Method for settling electricity charges, and distributed architecture settlement system, medium

Abstract

The application is applied to the technical field of smart grid, and discloses a method for settling electricity charges, a distributed architecture settlement system and a medium. The method comprises the following steps: obtaining and fusing original state data by using a data fusion module to obtain a real-time operation state section, and then generating situation index data flow based on the real-time operation state section; obtaining a real-time dynamic electricity price sequence and a power consumption plan corresponding to a plurality of power consumption areas based on the situation index data flow by using a distributed collaborative computing decision network; obtaining an actual power consumption power curve based on the power consumption plan by using a tracking metering module; then obtaining a cumulative power deviation; and calculating user cumulative electricity charges and user cumulative adjustment charges based on the real-time dynamic electricity price sequence, the actual power consumption power curve and the cumulative power deviation by using a parallel settlement processing module, and determining a user final electricity charge bill. In this way, the user can be guided to adjust power consumption behavior to meet the demand of source-grid-load-storage collaborative interaction.

Description

Technical Field

[0001] This invention relates to the field of smart grid technology, specifically to a method for settling electricity bills, a distributed architecture settlement system, and a medium. Background Technology

[0002] Currently, the global energy structure is undergoing rapid transformation. Renewable energy sources, represented by photovoltaics and wind power, along with distributed energy resources such as distributed power sources, distributed energy storage, and flexible loads, are being massively integrated into the power system. This is driving the traditional power system to shift from a one-way dispatch mode of "source follows load" to a new multi-faceted, collaborative, and two-way interactive mode of "source-grid-load-storage interaction." The new power system places higher demands on real-time balance, flexible regulation, and economical and efficient operation, urgently requiring a simultaneous upgrade of the electricity metering and settlement system to support real-time guidance of electricity price signals, flexible load response, and efficient grid control.

[0003] Current mainstream electricity billing and settlement systems generally adopt a centralized architecture and offline batch processing mode. The system uses a central server as its core, and based on a preset fixed electricity price model, it centrally collects, batch-calculates, and settles fees for historical electricity consumption data uploaded by users' smart meters. However, in new power system operating scenarios, this traditional approach suffers from a disconnect between electricity price signals and grid status, failing to guide users to adjust their electricity consumption behavior in a timely manner. Load response lags behind grid regulation needs, hindering the system's ability to absorb renewable energy and maintain safe and stable operation, and making it difficult to adapt to the requirements of coordinated interaction between power generation, grid, load, and storage.

[0004] Therefore, in order to overcome the above-mentioned technical problems, the present invention provides a method for settling electricity bills, as well as a distributed architecture settlement system and medium. Summary of the Invention

[0005] The technical problem to be solved by this invention is how to guide users to adjust their electricity consumption behavior in order to meet the needs of coordinated interaction between power generation, grid, load and storage. The purpose is to provide a method for settling electricity bills and a distributed architecture settlement system and medium to guide users to adjust their electricity consumption behavior in order to meet the needs of coordinated interaction between power generation, grid, load and storage.

[0006] This invention is achieved through the following technical solution:

[0007] Firstly, a method for settling electricity bills.

[0008] This method is applied to a distributed architecture settlement system. The distributed architecture settlement system includes a data fusion module, a distributed collaborative computing decision network, a tracking and metering module, and a parallel settlement processing module. The method includes: using the data fusion module to acquire and fuse the original state data of the power grid to obtain a real-time operating status profile of the power grid; then generating a status indicator data stream of the power grid within a preset time period based on the real-time operating status profile; using the distributed collaborative computing decision network to acquire a real-time dynamic electricity price sequence and electricity consumption plans corresponding to several electricity consumption areas based on the status indicator data stream; using the tracking and metering module to acquire the actual electricity consumption curve within the preset time period based on the electricity consumption plan; then acquiring the cumulative electricity consumption deviation between the planned electricity consumption curve and the actual electricity consumption curve; using the parallel settlement processing module to calculate the user's cumulative electricity bill and cumulative adjustment fee in parallel based on the real-time dynamic electricity price sequence, the actual electricity consumption curve, and the cumulative electricity consumption deviation; and determining the user's final electricity bill based on the user's cumulative electricity bill and cumulative adjustment fee.

[0009] In some embodiments, this method is applied to a distributed architecture settlement system; the distributed architecture settlement system includes: a data fusion module, a distributed collaborative computing decision network, a tracking and metering module, and a parallel settlement processing module; the method includes: using the data fusion module to acquire and fuse the original state data of the power grid to obtain a real-time operating status profile of the power grid, and then generating a status indicator data stream of the power grid within a preset time period based on the real-time operating status profile; using the distributed collaborative computing decision network to acquire a real-time dynamic electricity price sequence and electricity consumption plans corresponding to several electricity consumption areas based on the status indicator data stream; using the tracking and metering module to acquire the actual electricity consumption curve within the preset time period based on the electricity consumption plan; then acquiring the cumulative electricity consumption deviation between the planned electricity consumption curve and the actual electricity consumption curve; using the parallel settlement processing module to calculate the user's cumulative electricity bill and user's cumulative adjustment fee in parallel based on the real-time dynamic electricity price sequence, the actual electricity consumption curve, and the cumulative electricity consumption deviation, and determining the user's final electricity bill based on the user's cumulative electricity bill and user's cumulative adjustment fee.

[0010] In some embodiments, the raw state data includes: real-time power generation data, real-time power grid data, real-time user electricity consumption data, and real-time environmental data.

[0011] In some embodiments, generating the power grid status indicator data stream within a preset time period based on the real-time operating status profile includes: predicting the load and new energy output within the preset time period to obtain prediction data; and obtaining the status indicator data stream based on the prediction data and the real-time operating status profile.

[0012] In some embodiments, the distributed collaborative computing decision network includes: a central coordination server and intelligent agent server clusters corresponding to each of the electricity consumption areas; the step of using the distributed collaborative computing decision network to obtain the real-time dynamic electricity price sequence and the electricity consumption plan corresponding to several electricity consumption areas based on the situation indicator data stream includes: using the central coordination server to obtain a reference shadow price vector; in the first iteration, the reference shadow price vector is obtained through the situation indicator data stream; in the m-th iteration, the reference shadow price vector is obtained through the reference shadow price vector of the m-1th iteration; using each of the intelligent agent server clusters to determine the alternative electricity consumption plan corresponding to each of the electricity consumption areas based on the reference shadow price vector; the alternative electricity consumption plan corresponding to each of the electricity consumption areas includes the alternative planned power consumption curves of several users in each of the electricity consumption areas within a preset time period; using the central coordination server to obtain the total planned load curve of the entire network based on each of the alternative electricity consumption plans; then obtaining the total power supply capacity curve of the entire network based on the situation indicator data stream; then obtaining the supply and demand matching deviation based on the total planned load curve and the total power supply capacity curve of the entire network; and obtaining the real-time dynamic electricity price sequence and the electricity consumption plan based on the supply and demand matching deviation.

[0013] In some embodiments, during the m-th iteration, the reference shadow price vector is obtained by: obtaining the product of the supply-demand matching deviation at the (m-1)-th iteration and a preset step size factor; and determining the sum of the product and the reference shadow price vector at the (m-1)-th iteration as the reference shadow price vector at the m-th iteration.

[0014] In some embodiments, during the m-th iteration, the reference shadow price vector is obtained by: obtaining the product of the supply-demand matching deviation at the (m-1)-th iteration and a preset step size factor; and determining the sum of the product and the reference shadow price vector at the (m-1)-th iteration as the reference shadow price vector at the m-th iteration.

