A source-load coupling power splitting and loss allocation method and system

Abstract

The application provides a source-load coupling electric quantity splitting and loss allocation method and system, comprising: obtaining gateway metering data of a new type of main body under a source-load coupling scenario; determining the operation condition of the new type of main body in a metering period; constructing a source-load coupling time sequence characteristic matrix of the new type of main body in the metering period according to the operation condition; splitting total electric quantity of the new type of main body in the metering period into multiple electric quantity components according to the source-load coupling time sequence characteristic matrix; calculating a loss allocation coefficient corresponding to each electric quantity component according to the power flow path of each electric quantity component in a power transmission and distribution network; calculating a settlement electric quantity value of each electric quantity component according to each electric quantity component and the loss allocation coefficient corresponding to each electric quantity component, and outputting the settlement electric quantity value of each electric quantity component as an electric quantity splitting and loss allocation result. The application can realize accurate identification and reasonable loss allocation of multiple electric quantity components under a source-load coupling scenario, and provide accurate data support for subsequent electricity fee settlement and index accounting.

Description

Technical Field

[0001] This application relates to the field of power system settlement and metering technology, and more specifically, to a method and system for source-load coupled power splitting and loss allocation. Background Technology

[0002] With the accelerated construction of a unified national electricity market system, new types of entities that combine power generation and consumption characteristics (such as distributed photovoltaic + energy storage + load aggregation entities) are developing rapidly and gradually becoming an important part of the power system and market. These new entities generally have the operating characteristics of source-load coupling, that is, they may simultaneously engage in multiple electricity behaviors such as self-consumption, surplus power transmission to the grid, power purchase from the grid, and energy storage charging and discharging within the same time period, resulting in highly complex electricity flow.

[0003] The existing electricity metering and settlement system is mainly based on the traditional source-grid-load structure design, which has significant shortcomings in the identification of electricity components and loss allocation in source-load coupled scenarios. First, the traditional single metering point model cannot distinguish the respective proportions and flows of different electricity components at the same point, making it difficult to meet the accuracy requirements of refined settlement, carbon emission accounting, and green electricity index statistics. Second, existing loss allocation methods are mostly based on empirical proportions or simple average principles, which cannot accurately reflect the actual power flow path of each electricity component in the transmission and distribution network, resulting in unreasonable loss allocation results, which in turn affects the fairness and transparency of cost allocation.

[0004] Therefore, there is an urgent need for a technical solution that can adapt to source-load coupling scenarios and achieve accurate identification of multiple electrical components and reasonable loss distribution. Summary of the Invention

[0005] The present invention aims to overcome the shortcomings of the prior art and provide a method and system for source-load coupled power splitting and loss allocation, which aims to achieve accurate identification of multiple power components and reasonable loss allocation in source-load coupled scenarios, and provide accurate data support for subsequent electricity billing and indicator calculation.

[0006] In a first aspect, the present invention provides a method for source-load coupling power splitting and loss allocation, the method comprising: Acquire gateway measurement data for novel entities in source-load coupling scenarios; Based on the metering data of the checkpoints, the direction and magnitude of the electricity flow at each checkpoint within the metering cycle are identified, and the operating conditions of the new entity within the metering cycle are determined. Based on the operating conditions, a source-load coupling time-series feature matrix is ​​constructed for the novel entity within the metering cycle; the source-load coupling time-series feature matrix is ​​used to characterize the degree of coupling between the novel entity's power generation behavior and power consumption behavior within the same time period; Based on the source-load coupling time-series feature matrix, and using a preset power component identification rule, the total power consumption of the novel entity within the metering period is divided into multiple power components; the power components include self-generated and self-consumed power consumption, grid-connected power consumption, and grid-connected power consumption. Based on the power flow path of each of the electrical components in the transmission and distribution network, a loss allocation coefficient corresponding to each of the electrical components is calculated using a power flow tracing-based loss allocation method. Based on each of the power components and the corresponding loss sharing coefficient, the settled power value of each power component is calculated, and the settled power value of each power component is output as the power splitting and loss sharing result.

[0007] Preferably, the metering data includes power generation metering data, plant usage metering data, self-generation and self-consumption metering data, upstream gateway metering data, user gateway metering data, energy storage metering data, and tie-line metering data.

[0008] Preferably, the step of identifying the direction and amplitude of electricity flow at each checkpoint within the metering cycle based on the metering data, and determining the operating conditions of the novel entity within the metering cycle, includes: Read the first meter reading at the beginning of the metering cycle and the second meter reading at the end of the metering cycle for each checkpoint; Calculate the difference between the second meter reading and the first meter reading at each checkpoint. If the difference is positive, it is determined that the direction of electricity flow at that checkpoint during the metering cycle is positive. If the difference is negative, it is determined that the direction of electricity flow at that checkpoint during the metering cycle is negative. The absolute value of the difference at each checkpoint is taken as the magnitude of the electricity flow at that checkpoint during the metering period. Based on the power flow direction and power flow amplitude corresponding to the power generation gateway metering data, the power flow direction and power flow amplitude corresponding to the energy storage gateway metering data, and the power flow direction and power flow amplitude corresponding to the user gateway metering data, the operating conditions of the new entity within the metering cycle are determined. The operating conditions include at least one of the following: power generation, power consumption, energy storage charging, energy storage discharging, and quiescent state.

