Grid Load Resource Hierarchical Regulation Method, Device, Electronic Equipment and Storage Medium

Abstract

The present disclosure provides a method, device, electronic device and storage medium for hierarchical regulation of power grid load resources. The method includes: obtaining operation data of a distribution network and hierarchical management data of load resources; determining regulation potential information and power distribution characteristic information of load resources based on the operation data of the distribution network and the hierarchical management data; constructing a global optimal operation mode model of the distribution network, and constructing an objective function and constraint conditions based on the regulation potential information and the power distribution characteristic information; solving the global optimal operation mode model of the distribution network to obtain a model output result; dividing the load resources to obtain multiple local area load resources; generating load resource scheduling information for the multiple local area load resources according to the model output result and the load resource information; and controlling the operation state of target load resources in the multiple local area load resources based on the load resource scheduling information, so as to ensure the stable and efficient operation of the distribution network.

Description

Technical Field

[0001] The present disclosure relates to the field of power technologies, and in particular, to a method, device, electronic device, and storage medium for hierarchical regulation and control of grid load resources. Background Art

[0002] With the continuous economic development, load resources of different types and scales have grown rapidly, making it difficult to manage the load resources. Moreover, the existing optimization technology for the regulation sequence of the operation strategy of the distribution network has deficiencies when facing the increasing complexity and uncertainty of the distribution network, resulting in a reduction in power supply quality and reliability. Summary of the Invention

[0003] In view of this, the purpose of the present disclosure is to propose a method, device, electronic device, and storage medium for hierarchical regulation and control of grid load resources.

[0004] Based on the above purpose, the first aspect of the present disclosure provides a method for hierarchical regulation and control of grid load resources, including:

[0005] Obtain the operation data of the distribution network and the hierarchical management data of the load resources; wherein, the load resources are hierarchically managed based on the load attributes, and the data of the load resources after hierarchical management is obtained as the hierarchical management data;

[0006] Determine the regulation potential information and power distribution characteristic information of the load resources based on the operation data of the distribution network and the hierarchical management data;

[0007] Construct a global optimal operation mode model of the distribution network, and construct the objective function and constraint conditions of the global optimal operation mode model of the distribution network based on the regulation potential information and the power distribution characteristic information;

[0008] Solve the global optimal operation mode model of the distribution network based on the objective function and the constraint conditions to obtain the model output result;

[0009] Divide the load resources based on the hierarchical management data to obtain multiple local area load resources;

[0010] Obtain the load resource information of the multiple local area load resources, and generate load resource scheduling information for the multiple local area load resources according to the model output result and the load resource information;

[0011] Control the operation state of the target load resource in the multiple local area load resources based on the load resource scheduling information.

[0012] In some embodiments, the acquisition of the hierarchical management data includes:

[0013] Obtain load device data of the load device;

[0014] Obtain load terminal data of the load terminal, where the load terminal is used to manage at least one load device within the same preset range;

[0015] Obtain load group data of the load group, where the load group is used to manage at least one load terminal of the same type;

[0016] Obtain load aggregator data of the load aggregator, where the load aggregator is used to manage the load group;

[0017] Determine the hierarchical management data based on the load device data, the load terminal data, the load group data, and the load aggregator data.

[0018] In some embodiments, constructing a global optimal operation mode model of the distribution network, and constructing the objective function and constraint conditions of the global optimal operation mode model of the distribution network based on the regulation potential information and the power distribution characteristic information includes:

[0019] Construct a regulation potential model based on the regulation potential information, and set the maximization of the utilization rate of the adjustable potential of the load resources as the objective function item of the objective function;

[0020] Construct a power distribution model based on the power distribution characteristic information, and set the power distribution balance information as the constraint condition.

[0021] In some embodiments, it further includes:

[0022] Obtain the correlation relationship between the output of the power generation device and the regulation amount information of the load resources, determine the power generation cost based on the correlation relationship between the output of the power generation device and the regulation amount information of the load resources, and set the power generation cost as the objective function item of the objective function;

[0023] Obtain the distribution information of the load resources on the nodes and lines based on the hierarchical management data, determine the power flow distribution information of the distribution network based on the distribution information, and set the power flow distribution information as the constraint condition;

[0024] Determine the constraint condition based on at least one of the line capacity, the node voltage, and the load regulation information.

[0025] In some embodiments, constructing the global optimal operation mode model of the distribution network includes:

[0026] Generate a graph structure based on the distribution network operation data and the distribution network topology structure, where the nodes of the graph structure represent the devices in the distribution network, and the edges of the graph structure represent the connection relationships between the devices;

[0027] Feature extraction is performed on the graph structure through a graph neural network, and the feature representation of nodes is updated through a message propagation mechanism, and decision variables are dynamically adjusted through reinforcement learning;

[0028] The global optimal operation mode model of the distribution network is solved based on the objective function and the constraint conditions to obtain a model output result, including:

[0029] The global optimal operation mode model of the distribution network is iteratively solved, and the feasible region is reduced by dynamically adding cut-plane constraints, and the distribution network operation information that satisfies the minimized operation cost is output as the model output result.

[0030] In some embodiments, the division of the load resources based on the hierarchical management data includes:

[0031] Obtaining at least one of geographical distribution information, network topology structure association information, load characteristic information, and regulation potential information of the load resources based on the hierarchical management data;

[0032] Dividing the load resources based on at least one of the geographical distribution information, network topology structure association information, load characteristic information, and regulation potential information of the load resources.

[0033] In some embodiments, the generation of load resource scheduling information for multiple local area load resources according to the model output result and the load resource information includes:

[0034] Obtaining at least one of complementary information and regulation potential information of the local area load resources according to the load resource information;

[0035] Determining the load resource scheduling information based on at least one of the complementary information and the regulation potential information.

[0036] In some embodiments, before controlling the operating state of the target load resource in the load resources based on the load resource scheduling information, it further includes:

[0037] Generating at least one of load regulation cost and load regulation accuracy based on the load resource scheduling information;

[0038] Obtaining a comparison result between at least one of the load regulation cost and the load regulation accuracy and a preset threshold, and modifying the load resource scheduling information based on the comparison result.

[0039] A second aspect of the present disclosure provides a hierarchical regulation device for grid load resources, including:

[0040] An acquisition module, configured to: acquire the operation data of the distribution network and the hierarchical management data of the load resources; wherein, the load resources are hierarchically managed based on the load attributes, and the data of the load resources after hierarchical management is acquired as the hierarchical management data;

[0041] A determination module, configured to: determine the regulation potential information and the power distribution characteristic information of the load resources based on the operation data of the distribution network and the hierarchical management data;

[0042] A model construction module, configured to: construct a global optimal operation mode model of the distribution network, and construct the objective function and the constraint conditions of the global optimal operation mode model of the distribution network based on the regulation potential information and the power distribution characteristic information;

[0043] A model solving module, configured to: solve the global optimal operation mode model of the distribution network based on the objective function and the constraint conditions to obtain a model output result;

[0044] A division module, configured to: divide the load resources based on the hierarchical management data to obtain a plurality of local area load resources;

[0045] A generation module, configured to: acquire the load resource information of a plurality of the local area load resources, and generate load resource scheduling information for the plurality of local area load resources according to the model output result and the load resource information;

[0046] A control module, configured to: control the operation state of the target load resource among the plurality of local area load resources based on the load resource scheduling information.

[0047] A third aspect of the present disclosure provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein when the processor executes the computer program, the hierarchical regulation method for grid load resources as described in the first aspect is implemented.

[0048] A fourth aspect of the present disclosure provides a non-transitory computer-readable storage medium, which stores computer instructions for causing a computer to execute the hierarchical regulation method for grid load resources as described in the first aspect.