[0015] In some embodiments, the step of using the parallel settlement processing module to calculate the user's cumulative electricity bill and cumulative adjustment fee in parallel based on the real-time dynamic electricity price sequence, the actual power consumption curve, and the cumulative power consumption deviation, and determining the user's final electricity bill based on the user's cumulative electricity bill and the user's cumulative adjustment fee, includes: within a preset settlement period, using the parallel settlement processing module to obtain the real-time electricity bill increment for several users in each of the electricity consumption areas within a preset time period based on the real-time dynamic electricity price sequence and the actual power consumption curve; simultaneously, obtaining the real-time adjustment service fee for several users in each of the electricity consumption areas within a preset time period based on the cumulative power consumption deviation; updating the user's cumulative electricity bill based on the real-time electricity bill increment using the parallel settlement processing module; simultaneously, updating the user's cumulative adjustment fee based on the real-time adjustment service fee; and at the end of the settlement period, determining the sum of the user's cumulative electricity bill and the user's cumulative adjustment fee as the user's final electricity bill.

[0016] Secondly, a distributed architecture settlement system includes: a data fusion module configured to acquire and fuse raw state data of the power grid to obtain a real-time operating status profile of the power grid, and then generate a status indicator data stream of the power grid within a preset time period based on the real-time operating status profile; a distributed collaborative computing decision network configured to acquire a real-time dynamic electricity price sequence and electricity consumption plans corresponding to several electricity consumption areas based on the status indicator data stream; a tracking metering module configured to acquire an actual electricity consumption curve within a preset time period based on the electricity consumption plan; and then acquire the cumulative electricity consumption deviation between the planned electricity consumption curve and the actual electricity consumption curve; and a parallel settlement processing module configured to calculate the user's cumulative electricity bill and user's cumulative adjustment fee in parallel based on the real-time dynamic electricity price sequence, the actual electricity consumption curve, and the cumulative electricity consumption deviation, and determine the user's final electricity bill based on the user's cumulative electricity bill and user's cumulative adjustment fee.

[0017] Thirdly, a storage medium stores program instructions that, when executed, perform the aforementioned method for settling electricity bills.

[0018] Compared with existing technologies, this invention obtains and merges the original state data of the power grid using a data fusion module to obtain a real-time operating status profile of the power grid. Then, based on the real-time operating status profile, it generates a situation indicator data stream of the power grid within a preset time period. A distributed collaborative computing decision network obtains the real-time dynamic electricity price sequence and the electricity consumption plan corresponding to several electricity consumption areas based on the situation indicator data stream. Then, a tracking and metering module obtains the actual electricity consumption curve within the preset time period based on the electricity consumption plan. Next, it obtains the cumulative electricity consumption deviation between the planned electricity consumption curve and the actual electricity consumption curve. Then, a parallel settlement processing module calculates the user's cumulative electricity bill and user's cumulative adjustment fee in parallel based on the real-time dynamic electricity price sequence, the actual electricity consumption curve, and the cumulative electricity consumption deviation. Finally, it determines the user's final electricity bill based on the user's cumulative electricity bill and user's cumulative adjustment fee. In this way, compared with existing technologies, this solution directly couples the data fusion module and the distributed collaborative computing decision network to transform the raw state data representing the physical state of the power grid into an executable real-time dynamic electricity price sequence and electricity consumption plans corresponding to several electricity consumption areas. Then, the actual power consumption curve and cumulative power consumption deviation within a preset time period are obtained through the tracking metering module and the parallel settlement processing module. Based on the actual power consumption curve and cumulative power consumption deviation, the final electricity bill for users is obtained, realizing the real-time quantification and settlement of user electricity consumption. This binds the electricity price to the state of the power grid, making it easier to guide users to adjust their electricity consumption behavior in a timely manner based on the final electricity bill. This meets the needs of source-grid-load-storage collaborative interaction and effectively improves the dynamic response speed and stability of the power grid to fluctuations. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be considered as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort. In the drawings:

[0020] Figure 1 This is a schematic diagram of a distributed architecture settlement system provided in an embodiment of this disclosure;

[0021] Figure 2 This is a flowchart illustrating a method for settling electricity bills provided in an embodiment of this disclosure;

[0022] Figure 3 This is a schematic diagram of an electronic device provided in an embodiment of this disclosure. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0024] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0025] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0026] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0027] In this application, "multiple" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0028] Please see Figure 1 , Figure 1 This is a schematic diagram of a distributed architecture settlement system illustrated in an exemplary embodiment of this application.

[0029] like Figure 1 As shown, the distributed architecture settlement system 100 includes: a data fusion module 101, a distributed collaborative computing decision network 102, a tracking and metering module 103, and a parallel settlement processing module 104.

[0030] Among them, the data fusion module 101 is configured to acquire and fuse the original state data of the power grid to obtain the real-time operating status profile of the power grid, and then generate the status index data stream of the power grid within a preset time period based on the real-time operating status profile.

[0031] The distributed collaborative computing decision network 102 is configured to acquire real-time dynamic electricity price sequences and electricity consumption plans corresponding to several electricity consumption areas based on situation indicator data streams;

[0032] The tracking metering module 103 is configured to obtain the actual power consumption curve within a preset time period based on the power consumption plan; and then obtain the cumulative power consumption deviation between the planned power consumption curve and the actual power consumption curve.

[0033] The parallel settlement processing module 104 is configured to calculate the user's cumulative electricity bill and cumulative adjustment fee in parallel based on the real-time dynamic electricity price sequence, the actual power consumption curve and the cumulative power deviation, and determine the user's final electricity bill based on the user's cumulative electricity bill and cumulative adjustment fee.

[0034] The distributed architecture settlement system provided in this disclosure uses a data fusion module to acquire and fuse the original state data of the power grid to obtain a real-time operating status profile of the power grid. Then, based on the real-time operating status profile, a situation indicator data stream of the power grid within a preset time period is generated. A distributed collaborative computing decision network uses the situation indicator data stream to obtain a real-time dynamic electricity price sequence and electricity consumption plans corresponding to several electricity consumption areas. Then, a tracking and metering module uses the electricity consumption plan to obtain the actual electricity consumption curve within the preset time period. Then, the cumulative electricity consumption deviation between the planned electricity consumption curve and the actual electricity consumption curve is obtained. Then, a parallel settlement processing module uses the real-time dynamic electricity price sequence, the actual electricity consumption curve, and the cumulative electricity consumption deviation to calculate the user's cumulative electricity bill and the user's cumulative adjustment fee in parallel. Finally, the user's final electricity bill is determined based on the user's cumulative electricity bill and the user's cumulative adjustment fee. In this way, compared with existing technologies, this solution directly couples the data fusion module and the distributed collaborative computing decision network to transform the raw state data representing the physical state of the power grid into an executable real-time dynamic electricity price sequence and electricity consumption plans corresponding to several electricity consumption areas. Then, the actual power consumption curve and cumulative power consumption deviation within a preset time period are obtained through the tracking metering module and the parallel settlement processing module. Based on the actual power consumption curve and cumulative power consumption deviation, the final electricity bill for users is obtained, realizing the real-time quantification and settlement of user electricity consumption. This binds the electricity price to the state of the power grid, making it easier to guide users to adjust their electricity consumption behavior in a timely manner based on the final electricity bill. This meets the needs of source-grid-load-storage collaborative interaction and effectively improves the dynamic response speed and stability of the power grid to fluctuations.