[0009] Preferably, constructing the source-load coupling time-series feature matrix of the novel entity within the metering cycle based on the operating conditions includes: The metering period is divided into multiple equal-length sub-periods; Within each sub-period, the power generation time series value is determined based on the power generation gateway metering data, and the power consumption time series value is determined based on the power consumption gateway metering data; If, within the same sub-period, both the power generation time-series value and the power consumption time-series value are greater than a preset power threshold, then it is determined that a source-load coupling event exists in that sub-period. Record the occurrence status of source-load coupling events in each sub-time period and construct a source-load coupling indicator vector; The source-load coupling time sequence feature matrix is ​​constructed based on the source-load coupling indicator vector and the power generation time sequence value and power consumption time sequence value corresponding to each sub-time period.

[0010] Preferably, the step of dividing the total electricity consumption of the novel entity within the metering period into multiple electricity components based on the source-load coupling time-series feature matrix and using a preset electricity component identification rule includes: Based on the source-load coupling indication vector, identify the coupling and non-coupling periods within the metering cycle; During the uncoupled period, if the power generation time series value is greater than the preset power threshold and the power consumption time series value is less than or equal to the preset power threshold, then the power generation during the uncoupled period is identified as the power to be fed into the grid. During the uncoupled period, if the power consumption time series value is greater than the preset power threshold and the power generation time series value is less than or equal to the preset power threshold, then the power consumption during the uncoupled period is identified as grid power consumption. During the uncoupled period, if the power generation time series value is less than or equal to the preset power threshold and the power consumption time series value is less than or equal to the preset power threshold, then the period is a silent period and no power component is generated. During the coupling period, the time-series values ​​of the power generation are compared. With the power consumption time series value :like ≥ Then the self-generated and self-consumed electricity during the coupling period is The power consumption for internet access is The electricity consumption is 0; if < Then the self-generated and self-consumed electricity during the coupling period is The amount of electricity used by the grid is The battery level for internet access is 0; among which, This is the start time of the coupling period. The duration of the coupling period; By summarizing the identification results for each time period, the self-generated and self-consumed electricity, grid-connected electricity, and grid-connected electricity of the new entity within the metering period are obtained.

[0011] Preferably, the step of calculating the loss allocation coefficient corresponding to each of the electrical components using a power flow tracing-based loss allocation method based on the power flow path of each of the electrical components in the transmission and distribution network includes: Obtain the topology and line parameters of the power transmission and distribution network to which the new type of entity is connected; Based on the topology and line parameters, a power flow calculation model for the transmission and distribution network is established. Each of the aforementioned electrical components is used as an injection source and input into the power flow calculation model to calculate the power flow distribution generated by each of the aforementioned electrical components on each branch of the power transmission and distribution network. Based on the power flow distribution of each electrical component on each branch and the line loss of each branch, the loss allocation coefficient corresponding to each electrical component is calculated using the proportional allocation principle.

[0012] Preferably, calculating the settlement electricity value for each electricity component based on each of the electricity components and the loss sharing coefficient corresponding to each of the electricity components includes: Obtain the total power loss of the power transmission and distribution network during the metering period; According to the The loss allocation coefficient corresponding to each electrical component With the total network loss power Calculate the first Network loss power allocated to individual power components ; Calculate the first Settlement electricity value for each electricity component ,in For the first The original charge value of each charge component; Output the calculated electricity value for each component of the electricity consumption.

[0013] Secondly, the present invention provides a source-load coupling power splitting and loss sharing system, comprising: The gateway measurement data acquisition module is used to acquire gateway measurement data of new subjects in source-load coupling scenarios; The operating condition determination module is used to identify the direction and magnitude of power flow at each checkpoint within the metering cycle based on the metering data at the checkpoints, and to determine the operating condition of the new entity within the metering cycle. The feature matrix construction module is used to construct the source-load coupling time-series feature matrix of the new entity within the metering cycle according to the operating conditions; the source-load coupling time-series feature matrix is ​​used to characterize the degree of coupling between the new entity's power generation behavior and power consumption behavior in the same time period; The splitting module is used to split the total electricity consumption of the novel entity within the metering period into multiple electricity components based on the source-load coupling time-series feature matrix and using preset electricity component identification rules; the electricity components include self-generated and self-consumed electricity, grid-connected electricity, and grid-used electricity. The loss allocation coefficient calculation module is used to calculate the loss allocation coefficient corresponding to each of the electrical components based on the power flow path of each electrical component in the power transmission and distribution network and using the power flow tracing-based loss allocation method. The result output module is used to calculate the settled power value of each power component based on each power component and the loss sharing coefficient corresponding to each power component, and output the settled power value of each power component as the power splitting and loss sharing result.