[0049] As can be seen from the above, the hierarchical regulation method, device, electronic device and storage medium of grid load resources provided by the present disclosure can obtain detailed information of each load resource and the correlation relationship between them through hierarchical management of load resources. Furthermore, the regulation potential information and power distribution characteristic information of load resources can be obtained by combining the operation data of the distribution network, and the load resources can be evaluated from multiple dimensions. Then, modeling and solving for the global optimal operation mode are carried out according to the regulation potential information and power distribution characteristic information of load resources, and the dispatching strategy is determined by combining various information such as the operation data of the distribution network, regulation potential information and power distribution characteristic information, so as to achieve all-round optimization and effectively improve the operation efficiency and economy of the distribution network. At the same time, the hierarchical regulation method of grid load resources described in this embodiment can adapt to various complex requirements. Whether it is to cope with the power output fluctuations of intermittent energy sources such as photovoltaic and wind power, or to deal with the problems of intelligent load participation and multi-time scale optimization, the method described in this embodiment can provide effective solutions through its hierarchical management, modeling analysis and optimization control mechanisms to ensure the stable and efficient operation of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the present disclosure or related technologies, the following will briefly introduce the drawings required for use in the embodiments or related technology descriptions. Obviously, the drawings described below are only the embodiments of the present disclosure. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0051] Figure 1 The flowchart of an exemplary method provided by an embodiment of the present disclosure is shown.

[0052] Figure 2 The schematic diagram of the framework structure of hierarchical management of a load resource according to an embodiment of the present disclosure is shown.

[0053] Figure 3 The schematic diagram of an exemplary device provided by an embodiment of the present disclosure is shown.

[0054] Figure 4 The schematic diagram of the hardware structure of an exemplary computer device provided by an embodiment of the present disclosure is shown. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0055] In order to make the objectives, technical solutions and advantages of the present disclosure clearer and more understandable, the following further elaborates on the present disclosure in detail with reference to specific embodiments and the accompanying drawings.

[0056] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present disclosure should have the ordinary meanings understood by those of ordinary skill in the field to which the present disclosure belongs. The "first", "second" and similar terms used in the embodiments of the present disclosure do not denote any order, quantity or importance, but are only used to distinguish different components. Words such as "including" or "comprising" mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Words such as "connected" or "linked" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Upper", "lower", "left", "right", etc. are only used to represent relative position relationships. When the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0057] As Figure 1 shown, the embodiments of the present disclosure provide a method for hierarchical regulation of grid load resources, including:

[0058] Step S101, obtaining the operation data of the distribution network and the hierarchical management data of the load resources; wherein, the load resources are hierarchically managed based on the load attributes, and the data of the load resources after hierarchical management is obtained as the hierarchical management data.

[0059] In this embodiment, the operation data of the distribution network can be obtained from the distribution network monitoring system.

[0060] In this embodiment, the load resources include various different electrical equipment such as air conditioners, electric water heaters, lighting equipment, electric vehicle charging piles, etc., that is, specific load equipment.

[0061] In this embodiment, each specific load equipment is divided based on the load attributes, and the specific load equipment is divided into multiple groups. Each group of specific load equipment is associated with a load terminal, and the load equipment in this group is managed through this load terminal; multiple load terminals are divided into a group and associated with a load cluster, and the load terminals in this group are managed through this load cluster; each load cluster is associated with a load aggregator, and the load clusters are managed through the load aggregator, so as to hierarchically manage the load resources from four levels: specific load equipment, load terminal, load cluster, and load aggregator, so as to comprehensively cover various load resources in the distribution network. In this way, when new load resources are added to the distribution network, they can be added to the existing grouping of specific load equipment according to their load attributes and associated with the existing load terminals; or, the new load resources added to the distribution network with the same load attributes can also be used as a separate group and associated with a new load terminal and introduced into this load resource hierarchical management structure, so as to ensure that different types and scales of load resources can be well added to the distribution network.

[0062] In some embodiments, after obtaining the operation data of the distribution network and the hierarchical management data of the load resources, it is also necessary to integrate and process the operation data of the distribution network and the hierarchical management data of the load resources, so as to facilitate subsequent analysis and modeling, and provide comprehensive data support for subsequent global optimal mode analysis and local collaborative strategy formulation.

[0063] In some embodiments, processing the operation data of the distribution network and the hierarchical management data of the load resources includes: associating the data in the operation data of the distribution network with the corresponding data of the specific load devices in the hierarchical management data of the load resources.

[0064] In some embodiments, there are some resources in the distribution network that are not managed by the distribution network, such as third-party load resources. There are real-time data during the operation of third-party load resources, such as real-time power data, in the operation data of the distribution network, but there is no power data of the third-party load resources themselves. In this embodiment, by associating the real-time power data in the operation data of the distribution network with the power data of the specific load devices in the hierarchical management data of the load resources, the performance of each specific load device in the actual operation of the distribution network can be comprehensively understood.

[0065] In some embodiments, the real-time power data in the operation data of the distribution network and the power data of the specific load devices in the hierarchical management data of the load resources can be associated and matched through a unified data identifier and index. For example, the unique identification code of the specific load device (such as the IMEI code for smart devices) is used to associate the data of the specific load device with its operation data in the distribution network monitoring system to ensure accurate matching of data from different sources.

[0066] In some embodiments, processing the operation data of the distribution network and the hierarchical management data of the load resources includes: cleaning and validating the obtained data.

[0067] In this embodiment, after associating the data in the operation data of the distribution network with the corresponding data of the specific load devices in the hierarchical management data of the load resources, it is necessary to clean the data to remove incorrect data, duplicate data, and incomplete data. For example, there may be abnormal voltage values caused by sensor failures in the distribution network monitoring system. For the abnormal voltage values caused by sensor failures, they are identified and corrected by setting a reasonable threshold range.

[0068] After that, the data is verified to ensure the accuracy and consistency of the data. For example, the load demand prediction value in the load plan should be consistent with the historical data and actual operation of the distribution network. If there is a large deviation, it needs to be re-evaluated and adjusted.

[0069] In some embodiments, processing the operation data of the distribution network and the hierarchical management data of the load resources further includes: converting and normalizing the data. In this embodiment, according to the requirements of data analysis and modeling in subsequent steps, the data is converted, including: unifying the units of the data. For example, converting power data into values in megawatts and converting time data into a unified timestamp format. Then, the data of different magnitudes is normalized to facilitate calculation and analysis in the same model. For example, normalizing the voltage value to the interval [0, 1] enables the analysis of data of different voltage levels in the same evaluation system.

[0070] During the processing of the time data format, the unified formatting of the time series data can be expressed as:

[0071] = f ( T )

[0072] where: T is the original time series data, is the data after formatting, and f is the formatting function.

[0073] Step S103, determining the regulation potential information and power distribution characteristic information of the load resources based on the operation data of the distribution network and the hierarchical management data.

[0074] Different types of load resources have different regulation potential information, so corresponding regulation potential assessment methods need to be determined. For example, for air-conditioning loads, their regulation potential information can be achieved by adjusting the set temperature, operation mode (cooling / heating switching), etc. Specifically, according to the power-temperature curve of the air-conditioning equipment and the acceptable comfort range of users, the regulation potential information of the air-conditioning load under different conditions can be quantified. The regulation potential information of industrial loads can be determined according to factors such as the production process and the operation status of equipment. Specifically, by analyzing the operation time and power demand of key equipment in the industrial production process and combining the flexibility of the enterprise's production plan adjustment, the regulation potential information of industrial loads at different times is determined. For some special types of loads or newly emerging load types, flexible model construction methods are adopted according to the attributes of the load resources to determine their regulation potential information. In this embodiment, the load resources are classified and evaluated from multiple dimensions such as spatial scale, time scale, and operation mode.