[0035] Please see Figure 2 , Figure 2 This is a schematic flowchart illustrating a method for settling electricity bills, as shown in an exemplary embodiment of this application. The method is applied to, for example... Figure 1 The distributed architecture settlement system shown; such as Figure 2 As shown in the embodiments of this disclosure, a method for settling electricity bills is provided, the method comprising:

[0036] Step S101: Use the data fusion module to acquire and fuse the original state data of the power grid to obtain the real-time operating status profile of the power grid, and then generate the status index data stream of the power grid within a preset time period based on the real-time operating status profile.

[0037] Step S102: Use a distributed collaborative computing decision network to obtain real-time dynamic electricity price sequences and electricity consumption plans corresponding to several electricity consumption areas based on situation indicator data streams.

[0038] Step S103: Use the tracking metering module to obtain the actual power consumption curve within a preset time period based on the power consumption plan; then obtain the cumulative power consumption deviation between the planned power consumption curve and the actual power consumption curve.

[0039] Step S104: The parallel settlement processing module calculates the user's cumulative electricity bill and cumulative adjustment fee in parallel based on the real-time dynamic electricity price sequence, the actual power consumption curve and the cumulative power deviation, and determines the user's final electricity bill based on the user's cumulative electricity bill and cumulative adjustment fee.

[0040] In this embodiment, the original state data of the power grid is acquired and fused using a data fusion module to obtain a real-time operating status profile of the power grid. Then, based on the real-time operating status profile, a status indicator data stream of the power grid within a preset time period is generated. A distributed collaborative computing decision network is used to obtain a real-time dynamic electricity price sequence and electricity consumption plans corresponding to several electricity consumption areas based on the status indicator data stream. Then, a tracking and metering module is used to obtain the actual power consumption curve within the preset time period based on the power consumption plan. Then, the cumulative power consumption deviation between the planned power consumption curve and the actual power consumption curve is obtained. Then, a parallel settlement processing module is used to calculate the user's cumulative electricity bill and user's cumulative adjustment fee in parallel based on the real-time dynamic electricity price sequence, the actual power consumption curve, and the cumulative power consumption deviation. Finally, the user's final electricity bill is determined based on the user's cumulative electricity bill and user's cumulative adjustment fee. In this way, compared with existing technologies, this solution directly couples the data fusion module and the distributed collaborative computing decision network to transform the raw state data representing the physical state of the power grid into an executable real-time dynamic electricity price sequence and electricity consumption plans corresponding to several electricity consumption areas. Then, the actual power consumption curve and cumulative power consumption deviation within a preset time period are obtained through the tracking metering module and the parallel settlement processing module. Based on the actual power consumption curve and cumulative power consumption deviation, the final electricity bill for users is obtained, realizing the real-time quantification and settlement of user electricity consumption. This binds the electricity price to the state of the power grid, making it easier to guide users to adjust their electricity consumption behavior in a timely manner based on the final electricity bill. This meets the needs of source-grid-load-storage collaborative interaction and effectively improves the dynamic response speed and stability of the power grid to fluctuations.

[0041] Furthermore, the data fusion module is used to acquire and fuse the original state data of the power grid to obtain a real-time operating state profile of the power grid, including: acquiring the original state data using the data fusion module; cleaning and time-aligning the original state data using the data fusion module to obtain reference state data; and acquiring the real-time operating state profile based on the reference state data using the data fusion module based on a preset real-time state estimator.

[0042] In this way, by using the data fusion module to obtain real-time operating status sections based on the original state data of the power grid, an accurate description of the real-time state of the power grid is achieved.

[0043] Furthermore, the raw state data includes: real-time power generation data, real-time power grid data, real-time user electricity consumption data, and real-time environmental data.

[0044] It should be noted that the data fusion module is a distributed acquisition and computing module consisting of a front-end sensor network and a back-end regional data aggregation server cluster.

[0045] The front-end sensor network is a network composed of several front-end sensors. Specifically, the front-end sensors include: power transmitters deployed in new energy power plants on the power generation side, synchronous phasor measurement units deployed in the power grid transmission network, feeder terminal units deployed in the distribution network, meteorological sensors deployed in the load center of the power grid, and smart meters on the user side.

[0046] The backend regional data aggregation server cluster consists of multiple geographically distributed regional data aggregation servers; each regional data aggregation server is equipped with a high-precision GPS clock synchronization card, which can run simultaneously to process data, ensuring the time determinism of data processing.

[0047] It should be noted that new energy power plants can be photovoltaic power plants and / or wind power plants. Meteorological sensors may include temperature sensors, humidity sensors, light intensity sensors, anemometers, and wind direction sensors.

[0048] Real-time power generation data is acquired by power transmitters and feeder terminal units. Specifically, real-time power generation data includes one or more of the following from renewable energy power plants: active power output, reactive power output, transformer tap position, circuit breaker status, line active power flow data, and line reactive power flow data, acquired by the power transmitter; and one or more of the following from ordinary power plants: active power output, reactive power output, transformer tap position, circuit breaker status, line active power flow data, and line reactive power flow data, acquired by the feeder terminal units. In some embodiments, the frequency at which the power transmitters and feeder terminal units acquire real-time power generation data is a preset medium rate, such as once every 2 seconds, once every 3 seconds, or once every 4 seconds.

[0049] Real-time power grid data is acquired by a synchronous phasor measurement unit. Specifically, the real-time power grid data includes line load rate, phase angle difference, voltage phasor, current phasor, frequency, and rate of frequency change for several preset key nodes in the power grid. In some embodiments, the synchronous phasor measurement unit acquires real-time power grid data at a preset extremely high rate, for example, 50 frames per second. The synchronous phasor measurement unit packages the acquired line load rate, phase angle difference, voltage phasor, current phasor, frequency, and rate of frequency change into a frame format conforming to the IEEE C37.118 standard and uploads it in real-time to the backend regional data aggregation server cluster via a dedicated network.

[0050] Real-time user electricity consumption data is collected by smart meters. This data includes the user's load, cumulative electricity consumption, and instantaneous power. In some embodiments, the smart meter may collect real-time user electricity consumption data at a lower rate. This lower rate may be once every 15 minutes; the smart meter may also collect real-time user electricity consumption data at a rate of once per second or once per minute.

[0051] Real-time environmental data is acquired by meteorological sensors. This real-time environmental data includes ambient temperature, humidity, light intensity, wind speed, and wind direction. In some embodiments, the meteorological sensors may acquire real-time environmental data at a frequency of minutes.

[0052] Specifically, the data fusion module is used to acquire raw state data, including: acquiring real-time power generation data using power transmitters and feeder terminal units; acquiring real-time grid data using synchronous phasor measurement units; acquiring real-time user electricity consumption data using smart meters; and acquiring real-time environmental data using meteorological sensors.

[0053] It should be noted that the data fusion module is used to clean and align the original state data over time to obtain reference state data. In other words, the backend regional data aggregation server cluster is used to clean and align the original state data over time to obtain reference state data.

[0054] Specifically, in the backend regional data aggregation server cluster, each regional data aggregation server corresponds to a different geographical region. This geographical region can be the aforementioned power consumption area, or it can be a region distinguished by other methods. However, the set of geographical regions is the same as the set of power consumption areas. Each front-end sensor in the front-end sensor network can upload the region to which its collected raw state data belongs to to the regional data aggregation server. The regional data aggregation server then performs data cleaning and time alignment on the raw state data sent by each front-end sensor to obtain reference state data.

[0055] It should be noted that data cleaning aims to correct or remove errors, noise, and inconsistencies in the original state data, making the data complete, correctly formatted, and with values ​​within their corresponding specified ranges, thus transforming it into uniform and readable data. Time alignment involves aligning the original state data to a unified timeline.

