A random power flow crossing electric energy settlement error detection method and system

Abstract

The application discloses a kind of random tidal flow crossing electric energy settlement error detection method and system, the random tidal flow crossing electric energy settlement error detection method of the present application includes the basic parameter random generation multiple groups of simulation working condition data based on target electric energy settlement device;The theoretical electric energy settlement information corresponding to each group of simulation working condition data is obtained;Through control program control source and electric energy meter output each group of simulation working condition data, and the measured electric energy settlement information obtained using target electric energy settlement device is collected electric energy meter output simulation working condition data;Theoretical electric energy settlement information and measured electric energy settlement information are compared and the comprehensive electric energy settlement error detection result of target electric energy settlement device is calculated.The present application aims to realize the efficient detection of random tidal flow crossing electric energy settlement error by combining the actual operation output of electric energy meter, ensure the accuracy and timeliness of electric energy settlement error detection.

Description

Technical Field

[0001] This invention relates to electricity metering technology, specifically to a method and system for detecting errors in electricity settlement during random power flow. Background Technology

[0002] As a crucial energy management tool, the accuracy of electricity settlement is not only affected by the precision of electricity data but also closely related to the accuracy of the internal algorithms within the settlement terminal. Due to the intermittent, volatile, and unpredictable nature of renewable energy generation, grid-connected power flows are complex and ever-changing. Particularly in scenarios with distributed renewable energy multi-point access and multiple grid outgoing lines sharing a common busbar, the direction and magnitude of power flows change frequently, significantly impacting electricity metering and settlement. For example, when there is an imbalance between supply and demand on a large power grid line, power flowing off the grid at a certain node may flow back to other grid nodes via the busbar and reconnect to the grid, resulting in grid-side cross-connection power. If, within a settlement cycle, only the change in the base code of the meter at the gateway (current energy value base code minus initial energy value base code) is used for grid connection and settlement, it will lead to serious conflicts and disputes between renewable energy plants and the grid. In new power systems, the large-scale access of distributed power sources and energy storage devices results in a multi-node grid connection, requiring comprehensive analysis of electricity metering values ​​from multiple nodes, which increases the demands for accuracy and timeliness in settlement. However, existing power metering and testing technologies are insufficient to meet the accuracy requirements of new power systems for power settlement result detection. To achieve accurate detection of power transmission across multiple nodes, the following technical challenges exist: (1) Challenges related to the dynamic characteristics of power transmission: The dynamic characteristics of power transmission often lead to unstable power settlement results. Existing power metering verification standard sources are all high-precision outputs for steady-state conditions, which are difficult to simulate for dynamic changes in node power transmission. The widespread application of new energy and energy storage devices causes the direction of power flow and power level to change continuously. Power meters need to adapt to these changes, which increases the difficulty of power settlement error detection. (2) Error problems in discrete data acquisition: Existing power settlement error detection methods have large errors due to the discreteness of power meter readings. The data acquisition of power meters relied upon in the correctness detection of power settlement at each node is discrete and asynchronous in time. Directly performing settlement based on discrete power metering data will lead to large deviations in the results, affecting the accuracy of power settlement. (3) Currently, there are only multi-node power flow analysis methods, but no suitable simulation tools for detecting electricity settlement errors. It is difficult to fully reproduce the special attributes of actual power systems, such as losses and metering errors, and it is impossible to actually detect the correctness of the settlement results of multi-node power flow, which limits the accuracy and reliability of electricity settlement error detection. How to achieve accurate detection of multi-node power flow has become a key technical problem that urgently needs to be solved. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a method and system for detecting random power flow crossing power settlement errors, in view of the above-mentioned problems in the prior art. The present invention aims to achieve efficient detection of random power flow crossing power settlement errors by combining the actual operating output of the power meter, thereby ensuring the accuracy and timeliness of power settlement error detection.

[0004] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0005] A method for detecting electricity settlement errors during random power flow crossings includes the following steps: randomly generating multiple sets of simulated operating condition data based on the basic parameters of the target electricity settlement device; acquiring the theoretical electricity settlement information corresponding to each set of simulated operating condition data; outputting each set of simulated operating condition data by controlling the programmable source and the electricity meter, and using the target electricity settlement device to collect the simulated operating condition data output by the electricity meter to obtain the measured electricity settlement information; comparing the theoretical electricity settlement information and the measured electricity settlement information and calculating the comprehensive electricity settlement error detection result of the target electricity settlement device.

[0006] Optionally, when multiple sets of simulated operating condition data are randomly generated based on the basic parameters of the target electricity billing device, the basic parameters of the target electricity billing device include the number of grid-connected nodes and the number of user-side nodes, and the number of grid-connected nodes and the number of user-side nodes satisfy the following:

[0007] ;

[0008] in, This refers to the number of nodes on the grid-connected side. For the number of user-side nodes, This represents the total number of nodes.