[0075] Different types of load resources have different power distribution characteristic information, so it is necessary to determine the corresponding power distribution evaluation method. Taking the electric vehicle charging load as an example, its power distribution characteristic information can be determined according to factors such as the charging power, charging time, battery capacity and status of the electric vehicle. For industrial load groups, the power distribution characteristic information is determined according to the power requirements and production processes of each industrial device to ensure power balance and optimal operation of the distribution network while meeting production requirements.

[0076] Step S105, construct a global optimal operation mode model of the distribution network, and construct the objective function and constraint conditions of the global optimal operation mode model of the distribution network based on the regulation potential information and the power distribution characteristic information.

[0077] In this embodiment, a global optimal operation mode model of the distribution network based on mixed integer linear programming (MILP) can be constructed. When constructing this global optimal operation mode model of the distribution network, the regulation potential information and power distribution characteristic information of the load resources are considered, and the management of the load resources is incorporated into the objective function and constraint conditions of the global optimal operation mode model of the distribution network.

[0078] Step S107, solve the global optimal operation mode model of the distribution network based on the objective function and the constraint conditions to obtain the model output result.

[0079] In this embodiment, with the goal of minimizing the operating cost, the global optimal operation mode model of the distribution network is solved by a professional optimization algorithm solver (such as CPLEX or Gurobi) to obtain the model output result, that is, the global optimal operation mode.

[0080] In this embodiment, a single-objective optimization method (such as the method of minimizing the regulation cost) and a double-objective optimization method (considering the system stability and sustainability under extreme load scenarios) can be adopted. For example, in the multi-period optimal scheduling strategy, considering the grid frequency safety factor, balancing the regulation costs of each load group to ensure that the regulation power of each load group falls within the interval with lower costs; in extreme load scenarios, comprehensively considering the management cost and error cost, and realizing the stable operation of the system through a double-objective collaborative optimization method.

[0081] Step S109, divide the load resources based on the hierarchical management data to obtain the load resource information of multiple local area load resources.

[0082] In this embodiment, the distribution and characteristics of the load resources are obtained based on the hierarchical management data, and the load resources are divided based on the distribution and characteristics of the load resources to obtain multiple local area load resources, and each local area load resource contains load resources with similar load characteristics and regulation potential.

[0083] Step S111: Generate load resource scheduling information for multiple local area load resources based on the model output result and the load resource information of the multiple local area load resources.

[0084] In this embodiment, based on the global optimal operation mode and the load resource information of multiple local area load resources, determine the power exchange strategy and load regulation strategy between regions to improve the operation efficiency and stability of the entire distribution network.

[0085] Step S113: Control the operating state of the target load resource among the multiple local area load resources based on the load resource scheduling information.

[0086] In this embodiment, through hierarchical management of load resources, the detailed information of each load resource and the correlation relationship between them can be obtained. Furthermore, the regulation potential information and power distribution characteristic information of load resources can be obtained by combining the operation data of the distribution network, and load resources can be evaluated from multiple dimensions; then, based on the regulation potential information and power distribution characteristic information of load resources, modeling and solving for the global optimal operation mode are carried out, and then combined with the operation data of the distribution network, regulation potential information, power distribution characteristic information and other information to determine the scheduling strategy, so as to achieve all-round optimization and effectively improve the operation efficiency and economy of the distribution network; at the same time, the hierarchical regulation method of the grid load resources in this embodiment can adapt to various complex requirements. Whether it is to cope with the power output fluctuations of intermittent energy sources such as photovoltaic and wind power, or to handle the problems of intelligent load participation and multi-time scale optimization, the method in this embodiment can provide effective solutions through its hierarchical management, modeling analysis and optimization control mechanisms to ensure the stable and efficient operation of the power grid.

[0087] In some embodiments, the distribution network operation data may include the real-time operation section data of the distribution network and the boundary condition data under multiple plan scenarios.

[0088] In some embodiments, the real-time operation section data of the distribution network includes electrical quantity information, equipment operation state information, distributed power source information, load information, time information, etc.

[0089] In this embodiment, the electrical quantity information includes information such as voltage, current, and power. Among them, the voltage information includes information such as the node voltage amplitude (V) and voltage phase angle (θ).

[0090] The node voltage amplitude (V) is used to represent the current voltage magnitude of each node in the distribution network, with the unit of volt (V). For example, the voltage amplitude of node A is 10.5 kV. This is one of the important indicators to measure the power quality of the distribution network. Too high or too low voltage may affect the normal operation of electrical equipment.

[0091] The voltage phase angle (θ) is used to represent the phase angle of the node voltage, with the unit of degree (°). The phase angle information is crucial for analyzing the power flow distribution and power transmission in the distribution network. For example, in a three-phase AC system, the difference in the voltage phase angles of different nodes affects the reactive power flow on the line.

[0092] The current information includes the node current amplitude (I), the current phase angle (φ), etc.

[0093] The node current amplitude (I) is used to reflect the magnitude of the current flowing through each node, with the unit of ampere (A). For example, the current amplitude of node B is 300 A. By monitoring the current amplitude, the overload risk of the line can be detected in a timely manner.

[0094] The current phase angle (φ) is used to represent the phase angle of the node current, with the unit of degree (°). The difference between the current phase angle and the voltage phase angle can be used to calculate parameters such as the power factor.

[0095] The power information includes the active power (P), the reactive power (Q), the apparent power (S), etc.

[0096] The active power (P) is used to represent the actual electrical energy consumed or generated by the node, with the unit of watt (W). A positive value indicates that the node consumes power, and a negative value indicates that the node generates power (such as a distributed power source feeding power into the grid). For example, the active power of an industrial load node is 500 kW.

[0097] The reactive power (Q) is used to reflect the energy exchange situation in the grid for establishing magnetic fields and electric fields, with the unit of var. The reactive power has an important impact on the voltage stability of the grid and the operation efficiency of equipment. For example, the reactive power of a substation is 200 var.

[0098] The apparent power (S) is the product of the voltage and the current, with the unit of volt-ampere (VA). The apparent power is equal to the square root of the sum of the squares of the active power and the reactive power, that is . The apparent power (S) reflects the overall capacity demand of the grid.

[0099] In this embodiment, the equipment operation status information includes the switch status (SW), the protection device action signal (PROT), etc.

[0100] The switch status (SW) is used to represent the opening and closing status of each switch (such as circuit breakers, disconnectors, etc.) in the distribution network. Usually, 0 represents the switch is open, and 1 represents the switch is closed. For example, the incoming switch status of a certain line is 1, indicating that the switch is closed and the line is normally powered.

[0101] The protection device action signal (PROT) is used to mark the action status of the protection device. When the protection device in the distribution network (such as overcurrent protection, overvoltage protection, etc.) acts, corresponding signals will be sent. The action status of the protection device is recorded through the protection device action signal (PROT). For example, 1 represents that the protection device has acted, and 0 represents that it has not acted. If an overcurrent fault occurs on a certain line, the action signal of its corresponding overcurrent protection device is 1.

[0102] In this embodiment, the distributed power source information includes information such as the distributed power source output (P_gen) and the distributed power source connection point information (P_conn).

[0103] The distributed power source output (P_gen) is used to represent the current power generation of distributed power sources in the distribution network (such as solar photovoltaic power generation, small wind power generation, etc.), and the unit is watt (W). For example, the output of a certain solar photovoltaic power station at a certain moment is 20 kW.