[0056] It should be noted that the data fusion module uses a preset real-time state estimator to obtain a real-time operating state profile based on reference state data. In other words, the backend regional data aggregation server cluster uses a preset real-time state estimator to obtain a real-time operating state profile based on reference state data.

[0057] Specifically, each regional data aggregation server runs a preset real-time state estimator. After cleaning and aligning the raw state data to obtain reference state data, the real-time operating state profile can be obtained based on the reference state data using the real-time state estimator.

[0058] Specifically, the real-time operating state profile is obtained based on the reference state data by the real-time state estimator, that is, the real-time operating state profile is obtained by solving the preset objective function based on the reference state data by the real-time state estimator.

[0059] It should be noted that the goal of the core mathematical model of the real-time state estimator is to solve for the optimal system state variables, namely the voltage magnitude and phase angle of all nodes in the power grid, and to minimize the weighted sum of squares between the measurement estimates calculated based on these system state variables and all actual measurement values.

[0060] Specifically, the mathematical expression for the objective function of the real-time state estimator is as follows: Here, x is the system state vector based on the assumption of reference state data; it includes the voltage magnitude and phase angle of all nodes in the power grid. This is the essential variable describing the physical state of the power grid; once the system state vector is determined, all other electrical quantities in the power grid, such as line power flow and node injected power, can be calculated using physical laws. The total number of measurements is the number of valid data points collected. This is the i-th actual measurement value; The measurement function describes how to calculate the theoretical value of the i-th valid data point from the current state vector x, based on Kirchhoff's laws and Ohm's law. This represents the weighting coefficient corresponding to the i-th actual measurement value. In some embodiments, this weighting coefficient is inversely proportional to the accuracy of the measuring device; the higher the accuracy, the greater the weight; the lower the accuracy, the smaller the weight. The measuring device can be a front-end sensor.

[0061] It should be noted that valid data points are reference state data. One valid data point corresponds to one reference state data point, which is one actual measurement value.

[0062] It should be noted that the real-time state estimator can be solved iteratively using the pre-defined Gauss-Newton method, which is a standard nonlinear optimization algorithm and is existing technology, so it will not be elaborated here.

[0063] During each round of the solution process, the residual for each measurement value is calculated. If the residual of a certain actual measurement value is consistently large, it indicates that the measuring equipment corresponding to the actual measurement value is faulty, or that the actual measurement value is an undetected abnormal data. If the reliability of the measuring equipment is determined to be reduced, the real-time state estimator will automatically reduce the weight coefficient corresponding to the actual measurement value, thereby ensuring that the final output power grid state estimate always approximates the real physical state and will not lead to decision-making errors due to the failure of a single sensor.

[0064] After the objective function of the real-time state estimator converges iteratively, a real-time operating state profile can be generated based on the system state variables at the convergence point. This real-time operating state profile includes the voltage and phase angle of each node in the power grid, as well as line power flow and network losses derived from the voltage and phase angle. Thus, this real-time operating state profile is no longer the original, chaotic measurements, but rather a system state vector validated by the physical model and optimally estimated, representing a high-confidence, complete real-time operating state profile of the power grid.

[0065] The real-time operating state profile obtained by the real-time state estimator describes the current state of the power grid, while dynamic pricing requires prediction of the future state of the power grid.

[0066] Furthermore, based on the real-time operating status section, a power grid status indicator data stream is generated within a preset time period, including: predicting the load and new energy output within the preset time period to obtain prediction data; and obtaining the status indicator data stream based on the prediction data and the real-time operating status section.

[0067] This enables the situational indicator data stream to describe the future output trend of the power grid, thereby facilitating the accurate acquisition of predictions for future electricity prices for users.

[0068] It should be noted that the preset time period is the period from 15 minutes to 1 hour in the future from the current time. The forecast data includes forecast load data and forecast renewable energy data; the forecast load data is the regional total load curve within the preset time period, meaning each region can correspond to a specific regional total load curve. The forecast renewable energy data is the renewable energy output curve within the preset time period.

[0069] It should be noted that the data aggregation servers in each region also have built-in ultra-short-term forecasting models. These ultra-short-term forecasting models are used to predict load and renewable energy output within a preset future time period.

[0070] Specifically, the ultra-short-term forecasting model includes a load forecasting sub-model and a new energy forecasting sub-model. The load forecasting sub-model is a pre-defined time series analysis model, such as a differentially integrated moving average autoregressive model, an LSMM (Long Short-Term Memory) model, or a recurrent neural network model. It can use historical load data, temperature data within a preset time period, and the date type of the current day within that preset time period to forecast the load for that preset time period, thus obtaining load forecasting data. The date type includes weekdays or holidays.

[0071] The new energy prediction sub-model can predict the new energy output within a preset time period based on a preset physical model or statistical learning method, using wind speed within a preset time period, solar radiation forecast data within a preset time period, and real-time power output data of new energy power plants, thereby obtaining new energy prediction data.

[0072] It should be noted that the sunshine forecast data refers to the predicted data of solar radiation and daily irradiance, which can be obtained through existing technology and will not be elaborated here.

[0073] In some embodiments, load and renewable energy output within a preset time period are predicted to obtain prediction data. This includes acquiring historical load data, temperature data within the preset time period, the date type of the current day within the preset time period, wind speed within the preset time period, sunshine forecast data within the preset time period, and real-time output data of the renewable energy power plant. Historical load data, temperature data within the preset time period, and the date type of the current day within the preset time period are input into the load prediction sub-model to obtain load prediction data; wind speed within the preset time period, sunshine forecast data within the preset time period, and real-time output data of the renewable energy power plant are input into the renewable energy prediction sub-model to obtain renewable energy prediction data.

[0074] It should be noted that the situation indicator data stream includes: timestamp, region ID, the basic signal of the marginal electricity price of each node in the region corresponding to the region ID, network congestion coefficient matrix, flexibility adjustment requirements, and forecast data.

[0075] The timestamp is used to indicate a preset time period.

[0076] It should be noted that since the new energy forecast data for wind speed and sunshine, as well as data such as electricity prices, will vary depending on the geographical region, each region can be assigned a region ID to facilitate separate forecasting and electricity billing for each region.

[0077] The basic signal of the marginal electricity price of each node can be obtained through prediction data, real-time operation status profiles, preset generation cost curves, and network topology calculations of the power grid.

[0078] Specifically, real-time operational status profiles provide the voltage, phase angle, and load levels of each node in the current power grid, serving as an operational baseline. Forecast data provides predicted load and renewable energy output for the next 15-30 minutes, facilitating ultra-short-term forecasting. The preset generation cost curve is the incremental cost curve for each generator unit, indicating the additional cost required to generate one more kilowatt-hour. The power grid topology includes topological parameters such as line impedance, transformer turns ratio, and line transmission capacity limits.

[0079] During the calculation, it is necessary to solve the mathematical model. ,in, The generator set g has an output of Cost of time, These are the decision variables. Their constraints include: power balance equations, line power flow equations, node voltage constraints, and upper and lower limits of unit output, etc.

[0080] Solve In this process, a Lagrange multiplier is introduced for each constraint. The Lagrange multiplier corresponding to the system power balance constraint is called the energy component, which reflects the marginal energy cost of the system, i.e., the increase in the total system cost when the load increases by one unit (assuming a uniform increase). The Lagrange multiplier corresponding to the line flow constraint is called the congestion component, reflecting the cost increase caused by line congestion. This causes the electricity price at some nodes to be higher than the energy component and lower than the energy component, guiding load flow and power output to alleviate congestion. Superimposing the energy component and the congestion component yields the basic signal of the node marginal electricity price.