[0009] Optionally, the random generation of multiple sets of simulated operating condition data based on the basic parameters of the target electricity billing device includes: configuring the grid-connected and user-side nodes to be simulated according to the number of grid-connected nodes and user-side nodes; setting the node attributes of the grid-connected and user-side nodes, including node number, the actual electricity meter corresponding to the power topology, the node's enabled or disabled status, and the node's transformation ratio; based on the node attributes of the grid-connected and user-side nodes, randomly generating N sets of forward and reverse electricity data that meet preset constraints as N sets of simulated operating condition data within the single meter collection interval of the electricity meter; for grid-connected nodes, the electricity flowing into the bus from the grid-connected node is forward electricity data, and the electricity flowing back from the bus to the grid-connected side is reverse electricity data; for user-side nodes, the electricity flowing into the bus from the user-side node is reverse electricity data, and the electricity transmitted from the bus to the user side is forward electricity data; the functional expression of the preset constraints is:

[0010] ;

[0011] in, This refers to the number of nodes on the grid-connected side. For the number of user-side nodes, This represents the total number of nodes. For the positive electrical energy data of the i-th node in the n-th set of simulated operating conditions, This refers to the reverse electrical energy data of the i-th node in the n-th set of simulated operating conditions. This is the group number for the simulated operating condition data. , The number of sets of simulated operating condition data; in all In the nodes, the first One node is a grid-connected node, and the remaining nodes are user-side nodes.

[0012] Optionally, when obtaining the theoretical energy settlement information corresponding to each set of simulated operating condition data, the theoretical energy settlement information corresponding to any j-th set of simulated operating condition data includes the energy crossover flag of the j-th set of simulated operating condition data. and the power value of crossing If the j-th set of simulated operating data experiences a power crossing, then the power crossing occurrence flag is set. Set to 1; otherwise, the charge crossing flag will be set to 1. The battery level is 0, indicating the battery level during the journey. Let be the electricity consumption value of the j-th group of simulated operating conditions within a single meter sampling interval of the electricity meter.

[0013] Optionally, when outputting each set of simulated operating condition data by controlling the programmable source and the energy meter, the process includes providing a corresponding output voltage to each node through K programmable voltage sources, providing a corresponding output current to each node through K programmable current sources, and generating the output current of the K programmable current sources for each set of simulated operating condition data according to the following formula:

[0014] ;

[0015] in, The output current of the programmable current source corresponding to the i-th node in the n-th set of simulated operating data. For the positive electrical energy data of the i-th node in the n-th set of simulated operating conditions, This refers to the reverse electrical energy data of the i-th node in the n-th set of simulated operating conditions. Let i be the transformer ratio of the i-th node in the n-th set of simulated operating data. The output voltage of the programmable voltage source at the i-th node in the n-th set of simulated operating data is given. The output times of the programmable current source and programmable voltage source in the nth set of simulated operating data. The total number of nodes, in the subscript. This is the group number for the simulated operating condition data. , The number of sets of simulated operating data; and the output time of the programmable current source and programmable voltage source in the nth set of simulated operating data. Less than the single meter reading interval of the electricity meter.

[0016] Optionally, when obtaining the measured electricity settlement information by collecting simulated operating condition data output by the electricity meter using the target electricity settlement device, the measured electricity settlement information corresponding to any j-th group of simulated operating condition data includes the electricity crossover flag bit of the j-th group of simulated operating condition data. and battery level If the measured electrical crossover occurs in the j-th group of simulated operating data, then the electrical crossover occurrence flag is set. Set to 1; otherwise, the charge crossing flag will be set to 1. The battery level is 0, indicating the battery level during the journey. Let be the electricity consumption value of the j-th group of simulated operating conditions within a single meter sampling interval of the electricity meter.

[0017] Optionally, the functional expression for comparing theoretical and measured electricity settlement information and calculating the comprehensive electricity settlement error detection result of the target electricity settlement device is as follows:

[0018] ;

[0019] ;

[0020] ; ;