[0014] Thirdly, the present invention provides a readable medium including executable instructions, which, when executed by a processor of an electronic device, cause the electronic device to perform any of the methods described in the first aspect.

[0015] Fourthly, the present invention provides an electronic device including a processor and a memory storing execution instructions, wherein when the processor executes the execution instructions stored in the memory, the processor performs the method as described in any of the first aspects.

[0016] This invention provides a method and system for source-load coupled power decomposition and loss allocation. By acquiring multi-point metering data of a novel entity in a source-load coupled scenario, the method identifies the power flow direction and amplitude at each point within the metering cycle to determine the operating conditions. This leads to the construction of a source-load coupled time-series feature matrix that characterizes the degree of coupling between power generation and consumption behavior within the same time period. Based on this matrix, a preset power component identification rule is used to accurately decompose the total power into self-generated and self-consumed power, grid-connected power, and grid-used power. This effectively solves the problems of blurred boundaries and inability to distinguish the proportions of power components in the traditional single-metering-point model, providing an accurate data foundation for refined settlement. Simultaneously, for each decomposed power component, its actual power flow path in the transmission and distribution network is tracked, employing a power flow tracing-based approach. The loss allocation method calculates the loss allocation coefficient corresponding to each component and deducts the grid loss electricity allocated to each component accordingly to obtain the settlement electricity value. This avoids the shortcomings of the traditional empirical proportional allocation method, which cannot reflect the actual power flow path of each component, and significantly improves the rationality and transparency of loss allocation. In addition, by introducing a source-load coupling indicator vector and a power threshold determination mechanism, this invention realizes the automatic identification of coupled and uncoupled periods. During the coupled period, based on the real-time comparison of power generation and power consumption, it accurately calculates the electricity values ​​of self-generation and self-consumption, grid connection and grid use in integral form. It can adapt to the dynamic changes in the degree of source-load coupling, has strong adaptability and high feasibility, and fully supports the needs of new entities for refined settlement, carbon emission accounting and renewable energy indicator statistics in diversified trading scenarios.

[0017] The further effects of the aforementioned non-conventional preferred method will be explained below in conjunction with specific embodiments. Attached Figure Description

[0018] To more clearly illustrate the embodiments of the present invention or the existing technical solutions, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of a source-load coupling power splitting and loss amortization method provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of another source-load coupling power splitting and loss amortization method provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the composition of a source-load coupling power splitting and loss sharing system provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0021] See Figure 1 The image shows a specific embodiment of a source-load coupled power splitting and loss amortization method provided by the present invention. In this embodiment, the source-load coupled power splitting and loss amortization method includes:

[0022] See Figure 1 The image shows a specific embodiment of a source-load coupled power splitting and loss amortization method provided by the present invention. In this embodiment, the source-load coupled power splitting and loss amortization method includes:

[0023] Step 101: Obtain the gate measurement data of the new type of subject under the source-load coupling scenario; Specifically, this embodiment takes an industrial park equipped with distributed photovoltaic, energy storage systems and adjustable loads as the application object, and describes in detail the specific execution process of the method.

[0024] This new type of entity is a user-side unit that combines power generation capacity and electricity demand, internally containing photovoltaic power generation units, energy storage devices, and production loads. To comprehensively capture its power flow information, multiple metering checkpoints are set at its connections with the external power grid and internal electrical nodes. The metering data at these checkpoints specifically includes: power generation checkpoint metering data installed on the AC output side of the photovoltaic inverter, used to record the total photovoltaic power generation; plant service checkpoint metering data installed on the low-voltage side of the plant service transformer, used to record the power consumption of production auxiliary equipment; self-consumption checkpoint metering data installed on the self-consumption branch of the internal busbar, used to monitor the directly consumed renewable energy; upstream and downstream checkpoint metering data installed at the grid connection point (recorded by the same bidirectional energy meter for forward and reverse power respectively), used to measure the power transmitted to the grid and the power purchased from the grid, respectively; energy storage checkpoint metering data installed on the DC side of the energy storage converter, used to record the energy storage charging and discharging power; and tie-line checkpoint metering data connecting different voltage levels or different distribution zones, used to record cross-regional power exchange. All of the above-mentioned gates use multi-functional electronic energy meters that conform to the IEC 62053 standard. They have bidirectional metering, time-of-use recording and remote data transmission functions, and can provide instantaneous and cumulative values ​​of electrical quantities such as active / reactive power, voltage and current with time stamps.

