A smart meter energy management system and method supporting bidirectional measurement

Abstract

The present application provides a smart meter energy management system and method supporting bidirectional metering, which relates to the field of energy management technology. The system extracts the load power characteristics of energy-consuming units and the power generation characteristic curves of distributed power generation units from historical electric energy data, and constructs a mapping feature matrix that characterizes the power matching between energy-consuming units and distributed power generation units. The system determines the scheduling constraints of energy-consuming units in a typical time period based on the energy storage configuration, grid access capacity, and load category information in the operating configuration parameters, and then generates a priority matrix of energy distribution paths in the electric energy distribution process in combination with the mapping feature matrix. The system further generates an electric energy control strategy that optimizes the local absorption rate under the constraints of the priority matrix. The energy control variables are then dynamically adjusted based on the electric energy control strategy and the real-time bidirectional power metering data. The solution of the present invention can realize energy control based on the dynamic coordination of source-load-storage power and the optimization of local absorption capacity based on bidirectional metering data.

Description

Technical Field

[0001] The present application relates to the field of energy management technology, and more specifically, to a smart meter energy management system and method supporting bidirectional metering. Background Art

[0002] With the rapid development of distributed energy and smart grid technologies, energy management systems play an important role in improving the operating efficiency of power systems, ensuring power supply reliability, and promoting the consumption of green energy. Smart meters, as key equipment for electricity metering and information collection, can not only provide real-time monitoring of electricity consumption data, but also support two-way electricity metering, meeting the precise management of complex energy flows between distributed power generation and loads. Through scientific and reasonable energy management, they can achieve coordinated operation between energy-consuming units, distributed power generation units, and energy storage units, becoming a core link in promoting energy structure optimization and grid intelligence.

[0003] Existing technologies generally lack sufficient monitoring of power flow and energy matching during energy management. This is especially true in scenarios with complex interactions involving bidirectional energy flows. Traditional one-way metering and static scheduling strategies struggle to accurately reflect real-time power changes, leading to inefficient energy allocation and limited local energy consumption capacity. Furthermore, existing solutions often lack dynamic coordination mechanisms based on real-time bidirectional metering data, making it impossible to effectively resolve the conflict between the volatility of distributed generation output and load demand fluctuations. Furthermore, the regulatory role of energy storage systems is also difficult to fully leverage, hindering the efficient use of renewable energy and the stability of system operation. Therefore, achieving dynamic coordination of source-load-storage power based on bidirectional metering data and optimizing local energy consumption capacity has become a challenging issue for the industry. Summary of the Invention

[0004] The present application provides a smart meter energy management system and method that supports bidirectional metering, which can realize energy regulation based on dynamic coordination of source-load-storage power and optimization of local absorption capacity based on bidirectional metering data.

[0005] In a first aspect, the present application provides a smart meter energy management method supporting bidirectional metering, which is used for energy management in a smart meter energy management system. The smart meter energy management system includes: a smart meter, a power distribution unit, a distributed power generation unit, and an energy consumption unit, including the following steps:

[0006] Obtain historical power data of smart meters and operating configuration parameters of distribution units;

[0007] Extracting the load power characteristics of the energy-consuming unit and the power generation characteristic curve of the distributed power generation unit from the historical electric energy data, and then constructing a mapping feature matrix representing the power matching between the energy-consuming unit and the distributed power generation unit based on the load power characteristics and the power generation characteristic curve;

[0008] Determining the scheduling constraints of the energy-consuming units within a typical time period based on the energy storage configuration, grid connection capacity, and load category information in the operation configuration parameters, and then determining the priority matrix of the energy distribution path in the electric energy distribution process based on the scheduling constraints and the mapping characteristic matrix;

[0009] Predicting the local absorption capacity and reverse power carrying capacity in the next typical period, and then generating an electric energy regulation strategy that optimizes the local absorption rate under the constraints of the priority matrix;

[0010] Based on the electric energy control strategy, the output power of the distributed generation unit, the charging and discharging power of the energy storage unit and the power supply priority of the load are dynamically adjusted in combination with the bidirectional real-time electricity measurement data provided by the smart meter.

[0011] In this embodiment, extracting the load power characteristics of the energy consumption unit and the power generation characteristic curve of the distributed power generation unit from the historical power data specifically includes:

[0012] Separating the power consumption data of the energy-consuming units and the power output data of the distributed power generation units from the historical power data;

[0013] Extracting the power fluctuation range, peak frequency and duration of the energy consumption unit in different time periods through the power consumption data, and using them as the load power characteristics of the energy consumption unit;

[0014] The maximum value, minimum value and change trend of the power generation of the distributed power generation unit in different time periods are extracted through the power output data, and then the power generation characteristic curve of the distributed power generation unit is determined.

[0015] In this embodiment, constructing a mapping characteristic matrix representing power matching between energy consumption units and distributed power generation units based on the load power characteristics and the power generation characteristic curve specifically includes:

[0016] aligning the load power characteristic with the power generation characteristic curve according to time periods;

[0017] Calculate the matching degree between generated power and load power in each time period;

[0018] The matching degrees of each time period are arranged by time to generate a mapping feature matrix that represents the power matching between the energy consumption unit and the distributed generation unit.