[0081] It should be noted that in solving Furthermore, the expected active power flow of all lines can be determined based on the line power flow equation.

[0082] The network congestion coefficient matrix includes the expected load rates of several preset sections. Specifically, the expected load rate of the preset sections is obtained as follows: the sum of the expected active power flow of all lines in the preset critical sections is determined as the predicted transmission capacity; based on the predicted power flow, the expected load rate of the critical sections is calculated as (predicted transmission capacity / maximum transmission capacity). It should be noted that when the expected load rate exceeds a safety threshold, this coefficient will increase significantly, serving as a penalty signal for exacerbating congestion in subsequent pricing decisions.

[0083] It should be noted that a critical section refers to a "virtual channel" consisting of one or more key transmission lines, used to monitor important power transmission capacity between or within a region. For example, several AC lines in the "West-to-East Power Transmission" project can form a transmission section. This scheme is more concerned with whether the total power flow of this section exceeds its stability limit, rather than the power flow of each individual line within it.

[0084] The flexibility adjustment requirement is the reserve capacity requirement that the system needs to adjust upwards or downwards, calculated by predicting the ramp-up rate of load data and the volatility of renewable energy data.

[0085] It should be noted that the forecast data can determine several predicted values ​​for load and renewable energy output within a preset time period, as well as their forecast intervals. The forecast interval is the range within which each predicted value falls.

[0086] It should be noted that the reserve capacity demand for load and renewable energy output can be calculated separately. Specifically, the upward reserve demand is the upper limit of the forecast interval minus the point forecast value. The downward reserve demand is the point forecast value minus the lower limit of the forecast interval.

[0087] It should be noted that this power grid status indicator data stream, which has undergone "collection, cleaning, fusion, and prediction" four times, can be pushed to the distributed collaborative computing decision network in real time through a high-speed message bus to drive the next stage of dynamic pricing game.

[0088] Furthermore, the distributed collaborative computing decision network includes: a central coordination server and intelligent agent server clusters corresponding to each power consumption area; the distributed collaborative computing decision network uses situation indicator data streams to obtain real-time dynamic electricity price sequences and power consumption plans corresponding to several power consumption areas, including: using the central coordination server to obtain a reference shadow price vector; in the first iteration, the reference shadow price vector is obtained through the situation indicator data stream; in the m-th iteration, the reference shadow price vector is obtained through the reference shadow price vector of the m-1th iteration; using each intelligent agent server cluster to determine the alternative power consumption plan corresponding to each power consumption area based on the reference shadow price vector; the alternative power consumption plan corresponding to each power consumption area includes the alternative planned power consumption curves of several users in each power consumption area within a preset time period; using the central coordination server to obtain the total planned load curve of the entire network based on each alternative power consumption plan; then obtaining the total power supply capacity curve of the entire network based on the situation indicator data stream; then obtaining the supply and demand matching deviation based on the total planned load curve and the total power supply capacity curve of the entire network; and obtaining the real-time dynamic electricity price sequence and power consumption plan based on the supply and demand matching deviation.

[0089] In this way, the reference shadow price vector is obtained through the central coordination server, and the backup power selection plan corresponding to each power consumption area is determined by each intelligent agent server cluster based on the reference shadow price vector. Then, the central coordination server obtains the total planned load curve of the whole network based on each backup power selection plan, obtains the total power supply capacity curve of the whole network based on the situation indicator data stream, obtains the supply and demand matching deviation based on the total planned load curve and the total power supply capacity curve of the whole network, and then obtains the real-time dynamic electricity price sequence and power consumption plan based on the supply and demand matching deviation, thus realizing the accurate acquisition of the real-time dynamic electricity price sequence and power consumption plan.

[0090] It should be noted that in the distributed collaborative computing decision-making network, the decision-making nodes are connected through a high-speed dedicated network. The decision-making nodes include a central coordination server and intelligent agent server clusters corresponding to each power consumption area.

[0091] It should be noted that the central coordination server is a high-performance server deployed in the scheduling center. It runs a global coordination algorithm, does not access user privacy data, and only processes aggregated plan proposals and scalar price signals. Each server cluster is deployed on the load aggregator side or the user group side, consisting of multiple servers. Internally, it runs a local optimization solver and stores detailed power equipment models and historical data of the users under the aggregator's jurisdiction.

[0092] The central coordination server does not directly solve the global power consumption plan problem, but decomposes it into multiple sub-problems that can be solved in parallel. This decomposition is based on the natural division of load aggregators or user groups, such as by administrative region, voltage level, or user type. Each load aggregator or user group corresponds to a power consumption area and an independent cluster of intelligent agent servers, responsible for solving its internal sub-problems. Each load aggregator or user group includes multiple users, and each user corresponds to one intelligent agent server.

[0093] It should be noted that in the first iteration, the central coordination server generates an initial reference shadow price vector based on the received power grid status indicators, which includes the shadow price corresponding to each electricity consumption area. This shadow price is not the final electricity price, but rather serves as a "probing signal" to guide the optimization of each intelligent agent server cluster.

[0094] Specifically, in the first iteration, the reference shadow price vector is obtained as follows: by calculating... This yields the reference shadow price vector. The reference shadow price vector at the first iteration has a dimension equal to the number of key time periods, and the unit is yuan / kilowatt-hour. This provides the basic signal for the regional marginal electricity price corresponding to each key time period; This is a preset blocking penalty factor, which is an adjustable parameter used to convert the degree of physical blocking into an economic price penalty. This is the network congestion coefficient matrix corresponding to each critical time period. The critical time periods are several time periods after the preset time period has been divided.

[0095] Then the central coordination server can broadcast the reference shadow price vector to each intelligent agent server cluster, so that each intelligent agent server cluster can determine the corresponding backup power selection plan for each power consumption area based on the reference shadow price vector.

[0096] In this way, the originally isolated physical constraints of the power grid can be embedded into the optimization objective function of all subsequent user agents through mathematical transformation by using shadow prices, thus achieving deep coupling between the physical system and the economic system.

[0097] It should be noted that the regional marginal electricity price base signal here is a weighted average of the node marginal electricity price base signals of each node within the electricity consumption area under the jurisdiction of the intelligent agent server, and its weight is positively correlated with the load.

[0098] Furthermore, each intelligent agent server cluster determines the alternative power selection plan corresponding to each power consumption area based on the reference shadow price vector, including: using the intelligent agent servers in each intelligent agent server cluster to determine the shadow price corresponding to their respective power consumption area from the reference shadow price vector, and then determining the alternative power selection plan corresponding to each power consumption area based on the shadow price.

[0099] Thus, since the regional marginal electricity price base signals differ in different electricity consumption areas, each intelligent agent server cluster needs to use different shadow prices to determine alternative electricity selection plans.

[0100] Furthermore, in the m-th iteration, the reference shadow price vector is obtained as follows: the product of the supply and demand matching deviation in the (m-1)-th iteration and the preset step size factor is obtained; the sum of the product and the reference shadow price vector in the (m-1)-th iteration is determined as the reference shadow price vector in the m-th iteration.

[0101] In this way, the core idea of ​​the alternating direction multiplier method is adopted to update the reference shadow price vector, ensuring that the entire system can converge to the vicinity of the global optimum simply by exchanging prices and aggregation plans, without requiring the central server to know any user privacy data.