[0025] Step 102: Based on the metering data at each checkpoint, identify the direction and magnitude of electricity flow at each checkpoint within the metering cycle, and determine the operating conditions of the new entity within the metering cycle. Furthermore, the metering cycle is set to one calendar day (24 hours), starting at 00:00 on the current day and ending at 00:00 on the next day. For each checkpoint, the reading of the first meter (total positive active energy) at the start time and the reading of the second meter at the end time are read, and the difference between the second meter reading and the first meter reading is calculated. If the difference is positive, it indicates that the net energy flow direction of the checkpoint within the metering cycle is consistent with the preset positive direction of the energy meter, and is judged as positive flow; if the difference is negative, it indicates that the net flow direction is opposite to the preset positive direction, and is judged as reverse flow. The absolute value of the difference is taken as the energy flow amplitude of the checkpoint within the metering cycle, and its physical meaning is the net cumulative energy (unit: kilowatt-hour) passing through the checkpoint. Based on this, the following three key flow characteristics are used to infer operating conditions: the direction and amplitude of the power generation threshold reflect whether the new entity is injecting or absorbing power into the system; the direction (positive for charging, negative for discharging) and amplitude of the energy storage threshold reflect the charging and discharging behavior of the energy storage system; and the direction (positive for purchasing electricity from the grid, negative for selling electricity to the grid) and amplitude of the power consumption threshold reflect the exchange status with the public grid. By combining the above directions and amplitudes, the operating conditions of the new entity in each time period can be determined. For example, if the power generation threshold is positive, the power consumption threshold is negative, and the energy storage threshold is negative, it is determined to be a "photovoltaic power generation + energy storage discharge + grid connection" operating condition; if the power generation threshold is positive, the power consumption threshold is positive, and the energy storage threshold is positive, it is determined to be a "photovoltaic power generation + purchasing electricity from the grid + energy storage charging" operating condition. The set of operating conditions includes power generation state (only power generation, no power consumption), power consumption state (only power consumption, no power generation), energy storage charging state, energy storage discharging state, and quiescent state (power below the threshold), as well as any combination of the above states.

[0026] Step 103: Based on the operating conditions, construct the source-load coupling time-series characteristic matrix of the new entity within the metering cycle; the source-load coupling time-series characteristic matrix is ​​used to characterize the degree of coupling between the new entity's power generation behavior and power consumption behavior in the same time period. Furthermore, the source-load coupling time-series feature matrix is ​​used to quantitatively characterize the coupling strength of a novel entity simultaneously exhibiting power generation and power consumption behaviors within the same time period. The construction process is as follows: First, the 24-hour metering cycle is divided into several consecutive sub-periods at equal intervals. The duration of each sub-period can be set according to the metering accuracy requirements; in this embodiment, it is 15 minutes, resulting in 96 sub-periods. For each sub-period, the average active power within that period is extracted from the power generation metering data as the power generation time-series value; the average active power (absolute value) within that period is extracted from the power consumption metering data as the power consumption time-series value. Then, for each sub-period, it is determined whether the power generation time-series value and the power consumption time-series value are simultaneously greater than a preset power threshold. This threshold is set to the larger of 2% of the rated power or 0.5kW to avoid misjudgments caused by meter zero drift and measurement noise. If both are greater than the threshold, the sub-period is marked as having a source-load coupling event; otherwise, it is marked as having no coupling event. The coupling event marking results of all sub-time periods are arranged in chronological order to form a source-load coupling indicator vector (96 in length, with elements of 1 or 0). Finally, the source-load coupling indicator vector, the power generation time series value sequence, and the power consumption time series value sequence are concatenated column by column to form a 96-row, 3-column source-load coupling time series feature matrix, which fully preserves the coupling distribution and power magnitude information in the time dimension.

[0027] Step 104: Based on the source-load coupling time-series feature matrix, and using the preset power component identification rules, the total power of the new entity within the metering period is divided into multiple power components; the power components include self-generated and self-consumed power, grid-connected power, and grid-used power. Extract the source-load coupling indicator vector in the first column, the power generation time series value sequence in the second column, and the power consumption time series value sequence in the third column from the source-load coupling time series feature matrix constructed in step 103, and perform the following power component identification and splitting accordingly.

[0028] Furthermore, the identification rules use the source-load coupling indicator vector as the first-level criterion. First, sub-periods with elements of 1 in the indicator vector are classified as coupled periods, and sub-periods with elements of 0 are classified as uncoupled periods. For uncoupled periods, further subdivision is based on power thresholds: if the generation power time-series value is greater than the threshold and the consumption power time-series value is not greater than the threshold, then only generation occurs during this period, and all generated electricity is attributed to grid connection; if the consumption power time-series value is greater than the threshold and the generation power time-series value is not greater than the threshold, then only consumption occurs during this period, and all consumed electricity is attributed to grid connection; if neither is greater than the threshold, then this period is a silent period, producing no electricity. For coupled periods, a power comparison strategy is implemented: let the generation power time-series value during this period be... (Unit: kW), power consumption time sequence value (Unit: kW), start and end times of the time period are: and ( =0.25 hours). If ≥ This indicates that the power generation capacity fully covers the electricity demand. Therefore, during this period, the self-generated and self-consumed electricity is equal to the electricity consumption. The amount of electricity fed into the grid equals the remaining amount of electricity generated. The electricity consumption is zero; if < This indicates that the power generation capacity is insufficient to cover the electricity demand, therefore the self-generated and self-consumed electricity equals the total power generation. The amount of electricity used from the grid is equal to the shortfall. The electricity consumption to the grid is zero. After completing the above identification and calculation for all sub-time periods, the self-generated and self-consumed electricity, grid-connected electricity, and grid-connected electricity for each time period are summed to obtain the total value of the three electricity components of the new entity throughout the entire metering cycle.