[0104] The distributed power source connection point information (P_conn) is used to represent the connection location of the distributed power source in the distribution network, including information such as the connected node number and line number. For example, a certain distributed power source is connected to the 10 kV line L1 where node C is located.

[0105] In this embodiment, the load information includes information such as the load type (Load_type) and the load importance level (Load_importance).

[0106] The load type (Load_type) is used to classify and identify different types of loads, such as residential loads, industrial loads, commercial loads, etc. It can be represented by numbers or character codes. For example, 1 represents a residential load, 2 represents an industrial load, 3 represents a commercial load; or a represents a residential load, b represents an industrial load, c represents a commercial load, etc.

[0107] The load importance level (Load_importance) can divide different load importance levels according to the requirements of the load for power supply reliability. For example, first-level loads (such as important medical equipment in hospitals, important communication hubs, etc.), second-level loads (such as large shopping malls, office buildings, etc.) and third-level loads (such as ordinary residences, general factories, etc.). Different importance levels can also be represented by the numbers 1, 2, and 3 respectively.

[0108] The time information includes the data acquisition timestamp (Timestamp). The timestamp (Timestamp) is used to represent the specific time of data acquisition for the real-time operation section of the distribution network, and the format is usually year-month-day hour:minute:second. For example, 2024-01-01 10:30:00, which is used for subsequent data analysis and processing to understand the operation status of the distribution network at different time points.

[0109] The real-time operation section data of the distribution network also includes other auxiliary information such as a data quality flag (Data_quality) and remarks (Remarks).

[0110] The data quality flag (Data_quality) is used to identify the quality of the real-time operation section data. For example, 0 indicates good data quality, and 1 indicates data anomalies (such as sensor failures, communication interference, etc.). If the voltage data of a certain node is abnormal due to a sensor failure, its data quality flag is 1.

[0111] The remarks (Remarks) are used to represent other explanatory information related to the real-time operation section data, such as whether special operation operations have been carried out and whether there are temporary equipment repairs. For example, during a certain period, due to planned maintenance of the line, the operation data of relevant nodes may be affected, and the maintenance situation is noted in the remarks.

[0112] The boundary condition data in the multi-plan scenario includes load demand prediction information and distributed power source access plan information.

[0113] The load demand prediction information can be obtained according to the load plan. The load plan in the boundary condition data provides the expected demands of various types of loads in different future time periods. Based on these data, the load conditions of the distribution network at different times can be predicted, which helps to understand the load conditions of the distribution network at different future moments, thereby assisting in optimizing the power generation plan and load distribution. In some embodiments, the demand patterns of residential loads and commercial loads are different on weekdays and weekends, at different times, and in different seasons: on weekdays, the residential load during the evening peak > the residential load during the morning peak > the residential load at noon, and the commercial load during the evening peak > the commercial load during the morning peak > the commercial load at noon; on weekends, the residential load in the morning < the residential load in the afternoon, and the commercial load in the morning > the commercial load in the afternoon; the residential and commercial loads in the afternoon of summer increase compared to other seasons, and the residential load in the evening of winter increases compared to other seasons. The load plan can help predict these demand changes to obtain the load demand prediction information, and then adjust the power generation and load distribution strategies based on the load demand prediction information. For example, the residential electricity demand is relatively high on weekends, and it may be necessary to increase the power generation power of distributed power sources.

[0114] Distributed power access plan information includes information such as the access locations, capacities, and predicted power generation of distributed power sources such as solar photovoltaic power generation and small wind power generation devices. This information has an important impact on the power flow distribution and voltage control of the distribution network. If a large amount of solar photovoltaic power generation is planned to be connected in a certain area, the load plan can help predict the power generation of these distributed power sources, thereby optimizing the power flow distribution of the distribution network and avoiding overload and voltage fluctuations.

[0115] In some embodiments, as Figure 2 shown, hierarchical management of load resources is carried out at four levels from load equipment to load terminals, load groups, and then to load aggregators.

[0116] Load equipment includes various specific electrical equipment, such as air conditioners, electric water heaters, lighting equipment, electric vehicle charging piles, etc. The load equipment data obtained for different load equipment is different: for air conditioning equipment, load equipment data such as its model, power, operating mode (cooling / heating), and set temperature need to be obtained; for electric water heaters, load equipment data such as its capacity, heating power, and current water temperature need to be obtained; for lighting equipment, load equipment data such as power, switch status, and usage time preference need to be obtained; for electric vehicle charging piles, load equipment data such as its charging power, charging interface type, and communication protocol with the power grid need to be obtained.

[0117] Load terminals manage load equipment within a preset range, such as the same attribute loads within a user, a shopping mall, or a workshop. The data obtained from the load terminals includes the number, type distribution of the load equipment managed by the load terminals, and the communication status between the load terminals and the distribution network. For example, a load terminal in a shopping mall may manage a large number of lighting equipment, air conditioning equipment, and some commercial electrical equipment, and it is necessary to know the overall operating conditions of these equipment and whether the communication between the load terminal and the distribution network is stable.

[0118] A load group is a collection composed of the same type of load terminals. The data obtained from the load group includes the number of load terminals, the geographical location distribution of each load terminal (if relevant), and the overall power demand characteristics of the load group, etc. For example, a load group composed of air conditioning load terminals in multiple residential communities needs to understand the power demand change rules of this group in different seasons and different time periods.

[0119] A load aggregator is an entity that jointly optimizes and controls multiple load groups. The load group data obtained from the load aggregator includes the list of load groups it manages, the optimization control strategies for each load group, and the interaction protocols with the distribution network operator, etc. For example, the load aggregator may adopt a time-sharing aggregation strategy to adjust the power output of the load groups it manages according to the electricity price and grid load conditions at different time periods.

[0120] Among them, the load terminal is implemented through a Virtual Energy Node (VEN), the load group is implemented through a Virtual Energy Node or a Virtual Transformer Node (VTN), and the load aggregator is implemented through a Virtual Transformer Node.

[0121] A Virtual Energy Node refers to a unified energy supply point virtualized from multiple physical energy devices (such as distributed generation, energy storage devices, electric vehicles, etc.) through software and communication technologies in a smart grid. This virtualization technology enables these dispersed energy devices to participate in the operation and management of the power grid as a whole, thereby improving energy utilization efficiency and power grid stability.

[0122] A Virtual Transformer Node refers to a unified transformer node virtualized from multiple physical transformers and their related devices using software-defined network technology and intelligent control technology. This virtualization technology can achieve flexible configuration and dynamic adjustment of transformers to adapt to changes in grid load, improving grid operation efficiency and reliability.

[0123] The load aggregator, load group, and load terminal are managed and optimized through Virtual Energy Nodes and Virtual Transformer Nodes to achieve hierarchical management of load resources and coordinated control of the power source, grid, load, and energy storage.

[0124] Based on the above hierarchical management framework of load resources, the acquisition of the hierarchical management data includes: acquiring load device data of load devices; acquiring load terminal data of load terminals, where the load terminals are used to manage at least one load device within the same preset range; acquiring load group data of load groups, where the load groups are used to manage at least one load terminal of the same type; acquiring load aggregator data of load aggregators, where the load aggregators are used to manage the load groups; and determining the hierarchical management data based on the load device data, the load terminal data, the load group data, and the load aggregator data.

[0125] Among them, the load device data may include device identification information, device operation parameters, operation mode, set parameters, etc.

[0126] The device identification information includes device number, device type, etc. The device number is the unique identification code of each load device, used to accurately identify and manage the device in the system. The device type identifies the specific category of the load device, such as air conditioner, electric water heater, lighting device, electric vehicle charging pile, etc.