[0019] In this embodiment, determining the dispatch constraint conditions of the energy consumption unit in a typical period based on the energy storage configuration, grid connection capacity, and load category information in the operation configuration parameters specifically includes:

[0020] Determining energy storage unit constraints based on the maximum charge and discharge power, capacity upper limit, and charge and discharge efficiency in the energy storage configuration;

[0021] The reverse feeding power limit is set according to the grid access capacity to obtain the grid interaction power constraint;

[0022] The scheduling constraints of the energy consumption units in a typical period are constructed by the energy storage unit constraints, the grid-connected interaction power constraints and the minimum power supply guarantee power of different types of loads.

[0023] In this embodiment, determining the priority matrix of the energy distribution path in the electric energy distribution process based on the scheduling constraint condition and the mapping characteristic matrix specifically includes:

[0024] Identify all possible paths in the power distribution process, including distributed generation units directly supplying energy users, energy storage units supplying power, grid-interactive power supply, and hybrid power supply paths;

[0025] Filtering out feasible allocation paths that meet the constraints from all possible paths according to the scheduling constraints;

[0026] Calculating a power matching rate of each feasible allocation path based on the mapping characteristic matrix;

[0027] The feasible distribution paths are sorted from high to low according to the power matching rate, and the sorting results are quantified into priority values ​​to obtain the priority matrix of the energy distribution path in the power distribution process.

[0028] In this embodiment, predicting the local absorption capacity and reverse power carrying capacity in the next typical period, and then generating an electric energy regulation strategy that optimizes the local absorption rate under the constraints of the priority matrix specifically includes:

[0029] Predict local energy consumption capacity within the next typical period based on historical electricity data;

[0030] Predict the reverse power carrying capacity in the next typical period based on historical grid operation data;

[0031] With the goal of maximizing the local absorption rate, under the priority matrix constraints, a control instruction sequence for distributed power generation output, energy storage charging and discharging, and load switching is generated by combining the local absorption capacity and the reverse power carrying capacity, and the control instruction sequence is used as the power control strategy.

[0032] In this embodiment, based on the power control strategy, the output power of the distributed generation unit, the charge and discharge power of the energy storage unit, and the power supply priority of the load are dynamically adjusted in combination with the bidirectional real-time power measurement data provided by the smart meter. Specifically, the following steps are performed:

[0033] Obtaining the target output power of the distributed generation unit, the target charge and discharge power of the energy storage unit, and the target power supply priority of the load from the power control strategy;

[0034] Determine the deviation between actual power and control strategy based on real-time bidirectional power measurement data;

[0035] If the deviation is within the preset range, the current output power, charge and discharge power, and power supply priority remain unchanged;

[0036] If the deviation exceeds the preset range, the output power of the distributed power generation unit is adjusted to the target output power according to the size of the deviation, the charging and discharging power of the energy storage unit is adjusted to compensate for the deviation, and the power supply priority of the load is dynamically adjusted to match the current energy supply capacity.

[0037] In this embodiment, the smart meter refers to a digital electric energy metering device with bidirectional electricity metering, data communication and remote control functions.

[0038] In this embodiment, historical electric energy data of the smart meter is obtained from a database.

[0039] In a second aspect, the present application provides a smart meter energy management system supporting bidirectional metering, which is configured to execute a smart meter energy management method supporting bidirectional metering. The smart meter energy management system includes:

[0040] An acquisition module is used to obtain historical electric energy data of smart meters and operating configuration parameters of distribution units;

[0041] a feature processing module for extracting the load power characteristics of the energy-consuming unit and the power generation characteristic curve of the distributed power generation unit from the historical electric energy data, and then constructing a mapping feature matrix representing the power matching between the energy-consuming unit and the distributed power generation unit based on the load power characteristics and the power generation characteristic curve;

[0042] The feature processing module is further configured to determine the scheduling constraints of the energy-consuming units within a typical time period based on the energy storage configuration, grid-connected capacity, and load category information in the operation configuration parameters, and further determine the priority matrix of the energy distribution path in the electric energy distribution process based on the scheduling constraints and the mapping feature matrix;

[0043] The feature processing module is further used to predict the local absorption capacity and reverse power carrying capacity in the next typical period, and then generate an electric energy control strategy that optimizes the local absorption rate under the constraints of the priority matrix;

[0044] The execution module is used to dynamically adjust the output power of the distributed generation unit, the charging and discharging power of the energy storage unit and the power supply priority of the load based on the power control strategy and the two-way real-time power measurement data provided by the smart meter.

[0045] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects:

[0046] First, the historical electric energy data of the smart meter and the operating configuration parameters of the distribution unit are obtained; the load power characteristics of the energy-consuming unit and the power generation characteristic curve of the distributed power generation unit are extracted from the historical electric energy data, and then a mapping feature matrix representing the power matching between the energy-consuming unit and the distributed power generation unit is constructed based on the load power characteristics and the power generation characteristic curve; the scheduling constraints of the energy-consuming unit in a typical time period are determined according to the energy storage configuration, grid access capacity and load category information in the operating configuration parameters, and then a priority matrix of the energy distribution path in the electric energy distribution process is determined based on the scheduling constraints and the mapping feature matrix; the local absorption capacity and reverse power carrying capacity in the next typical time period are predicted, and then an electric energy control strategy with the optimal local absorption rate is generated under the constraints of the priority matrix; based on the electric energy control strategy, the output power of the distributed power generation unit, the charging and discharging power of the energy storage unit and the power supply priority of the load are dynamically adjusted in combination with the bidirectional real-time metering data of electric power provided by the smart meter.