[0021] in, The overall electricity settlement error detection results for the target electricity settlement device; and These are the weighting coefficients, and and The sum of the two is 1; This represents the total number of nodes. The number of sets of simulated operating condition data; The flag indicating that the energy crossover did not occur simultaneously for the theoretical and measured energy settlement information of the j-th group of simulated operating data is set. If both the theoretical and measured energy crossover flags are 0, then... The value is 1 if it is 1, otherwise the value is 0. The flag indicating simultaneous occurrence of energy crossover for both theoretical and measured energy settlement information in the j-th group of simulated operating data is set. If both the theoretical and measured energy settlement information flags are 1, then... The value is 1 if it is 1, otherwise the value is 0. The relative error between the theoretical and measured electricity consumption values ​​of the j-th set of simulated operating data is given by the formula . and Let N be the number of operating conditions in the theoretical energy settlement information where electricity consumption crosses over, and the number of simulated operating condition data sets where electricity consumption crosses over. This is the preset allowable error. and These are the electricity crossing occurrence flag and the crossing electricity value, respectively, for the theoretical electricity settlement information of the j-th group of simulated operating data. and These are the power crossing flag and the power crossing value, respectively, of the measured power settlement information for the j-th group of simulated operating conditions.

[0022] The present invention also provides a random power flow crossing power settlement error detection system, comprising a microprocessor and a memory interconnected thereto, wherein the microprocessor is programmed or configured to execute the random power flow crossing power settlement error detection method.

[0023] The present invention also provides a computer-readable storage medium storing a computer program or instructions that are programmed or configured to execute the random power flow crossing energy settlement error detection method by a processor.

[0024] The present invention also provides a computer program product, including a computer program or instructions, which are programmed or configured to execute the random power flow crossing energy settlement error detection method via a processor.

[0025] Compared with the prior art, the present invention can mainly achieve the following beneficial effects:

[0026] 1. Addressing the current challenge of having only multi-node power flow analysis methods but lacking suitable simulation tools for detecting electricity billing errors, this invention combines real-time data from electricity meters to calculate billing results, thus accurately reflecting the performance of electricity billing algorithms under actual operating conditions. Specifically, this invention utilizes real-time data collected from electricity meters and compares it with simulated operating conditions to dynamically detect the accuracy of electricity billing. This method effectively avoids the problem of traditional methods failing to respond to changes in grid conditions in real time, ensuring the timeliness and accuracy of electricity billing error detection. Furthermore, compared to traditional complex simulation processes, this invention simplifies the verification framework, reduces computational costs and time consumption, and better meets the requirements of modern power systems for rapid response and real-time performance. Through this innovative approach, this invention significantly improves the efficiency of electricity billing error detection, making it more adaptable to the rapid changes and complex needs of modern power systems.

[0027] 2. To address the problem of significant errors in existing electricity billing error detection methods due to the discreteness of electricity meter readings and the influence of random power flow fluctuations, this invention employs multiple electricity meters to synchronously simulate complex power flow conditions. This effectively avoids metering errors caused by the discreteness of electricity meter readings. Specifically, the synchronous simulation output of multiple electricity meters generates more continuous and accurate electricity metering data, which better reflects the actual power flow, thus providing a more accurate basis for electricity billing error detection. Simultaneously, this method helps to cope with the volatility of random power flow, ensuring reliable detection results even under frequent changes in power flow. Compared with traditional methods, this method not only improves detection accuracy but also significantly enhances detection efficiency. By synchronously simulating complex power flow conditions, this invention better adapts to the needs of multi-node grid connection and frequent power direction switching in new power systems, providing a more reliable and practical method for electricity billing error detection. Attached Figure Description

[0028] Figure 1 This is a schematic diagram of the basic process of the method in an embodiment of the present invention. Detailed Implementation

[0029] To enable those skilled in the art to better understand the technical solutions of the present invention, the technical solutions of the present invention will be further described in detail below with reference to the accompanying drawings in the embodiments of the present invention.

[0030] like Figure 1 As shown, the random power flow crossing power settlement error detection method in this embodiment includes the following steps:

[0031] S101, randomly generate multiple sets of simulated operating condition data based on the basic parameters of the target electricity billing device;

[0032] S102, Obtain the theoretical energy settlement information corresponding to each set of simulated operating condition data;

[0033] S103 controls the programmable source and the energy meter to output each set of simulated operating condition data, and uses the target energy settlement device to collect the simulated operating condition data output by the energy meter to obtain the measured energy settlement information.

[0034] S104, compare the theoretical power settlement information and the measured power settlement information and calculate the comprehensive power settlement error detection result of the target power settlement device. The random power flow crossing power settlement error detection method in this embodiment can achieve efficient detection of random power flow crossing power settlement error by combining the actual operating output of the power meter, ensuring the accuracy and timeliness of power settlement error detection.