[0127] The operating parameters of the device include power-related information such as rated power and real-time power. The rated power is the rated power of the device under normal operating conditions, with the unit of watt (W). For example, the rated power of a certain model of air conditioner is 2500W. The real-time power is the current operating power of the device, reflecting the actual electrical energy consumed by the device, with the unit of watt (W). By monitoring the real-time power in real time, the energy consumption situation of the device can be understood.

[0128] Different types of devices have different operating modes. For example, the operating modes of air conditioner devices include refrigeration, heating, dehumidification, ventilation and other modes, the operating mode of lighting devices includes the switch state (on / off), and the operating mode of electric vehicle charging piles includes the charging state (charging / stopping charging), etc.

[0129] The set parameters include temperature setting and charging parameters. For devices such as air conditioners and electric water heaters, the temperature setting is used to record the temperature value set by the user. For electric vehicle charging piles, the charging parameters include charging power setting, charging time setting, etc.

[0130] The load terminal data includes information such as terminal basic information, terminal management information, terminal communication and operating status.

[0131] Among them, the terminal basic information includes information such as terminal number and terminal location. The terminal number is the unique identifier of the load terminal, used to distinguish different terminal devices. The terminal location is used to record the specific geographical location or the affiliated area where the load terminal is located, such as a certain shopping mall, a certain community, a certain factory workshop, etc.

[0132] The terminal management information includes information such as the number of load devices managed and the distribution of load device types. The number of load devices managed is used to count the total number of various types of load devices managed by this terminal. For example, the number of load devices managed by a load terminal in a shopping mall is 500 units. The distribution of load device types is used to detail the number and proportion of different types of load devices managed by this terminal. Such as 200 lighting devices, accounting for 40%; 150 air conditioner devices, accounting for 30%; 150 commercial electricity devices, accounting for 30%.

[0133] The terminal communication and operating status includes information such as the communication status with the distribution network and the data transmission success rate. The communication status with the distribution network can be represented by 0 for normal communication and 1 for abnormal communication; or 1 for normal communication and 0 for abnormal communication. For example, the current communication status of the terminal with the distribution network is 0, indicating normal communication. The data transmission success rate is used to count the proportion of the number of times the terminal successfully transmits data to the management system within a certain period of time to the total number of transmission times, expressed as a percentage. Such as the data transmission success rate is 98%.

[0134] The load group data includes information such as load group basic information and load group characteristic data.

[0135] Among them, the basic information of the load group includes information such as the load group number, the number of load terminals included, and the geographical area coverage. The load group number is the unique identification code for each load group. The number of load terminals included is used to record the specific number of load terminals that make up the load group. For example, an air-conditioning load group in a residential community includes 200 load terminals. The geographical area coverage is used to describe the geographical area covered by the load group, such as several adjacent residential communities, specific floors in a commercial area, etc.

[0136] The characteristic data of the load group includes information such as the overall power demand characteristics, the power demand curves at different times, and the seasonal power demand variation rules. The power demand curves at different times are used to record the changes in the power demand of the load group at different time periods, usually with time as the horizontal axis and power demand as the vertical axis to draw the curves. For example, during the period from 19:00 to 21:00 in the evening in summer, the power demand of the air-conditioning load group in this residential community reaches its peak. The seasonal power demand variation rules are used to identify the power demand characteristics of the load group in different seasons. For example, more heating equipment is used in winter, and the power demand is relatively high; more cooling equipment is used in summer, and the power demand is also large.

[0137] The data of the load aggregator includes information such as the basic information of the aggregator, the load group management information, the optimization control strategies for each load group, and the interaction information with the distribution network operator.

[0138] Among them, the basic information of the aggregator includes information such as the aggregator number, the aggregator name, and the affiliated region. The aggregator number is the unique identifier of the load aggregator; the aggregator name is the enterprise name of the load aggregator; the affiliated region is used to identify the geographical location where the load aggregator is located, such as a certain city or a certain region.

[0139] The load group management information includes the list of load groups under management, and the list of load groups under management is used to list the numbers, names, and basic situations of all load groups that the load aggregator is responsible for managing.

[0140] The optimization control strategies for each load group include time-of-use aggregation strategies and demand response strategies. The time-of-use aggregation strategy is used to formulate power adjustment strategies for the load group according to the electricity price and grid load conditions at different times. For example, during low electricity price periods, increase the power consumption of the load group; during high electricity price periods, reduce the power demand of the load group. The demand response strategy is the demand response measures taken when there is an emergency in the power grid or load adjustment is required. For example, require some load groups to suspend power consumption for a period of time to relieve the power grid pressure.

[0141] The information exchanged with the distribution network operator includes the interaction protocol and data interaction records. The interaction protocol is used to clarify the rules such as the data interaction format, content, and frequency between the load aggregator and the distribution network operator. For example, power prediction data, adjustment instructions, and other information of the load group are exchanged regularly every day. The data interaction records are used to identify information such as the time, content, and result of each data interaction with the distribution network operator for subsequent query and analysis.

[0142] In this embodiment, through the hierarchical management of load resources at four levels from load equipment to load terminal, load group, and load aggregator, various load resources in the distribution network can be comprehensively covered; in the source-network-load-storage coordination, this hierarchical structure helps to clarify the management focus and coordination methods of loads at different levels; the load terminal manages the loads within a single user or location, the load group aggregates the same type of terminals, and the load aggregator conducts joint optimization control on different types of load groups, thereby achieving orderly coordination from the underlying equipment to the higher level and improving the ability of the distribution network to cope with the fluctuations of renewable energy; for the participation of intelligent loads, this hierarchical structure facilitates the incorporation of intelligent loads (such as electric vehicle charging facilities, controllable loads, etc.) into the corresponding management levels. For example, electric vehicle charging facilities can be incorporated into the management scope of a suitable load group or load aggregator according to their distribution and usage characteristics in the distribution network, and their charging behaviors can be effectively regulated through hierarchical management, improving the flexibility and adaptability of the distribution network.

[0143] In this embodiment, through the hierarchical management of load resources, comprehensive and accurate load information is provided, covering multi-level data from specific equipment to load groups and load aggregators, such as the model, power, etc. of specific load equipment, the number of devices, communication status, etc. managed by the load terminal, and the overall power demand characteristics of the load group, providing refined data support for the operation analysis of the distribution network; deeply analyzing the characteristics and behaviors of load resources provides a rich foundation for the operation analysis of the distribution network. For example, analyzing the characteristics and behavior patterns of different types of load resources provides a basis for formulating operation strategies; incorporating information such as the regulation potential of load resources into the global optimal operation mode model, such as quantifying the regulation potential of different types of loads and adding the regulation potential to the objective function to reflect the relationship between the regulation potential and the optimization of generation cost, network loss cost, and load resources, supporting the global optimal operation of the distribution network; establishing a power distribution model for different types of loads and reflecting it in the constraint conditions to ensure reasonable power distribution and achieve power balance and optimal operation of the distribution network; reasonably dividing local areas so that the characteristics and regulation potential of load resources within the area are similar, promoting the coordinated operation of local areas and facilitating management and control; formulating inter-regional coordination strategies, using the complementarity and regulation potential differences of load resources to achieve power exchange and load regulation, and improving the operation efficiency and stability of the distribution network.

[0144] In some embodiments, in step S105, constructing a global optimal operation mode model of the distribution network, and constructing the objective function and constraint conditions of the global optimal operation mode model of the distribution network based on the regulation potential information and the power distribution characteristic information, includes:

[0145] Step S201, constructing a regulation potential model based on the regulation potential information, and setting the maximization of the utilization rate of the adjustable potential of the load resources as an objective function term of the objective function.