[0047] It can be seen that this application is based on the electric energy regulation strategy, combined with the two-way real-time electricity metering data provided by the smart meter to dynamically adjust the output power of the distributed generation unit, the charging and discharging power of the energy storage unit and the power supply priority of the load; first, the mapping characteristic matrix constructed based on the load power characteristics and the power generation characteristic curve provides a reliable quantitative basis for reflecting the time synchronization matching relationship between the load and the power generation output, significantly improves the accuracy and real-time performance of power matching, and helps to optimize the electric energy distribution strategy; secondly, by introducing energy storage configuration, grid-connected capacity and load category information, detailed scheduling constraints are constructed to ensure that the energy scheduling process is carried out under the premise of meeting equipment capacity limitations and power supply safety, effectively prevent system overload and reverse power feeding risks, and enhance the executability and stability of the scheduling strategy; then, based on the scheduling constraints and mapping characteristic matrix screening Select and sort energy distribution paths to form a priority matrix, realize scientific evaluation and priority sorting of power matching efficiency of different paths, optimize energy flow selection, and improve overall energy utilization efficiency; finally, use historical data to predict local absorption capacity and reverse power carrying capacity, provide dynamic constraint support for scheduling strategy formulation, ensure the adaptability and foresight of the strategy in actual operation, further combine bidirectional electricity real-time metering data, dynamically adjust distributed power generation, energy storage charging and discharging, and load power supply priority, realize coordinated dynamic regulation between source-load-storage, maximize local absorption rate and ensure stable operation of the power grid; in summary, the present invention can realize energy regulation based on bidirectional metering data to realize dynamic coordination of source-load-storage power and optimization of local absorption capacity, thereby promoting performance optimization of smart meter energy management system and efficient utilization of renewable energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0049] Figure 1 This is a flow chart of a smart meter energy management method supporting bidirectional metering provided in accordance with the present application;

[0050] Figure 2 is an exemplary flow chart for determining a mapping feature matrix according to the present application;

[0051] Figure 3 is an exemplary flow chart for determining scheduling constraints provided in this application;

[0052] Figure 4This is a module structure diagram of the smart meter energy management system supporting bidirectional metering provided in this application. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0054] The embodiment of the present application provides a smart meter energy management system and method that supports bidirectional metering. The core of the management system is to extract the load power characteristics of the energy-consuming unit and the power generation characteristic curve of the distributed power generation unit from the historical electric energy data, and construct a mapping feature matrix that characterizes the power matching between the energy-consuming unit and the distributed power generation unit; determine the scheduling constraints of the energy-consuming unit in a typical time period based on the energy storage configuration, grid access capacity and load category information in the operating configuration parameters, and then generate a priority matrix of the energy distribution path in the electric energy distribution process in combination with the mapping feature matrix; further generate an electric energy control strategy that optimizes the local absorption rate under the constraint of the priority matrix; and then dynamically adjust the energy control variables based on the electric energy control strategy and the real-time metering data of bidirectional electricity. The solution of the present invention can realize the dynamic coordination of source-load-storage power based on bidirectional metering data and the energy control that optimizes the local absorption capacity.

[0055] Example 1: In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods. Figure 1 As shown in FIG, this figure is an exemplary flow chart of a smart meter energy management method supporting bidirectional metering according to this embodiment of the present application. The smart meter energy management method supporting bidirectional metering includes the following steps:

[0056] In step S1, historical electric energy data of the smart meter and operating configuration parameters of the power distribution unit are obtained.

[0057] It should be noted that the smart meter in this application refers to a digital electricity metering device with two-way electricity metering, data communication and remote control functions.

[0058] In specific implementation, the acquisition equipment connected to the smart meter can use the DL / T 645 communication protocol to regularly read bidirectional active power, voltage, current and other data, and store these data in a database in time series. The historical electric energy data of the smart meter can be obtained from the database; the operating configuration parameters can be extracted from the distribution system, including: energy storage capacity, grid-connected capacity and load type. If the system supports the IEC 61850 protocol, the structured parameters in the configuration model can be directly read.

[0059] In step S2, the load power characteristics of the energy-consuming unit and the power generation characteristic curve of the distributed power generation unit are extracted from the historical electric energy data, and then a mapping feature matrix characterizing the power matching between the energy-consuming unit and the distributed power generation unit is constructed based on the load power characteristics and the power generation characteristic curve.