[0035] In this embodiment, when multiple sets of simulated operating condition data are randomly generated based on the basic parameters of the target electricity billing device, the basic parameters of the target electricity billing device include the number of grid-connected nodes and the number of user-side nodes, and the number of grid-connected nodes and the number of user-side nodes satisfy the following:

[0036] ;

[0037] in, This refers to the number of nodes on the grid-connected side. For the number of user-side nodes, This represents the total number of nodes. The number of grid-connected nodes and user-side nodes can be obtained from the power plant's operational status information. This information includes, but is not limited to, historical cumulative electricity data, the power plant's internal grid connection and disconnection paths and wiring methods, and the installation locations of electricity meters. "A set of simulated operating condition data" refers to K nodes, including... Each grid-connected node and Each user-side node, within the single meter data collection interval Within this system, software randomly generates forward and reverse cumulative electricity data that meet corresponding constraints. This data is used to simulate and describe the power plant's operating conditions within a single meter collection interval and is referred to as "theoretical electricity settlement information." In this embodiment, based on the historical cumulative power generation data of a photovoltaic power plant, the power plant's internal grid connection and grid disconnection paths, and the installation location of the metering energy meters, the basic parameters of the energy settlement device configured corresponding to the power plant's operating status information can be obtained. The information of the grid-connected nodes and user-side nodes is as follows: ① 220kV-D1 line 201 circuit breaker: Forward power generation data is the grid connection power generation from the D1 line to the 220kV busbar; ② 220kV-D2 line 202 circuit breaker: Forward power generation data is the grid connection power generation from the D2 line to the 220kV busbar; ③ U1 line 291 circuit breaker: Reverse power generation data is the grid connection power generation from the U1 line to the photovoltaic power plant area; ④ U2 line 292 circuit breaker: Reverse power generation data is the grid connection power generation from the U2 line to the photovoltaic power plant area; ⑤ U3 line 293 circuit breaker: Reverse power generation data is the grid connection power generation from the U3 line to the photovoltaic power plant area. The final number of grid-connected nodes is determined. 2. Number of user-side nodes 3. Total number of nodes It is 5.

[0038] To accommodate the complex and changing characteristics of random power flow crossings, each node's data should include both forward and reverse power flow attributes. The power flow directions are defined as follows: power flowing from the grid-connected node into the bus is forward power flow data, and power flowing back from the bus to the grid-connected side is reverse power flow data; power flowing from the user-side node into the bus is reverse power flow data, and power transmitted from the bus to the user side is forward power flow data. For the positive electrical energy data of the i-th node in the n-th set of simulated operating conditions, This represents the reverse power data of the i-th node in the n-th set of simulated operating conditions. Specifically, it requires that each node has a power consumption data set within a single meter data collection interval. Only positive or negative power data is allowed within the node (ensuring a unique power flow direction). Each node accumulates only positive or negative power. Therefore:

[0039] ;

[0040] Based on the energy conservation law formula that must be satisfied in the node line (the net energy flowing from the grid-connected node to the bus in the node line is equal to the net energy flowing from the bus to the user-side node), a suitable random data generation calculation method is selected to assign values ​​to the forward and reverse energy data of each node. For example, it can be done by first generating... The forward or reverse electrical energy data of a node with randomness is then processed according to the following constraints:

[0041] ;

[0042] Determining the forward or reverse electrical energy data of the last node can improve computational efficiency. Repeating the above steps generates N sets of simulated operating condition data with the same properties, and preset constraints between the data. In this embodiment, the random generation of multiple sets of simulated operating condition data based on the basic parameters of the target electricity billing device includes: configuring the grid-connected and user-side nodes to be simulated according to the number of grid-connected nodes and user-side nodes; setting the node attributes of the grid-connected and user-side nodes, including node number, the actual electricity meter corresponding to the power topology, the node's enabled or disabled status, and the node's transformation ratio; based on the node attributes of the grid-connected and user-side nodes, within the single meter collection interval of the electricity meter, randomly generating N sets of forward and reverse electricity data that satisfy preset constraints as N sets of simulated operating condition data. For the grid-connected nodes, the electricity flowing into the bus from the grid-connected nodes is forward electricity data, and the electricity flowing back from the bus to the grid-connected side is reverse electricity data; for the user-side nodes, the electricity flowing into the bus from the user-side nodes is reverse electricity data, and the electricity transmitted from the bus to the user side is forward electricity data; the functional expression of the preset constraints is:

[0043] ;

[0044] in, This refers to the number of nodes on the grid-connected side. For the number of user-side nodes, This represents the total number of nodes. For the positive electrical energy data of the i-th node in the n-th set of simulated operating conditions, This refers to the reverse electrical energy data of the i-th node in the n-th set of simulated operating conditions. This is the group number for the simulated operating condition data. , The number of sets of simulated operating condition data; in all In the nodes, the first One node is the grid-connected node, and the remaining nodes are user-side nodes. The randomly generated nth set of simulation data includes... One grid-connected node, Individual user-side nodes, single meter data collection interval The forward and reverse cumulative power data of the i-th node and In this embodiment, the data acquisition interval time is... All were set to 15 minutes; a total of 100 simulated operating condition data were generated (N=100), as shown in Table 1.