[0102] In some embodiments, the reference shadow price vector at the m-th iteration .in, This is the reference shadow price vector at the m-th iteration; This is the reference shadow price vector at the (m-1)th iteration; This is the step size factor, which is a positive number used to control the magnitude of price adjustments. Its size directly affects the convergence speed and stability. This is due to a mismatch between supply and demand.

[0103] It should be noted that each intelligent agent server maintains a user electricity consumption preference and constraint model library. This model library contains physical behavior models of different types of adjustable loads (i.e., electrical equipment) corresponding to each user within the electricity consumption area of ​​the load aggregator or user group, as well as user utility functions for these adjustable loads.

[0104] The backup power plan for each power consumption area is determined by using each intelligent agent server cluster based on the reference shadow price vector. Specifically, the shadow price corresponding to the power consumption area of ​​each intelligent agent server cluster is extracted based on the reference shadow price vector. Then, the backup power plan for each power consumption area is determined based on the shadow price.

[0105] For the k-th intelligent agent server cluster, the local optimization problem that needs to be solved to determine the alternative power selection plan can be abstracted as: ; ;in, is a decision variable, representing the power consumption of the j-th adjustable load under the agency's jurisdiction during each critical time period, and it is a vector parameter. Let be the user utility function corresponding to the j-th adjustable load, which quantifies the user's satisfaction from electricity consumption. For example, for electric vehicles, the user utility function is related to the degree to which the battery's state of charge reaches the target value; for air conditioners, the user utility function is related to the degree to which the indoor temperature deviates from the set point. This is a concave function, reflecting the law of diminishing marginal utility. This is the electricity cost item, calculated from the shadow price p, representing the expected electricity cost. M indicates transpose. It is a vector dot product, representing the total electricity cost of the j-th adjustable load. It is a matrix representation of physical constraints. It is a coefficient matrix. These are constant vectors. These constraints are determined by physical behavior models in the user electricity preference and constraint model library. For example: the charging power cannot exceed the maximum power of the corresponding charging pile in the area; electric vehicles must be charged to a specified level before leaving, etc.

[0106] Each intelligent agent server cluster can invoke its built-in optimization solver to solve the aforementioned local optimization problem using gradient descent, interior-point methods, or a dedicated mixed-integer linear programming solver. The optimization solver uses the shadow price *p* as input parameters and the utility function and physical constraints from the user model library as boundary conditions, performing iterative calculations to find the alternative planned power consumption curve that maximizes the objective function. It should be noted that, This includes the power consumption of each adjustable load corresponding to the kth intelligent agent server cluster during each critical time period, which is a vector parameter.

[0107] Since each agent only solves its own subproblem, and the size of the subproblem is much smaller than the global problem, all agents can run simultaneously on their own servers, making full use of distributed computing resources and improving the solution speed.

[0108] After the solution is obtained, each intelligent agent server cluster obtains its own locally optimal alternative power consumption plan, which is the power consumption curve that the intelligent agent server cluster most wants to execute under a given shadow price. The intelligent agent server cluster then sends this alternative power consumption plan to the central coordination server through the communication network.

[0109] Furthermore, the central coordination server is used to obtain the total planned load curve of the entire network based on each alternative power selection plan, including: using the central coordination server to aggregate the power consumption curves of each alternative plan to obtain the total planned load curve of the entire network.

[0110] The total power supply capacity curve of the entire network is obtained by extracting the total expected output of all generator units based on the situation indicator data stream.

[0111] It should be noted that the situation indicator data stream includes forecast data, namely load forecast data and renewable energy forecast data. Adding the renewable energy forecast data to the output data of traditional generating units yields the total power supply capacity curve for the entire grid. The output data of traditional generating units is known and does not require forecasting.

[0112] The supply-demand matching deviation is obtained by measuring the difference between the total planned load curve and the total power supply capacity curve of the entire network.

[0113] It should be noted that the supply-demand mismatch is a vector. It represents the difference between planned electricity consumption and available power supply in several future periods within a preset time period. A value greater than 0 indicates insufficient power supply, requiring load reduction or increased power generation. A value less than 0 indicates an oversupply of electricity, requiring an increase in load or a reduction in power generation.

[0114] Furthermore, based on the supply-demand matching deviation, real-time dynamic electricity price sequence and electricity consumption plan are obtained, including: using the central coordination server to determine the reference shadow price vector as the real-time dynamic electricity price sequence when the supply-demand matching deviation meets the preset convergence conditions; determining the alternative electricity consumption plan as the electricity consumption plan; and re-obtaining the reference shadow price vector for the next iteration when the supply-demand matching deviation does not meet the convergence conditions.

[0115] In this way, by iteratively adjusting shadow prices, the aggregate demand and aggregate supply of the system can eventually reach or nearly reach equilibrium in each time period. This helps reduce the peak-valley difference in the power system and improves the stability and reliability of the power grid.

[0116] It should be noted that the determination of whether the supply and demand matching deviation meets the preset convergence condition is made in the following way: obtaining the norm of the supply and demand matching deviation; if the norm is less than the preset convergence tolerance, it is determined that the preset convergence condition is met; otherwise, it is determined that the preset convergence condition is not met.

[0117] It should be noted that the norm of the supply-demand matching deviation Used to measure the degree of supply-demand mismatch across the entire network. Preset convergence tolerance. It is a positive number close to 0, when the norm of the supply-demand matching deviation is less than 0. When the system reaches an acceptable equilibrium state, the iteration stops.

[0118] After obtaining the real-time dynamic electricity price sequence and electricity consumption plan, the distributed collaborative computing decision network sends the real-time dynamic electricity price sequence and electricity consumption plan to the parallel settlement processing module as the price benchmark for subsequent electricity bill calculation; and sends the electricity consumption plan to the corresponding tracking and metering module as the assessment benchmark for the user's actual electricity consumption behavior.

[0119] It should be noted that there are multiple tracking and metering modules, and each tracking and metering module corresponds to a different intelligent agent server cluster.

[0120] It should be noted that the tracking and metering module is not a single device, but an edge metering and control network consisting of a front-end execution terminal and a back-end concentrator.

[0121] The front-end execution terminals include smart meters, smart gateways, programmable logic controllers, and various load control devices deployed on the user side, such as smart charging pile controllers and air conditioning thermostats. These devices have two-way communication capabilities and local computing capabilities.

[0122] The back-end concentrator is deployed on the transformer side or load aggregator side of the distribution area. It is responsible for communicating with all front-end terminals in the area, performing data aggregation and preliminary processing, and interacting with the upper-level system, namely the distributed collaborative computing decision network and the parallel settlement processing module.

[0123] It should be noted that the tracking and metering module obtains the actual power consumption curve within a preset time period based on the power consumption plan, that is, the back-end concentrator sends the power consumption plan to the front-end execution terminal; and the front-end execution terminal obtains the actual power consumption curve within a preset time period based on the power consumption plan.

[0124] The back-end concentrator receives the electricity consumption plan from the distributed collaborative computing decision-making network via a secure communication network. This electricity consumption plan includes:

[0125] Agent identifier, which uniquely identifies the load aggregator or user group to which the electricity plan belongs;

[0126] A time window is the start and end time to which the plan applies, i.e., a preset time period.

[0127] The planned power consumption curve includes the expected power values ​​at a series of discrete time points. (t), representing a desired power value for each critical time period, indicating the target power that the total load of all users proxied by this intelligent agent server cluster should reach at each moment within the time window. It can also include user-level planned power curves. (t) is obtained by aggregating the planned power of each adjustable load by different users.

[0128] Then, the back-end concentrator will display the user-level planned power curve. (t) is sent to the corresponding front-end execution terminal.