[0060] In this embodiment, extracting the load power characteristics of the energy-consuming unit and the power generation characteristic curve of the distributed power generation unit from the historical power data can be achieved by using the following steps:

[0061] Separating the power consumption data of the energy-consuming units and the power output data of the distributed power generation units from the historical power data;

[0062] Extracting the power fluctuation range, peak frequency and duration of the energy consumption unit in different time periods through the power consumption data, and using them as the load power characteristics of the energy consumption unit;

[0063] The maximum value, minimum value and change trend of the power generation of the distributed power generation unit in different time periods are extracted through the power output data, and then the power generation characteristic curve of the distributed power generation unit is determined.

[0064] It should be noted that the load power characteristics in this application are used to characterize the intensity of the energy demand of the energy-consuming unit and its time distribution characteristics; the power generation characteristic curve in this application is a curve used to describe the output power change trend and energy supply capacity characteristics of the distributed power generation unit in different time periods.

[0065] In specific implementation, first, the historical electric energy data is filtered according to the device identifier to distinguish the electric energy consumption data of the energy-consuming unit from the electric energy output data of the distributed power generation unit. In this process, the grouping can be carried out according to the device address code or the acquisition channel number; secondly, for the electric energy consumption data of the energy-consuming unit, a fixed time window (which can be set to 30 minutes in this application) is used for statistics, and the maximum power, minimum power and average power in each time period are calculated. The power fluctuation range is extracted by sliding window method, and the number of peak occurrences and duration are counted in combination with the local extreme value detection algorithm. Commonly used algorithms include threshold-based local maximum value search or continuous threshold judgment method; then, for the electric energy output data of the distributed power generation unit, statistics are performed according to the same step length, the maximum and minimum power generation power in each cycle are extracted, and the time series is smoothed using the moving average method or polynomial regression method to obtain a trend curve of power generation over time.

[0066] In this embodiment, reference Figure 2 As shown in FIG. 1 , this figure is an exemplary flow chart for determining a mapping characteristic matrix in an embodiment of the present application. In this embodiment, the mapping characteristic matrix representing the power matching between the energy consumption unit and the distributed generation unit based on the load power characteristics and the power generation characteristic curve can be constructed by the following steps:

[0067] In step S21, the load power characteristic is aligned with the power generation characteristic curve according to time periods;

[0068] In step S22, the matching degree between the generated power and the load power in each time period is calculated;

[0069] In step S23, the matching degrees of each time period are arranged by time to generate a mapping feature matrix representing the power matching between the energy consumption unit and the distributed generation unit.

[0070] It should be noted that the matching degree in this application is an indicator to measure the degree of supply and demand adaptation between the output power of the distributed power generation unit and the load power of the energy-consuming unit in each time period; the mapping characteristic matrix in this application is a matrix that reflects the power matching relationship between the energy-consuming unit and the distributed power generation unit and its time-series change characteristics.

[0071] In the specific implementation, first, in the time alignment stage, with a unified time resolution, the time resolution in this application can be set to 15 minutes, the load power characteristics and the power generation characteristic curve are synchronized to ensure that the two power series have corresponding data points in each time period; secondly, for each time period, the normalized difference method is used to calculate the power generation power and load power The matching degree between , the matching index can be set as , and set a threshold to limit the matching degree to no more than 1. If multi-dimensional factors need to be considered, a weighting factor can be introduced to deal with the differences in power stability and volatility; finally, the matching degree of each time period is arranged in chronological order and constructed as a one-dimensional time series or a two-dimensional matrix structure, where the rows represent time periods and the columns can be expanded to combinations of different loads or power generation units. The mapping feature matrix representing the power matching between energy-consuming units and distributed power generation units is obtained, which can be used for subsequent energy path priority analysis and regulation strategy formulation.

[0072] In step S3, the scheduling constraints of the energy-consuming units within a typical time period are determined based on the energy storage configuration, grid connection capacity and load category information in the operating configuration parameters, and then the priority matrix of the energy distribution path in the electric energy distribution process is determined based on the scheduling constraints and the mapping characteristic matrix.

[0073] It should be noted that the typical time period in this application refers to a representative time period that reflects the load and power generation rules.

[0074] In this embodiment, reference Figure 3 As shown in FIG, this figure is an exemplary flow chart for determining scheduling constraints in an embodiment of the present application. In this embodiment, the scheduling constraints of the energy-consuming unit in a typical period are determined based on the energy storage configuration, grid connection capacity, and load category information in the operating configuration parameters. The following steps can be used to implement this:

[0075] In step S31, energy storage unit constraints are determined based on the maximum charge and discharge power, capacity upper limit, and charge and discharge efficiency in the energy storage configuration;

[0076] In step S32, a reverse power feeding power limit is set according to the grid access capacity to obtain a grid interaction power constraint;

[0077] In step S33, the scheduling constraints of the energy consumption units in a typical period are constructed by using the energy storage unit constraints, the grid-connected interaction power constraints and the minimum power supply guarantee powers of different types of loads.

[0078] It should be noted that the energy storage unit constraints in this application are restrictive conditions that describe the changes in the charging and discharging power and charge state of the energy storage system within the safe operating range; the grid-connected interaction power constraints in this application refer to the power range that limits the distributed generation and energy storage systems from transmitting or absorbing electricity to the grid; the scheduling constraints in this application refer to the comprehensive restrictions imposed on the equipment operating capacity and power supply guarantee requirements during the energy management process.