[0045]

[0046] In Table 1, and These are the forward and reverse electrical data for the first node (circuit breaker 201), respectively. and These are the forward and reverse electrical data for the second node (circuit breaker 202), and so on.

[0047] In this embodiment, when obtaining the theoretical energy settlement information corresponding to each set of simulated operating condition data, the theoretical energy settlement information corresponding to any j-th set of simulated operating condition data includes the energy crossover flag of the j-th set of simulated operating condition data. and battery level If the j-th set of simulated operating data experiences a power crossing, then the power crossing occurrence flag is set. Set to 1; otherwise, the charge crossing flag will be set to 1. The battery level is 0, indicating the battery level during the journey. This represents the electricity consumption crossing value within a single meter reading interval of the j-th set of simulated operating data. The electricity consumption crossing occurrence flag is also included. When it is 0, the crossing power value The value is 0. In this embodiment, the theoretical energy settlement information corresponding to the 100 sets of simulated operating condition data is shown in Table 2.

[0048]

[0049] In Table 2, and These are the forward and reverse electrical data for the first node (circuit breaker 201), respectively. and These are the forward and reverse electrical data for the second node (circuit breaker 202), and so on.

[0050] In this embodiment, when controlling the programmable source and the energy meter to output each set of simulated operating condition data, it includes providing corresponding output voltages to each node through K programmable voltage sources, providing corresponding output currents to each node through K programmable current sources, and generating the output currents of the K programmable current sources for each set of simulated operating condition data according to the following formula:

[0051] ;

[0052] in, The output current of the programmable current source corresponding to the i-th node in the n-th set of simulated operating data. For the positive electrical energy data of the i-th node in the n-th set of simulated operating conditions, This refers to the reverse electrical energy data of the i-th node in the n-th set of simulated operating conditions. Let i be the transformer ratio of the i-th node in the n-th set of simulated operating data. The output voltage of the programmable voltage source at the i-th node in the n-th set of simulated operating data is given. The output times of the programmable current source and programmable voltage source in the nth set of simulated operating data. The total number of nodes, in the subscript. This is the group number for the simulated operating condition data. , The number of sets of simulated operating data; and the output time of the programmable current source and programmable voltage source in the nth set of simulated operating data. The interval between single meter data collection is less than that of the electricity meter. The programmable source can be divided into programmable current sources and programmable voltage sources. For programmable voltage sources, different voltage sources can be selected as the main constant voltage source for stable output and connected to the electricity meter corresponding to each node. For programmable current sources, K programmable current sources need to be selected to adjust the output power of the electricity meters at the corresponding nodes to match each set of randomly generated simulated operating condition data. This can be regarded as a regulator of the electricity meter output power. By adjusting the output magnitude of the programmable voltage and current sources, the generated simulated operating condition data can be run in real time on each electricity meter. The adjustment method is calculated according to the above formula. To adapt to the system's time delay, the output duration of the programmable source in each set of simulated operating condition data is... It must be less than or equal to the interval between single meter readings. , can be represented as: In this embodiment, the output voltage of each node corresponding to the programmable voltage source is set. Both are 220V, , , , , =[5000,5000,5000,5000,5000], the output duration of the programmable source in each set of simulated operating condition data. With all values ​​set to 15 minutes, the output values ​​of the programmable current source in the energy meter corresponding to each node can be calculated, as shown in Table 3.

[0053]

[0054] In Table 3, ~ These are the output currents of the programmable current sources corresponding to the five nodes' corresponding energy meters.