[0129] After the front-end execution terminal enters the planned time window of the preset time period, the real-time clock inside the front-end execution terminal triggers the control program.

[0130] The control program controls the front-end execution terminal to read the local clock at fixed time intervals, such as 1 second or 1 minute, depending on the control precision requirements. Based on the current time t, it queries the locally stored planned power curve to obtain the desired power value. (t). Then, the control front-end execution terminal, through its built-in power regulation module, such as a pulse width modulation signal generator or a relay array, controls the connected load equipment to make its actual operating power approximate that of the load. .

[0131] Specifically, the connected load equipment can be controlled. When the load equipment is a continuously adjustable device such as a charging pile, analog control signals can be output to it. When the load equipment is a discrete start-stop device such as a regular air conditioner, equivalent power regulation can be achieved by adjusting its duty cycle.

[0132] While performing control, the smart meter at the front-end execution terminal continuously performs high-frequency metering. The metering chip inside the smart meter samples the voltage and current at an extremely high rate and accumulates and calculates the active energy. The smart meter outputs "frozen" data of instantaneous power or energy at a granularity of minutes or even seconds. That is, at every whole minute or second, the smart meter records the instantaneous power at that moment. This forms a high-time-resolution data sequence, i.e., the actual electricity consumption data stream, which is then reported to the back-end concentrator. It should be noted that... The sampling interval can be related to The sampling intervals are the same.

[0133] Furthermore, the cumulative power consumption deviation between the planned power consumption curve and the actual power consumption curve is obtained, including by calculating... Obtain the planned electricity consumption corresponding to the planned power consumption curve; among which, The planned electricity consumption curve represents the planned electricity consumption of user from the start time to the end time of the preset time period. The preset measurement time interval is in hours, which is used for data collection. The time interval. By calculating To obtain the planned electricity consumption corresponding to the actual power consumption curve; among which, This represents the planned electricity consumption corresponding to the actual power consumption curve, indicating the actual electricity consumption of user from the start time to the end time of the preset time period; it is calculated... The cumulative power deviation is obtained; among which, This represents the cumulative power deviation.

[0134] The backend concentrator can package the planned electricity consumption deviation data of its users and send it to the billing engine. The planned electricity consumption deviation data includes the user's actual electricity consumption data stream and the user's cumulative electricity consumption deviation.

[0135] Furthermore, the parallel settlement processing module calculates the user's cumulative electricity bill and cumulative adjustment fee in parallel based on the real-time dynamic electricity price sequence, actual power consumption curve, and cumulative power consumption deviation. The final electricity bill is then determined based on these two parameters. This process includes: within a preset settlement period, the parallel settlement processing module obtains the real-time electricity bill increments for several users in each electricity consumption area within a preset time period based on the real-time dynamic electricity price sequence and actual power consumption curve; simultaneously, it obtains the real-time adjustment service fees for several users in each electricity consumption area within a preset time period based on the cumulative power consumption deviation; the parallel settlement processing module updates the user's cumulative electricity bill based on the real-time electricity bill increments; and simultaneously, it updates the user's cumulative adjustment fee based on the real-time adjustment service fees; at the end of the settlement period, the sum of the user's cumulative electricity bill and cumulative adjustment fee is determined as the user's final electricity bill.

[0136] In this way, within the preset settlement period, based on the real-time dynamic electricity price sequence, actual power consumption curve, and cumulative power consumption deviation, the system realizes the real-time calculation and updating of users' cumulative electricity charges and adjustment fees, and ultimately generates the user's final electricity bill, greatly improving settlement speed and efficiency and reducing processing time. At the same time, the introduction of the user's cumulative adjustment fee caused by the user's cumulative power consumption deviation into the user's final electricity bill can guide users to optimize their electricity consumption behavior and achieve a balance between supply and demand in the power system.

[0137] It should be noted that a settlement cycle can correspond to a preset time period.

[0138] The real-time dynamic electricity price sequence includes the real-time dynamic electricity price at each point in time; the actual power consumption curve includes the actual power consumption of each user at each point in time.

[0139] Based on the real-time dynamic electricity price sequence and the actual power consumption curve, the real-time electricity charge increment for several users in each electricity consumption area within a preset time period is obtained. That is, for any user, at time point t, the product of the real-time dynamic electricity price corresponding to that time point and the actual power consumption corresponding to that time point is determined as the real-time electricity charge increment.

[0140] The real-time adjustment service fee for several users in each power consumption area within a preset time period is obtained based on the cumulative power consumption deviation, including: for any user, at time point t, the real-time adjustment service fee is determined based on the cumulative power consumption deviation corresponding to that time point according to the preset reward and punishment rules.

[0141] In some embodiments, the reward and penalty rules can determine the real-time adjustment service fee in a tiered manner based on the value of the accumulated power deviation. For example, if the accumulated power deviation is within ±5%, the real-time adjustment service fee is 0; otherwise, the real-time adjustment service fee is a preset penalty fee.

[0142] For example, the reward and punishment rules are: set up real-time adjustment of service fees. ,in, To adjust service fees in real time; This represents the cumulative electricity consumption deviation; a positive value indicates over-consumption, i.e., actual electricity consumption > planned electricity consumption, and a negative value indicates under-consumption, i.e., actual electricity consumption < planned electricity consumption.

[0143] The preset first penalty coefficient; This is the preset second penalty coefficient; >0 indicates that exceeding the limit will incur charges; >0 indicates that less use may result in compensation or payment. The specific symbols and values ​​can be determined according to market rules and are not specified here.

[0144] The parallel settlement processing module updates the user's cumulative electricity bill based on the real-time electricity bill increment. This involves adding the user's cumulative electricity bill before the update to the real-time electricity bill increment to obtain the updated user's cumulative electricity bill. The user's cumulative electricity bill before the update is the accumulated cost of the real-time electricity bill increments over the previous t-1 time points.

[0145] The user's cumulative adjustment fee is updated based on the real-time adjustment service fee. This is achieved by adding the user's cumulative adjustment fee before the update to the real-time adjustment service fee to obtain the updated user's cumulative adjustment fee. The user's cumulative adjustment fee before the update is the sum of the real-time adjustment service fees for the previous t-1 time points.

[0146] It should be noted that this embodiment uses a distributed architecture to decompose the massive optimization and settlement tasks that were originally concentrated on a single server into a large number of parallel nodes. This not only overcomes the performance bottlenecks and single-point-of-failure risks of centralized architectures, but also significantly improves the overall system throughput and reliability through parallel processing.

[0147] Combination Figure 3 As shown, this disclosure provides an electronic device 300, including a processor 301 and a memory 302. Optionally, the device may further include a communication interface 303 and a bus 304. The processor 301, communication interface 303, and memory 302 can communicate with each other via the bus 304. The communication interface 303 can be used for information transmission. The processor 301 can call logical instructions in the memory 302 to execute the method for settling electricity bills described in the above embodiment.

[0148] Furthermore, the logic instructions in the aforementioned memory 302 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium.

[0149] The memory 302, as a storage medium, can be used to store software programs and computer-executable programs, such as program instructions / modules corresponding to the methods in the embodiments of this disclosure. The processor 301 executes functional applications and data processing by running the program instructions / modules stored in the memory 302, thereby implementing the method for settling electricity bills in the above embodiments.

[0150] The memory 302 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the terminal device. Furthermore, the memory 302 may include high-speed random access memory and may also include non-volatile memory.

[0151] This disclosure provides a storage medium storing computer-executable instructions configured to execute the above-described method for settling electricity bills.