[0146] In this embodiment, in the objective function of the global optimal operation mode model of the distribution network based on MILP, an objective function term determined based on the regulation potential of the load resources is added. For example, the maximization of the utilization rate of the adjustable potential of the load resources is added as an objective function term to the objective function of the global optimal operation mode model of the distribution network to improve the ability of the distribution network to cope with renewable energy fluctuations and load changes.

[0147] In addition to the traditional power generation cost and network loss cost, an objective term related to the regulation potential of the load resources needs to be added. For example, an objective function term can be set as the maximization of the utilization rate of the adjustable potential of the load resources to improve the ability of the distribution network to cope with renewable energy fluctuations and load changes.

[0148] Express the regulation potential of the load resources as P reg (for each load resource or load group), the total regulation potential is ∑P reg , then the objective function can be expressed as:

[0149] minZ = C gen +C loss -λ∑P reg

[0150] Where, C gen is the power generation cost, C loss is the network loss cost, and λ is a weight coefficient used to balance the relationship between the power generation cost, the network loss cost and the regulation potential of the load resources.

[0151] In some embodiments, information such as the power generation cost and the network loss cost can also be used as objective function terms of the objective function of the global optimal operation mode model of the distribution network together with the objective function term determined based on the regulation potential of the load resources.

[0152] Step S203, constructing a power distribution model based on the power distribution characteristic information, and setting the power distribution balance information as the constraint condition.

[0153] In this embodiment, in the constraint conditions of the global optimal operation mode model of the distribution network, constraints related to the power distribution characteristic information of the load resources are added. Among them, the power distribution characteristic information may include power distribution balance information. For example, for the distribution network of a certain local area, its total power input should be equal to the sum of the power distributions of each load resource, that is:

[0154] P total =∑P i

[0155] Where P total is the total power input of the local area, and P i is the power distribution of the i-th load resource.

[0156] At the same time, it is also necessary to consider the impact of the power distribution of the load resources on their own operation characteristics and the stability of the distribution network. For example, for an air-conditioning load group, its power distribution cannot cause the voltage fluctuation to exceed the allowable range, that is:

[0157] ∣V i - V0∣≤ΔV max

[0158] Where V i is the node voltage after power distribution, V0 is the initial node voltage, and ΔV max is the allowable voltage fluctuation range.

[0159] In this embodiment, when constructing the global optimal operation mode model of the distribution network, it is necessary to clarify the variable definitions related to the load resource management. For example, define the variable x ij indicating whether the i-th load resource participates in regulation in the j-th time period (0 means not participating, 1 means participating), and the variable y ij indicating the power distribution amount of the i-th load resource in the j-th time period.

[0160] Relate these variables to other operation parameters of the distribution network (such as the output of power generation equipment, the power flow of lines, etc.) and establish the mathematical relationship between them.

[0161] In some embodiments, the method further includes: obtaining the correlation relationship between the output of power generation equipment and the regulation amount information of load resources, determining the power generation cost based on the correlation relationship between the output of power generation equipment and the regulation amount information of load resources, and setting the power generation cost as the objective function item of the objective function.

[0162] There is a balance relationship between the regulation of load resources and the output of power generation equipment. When load resources participate in regulation, it will affect the energy balance of the entire distribution network. For example, during peak load periods, if the total load demand is reduced by regulating the load (such as reducing some interruptible loads), the output of power generation equipment can be correspondingly reduced.

[0163] Assume that the output of the power generation equipment is P gen , and the regulation amount of the load resources is ΔP load . The initial power balance equation of the distribution network is P gen0 = P load0 + P loss0 (where P load0 is the initial load, and P loss0 is the initial line loss). After the load resources are regulated, the new power balance equation becomes P gen1 = (P load0 - ΔP load ) + P loss1 (where P gen1 is the output of the regulated power generation equipment, and P loss1 is the regulated line loss).

[0164] For different types of power generation equipment, the relationship with load regulation is also different. For example, renewable energy power generation (such as solar energy and wind energy) is restricted by natural conditions, and its output has certain intermittency and uncertainty. When the load resource regulation potential is large, it can better match the output fluctuations of renewable energy. For example, during the peak period of solar power generation, if the load resources can be appropriately increased (such as an increase in the charging demand of electric vehicles), the dependence on traditional fossil fuel power generation equipment can be reduced.

[0165] In this embodiment, this relationship is described by establishing an objective function based on an optimization algorithm. For example, in the distribution network global optimal operation mode model based on mixed integer linear programming (MILP), the objective function is minZ = C gen + C loss λ - ∑P reg (where C gen is the power generation cost, C loss is the network loss cost, λ is the weight coefficient, and P reg is the regulation potential of the load resources). Here, C gen is related to the output of the power generation equipment. By adjusting the regulation potential and power distribution of the load resources, the optimal output of the power generation equipment can be affected, thereby achieving cost minimization.

[0166] In some embodiments, the method further includes: obtaining the distribution information of the load resources on the nodes and lines based on the hierarchical management data, determining the power flow distribution information of the distribution network based on the distribution information, and setting the power flow distribution information as the constraint condition.

[0167] In this embodiment, the distribution of load resources among different nodes and lines affects the power flow distribution of the lines. For example, in a distribution network, if the load in a certain local area increases, according to Kirchhoff's law, the current in the lines connected to this area will increase, thereby changing the power flow distribution of the lines.

[0168] In this embodiment, based on the hierarchical management data, the distribution information of load resources among nodes and lines is obtained, and then based on this distribution information, the power flow distribution information of the distribution network is determined, and the power flow distribution information is set as the constraint condition.

[0169] Specifically, let the impedance of a certain line be Z, and the voltages at both ends be V1 and V2 respectively. According to Ohm's law, the current I on the line = (V1 - V2) / Z. When power flows on this line due to load resources, the difference between V1 and V2 will change, thereby affecting the magnitude of the current.

[0170] In the steady-state analysis of a distribution network, the node voltage equation is usually used to describe the power flow distribution. For a distribution network with n nodes, its node voltage equation is:

[0171] ∑

[0172] where, Y j is the element of the node admittance matrix, I i is the node injection current, S i is the node complex power, S i ∗ is its conjugate complex number, and V is the node voltage.

[0173] When the load resources change, it will affect the node injection current Ii, thereby changing the node voltage Vi and the line power flow. For example, when the load equipment managed by a certain load terminal increases, the injection current of this node increases, and the voltage changes of other nodes and the power flow changes on the lines can be calculated through the node voltage equation.

[0174] In this embodiment, when constructing the global optimal operation mode model of the distribution network, it is necessary to incorporate load resource management variables such as the regulation potential information P reg , power distribution quantity y ij and other operation parameters such as the output of power generation equipment and line power flow into the same mathematical model.

[0175] For example, in this model, the power balance equation ( =P total , where P i is the power distribution of each load resource, and P total is the total power demand), line capacity limit (such as P line ≤P maxSet the following (etc.) as constraint conditions, and these constraint conditions are all interrelated with the load resource management variables and other operating parameters. Through this setting, a comprehensive description and optimal control of the operating state of the distribution network can be achieved, ensuring that the distribution network can operate efficiently and be optimally managed under different resources and constraint conditions.

[0176] In some embodiments, the method further includes: determining the constraint conditions based on at least one of line capacity, node voltage, and load regulation information.

[0177] In this embodiment, at least one of line capacity constraint, node voltage constraint, and load regulation information constraint can also be added to the constraint conditions of the global optimal operation mode model of the distribution network.

[0178] Among them, power balance constraint: ∑ Pgen，i =∑ Pload，k +∑P loss，j

[0179] Line capacity constraint: P line，j ≤P max，j

[0180] Voltage constraint: V min ≤V i ≤V max

[0181] Load regulation constraint: ∣V i - V0∣≤ΔV max , where V i is the node voltage after regulation.