[0079] In the specific implementation, first, for the energy storage unit, the maximum charge and discharge power is collected according to its operating configuration parameters. The maximum charge and discharge power is the maximum charging power and maximum discharge power allowed by the energy storage system. The capacity upper limit is the energy capacity upper limit of the energy storage device. The charge and discharge efficiency is the charging efficiency and the discharge efficiency. By establishing an energy storage state variable model, the state of charge (State of Charge, SOC), and use inequality constraints to ensure that the charge and discharge power does not exceed the maximum power limit, and the SOC is always kept within the allowable range to ensure the safe operation and long life of the energy storage equipment. It should be further explained that the energy storage state variable model in this application is a mathematical model used to describe the change of the state of charge of the energy storage system over time. Its core is to reflect the effective energy currently stored in the battery through state variables. Its technical principle is based on the law of conservation of energy. The change of SOC at any point in time is determined by the SOC, charging power, discharging power and charging and discharging efficiency at the previous moment. When calculating specifically, SOC is expressed as a percentage or unit capacity. The dynamic change of SOC is obtained by integrating the actual charge and discharge power of the energy storage unit (after adjusting the efficiency). The expression is usually: the SOC at the next moment is equal to the current SOC plus the charging energy minus the discharging energy, and is constrained within the physical capacity range. The energy storage state variable model takes into account It measures the energy loss and efficiency during battery charging and discharging, accurately reflects the remaining available energy of energy storage equipment, supports real-time scheduling decisions and safe operation control, and is the basis for optimized scheduling and life management of energy storage systems. Then, based on the grid-connected access capacity of the distribution system, a reverse feeding power limit is set to prevent distributed generation or energy storage from sending back more than the allowable range of the grid, avoiding grid overload and the risk of reverse flow of electric energy. This constraint uses an upper limit constraint expression to limit the reverse feeding power to a specified range by real-time monitoring of the power flow at the grid connection point and combining it with the grid connection capacity parameters. Finally, for different load categories, a minimum power supply power threshold is defined based on their power supply level and security requirements to ensure continuous power supply to critical loads. The above three types of constraints are then integrated through a mathematical model to form the scheduling constraint conditions for energy-consuming units in a typical period, and are usually input into the scheduling optimization model in the form of linear constraints to support the feasibility of subsequent energy allocation and strategy optimization.

[0080] In this embodiment, determining the priority matrix of the energy distribution path in the electric energy distribution process based on the scheduling constraints and the mapping characteristic matrix can be implemented by the following steps:

[0081] Identify all possible paths in the power distribution process, including distributed generation units directly supplying energy users, energy storage units supplying power, grid-interactive power supply, and hybrid power supply paths;

[0082] Filtering out feasible allocation paths that meet the constraints from all possible paths according to the scheduling constraints;

[0083] Calculating a power matching rate of each feasible allocation path based on the mapping characteristic matrix;

[0084] The feasible distribution paths are sorted from high to low according to the power matching rate, and the sorting results are quantified into priority values ​​to obtain the priority matrix of the energy distribution path in the power distribution process.

[0085] It should be noted that the energy distribution path in this application refers to the combination of paths for transmitting electric energy to the load between different energy supply modes; the power matching rate in this application is a characteristic indicator for measuring the degree of matching between electric energy supply capacity and energy demand on a time scale; the priority matrix in this application is a characteristic matrix that reflects the ranking relationship of the degree of contribution of various energy distribution paths to overall energy efficiency under the conditions of meeting scheduling objectives and constraints.

[0086] In specific implementation, first, the path identification stage can rely on the system topology and device connection relationship, and use a graph traversal algorithm (depth-first search or breadth-first search can be used in this application) to enumerate all possible power distribution paths, covering power supply paths in which distributed power generation units directly supply energy-consuming units, energy storage units participate in charging and discharging, bidirectional energy exchange paths between the power grid and the local system, and multi-path hybrid power supply schemes; second, the previously established scheduling constraints are applied to each possible path one by one, and paths that do not meet the conditions are screened out through logical judgment or linear constraint testing to ensure the feasibility and security of the power distribution scheme; then, based on the power matching degree of the corresponding time period in the mapping feature matrix, the weighted average method or integral method can be used to summarize the matching degree of the path in the entire typical time period to obtain the power matching rate of the path; finally, based on the calculated matching rate value, the paths are sorted from high to low using a sorting algorithm (quick sorting method can be used in this application), and the sorting results are quantified into a priority matrix through numerical mapping. The priority matrix structure clearly indicates the priority order of each path.

[0087] In step S4, the local absorption capacity and reverse power carrying capacity in the next typical time period are predicted, and then an electric energy regulation strategy for optimizing the local absorption rate is generated under the constraint condition of the priority matrix.

[0088] In this embodiment, the following steps are used to predict the local absorption capacity and reverse power carrying capacity in the next typical period, and then generate an electric energy control strategy that optimizes the local absorption rate under the constraints of the priority matrix:

[0089] Predict local energy consumption capacity within the next typical period based on historical electricity data;

[0090] Predict the reverse power carrying capacity in the next typical period based on historical grid operation data;

[0091] With the goal of maximizing the local absorption rate, under the priority matrix constraints, a control instruction sequence for distributed power generation output, energy storage charging and discharging, and load switching is generated by combining the local absorption capacity and the reverse power carrying capacity, and the control instruction sequence is used as the power control strategy.