[0055] Based on the simulated operating condition data generated in real time from each energy meter output by the access programmable power source, the energy billing device under test is analyzed according to the interval of the energy meter data collection. The electricity consumption data of the electricity meters is collected. The electricity consumption data obtained from each meter by the electricity settlement device under test is called the measured electricity settlement information. It should be noted that the measured electricity settlement information is generated randomly by software simulation, consisting of multiple sets of simulated operating condition data that satisfy energy conservation constraints. During the generation of the simulated operating conditions, electricity settlement information for each set of simulated operating condition data is obtained and recorded as the measured electricity settlement information. The measured electricity settlement information is based on "theoretical electricity settlement information," which is generated by controlling the output of a programmable power source, running through the electricity meter, and finally processed by the electricity settlement device under test according to the meter's data collection interval. The forward and reverse power consumption data of each electricity meter are obtained. The electricity billing device under test calculates electricity billing information in real time based on the measured electricity billing information using its internal electricity billing algorithm, and this information is recorded as the measured electricity billing information. Similar to the measured electricity billing information, the electricity billing information (also called the measured electricity billing information) is calculated in real time based on the electricity meter output data using the electricity billing algorithm within the electricity billing device. This also includes the data collected during a single meter data collection interval. This includes determining whether a power flow occurred during the operation of each set of operating conditions and the value of the power flow during the operation of each set of operating conditions. Specifically, in this embodiment, when obtaining the measured power settlement information by collecting the simulated operating condition data output by the power meter using the target power settlement device, the measured power settlement information corresponding to any j-th set of simulated operating condition data includes the power flow occurrence flag bit of the j-th set of simulated operating condition data. and battery level If the measured electrical crossover occurs in the j-th group of simulated operating data, then the electrical crossover occurrence flag is set. Set to 1; otherwise, the charge crossing flag will be set to 1. The battery level is 0, indicating the battery level during the journey. This represents the electricity consumption crossing value within a single meter reading interval of the j-th set of simulated operating data. The electricity consumption crossing occurrence flag is also included. When it is 0, the crossing power value The value is 0. In this embodiment, the power data of the energy meters corresponding to each node in the 00 simulated operating condition data are shown in Table 4.

[0056]

[0057] In Table 4, and These are the forward and reverse electrical data for the first node (circuit breaker 201), respectively. and The forward and reverse power data are for the second node (circuit breaker 202), and so on. The target power settlement device calculates the power settlement information in real time based on the measured power settlement information using its internal power settlement algorithm. This information is recorded as the measured power settlement information, and the final measured power settlement information is shown in Table 5.

[0058]

[0059] In this embodiment, the function expression for comparing theoretical and measured electricity settlement information and calculating the comprehensive electricity settlement error detection result of the target electricity settlement device is as follows:

[0060] ;

[0061] ;

[0062] ; ;

[0063] in, The overall electricity settlement error detection results for the target electricity settlement device; and , where are weighting coefficients, representing the relative importance of the judgment results of no crossing and the judgment results of crossing in relation to the final overall accuracy when calculating the accuracy of the target electricity billing device, respectively. and The sum of the two is 1, that is: ; This represents the total number of nodes. The number of sets of simulated operating condition data; The flag indicating that the energy crossover did not occur simultaneously for the theoretical and measured energy settlement information of the j-th group of simulated operating data is set. If both the theoretical and measured energy crossover flags are 0, then... A value of 1 or 0 can be represented as:

[0064] ;

[0065] Since the target electricity billing device identifies that no electricity crossing has occurred, the value of the crossed electricity is... and All are 0, therefore no further error verification is needed for the calculation of the crossing power value; with N sets of simulated operating data, the number of valid verifications for the target power billing device under the condition that no power crossing occurs is:

[0066] .

[0067] The flag indicating simultaneous occurrence of energy crossover for both theoretical and measured energy settlement information in the j-th group of simulated operating data is set. If both the theoretical and measured energy settlement information flags are 1, then... A value of 1 or 0 can be represented as:

[0068] ;

[0069] Even if the target electricity billing device can accurately identify whether an electricity crossing has occurred, the calculated value of the crossed electricity amount is still uncertain. and There will be errors, therefore a function is introduced. Further error verification is performed based on the original judgment to accurately count the number of valid verifications of the target electricity billing device in the event of electricity crossover.

[0070] The relative error between the theoretical and measured electricity consumption values ​​of the j-th set of simulated operating data is given by the formula . This indicates the allowable threshold for the relative error between the electricity crossing amount in the "Theoretical Electricity Settlement Information" and the electricity crossing amount in the "Theoretical Electricity Settlement Information" for operating conditions where electricity crossing occurs; if the relative error does not meet the allowable error... The output is 1 if the condition is met, otherwise it is 0. Under N operating conditions, the number of valid verifications of the target energy billing device in the event of energy ride-through can be expressed as:

[0071] ;

[0072] and Let N be the number of operating conditions in the theoretical energy settlement information where electricity consumption crosses over, and the number of simulated operating condition data sets where electricity consumption crosses over. This is the preset allowable error. and These are the electricity crossing occurrence flag and the crossing electricity value, respectively, for the theoretical electricity settlement information of the j-th group of simulated operating data. and These are the power crossing occurrence flag and the crossing power value, respectively, of the measured power settlement information for the j-th group of simulated operating conditions. Specifically, in this embodiment, we take... and The values ​​are 0.2 and 0.8 respectively. By comparing the theoretical and measured electricity settlement information, the accuracy of the target electricity settlement device is statistically analyzed, and a comprehensive electricity settlement error detection result is obtained. The result is 98.76%. The calculation process is shown in Table 6.