[0029] Step 105: Based on the power flow path of each electrical component in the transmission and distribution network, calculate the loss allocation coefficient corresponding to each electrical component using the power flow tracing-based loss allocation method. Furthermore, the purpose of loss allocation is to reasonably attribute network losses generated by the public power grid during transmission to different electrical components. First, the complete topology of the transmission and distribution network to which the new entity is connected (including node numbers, branch connections, transformer tap positions, etc.) and line parameters (resistance, reactance, ground susceptance, line length, etc.) are obtained. Based on this data, a steady-state power flow calculation model for the transmission and distribution network is established using the Newton-Raphson method. This model takes the active and reactive power injected by each node as input and iteratively solves for the active power flow, reactive power flow, and total network loss of each branch. Then, the three electrical components obtained in step 104 (self-generated and self-consumed electricity, grid-connected electricity, and grid-used electricity) are treated as independent injection sources, and each is input as a boundary condition into the power flow calculation model to simulate the power flow distribution of each branch when only this component exists. The specific approach is as follows: Each power component is equivalent to the injected power at the grid connection node (positive injection for grid-connected power, negative injection (i.e., load) for grid-connected power, and considered as a net effect of self-generated and self-consumed power after internal offsetting, without being injected into the grid). Power flow calculations are then performed again, recording the power flow component caused by that component on each branch. Finally, the comprehensive loss allocation coefficient is calculated according to the proportional allocation principle: for any power component… Its apportionment coefficient It equals the proportion of the sum of squares of tidal currents caused by this component on all branches to the total sum of squares of tidal currents caused by all components (or weighted by active power proportion), and satisfies This embodiment employs an algorithm that weights losses according to branch loss ratios to ensure that the allocation result is positively correlated with the actual utilization of each component in the network.

[0030] Step 106: Calculate the settlement electricity value of each electricity component based on each electricity component and the corresponding loss allocation coefficient, and output the settlement electricity value of each electricity component as the result of electricity splitting and loss allocation.

[0031] Furthermore, the total network loss of the transmission and distribution network during the metering period is obtained from the power flow calculation results in step 105. (Unit: kWh). For the first Each electrical component ( =1, 2, and 3 correspond to self-consumption, internet access, and internet usage, respectively, and their respective share of network loss electricity should be allocated. The original charge value of this component. This refers to the un-loss-deducted electricity volume obtained in step 104. Its settlement electricity value is... This settled electricity value is directly used for subsequent electricity bill settlement, carbon emission factor calculation, and renewable energy consumption statistics. Ultimately, the system outputs three settled electricity values: self-consumption settled electricity, grid-connected settled electricity, and grid-connected settled electricity, which serve as the final result of this method.

[0032] At this point, the system outputs three settlement electricity values ​​after loss allocation and deduction: self-consumption settlement electricity, grid connection settlement electricity, and grid usage settlement electricity. These three values ​​are the final results obtained after the dual steps of accurate electricity breakdown and reasonable grid loss allocation. They are directly used in subsequent stages such as electricity bill clearing and settlement, carbon emission factor conversion, and electricity determination when issuing renewable energy green certificates, ensuring clear cost boundaries and fair loss sharing for new entities in diversified trading scenarios.

[0033] As can be seen from the above technical solutions, the beneficial effects of this embodiment are: by constructing a source-load coupling time-series feature matrix and performing time-period-level splitting based on power comparison, combined with the proportional allocation of power flow tracking, the accurate stripping of power components and reasonable aggregation of network losses in coupled scenarios are achieved, which significantly improves the traceability and fairness of settlement data.

[0034] Figure 1 The embodiments shown are merely basic examples of the method of the present invention. Other preferred embodiments of the method can be obtained by making certain optimizations and extensions based on them.

[0035] like Figure 2 The image shows another specific embodiment of the source-load coupling power splitting and loss amortization method of the present invention. This embodiment is a further description based on the foregoing embodiments. In this embodiment, the method includes the following steps:

[0036] Step 201: Divide the metering period into multiple sub-periods of equal length; Specifically, this embodiment is a refined process for constructing the source-load coupling time-series characteristic matrix. This process is a further specification and optimization of step 103 in the previous embodiment, and is particularly suitable for high-frequency sampling scenarios with numerous metering points and severe power fluctuations. This embodiment uses a data acquisition system with a sampling interval of 1 minute as an example for illustration.