[0092] It should be noted that the local absorption capacity in this application refers to the real-time absorption upper limit of the distributed power output within the local load and energy storage system; the reverse power carrying capacity in this application refers to the maximum power upper limit that the power grid can accept to feedback the surplus electric energy from the user side under safe operating conditions.

[0093] In the specific implementation, first, a historical database of local electricity load and renewable energy output is constructed, including load, voltage, power factor and meteorological information (including light intensity and temperature) recorded at a granularity of 15 minutes or 1 hour. In the data preprocessing stage, Z-score standardization is used to remove outliers and noise, and wavelet transform is used to smooth the time series data to ensure data stability. According to the periodic characteristics of load changes, the autoregressive sliding average method is used to identify short-term trends, and then a long short-term memory network model is introduced to train historical data. The input features include historical load, current time period, working day type and meteorological conditions. The model establishes a mapping of the previous T time period series through a time step window. The load forecast value of the future target period is output through the radiation relationship, and the local available energy in the period is estimated through the existing local distributed power supply historical output records. The local absorption capacity is calculated by the difference between the load forecast value and the local available energy. Then, a historical data set containing operating parameters such as substation feeder voltage, current, active power, reactive power, bus voltage level, load flow state, etc. is established, and the corresponding time information and operating environment conditions (such as temperature, season, and grid topology) are labeled. In the data preprocessing stage, median interpolation is used to handle missing values, and then the principal component analysis method is used to compress multidimensional input features and eliminate redundant interference dimensions. In the modeling stage, support vector regression is selected as the regression model. The model is designed with input parameters such as the voltage level of each node, bus power distribution and terminal load power change rate under multiple typical operating conditions. The training target is the maximum reverse power flow value in the corresponding period (that is, the maximum power allowed to be sent back to the grid). During the model training process, grid search is used to determine the kernel function parameters, and five-fold cross-validation is used to improve stability. In the prediction stage, the operating data characteristics of the current and multiple historical adjacent time periods are input, and the maximum reverse power value of each node in the next typical period under the premise of voltage compliance is output, and this power value is then used as the reverse power carrying capacity. Finally, the control variables are defined first, including the output power of the distributed power source, the charging and discharging power of the energy storage system, and the switching state of the controllable load. Ensure that the variables are within the rated range of the equipment, and then use linear or mixed integer linear programming methods to establish the objective function expression. The objective function is quantified as maximizing the proportion of distributed electric energy absorbed by the local load, while incorporating energy storage energy conservation and charging and discharging efficiency constraints. The priority matrix is ​​integrated into the scheduling constraints to guide path selection in the form of weights. The reverse power carrying capacity constraint limits the reverse feeding power of the power grid. The model solving link uses the Gurobi solver to perform iterative optimization to ensure the global optimal or near-optimal solution. The scheduling results are further converted into control instructions in the form of time series, including distributed power generation output settings, energy storage charging and discharging plans, and load start and stop timing, which are sent to the control terminal as a real-time power control strategy.

[0094] In step S5, based on the power control strategy, combined with the bidirectional real-time power measurement data provided by the smart meter, the output power of the distributed power generation unit, the charging and discharging power of the energy storage unit and the power supply priority of the load are dynamically adjusted.

[0095] In this embodiment, based on the power control strategy, the output power of the distributed generation unit, the charge and discharge power of the energy storage unit, and the power supply priority of the load are dynamically adjusted in combination with the bidirectional real-time power measurement data provided by the smart meter. The following steps can be used to achieve this:

[0096] Obtaining the target output power of the distributed generation unit, the target charge and discharge power of the energy storage unit, and the target power supply priority of the load from the power control strategy;

[0097] Determine the deviation between actual power and control strategy based on real-time bidirectional power measurement data;

[0098] If the deviation is within the preset range, the current output power, charge and discharge power, and power supply priority remain unchanged;

[0099] If the deviation exceeds the preset range, the output power of the distributed power generation unit is adjusted to the target output power according to the size of the deviation, the charging and discharging power of the energy storage unit is adjusted to compensate for the deviation, and the power supply priority of the load is dynamically adjusted to match the current energy supply capacity.

[0100] It should be noted that the bidirectional real-time electricity metering data in this application refers to the real-time electricity information of the directions of electricity inflow and outflow in the power grid that is simultaneously measured and recorded.

[0101] In specific implementation, first, the target output power of the distributed generation unit, the charging and discharging power of the energy storage unit, and the power supply priority of each load are extracted from the power regulation strategy. These values ​​are used as the basis for the control instructions. Secondly, the bidirectional real-time electricity measurement data of the smart meter is used to calculate the deviation between the current actual power and the target power. This deviation reflects the degree of imbalance between supply and demand in the current system. Then, a deviation threshold range is set. When the deviation is within an acceptable range, the existing control parameters are maintained unchanged to ensure system stability. Finally, when the deviation exceeds the preset threshold, the proportional integral or model predictive control (MPC) algorithm is used to adjust the output power of the distributed generation unit based on the size and direction of the deviation. The charging and discharging power of the energy storage unit is adjusted first to compensate for the energy difference. At the same time, the power supply priority of the load is dynamically adjusted to ensure load-side power matching.