[0152] The aforementioned storage media can be either transient computer-readable storage media or non-transitory computer-readable storage media. Non-transitory storage media include various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks, and can also be transient storage media.

[0153] The foregoing description and accompanying drawings fully illustrate embodiments of this disclosure to enable those skilled in the art to practice them. Other embodiments may include structural, logical, electrical, procedural, and other changes. The embodiments represent only possible variations. Individual components and functions are optional unless explicitly required, and the order of operation may vary. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. Moreover, the terminology used in this application is for describing embodiments only and is not intended to limit the claims. As used in the description of embodiments and claims, the singular forms “a,” “an,” and “the” are intended to equally include the plural forms unless the context clearly indicates otherwise. Similarly, the term “and / or” as used in this application means including one or more of the associated listed items and all possible combinations thereof. Additionally, when used in this application, the term "comprise" and its variations "comprises" and / or "comprising" refer to the presence of stated features, integrals, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof. Without further limitations, an element defined by the phrase "comprises a..." does not exclude the presence of other identical elements in the process, method, or apparatus that includes said element. In this document, each embodiment may focus on the differences from other embodiments, and similar or identical parts between embodiments can be referred to mutually. For methods, products, etc., disclosed in the embodiments, if they correspond to the method section disclosed in the embodiments, the relevant parts can be referred to the description of the method section.

[0154] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the embodiments of this disclosure. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0155] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. Each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

Claims

1. A method for settling electricity bills, characterized in that, Applied to distributed architecture settlement systems; The distributed architecture settlement system includes: a data fusion module, a distributed collaborative computing decision network, a tracking and metering module, and a parallel settlement processing module; the method includes: The data fusion module is used to acquire and fuse the original state data of the power grid to obtain a real-time operating status profile of the power grid. Then, based on the real-time operating status profile, a status indicator data stream of the power grid within a preset time period is generated. The distributed collaborative computing decision network is used to obtain real-time dynamic electricity price sequences and electricity consumption plans corresponding to several electricity consumption areas based on the situation indicator data stream; The tracking and metering module uses the electricity consumption plan to obtain the actual electricity consumption curve within a preset time period; then, the cumulative electricity consumption deviation between the planned electricity consumption curve and the actual electricity consumption curve is obtained. The parallel settlement processing module uses the real-time dynamic electricity price sequence, the actual power consumption curve, and the cumulative power deviation to calculate the user's cumulative electricity bill and cumulative adjustment fee in parallel, and determines the user's final electricity bill based on the user's cumulative electricity bill and cumulative adjustment fee.

2. The method according to claim 1, characterized in that, The step of acquiring and fusing the original state data of the power grid using the data fusion module to obtain a real-time operating status profile of the power grid includes: The original state data is obtained using the data fusion module. The data fusion module is used to clean and time-align the original state data to obtain reference state data. The data fusion module uses a preset real-time state estimator to obtain the real-time operating state profile based on the reference state data.

3. The method according to claim 2, characterized in that, The raw state data includes: real-time power generation data, real-time power grid data, real-time user electricity consumption data, and real-time environmental data.

4. The method according to claim 1, characterized in that, The step of generating the power grid status indicator data stream within a preset time period based on the real-time operating status section includes: The load and renewable energy output within a preset time period are predicted to obtain forecast data; The situation indicator data stream is obtained based on the predicted data and the real-time operating status profile.

5. The method according to claim 1, characterized in that, The distributed collaborative computing decision network includes: a central coordination server and intelligent agent server clusters corresponding to each of the electricity consumption areas; the step of using the distributed collaborative computing decision network to obtain real-time dynamic electricity price sequences and electricity consumption plans corresponding to several electricity consumption areas based on the situation indicator data stream includes: The reference shadow price vector is obtained using the central coordination server; in the first iteration, the reference shadow price vector is obtained through the situation indicator data stream; in the m-th iteration, the reference shadow price vector is obtained through the reference shadow price vector of the (m-1)th iteration. Each of the aforementioned intelligent agent server clusters determines the alternative power consumption plan corresponding to each of the aforementioned power consumption areas based on the reference shadow price vector; the alternative power consumption plan corresponding to each of the aforementioned power consumption areas includes the alternative power consumption curves of several users in each of the aforementioned power consumption areas within a preset time period; The central coordination server is used to obtain the total planned load curve of the entire network based on each of the alternative power selection plans; then, the total power supply capacity curve of the entire network is obtained based on the situation indicator data stream; then, the supply and demand matching deviation is obtained based on the total planned load curve and the total power supply capacity curve of the entire network; and the real-time dynamic electricity price sequence and the electricity consumption plan are obtained based on the supply and demand matching deviation.

6. The method according to claim 5, characterized in that, In the m-th iteration, the reference shadow price vector is obtained in the following way: Obtain the product of the supply-demand matching deviation at the (m-1)th iteration and the preset step size factor; The sum of the product and the reference shadow price vector of the (m-1)th iteration is determined as the reference shadow price vector at the m-th iteration.

7. The method according to claim 5, characterized in that, The step of obtaining the real-time dynamic electricity price sequence and the electricity consumption plan based on the supply-demand matching deviation includes: When the supply-demand matching deviation meets the preset convergence condition, the central coordination server determines the reference shadow price vector as the real-time dynamic electricity price sequence and the alternative electricity consumption plan as the electricity consumption plan; when the supply-demand matching deviation does not meet the convergence condition, the reference shadow price vector is re-acquired for the next iteration.

8. The method according to claim 1, characterized in that, The method of using the parallel settlement processing module to calculate the user's cumulative electricity bill and cumulative adjustment fee in parallel based on the real-time dynamic electricity price sequence, the actual power consumption curve, and the cumulative power deviation, and determining the user's final electricity bill based on the user's cumulative electricity bill and cumulative adjustment fee, includes: Within a preset settlement period, the parallel settlement processing module uses the real-time dynamic electricity price sequence and the actual power consumption curve to obtain the real-time electricity fee increment of several users in each of the electricity consumption areas within a preset time period. At the same time, it uses the cumulative power consumption deviation to obtain the real-time adjustment service fee of several users in each of the electricity consumption areas within a preset time period. The parallel settlement processing module updates the user's cumulative electricity bill based on the real-time electricity bill increment, and simultaneously updates the user's cumulative adjustment fee based on the real-time adjustment service fee. Upon the end of the settlement period, the sum of the user's accumulated electricity charges and the user's accumulated adjustment charges will be determined as the user's final electricity bill.

9. A distributed architecture settlement system, characterized in that, include: The data fusion module is configured to acquire and fuse the original state data of the power grid to obtain a real-time operating status profile of the power grid, and then generate a status indicator data stream of the power grid within a preset time period based on the real-time operating status profile. A distributed collaborative computing decision network is configured to acquire real-time dynamic electricity price sequences and electricity consumption plans corresponding to several electricity consumption areas based on the situation indicator data stream. The tracking metering module is configured to obtain the actual power consumption curve within a preset time period based on the power consumption plan; and then obtain the cumulative power consumption deviation between the planned power consumption curve and the actual power consumption curve. The parallel settlement processing module is configured to calculate the user's cumulative electricity bill and cumulative adjustment fee in parallel based on the real-time dynamic electricity price sequence, the actual power consumption curve, and the cumulative power deviation, and determine the user's final electricity bill based on the user's cumulative electricity bill and cumulative adjustment fee.

10. A storage medium storing program instructions, characterized in that, When the program instructions are executed, they perform the smart home control method as described in any one of claims 1 to 8.

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