[0037] The measurement period is still set to one calendar day (24 hours). Based on the sampling frequency of the data acquisition system and the time resolution requirements of the service, the 24 hours are evenly divided into several equal-length sub-periods. In this embodiment, to balance computational efficiency and accuracy, the sub-period length is set to 5 minutes, resulting in a total of 24 × 60 / 5 = 288 sub-periods. Each sub-period contains multiple sampling points (in this embodiment, each sub-period contains 5 one-minute sampling points), facilitating robust estimation of subsequent time series values.

[0038] Step 202: Within each sub-period, determine the power generation time series value based on the power generation gate metering data, and determine the power consumption time series value based on the power consumption gate metering data; Furthermore, for the first Sub-time period ( =1,2,...,288), extract the instantaneous power values ​​of all sampling points within this sub-period from the power generation metering data, and take the arithmetic mean as the power generation time series value of this sub-period. The unit is kW. Similarly, the absolute values ​​of instantaneous power at all sampling points within the sub-period are extracted from the metering data at the gateway, and the arithmetic mean is taken as the time-series value of power consumption. Using average values ​​instead of instantaneous values ​​can effectively reduce the interference of random noise and short-term fluctuations on feature extraction. If there are missing data or abnormal sampling points within the sub-period, linear interpolation or forward imputation methods are used for data cleaning to ensure the integrity of the time series value sequence.

[0039] Step 203: If both the power generation time series value and the power consumption time series value are greater than the preset power threshold within the same sub-period, it is determined that there is a source-load coupling event in that sub-period. Furthermore, the preset power threshold The value is set to the larger of 1% and 0.3kW of the rated installed capacity of the new main body. In this embodiment, the rated installed capacity is 500kW. =5kW. The principle for selecting this threshold is: it should not be too low, so as to misjudge meter noise as effective power, nor should it be too high, so as to miss the actual coupling behavior under low power operation. If and only if and When both are established, the judgment is made on the first There are source-load coupling events in each sub-period, that is, within this 5-minute window, the new entity simultaneously outputs electrical energy to the outside and consumes electrical energy from the inside, which reflects a typical dual source-load identity.

[0040] Step 204: Record the occurrence status of source-load coupling events in each sub-time period and construct the source-load coupling indicator vector; Iterate through all 288 sub-time periods, recording the coupling event determination result for each sub-time period: if a coupling event exists, mark it as 1; otherwise, mark it as 0. Arrange all the marking results in chronological order of the sub-time periods to form a one-dimensional vector of length 288. ,in This vector is the source-load coupling indicator vector. This vector can be used not only for subsequent energy decomposition, but also to intuitively reflect the coupling activity distribution of the novel entity at different times of the day.

[0041] Step 205: Construct the source-load coupling time sequence feature matrix based on the source-load coupling indicator vector and the corresponding power generation time sequence value and power consumption time sequence value for each sub-time period.

[0042] The power generation time series obtained in step 202 and power consumption time series The source-load coupling indicator vector obtained in step 204 By concatenating the columns, a numerical matrix M with rows of 288 and columns of 3 is formed. Behavior This matrix is ​​the source-load coupling time-series characteristic matrix, with row numbers corresponding to time sequence and columns storing coupling identifiers, generation power, and consumption power, respectively. This matrix can be directly used as the input data source for subsequent power decomposition steps without additional preprocessing. The construction of this matrix relies on high-precision metering and a stable data acquisition system. In this embodiment, a digital substation communication architecture based on the IEC 61850 protocol is adopted to ensure data synchronization and timeliness.

[0043] As can be seen from the above technical solutions, the beneficial effects of this embodiment are: by refining the sub-time period division and introducing robust mean estimation and threshold discrimination, the noise resistance and temporal resolution of source-load coupling event identification are significantly improved, providing a more reliable temporal feature basis for subsequent power segmentation.

[0044] This invention also provides a source-load coupling power splitting and loss sharing system. See also Figure 3 The image shows a specific embodiment of a source-load coupling power splitting and loss sharing system provided by the present invention. This embodiment of the system is used to execute... Figures 1-2 The physical apparatus of the method. Its technical solution is essentially the same as the embodiments described above, and the corresponding descriptions in the embodiments above also apply to this embodiment. The system includes:

[0045] The checkpoint measurement data acquisition module 301 is configured to acquire checkpoint measurement data of a new type of subject under the source-load coupling scenario; The operating condition determination module 302 is configured to identify the direction and magnitude of electricity flow at each checkpoint within the metering cycle based on the metering data, and determine the operating condition of the new entity within the metering cycle. The feature matrix construction module 303 is configured to construct a source-load coupling time-series feature matrix of the new entity within the metering cycle based on the operating conditions; the source-load coupling time-series feature matrix is ​​used to characterize the degree of coupling between the new entity's power generation behavior and power consumption behavior in the same time period. The splitting module 304 is configured to split the total electricity consumption of the new entity within the metering period into multiple electricity components based on the source-load coupling time-series feature matrix and using preset electricity component identification rules; the electricity components include self-generated and self-consumed electricity, grid-connected electricity, and grid-used electricity. The loss allocation coefficient calculation module 305 is configured to calculate the loss allocation coefficient corresponding to each electrical component based on the power flow path of each electrical component in the transmission and distribution network and using the power flow tracing-based loss allocation method. The result output module 306 is configured to calculate the settlement power value of each power component based on each power component and the loss allocation coefficient corresponding to each power component, and output the settlement power value of each power component as the power splitting and loss allocation result.