[0102] It can be seen that this application is based on the electric energy regulation strategy, combined with the two-way real-time electricity metering data provided by the smart meter to dynamically adjust the output power of the distributed generation unit, the charging and discharging power of the energy storage unit and the power supply priority of the load; first, the mapping characteristic matrix constructed based on the load power characteristics and the power generation characteristic curve provides a reliable quantitative basis for reflecting the time synchronization matching relationship between the load and the power generation output, significantly improves the accuracy and real-time performance of power matching, and helps to optimize the electric energy distribution strategy; secondly, by introducing energy storage configuration, grid-connected capacity and load category information, detailed scheduling constraints are constructed to ensure that the energy scheduling process is carried out under the premise of meeting equipment capacity limitations and power supply safety, effectively prevent system overload and reverse power feeding risks, and enhance the executability and stability of the scheduling strategy; then, based on the scheduling constraints and mapping characteristic matrix screening Select and sort energy distribution paths to form a priority matrix, realize scientific evaluation and priority sorting of power matching efficiency of different paths, optimize energy flow selection, and improve overall energy utilization efficiency; finally, use historical data to predict local absorption capacity and reverse power carrying capacity, provide dynamic constraint support for scheduling strategy formulation, ensure the adaptability and foresight of the strategy in actual operation, further combine bidirectional electricity real-time metering data, dynamically adjust distributed power generation, energy storage charging and discharging, and load power supply priority, realize coordinated dynamic regulation between source-load-storage, maximize local absorption rate and ensure stable operation of the power grid; in summary, the present invention can realize energy regulation based on bidirectional metering data to realize dynamic coordination of source-load-storage power and optimization of local absorption capacity, thereby promoting performance optimization of smart meter energy management system and efficient utilization of renewable energy.

[0103] In the second embodiment, the present application provides a smart meter energy management system that supports bidirectional metering. Figure 4 As shown in FIG, this figure is a schematic diagram of a smart meter energy management system supporting bidirectional metering according to this embodiment of the present application. The smart meter energy management system supporting bidirectional metering includes:

[0104] An acquisition module 100 is used to acquire historical electric energy data of the smart meter and operating configuration parameters of the power distribution unit;

[0105] a feature processing module 200 for extracting the load power characteristics of the energy-consuming unit and the power generation characteristic curve of the distributed power generation unit from the historical power data, and then constructing a mapping feature matrix representing the power matching between the energy-consuming unit and the distributed power generation unit based on the load power characteristics and the power generation characteristic curve;

[0106] The feature processing module 200 is further configured to determine the scheduling constraints of the energy-consuming units within a typical time period based on the energy storage configuration, grid-connected capacity, and load category information in the operation configuration parameters, and further determine the priority matrix of the energy distribution path in the electric energy distribution process based on the scheduling constraints and the mapping feature matrix;

[0107] The feature processing module 200 is further used to predict the local absorption capacity and reverse power carrying capacity in the next typical period, and then generate an electric energy control strategy that optimizes the local absorption rate under the constraints of the priority matrix;

[0108] The execution module 300 is used to dynamically adjust the output power of the distributed generation unit, the charging and discharging power of the energy storage unit and the power supply priority of the load based on the power control strategy and the two-way real-time power measurement data provided by the smart meter.

[0109] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0110] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program. The program can be stored in a computer-readable storage medium, including a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM), or other optical disc storage, magnetic disk storage, or magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0111] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

Claims

1. A smart meter energy management method supporting bidirectional metering, used in a smart meter energy management system for energy management, the smart meter energy management system comprising: The smart meter, the power distribution unit, the distributed power generation unit and the energy consumption unit are characterized by comprising the following steps: Obtain historical power data of smart meters and operating configuration parameters of distribution units; Extracting the load power characteristics of the energy-consuming unit and the power generation characteristic curve of the distributed power generation unit from the historical electric energy data, and then constructing a mapping feature matrix representing the power matching between the energy-consuming unit and the distributed power generation unit based on the load power characteristics and the power generation characteristic curve; Determining the scheduling constraints of the energy-consuming units within a typical time period based on the energy storage configuration, grid connection capacity, and load category information in the operation configuration parameters, and then determining the priority matrix of the energy distribution path in the electric energy distribution process based on the scheduling constraints and the mapping characteristic matrix; Predicting the local absorption capacity and reverse power carrying capacity in the next typical period, and then generating an electric energy regulation strategy that optimizes the local absorption rate under the constraints of the priority matrix; Based on the power control strategy, the output power of the distributed generation unit, the charge and discharge power of the energy storage unit, and the power supply priority of the load are dynamically adjusted in combination with the bidirectional real-time power measurement data provided by the smart meter; The prediction of the local absorption capacity and reverse power carrying capacity in the next typical period, and the generation of an energy regulation strategy that optimizes the local absorption rate under the constraints of the priority matrix, specifically include: Predict local energy consumption capacity within the next typical period based on historical electricity data; Predict the reverse power carrying capacity in the next typical period based on historical grid operation data; With the goal of maximizing the local absorption rate, under the priority matrix constraints, a control instruction sequence for distributed power generation output, energy storage charging and discharging, and load switching is generated by combining the local absorption capacity and the reverse power carrying capacity, and the control instruction sequence is used as the power control strategy.