[0182] In some embodiments, in step S105, constructing the global optimal operation mode model of the distribution network includes:

[0183] Generating a graph structure based on the distribution network operation data and the distribution network topology. Among them, the nodes of the graph structure represent the devices in the distribution network, and the edges of the graph structure represent the connection relationships between the devices; extracting features from the graph structure through a graph neural network, updating the feature representations of the nodes through a message propagation mechanism, and dynamically adjusting the decision variables through reinforcement learning.

[0184] In this embodiment, the topology and operation data of the distribution network are transformed into a graph structure, where the nodes represent devices (such as generators, loads, switches), and the edges represent the connection relationships between the devices. Among them, the device node set V and the edge set E are expressed as:

[0185] V={v1, v2,..., v N},E={(v i , v j )∣vi, vj∈V},

[0186] Among them, for each node v i the eigenvector x i includes the power, voltage, regulation potential, etc. of the device.

[0187] In this embodiment, the graph neural network GNN is used to extract features from the graph structure, and the feature representations of variable nodes and constraint nodes are updated through the message propagation mechanism. Among them, the message propagation mechanism of GNN can be expressed as:

[0188]

[0189] Among them, h i (l + 1) is the hidden state of node i at the L-th layer, N(i) is the neighborhood of node i, W (l) and b (l) are learnable weights and biases, and σ is the activation function.

[0190] The decision variables in the optimization process are dynamically adjusted through the reinforcement learning RL strategy. Among them, the decision variables include continuous variables and integer variables. The continuous variables include the output P gen，i of the power generation device, the line power flow P line，j and other information, and the integer variables include the start-stop state x load，k (0 means not participating in regulation, 1 means participating in regulation) and other information.

[0191] The reward function R(τ) of the reinforcement learning RL is designed according to the quality of the current solution (such as the objective function value, constraint satisfaction situation). For example, for the trajectory τ, the reward function can be expressed as:

[0192]

[0193] Among them, rt is the immediate reward at time step t, and γ is the discount factor.

[0194] In this embodiment, a deep learning model (such as Transformer) is also used as the policy network to select the optimal action according to the current state. The update of the policy network can be achieved through the policy gradient method:

[0195]

[0196] Among them, pθ(τ) is the probability of the trajectory τ under the policy parameter θ.

[0197] In this embodiment, the proximal policy optimization (PPO) algorithm can also be used to improve the stability of policy update. The optimization objective of PPO can be expressed as:

[0198]

[0199] where δtn is the temporal difference error (TD-error), is the clipping parameter.

[0200] In some embodiments, in step S107, solving the global optimal operation mode model of the distribution network based on the objective function and the constraint conditions to obtain the model output result includes:

[0201] Iteratively solving the global optimal operation mode model of the distribution network, shrinking the feasible region by dynamically adding cutting plane constraints, and outputting the distribution network operation information that satisfies the minimized operation cost as the model output result.

[0202] In this embodiment, based on the power generation cost C gen , the network loss cost C loss and the utilization rate P of the load resource regulation potential reg generate an objective function, and aim to minimize the operation cost: minZ = C gen + C loss - λ∑P reg , where: λ is the weight coefficient, which is used to balance the relationship between different objectives.

[0203] In this embodiment, solving the global optimal operation mode model of the distribution network by combining the features extracted by the graph neural network and the reinforcement learning strategy includes: temporarily relaxing the integer constraints, solving the linear programming (LP) problem to obtain the relaxed solution; branching the relaxed solution to create sub-problems, and selecting the optimal branch through the RL strategy; during the solution process, adding cutting plane constraints as needed to shrink the feasible region to strengthen the model; dynamically adjusting the variable values through the RL strategy to gradually approach the optimal solution, and obtaining the distribution network operation information that satisfies the minimized operation cost as the model output result. In some embodiments, in step S109, dividing the load resources based on the hierarchical management data includes:

[0204] Step S301, obtaining at least one of the geographical distribution information, network topology structure association information, load characteristic information, and regulation potential information of the load resources based on the hierarchical management data.

[0205] The hierarchical management of load resources provides rich information such as the geographical distribution information of load resources, load characteristic information, and regulation potential information, etc., which are important bases for dividing local areas. For example, according to the distribution of load resources in the geographical space (the load differences in different functional areas), areas with close geographical locations and similar load types are divided into a local area; then, combined with load characteristic information (such as the seasonality and time period characteristics of air-conditioning loads, the relative stability of industrial loads) and regulation potential information (the regulation potential of electric vehicle charging loads is large, and the regulation potential of some industrial loads is small), the division of local areas is further optimized to make the load characteristics and regulation potential of load resources in the same area similar, reducing the communication cost and power transmission loss between areas and improving the management efficiency of the distribution network.

[0206] Step S303, divide the load resources based on at least one of the geographical distribution information, network topology structure association information, load characteristic information, and regulation potential information of the load resources.

[0207] In this embodiment, the distribution of load resources in the geographical space affects its regulation efficiency. For example, in the urban distribution network, there are obvious differences in the distribution of load resources in different functional areas (such as commercial areas, residential areas, industrial areas). Commercial areas usually concentrate a large number of commercial electrical equipment, such as the lighting, air-conditioning and other loads in large shopping malls; residential areas are mainly for residential living electrical equipment, such as household appliances, etc.; the load resources in industrial areas are mainly the electricity consumption demands of various industrial production equipment. According to the geographical distribution information, areas with close geographical locations and similar load types are divided into a local area. In this way, the communication cost and power transmission loss between areas can be reduced during subsequent regulation, and the management efficiency of the distribution network can be improved.

[0208] In this embodiment, the network topology structure of the distribution network also has an important impact on the division of local areas. For example, in a distribution network with a ring network structure, it can be divided according to the load resource conditions on each branch line of the ring network. The load resources with similar load characteristics on the same branch line are divided into a local area, which is convenient for monitoring and controlling the operation status of the branch line.

[0209] In this embodiment, the load characteristics can affect the regulation of load resources. Different types of load resources have different load characteristics. For example, air-conditioning loads have obvious seasonal and time-of-day characteristics, with relatively large load demands in summer and winter, and their power consumption is closely related to factors such as the set temperature and outdoor temperature; while industrial loads are relatively stable, but may have large power fluctuations at different stages of the production process. According to the similarity of load characteristics, local areas are divided, and load resources with similar load change rules (such as the same peak-valley periods, the same sensitivity to electricity prices, etc.) are divided into the same local area, so that subsequent scheduling based on regions can be realized based on the load characteristics of the load resources.

[0210] In this embodiment, the regulation potential of each load resource is evaluated. For example, the charging load of electric vehicles has a relatively large regulation potential and can adapt to the needs of the distribution network by adjusting the charging time, charging power, etc.; while some industrial loads have relatively small regulation potential due to the particularity of their production processes. Based on the regulation potential information, the load resources are divided to ensure that each local area contains load resources with similar regulation potential. This can facilitate the formulation of a unified regulation strategy and improve the regulation efficiency. For example, in a local area, if most of the load resources have a relatively large regulation potential, a more flexible regulation strategy such as demand response can be adopted.