[0046] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. The memory may include main memory, such as high-speed random-access memory (RAM), or it may also include non-volatile memory, such as at least one disk storage device. Of course, the electronic device may also include other hardware required for other services.

[0047] The processor, network interface, and memory can be interconnected via an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 4The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0048] Memory is used to store instructions for execution. Specifically, instructions for execution are computer programs that can be executed. Memory can include main memory and non-volatile memory, and it provides the processor with execution instructions and data.

[0049] In one possible implementation, the processor reads the corresponding execution instructions from non-volatile memory into memory and then executes them. Alternatively, it can obtain the corresponding execution instructions from other devices to form a source-load coupling power splitting and loss-sharing device at the logical level. The processor executes the execution instructions stored in memory to implement a source-load coupling power splitting and loss-sharing method provided in any embodiment of the present invention through the executed execution instructions.

[0050] The above is as described in the present invention. Figure 3 The method for source-load coupling power splitting and loss sharing system provided in the illustrated embodiment can be applied to or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor.

[0051] The steps of the method disclosed in the embodiments of this invention can be directly manifested as being executed by a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0052] This invention also proposes a readable medium storing execution instructions. When these instructions are executed by a processor of an electronic device, the device can perform a source-load coupling power splitting and loss amortization method provided in any embodiment of this invention, specifically for executing, as... Figure 1 , Figure 2 The method shown.

[0053] The electronic devices in the foregoing embodiments may be computers.

[0054] Those skilled in the art will understand that embodiments of the present invention can be provided as methods or computer program products. Therefore, the present invention can be implemented in a completely hardware embodiment, a completely software embodiment, or a combination of software and hardware.

[0055] The various embodiments in this invention are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the apparatus embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0056] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0057] The above are merely embodiments of the present invention and are not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the present invention should be included within the scope of the claims of the present invention.

Claims

1. A method for source-load coupling energy splitting and loss allocation, characterized in that, The method includes: Acquire gateway measurement data for novel entities in source-load coupling scenarios; Based on the metering data of the checkpoints, the direction and magnitude of the electricity flow at each checkpoint within the metering cycle are identified, and the operating conditions of the new entity within the metering cycle are determined. Based on the operating conditions, a source-load coupling time-series feature matrix is ​​constructed for the novel entity within the metering cycle; the source-load coupling time-series feature matrix is ​​used to characterize the degree of coupling between the novel entity's power generation behavior and power consumption behavior within the same time period; Based on the source-load coupling time-series feature matrix, and using a preset power component identification rule, the total power consumption of the novel entity within the metering period is divided into multiple power components; the power components include self-generated and self-consumed power consumption, grid-connected power consumption, and grid-connected power consumption. Based on the power flow path of each of the electrical components in the transmission and distribution network, a loss allocation coefficient corresponding to each of the electrical components is calculated using a power flow tracing-based loss allocation method. Based on each of the power components and the corresponding loss sharing coefficient, the settled power value of each power component is calculated, and the settled power value of each power component is output as the power splitting and loss sharing result.

2. The method according to claim 1, characterized in that, The metering data at the control points includes metering data at the power generation control points, metering data at the plant use control points, metering data at the self-generation and self-consumption control points, metering data at the upstream control points, metering data at the user control points, metering data at the energy storage control points, and metering data at the tie line control points.

3. The method according to claim 2, characterized in that, The step of identifying the direction and magnitude of electricity flow at each checkpoint within the metering cycle based on the metering data, and determining the operating conditions of the novel entity within the metering cycle, includes: Read the first meter reading at the beginning of the metering cycle and the second meter reading at the end of the metering cycle for each checkpoint; Calculate the difference between the second meter reading and the first meter reading at each checkpoint. If the difference is positive, it is determined that the direction of electricity flow at that checkpoint during the metering cycle is positive. If the difference is negative, it is determined that the direction of electricity flow at that checkpoint during the metering cycle is negative. The absolute value of the difference at each checkpoint is taken as the magnitude of the electricity flow at that checkpoint during the metering period. Based on the power flow direction and power flow amplitude corresponding to the power generation gateway metering data, the power flow direction and power flow amplitude corresponding to the energy storage gateway metering data, and the power flow direction and power flow amplitude corresponding to the user gateway metering data, the operating conditions of the new entity within the metering cycle are determined. The operating conditions include at least one of the following: power generation, power consumption, energy storage charging, energy storage discharging, and quiescent state.