[0073]

[0074] Comprehensive Electricity Settlement Error Detection Results This refers to the quantitative result of the electricity billing error of the target electricity billing device, which can be used to determine the availability of the target electricity billing device. For example, a four-level (unacceptable, pending calibration, available, and recommended) grading evaluation method can be used to determine the evaluation level of the electricity billing error of the target electricity billing device. As an optional implementation method, the evaluation reference table used for the four-level grading evaluation method is shown in Table 7.

[0075]

[0076] The results of the comprehensive electricity billing error detection Based on Table 7, the evaluation level of the random power flow crossing power settlement error of the target power settlement device can be determined as "Recommended" (98.76%).

[0077] In summary, the random power flow crossing power settlement error detection method of this embodiment first obtains the basic parameters of the target power settlement device based on the power plant's operating status information, including the number of grid-connected nodes and the number of user-side nodes. Then, based on the above parameter information, it generates multiple sets of simulated operating condition data with randomness and satisfying the law of energy conservation, referred to as "theoretical power settlement information". Next, it obtains the power settlement information of each set of simulated operating condition data, referred to as measured power settlement information, such as whether power flow crossing occurs during operation under a certain operating condition, and the calculated value of the crossing power flow under a certain operating condition. Based on simulated operating condition data, the output values ​​of each programmable current source and programmable voltage source are controlled and connected to the energy meter corresponding to the node in the power topology. The energy meter then operates the operating condition data in real time. Next, the energy settlement device under test collects the output data of the energy meter, which is called "collected settlement data". The energy settlement information based on the energy meter output data is calculated in real time by the energy settlement algorithm inside the energy settlement device, which is called measured energy settlement information. Finally, the measured energy settlement information is compared and verified with the measured energy settlement information. The accuracy of the target energy settlement device is statistically analyzed to obtain the quantitative result of the energy settlement error of the target energy settlement device, and an evaluation level can be further determined.

[0078] Those skilled in the art will understand that the technical solutions provided by this invention can take the form of methods, systems, or computer program products. For example, this invention can provide a stochastic power flow crossing energy billing error detection system, including a microprocessor and a memory interconnected, wherein the microprocessor is programmed or configured to execute the stochastic power flow crossing energy billing error detection method. This invention can provide a computer-readable storage medium storing a computer program or instructions programmed or configured to execute the stochastic power flow crossing energy billing error detection method via a processor. This invention can provide a computer program product including a computer program or instructions programmed or configured to execute the stochastic power flow crossing energy billing error detection method via a processor. Furthermore, this invention can also take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, this invention can take the form of a computer program product implemented on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each block of a flowchart and / or block diagram, and combinations of blocks in a flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing device, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0079] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A method for detecting errors in electricity billing during random power flow crossings, characterized in that, The process includes the following steps: Randomly generating multiple sets of simulated operating condition data based on the basic parameters of the target electricity billing device, including: configuring the grid-connected and user-side nodes to be simulated according to the number of grid-connected nodes and user-side nodes; setting the node attributes of the grid-connected and user-side nodes, including node number, the corresponding actual electricity meter in the power topology, the node's enabled or disabled status, and the node's transformation ratio; based on the node attributes of the grid-connected and user-side nodes, randomly generating N sets of forward and reverse electricity data satisfying preset constraints as N sets of simulated operating condition data within the single meter data collection interval of the electricity meter. For grid-connected nodes, the electricity flowing into the bus from the grid-connected node is forward electricity data, and the electricity flowing back from the bus to the grid-connected side is reverse electricity data; for user-side nodes, the electricity flowing into the bus from the user-side node is reverse electricity data, and the electricity transmitted from the bus to the user side is forward electricity data; the functional expression of the preset constraints is: ； in, This refers to the number of nodes on the grid-connected side. For the number of user-side nodes, This represents the total number of nodes. For the positive electrical energy data of the i-th node in the n-th set of simulated operating conditions, This refers to the reverse electrical energy data of the i-th node in the n-th set of simulated operating conditions. This is the group number for the simulated operating condition data. , The number of sets of simulated operating condition data; in all In the nodes, the first One node is the grid-connected node, and the remaining nodes are user-side nodes; acquire the theoretical energy settlement information corresponding to each set of simulated operating condition data; output each set of simulated operating condition data by controlling the programmable source and energy meter, and use the target energy settlement device to collect the measured energy settlement information obtained from the simulated operating condition data output by the energy meter; compare the theoretical energy settlement information and the measured energy settlement information and calculate the comprehensive energy settlement error detection result of the target energy settlement device: ； ； ； ； in, The overall electricity settlement error detection results for the target electricity settlement device; and These are the weighting coefficients, and and The sum of the two is 1; This represents the total number of nodes. The number of sets of simulated operating condition data; The flag indicating that the energy crossover did not occur simultaneously for the theoretical and measured energy settlement information of the j-th group of simulated operating data is set. If both the theoretical and measured energy crossover flags are 0, then... The value is 1 if it is not 1, otherwise the value is 0. The flag indicating simultaneous occurrence of energy crossover for both theoretical and measured energy settlement information in the j-th group of simulated operating data is set. If both the theoretical and measured energy settlement information flags are 1, then... The value is 1 if it is not 1, otherwise the value is 0. The relative error between the theoretical and measured electricity consumption values ​​of the j-th set of simulated operating data is given by the formula . and Let N be the number of operating conditions in the theoretical energy settlement information where electricity consumption crosses over, and the number of simulated operating condition data sets where electricity consumption crosses over. This is the preset allowable error. and These are the electricity crossing occurrence flag and the crossing electricity value, respectively, for the theoretical electricity settlement information of the j-th group of simulated operating data. and These are the power crossing flag and the power crossing value, respectively, of the measured power settlement information for the j-th group of simulated operating conditions.

