electric energy metering algorithm is used for electric energy metering. The dynamic electric energy metering algorithm includes the estimated fundamental frequency electric energy metering algorithm, the improved fast Fourier transform electric energy metering algorithm and the time-frequency analysis electric energy metering algorithm.

7. An electric energy metering system based on adjustable resource scenario analysis, applied to an electric energy metering method based on adjustable resource scenario analysis as claimed in any one of claims 1 to 6, characterized in that: The system comprises: A data acquisition module, used to divide the electrical equipment into fixed load equipment and elastic load equipment based on the working characteristics of the electrical equipment and to acquire equipment operation data of the elastic load equipment; An adjustable potential evaluation module is used to evaluate the adjustable potential of elastic load equipment based on equipment operation data; The load fluctuation prediction module is used to predict the grid load fluctuation of the elastic load equipment during the regulation process based on the adjustable potential evaluation results; The accuracy loss estimation module is used to estimate the risk of energy metering accuracy loss of elastic load equipment during the regulation process based on the grid load fluctuations; The metering strategy optimization module is used to optimize the electric energy metering strategy based on the risk of loss of electric energy metering accuracy.

8. An electronic device comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; characterized in that the processor executes the electric energy metering method based on adjustable resource scenario analysis as described in any one of claims 1 to 6 by calling the computer program stored in the memory.

9. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are executed on a computer, the computer is caused to execute the electric energy metering method based on adjustable resource scenario analysis as described in any one of claims 1 to 6.

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