2. The method according to claim 1, wherein Extracting the load power characteristics of the energy-consuming unit and the power generation characteristic curve of the distributed power generation unit from the historical power data specifically includes: Separating the power consumption data of the energy-consuming units and the power output data of the distributed power generation units from the historical power data; Extracting the power fluctuation range, peak frequency and duration of the energy consumption unit in different time periods through the power consumption data, and using them as the load power characteristics of the energy consumption unit; The maximum value, minimum value and change trend of the power generation of the distributed power generation unit in different time periods are extracted through the power output data, and then the power generation characteristic curve of the distributed power generation unit is determined.

3. The method according to claim 1, wherein Constructing a mapping characteristic matrix representing power matching between energy consumption units and distributed generation units based on the load power characteristics and the power generation characteristic curve specifically includes: aligning the load power characteristic with the power generation characteristic curve according to time periods; Calculate the matching degree between generated power and load power in each time period; The matching degrees of each time period are arranged by time to generate a mapping feature matrix that represents the power matching between the energy consumption unit and the distributed generation unit.

4. The method according to claim 1, wherein The scheduling constraints of the energy consumption unit during a typical period are determined based on the energy storage configuration, grid connection capacity, and load category information in the operation configuration parameters, specifically including: Determining energy storage unit constraints based on the maximum charge and discharge power, capacity upper limit, and charge and discharge efficiency in the energy storage configuration; The reverse feeding power limit is set according to the grid access capacity to obtain the grid interaction power constraint; The scheduling constraints of the energy consumption units in a typical period are constructed by the energy storage unit constraints, the grid-connected interaction power constraints and the minimum power supply guarantee power of different types of loads.

5. The method according to claim 1, wherein Determining the priority matrix of the energy distribution path in the electric energy distribution process based on the scheduling constraint condition and the mapping characteristic matrix specifically includes: Identify all possible paths in the power distribution process, including distributed generation units directly supplying energy users, energy storage units supplying power, grid-interactive power supply, and hybrid power supply paths; Filtering out feasible allocation paths that meet the constraints from all possible paths according to the scheduling constraints; Calculating a power matching rate of each feasible allocation path based on the mapping characteristic matrix; The feasible distribution paths are sorted from high to low according to the power matching rate, and the sorting results are quantified into priority values ​​to obtain the priority matrix of the energy distribution path in the power distribution process.

6. The method according to claim 1, wherein Based on the power control strategy, combined with the bidirectional real-time power measurement data provided by the smart meter, the output power of the distributed generation unit, the charge and discharge power of the energy storage unit, and the power supply priority of the load are dynamically adjusted, specifically including: Obtaining the target output power of the distributed generation unit, the target charge and discharge power of the energy storage unit, and the target power supply priority of the load from the power control strategy; Determine the deviation between actual power and control strategy based on real-time bidirectional power measurement data; If the deviation is within the preset range, the current output power, charge and discharge power, and power supply priority remain unchanged; If the deviation exceeds the preset range, the output power of the distributed power generation unit is adjusted to the target output power according to the size of the deviation, the charging and discharging power of the energy storage unit is adjusted to compensate for the deviation, and the power supply priority of the load is dynamically adjusted to match the current energy supply capacity.

7. The method according to claim 1, wherein The smart meter refers to a digital electric energy metering device with two-way electricity measurement, data communication and remote control functions.

8. The method according to claim 1, wherein Get the historical electric energy data of smart meters from the database.

9. A smart meter energy management system supporting bidirectional metering, configured to execute the smart meter energy management method supporting bidirectional metering according to any one of claims 1 to 8, characterized in that: The smart meter energy management system includes: An acquisition module is used to obtain historical electric energy data of smart meters and operating configuration parameters of distribution units; a feature processing module for extracting the load power characteristics of the energy-consuming unit and the power generation characteristic curve of the distributed power generation unit from the historical electric energy data, and then constructing a mapping feature matrix representing the power matching between the energy-consuming unit and the distributed power generation unit based on the load power characteristics and the power generation characteristic curve; The feature processing module is further configured to determine the scheduling constraints of the energy-consuming units within a typical time period based on the energy storage configuration, grid-connected capacity, and load category information in the operation configuration parameters, and further determine the priority matrix of the energy distribution path in the electric energy distribution process based on the scheduling constraints and the mapping feature matrix; The feature processing module is further used to predict the local absorption capacity and reverse power carrying capacity in the next typical period, and then generate an electric energy control strategy that optimizes the local absorption rate under the constraints of the priority matrix; The execution module is used to dynamically adjust the output power of the distributed generation unit, the charging and discharging power of the energy storage unit and the power supply priority of the load based on the power control strategy and the two-way real-time power measurement data provided by the smart meter.

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