4. The method according to claim 2, characterized in that, The step of constructing the source-load coupling time-series feature matrix of the novel entity within the metering cycle based on the operating conditions includes: The metering period is divided into multiple equal-length sub-periods; Within each sub-period, the power generation time series value is determined based on the power generation gateway metering data, and the power consumption time series value is determined based on the power consumption gateway metering data; If, within the same sub-period, both the power generation time-series value and the power consumption time-series value are greater than a preset power threshold, then it is determined that a source-load coupling event exists in that sub-period. Record the occurrence status of source-load coupling events in each sub-time period and construct a source-load coupling indicator vector; The source-load coupling time sequence feature matrix is ​​constructed based on the source-load coupling indicator vector and the power generation time sequence value and power consumption time sequence value corresponding to each sub-time period.

5. The method according to claim 4, characterized in that, The step of dividing the total electricity of the novel entity into multiple electricity components within the metering period based on the source-load coupling time-series feature matrix and using a preset electricity component identification rule includes: Based on the source-load coupling indication vector, identify the coupling and non-coupling periods within the metering cycle; During the uncoupled period, if the power generation time series value is greater than the preset power threshold and the power consumption time series value is less than or equal to the preset power threshold, then the power generation during the uncoupled period is identified as the power to be fed into the grid. During the uncoupled period, if the power consumption time series value is greater than the preset power threshold and the power generation time series value is less than or equal to the preset power threshold, then the power consumption during the uncoupled period is identified as grid power consumption. During the uncoupled period, if the power generation time series value is less than or equal to the preset power threshold and the power consumption time series value is less than or equal to the preset power threshold, then the period is a silent period and no power component is generated. During the coupling period, the time-series values ​​of the power generation are compared. With the power consumption time series value :like ≥ Then the self-generated and self-consumed electricity during the coupling period is The power consumption for internet access is The electricity consumption is 0; if < Then the self-generated and self-consumed electricity during the coupling period is The amount of electricity used by the grid is The battery level for internet access is 0; among which, This is the start time of the coupling period. The duration of the coupling period; By summarizing the identification results for each time period, the self-generated and self-consumed electricity, grid-connected electricity, and grid-connected electricity of the new entity within the metering period are obtained.

6. The method according to claim 1, characterized in that, The step of calculating the loss allocation coefficient corresponding to each of the electrical components based on the power flow path of each component in the power transmission and distribution network, using a power flow tracing-based loss allocation method, includes: Obtain the topology and line parameters of the power transmission and distribution network to which the new type of entity is connected; Based on the topology and line parameters, a power flow calculation model for the transmission and distribution network is established. Each of the aforementioned electrical components is used as an injection source and input into the power flow calculation model to calculate the power flow distribution generated by each of the aforementioned electrical components on each branch of the power transmission and distribution network. Based on the power flow distribution of each electrical component on each branch and the line loss of each branch, the loss allocation coefficient corresponding to each electrical component is calculated using the proportional allocation principle.

7. The method according to claim 1, characterized in that, The step of calculating the settlement electricity value for each electricity component based on each electricity component and the corresponding loss sharing coefficient includes: Obtain the total power loss of the power transmission and distribution network during the metering period; According to the The loss allocation coefficient corresponding to each electrical component With the total network loss power Calculate the first Network loss power allocated to individual power components ; Calculate the first Settlement electricity value for each electricity component ,in For the first The original charge value of each charge component; Output the calculated electricity value for each component of the electricity consumption.

8. A source-load coupling power splitting and loss sharing system, characterized in that, include: The gateway measurement data acquisition module is used to acquire gateway measurement data of new subjects in source-load coupling scenarios; The operating condition determination module is used to identify the direction and magnitude of power flow at each checkpoint within the metering cycle based on the metering data at the checkpoints, and to determine the operating condition of the new entity within the metering cycle. The feature matrix construction module is used to construct the source-load coupling time-series feature matrix of the novel subject within the metering period based on the operating conditions. The source-load coupling time-series feature matrix is ​​used to characterize the degree of coupling between the novel entity's simultaneous power generation and power consumption behaviors within the same time period; The splitting module is used to split the total electricity consumption of the novel entity within the metering period into multiple electricity components based on the source-load coupling time-series feature matrix and using preset electricity component identification rules; the electricity components include self-generated and self-consumed electricity, grid-connected electricity, and grid-used electricity. The loss allocation coefficient calculation module is used to calculate the loss allocation coefficient corresponding to each of the electrical components based on the power flow path of each electrical component in the power transmission and distribution network and using the power flow tracing-based loss allocation method. The result output module is used to calculate the settled power value of each power component based on each power component and the loss sharing coefficient corresponding to each power component, and output the settled power value of each power component as the power splitting and loss sharing result.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein the program, when executed, performs the method of any one of claims 1 to 7.

10. An electronic device, characterized in that, The electronic device includes: processor; Memory used to store the processor's executable instructions; The processor is configured to read the executable instructions from the memory and execute the instructions to implement the method described in any one of claims 1 to 7.

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