2. The method for detecting errors in random power flow crossing electricity calculation according to claim 1, characterized in that, When multiple sets of simulated operating condition data are randomly generated based on the basic parameters of the target electricity billing device, the basic parameters of the target electricity billing device include the number of grid-connected nodes and the number of user-side nodes, and the number of grid-connected nodes and the number of user-side nodes satisfy the following: ； in, This refers to the number of nodes on the grid-connected side. For the number of user-side nodes, This represents the total number of nodes.

3. The method for detecting errors in random power flow crossing electricity calculation according to claim 1, characterized in that, When acquiring the theoretical energy settlement information corresponding to each set of simulated operating condition data, the theoretical energy settlement information corresponding to any j-th set of simulated operating condition data includes the energy crossover flag of the j-th set of simulated operating condition data. and the power value of crossing If the j-th set of simulated operating data experiences a power crossing, then the power crossing occurrence flag is set. Set to 1; otherwise, the charge crossing flag will be set to 1. The value is 0, indicating the battery level during the crossing. Let be the electricity consumption value of the j-th group of simulated operating conditions within a single meter sampling interval of the electricity meter.

4. The method for detecting errors in random power flow crossing electricity calculation according to claim 1, characterized in that, When outputting each set of simulated operating condition data by controlling the programmable source and the energy meter, this includes providing corresponding output voltages to each node through K programmable voltage sources, providing corresponding output currents to each node through K programmable current sources, and generating the output currents of the K programmable current sources for each set of simulated operating condition data according to the following formula: ； in, The output current of the programmable current source corresponding to the i-th node in the n-th set of simulated operating data. For the positive electrical energy data of the i-th node in the n-th set of simulated operating conditions, This refers to the reverse electrical energy data of the i-th node in the n-th set of simulated operating conditions. Let i be the transformer ratio of the i-th node in the n-th set of simulated operating data. The output voltage of the programmable voltage source at the i-th node in the n-th set of simulated operating data is given. The output times of the programmable current source and programmable voltage source in the nth set of simulated operating data. The total number of nodes, in the subscript. This is the group number for the simulated operating condition data. , The number of sets of simulated operating data; and the output time of the programmable current source and programmable voltage source in the nth set of simulated operating data. Less than the single meter reading interval of the electricity meter.

5. The method for detecting errors in random power flow crossing energy calculation according to claim 3, characterized in that, When obtaining the measured electricity settlement information by collecting simulated operating condition data output by the electricity meter using the target electricity settlement device, the measured electricity settlement information corresponding to any j-th group of simulated operating condition data includes the electricity crossover flag bit of the j-th group of simulated operating condition data. and the power value of crossing If the measured electrical crossover occurs in the j-th group of simulated operating data, then the electrical crossover occurrence flag is set. Set to 1; otherwise, the charge crossing flag will be set to 1. The value is 0, indicating the battery level during the crossing. Let be the electricity consumption value of the j-th group of simulated operating conditions within a single meter sampling interval of the electricity meter.

6. A random power flow crossing electricity billing error detection system, comprising a microprocessor and a memory interconnected, characterized in that, The microprocessor is programmed or configured to perform the random power flow crossing power settlement error detection method according to any one of claims 1 to 5.

7. A computer-readable storage medium storing a computer program or instructions, characterized in that, The computer program or instructions are programmed or configured to execute, via a processor, the random power flow crossing power settlement error detection method according to any one of claims 1 to 5.

8. A computer program product, comprising a computer program or instructions, characterized in that, The computer program or instructions are programmed or configured to execute, via a processor, the random power flow crossing power settlement error detection method according to any one of claims 1 to 5.

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