[0211] In this embodiment, based on factors such as the geographical distribution information, network topology structure association information, load characteristic information, and regulation potential information of the load resources, the reasonable division and management of local areas are realized, reducing the communication cost and power transmission loss between regions and improving the management efficiency of the distribution network. For example, areas with close geographical locations and similar load types are divided into a local area to facilitate the monitoring and control of the operating status of the area. This embodiment analyzes the complementarity of load resources between regions and formulates an effective inter-regional cooperation strategy to achieve power complementarity between regions. For example, interruptible loads in commercial areas are reduced during peak load periods of the power grid, and energy storage devices in residential areas store electrical energy during low load periods and release it during peak periods to meet the needs of the entire distribution network. This embodiment formulates a joint regulation strategy, determines the regulation task allocation of different regions according to the regulation potential and response speed of each region, and improves the operating efficiency and stability of the distribution network. For example, when the distribution network faces a sudden power deficit, load resources in multiple local areas are reasonably called for regulation to avoid the spread of faults.

[0212] In some embodiments, the generating, according to the model output result and the load resource information, the load resource scheduling information for the load resources of multiple local areas in step S111 includes:

[0213] Step S401: Obtain at least one of the complementary information and regulation potential information of the local area load resources according to the load resource information.

[0214] The hierarchical management of load resources clarifies the regulation potential of load resources of different types and regions, which helps to analyze the complementarity of load resources within each local area. For example, the interruptible loads in the business district (such as partial lighting and air conditioners in large shopping malls) and the energy storage devices in the residential area (such as household energy storage batteries) have different regulation capabilities at different time periods. After obtaining this information through the hierarchical management of load resources, corresponding power exchange strategies can be formulated to achieve power complementarity between regions.

[0215] When formulating a joint regulation strategy, it is necessary to consider the regulation potential information of load resources in different local areas, which is determined based on the regulation potential data of load resources in each area provided by the hierarchical management of load resources. For example, when the distribution network faces a sudden power deficit, according to the hierarchical management data, understand the regulation potential and response speed of each area, and reasonably allocate regulation tasks to ensure that each local area can respond and execute the regulation tasks within the specified time, avoid regulation conflicts, and improve the operation efficiency and stability of the entire distribution network.

[0216] Step S403: Determine the load resource scheduling information based on at least one of the complementary information and the regulation potential information.

[0217] In this embodiment, different types of load resources are complementary in terms of regulation characteristics. For example, the interruptible loads in the business district (such as partial lighting and air conditioners in large shopping malls) can be reduced during the peak load period of the power grid, while the energy storage devices in the residential area (such as household energy storage batteries) can store electrical energy during the low load period and release electrical energy during the peak period, so as to achieve power complementarity between regions. Analyze the complementary information of load resources within each local area, formulate corresponding power exchange strategies, and thus the load resource scheduling information can be determined. For example, when the load demand in a certain local area is low and there is excess regulation capacity, part of the electrical energy can be transmitted to an adjacent area with higher load demand.

[0218] In this embodiment, the regulation potential of the same type of load resources also varies at different time periods. For example, there are differences in the load demand and regulation potential of industrial loads on weekdays and rest days. On weekdays, due to production task arrangements, the regulation potential of some industrial loads is limited; while on rest days, its regulation potential may increase. According to the load resource complementarity at different time periods, formulate a dynamic inter-regional coordination strategy, and thus the load resource scheduling information can be determined. For example, during the peak load period of the power grid, preferentially call resources in areas with greater regulation potential (such as industrial load areas on rest days) to meet the needs of the entire distribution network.

[0219] In this embodiment, a combined regulation strategy is also formulated based on the regulation potential of the load resources in each local area. For example, when the distribution network faces a sudden power deficit, the load resources in multiple local areas can be simultaneously called for regulation. According to the regulation potential and response speed of each local area, the regulation task allocation for different local areas is determined. When formulating the combined regulation strategy, it is also necessary to consider the communication delay and coordination control mechanism between regions to ensure that each local area can respond and execute the regulation task within the specified time and avoid regulation conflicts.

[0220] In this embodiment, by reasonably utilizing the complementary information and regulation potential information of the load resources, power exchange and load regulation among multiple local areas are realized, which can improve the operation efficiency of the entire distribution network. For example, reducing unnecessary investment in power generation capacity and reducing network losses, etc. At the same time, this inter-regional cooperation strategy also helps to improve the stability of the distribution network. When a fault or load fluctuation occurs in a certain local area, other areas can provide support through power exchange and load regulation to avoid the spread of the fault and its impact on the entire distribution network.

[0221] In this embodiment, the global optimal operation mode model of the distribution network is solved based on the objective function and the constraint conditions, and the model output results are obtained, that is, the optimal output information of each distributed power source, the optimal voltage value of each node, and the optimal power flow distribution of each line can be obtained.

[0222] In this embodiment, the load resource information of multiple local area load resources is obtained, and based on the model output results and the load resource information, the load resource scheduling information for multiple local area load resources is generated, that is, the power exchange strategy between local areas and the load regulation strategy for each local area can be obtained.

[0223] In some embodiments, before controlling the operating state of the target load resource in the load resources based on the load resource scheduling information in step S111, it further includes:

[0224] Step S501, generating at least one of the load regulation cost and the load regulation accuracy based on the load resource scheduling information.

[0225] In some embodiments, the performance of the distribution network during the state transition process is evaluated based on stability indicators and economic indicators, and then improvement suggestions can be put forward based on the evaluation results to optimize the operation strategy and control method of the distribution network.

[0226] Among them, the stability indicator is calculated by the root mean square value of voltage fluctuation (VRMS). The root mean square value of voltage fluctuation (VRMS) is used to measure the degree of voltage fluctuation of each node during the state transition process, and its calculation formula is:

[0227] VRMS =

[0228] Among them, V i is the voltage value of node i during the state transition process, V0 is the voltage value of node i in the initial state, and n is the total number of nodes.

[0229] The economic index is calculated through the additional operating cost. The additional operating cost is used to evaluate the increase in the additional power generation cost and network loss cost generated by adjusting the strategy during the state transition process. Its calculation formula is:

[0230] Economic Cost =Δ Cgen +Δ Closs

[0231] Among them, ΔC gen is the additional power generation cost, and ΔC loss is the increase in network loss cost.

[0232] In this embodiment, in addition to the stability index and the economic index, it is also necessary to add an evaluation index for the load resource regulation effect. Through these newly added evaluation indexes for the load resource regulation effect, the performance of the distribution network during the state transition process can be more comprehensively reflected.

[0233] In this embodiment, the evaluation indexes for the load resource regulation effect include at least one of the load regulation cost and the load regulation accuracy.

[0234] Among them, the load regulation cost is used to reflect the cost required to regulate the load resources during the state transition process, including direct costs (such as equipment operation costs) and indirect costs (such as loss of user satisfaction). Its calculation formula is:

[0235] Cost regulation =

[0236] Among them, C direct,i is the direct regulation cost of the i-th load resource, C indirect,i is the indirect regulation cost of the i-th load resource, and m is the total number of load resources.

[0237] Among them, the load regulation accuracy is used to measure the deviation degree between the actual regulation effect and the expected regulation target. Its calculation formula is:

[0238] Accuracy regulation =1 - ( P actual,i - P target,i ∣ / )

[0239] Among them, P actual,iis the actual regulation power of the i-th load resource, P target,i is the target regulation power of the i-th load resource, and m is the total number of load resources.

[0240] In this embodiment, according to the real-time operation data of the distribution network, the voltage values of each node during the state transition process can be obtained, and the root mean square value (VRMS) of the voltage fluctuation of each node can be calculated, so as to obtain the stability index; then, according to the model output result of the global optimal operation mode model of the distribution network in step S107, the additional power generation cost and the increase in network loss cost during the state transition process can be obtained, so as to calculate the total economic index value; then, according to the load resource scheduling information of multiple local area load resources in step S111, the regulation strategies and cost data of each load resource can be obtained, and the direct regulation cost and indirect regulation cost of each load resource can be calculated. Summarize the regulation costs of all load resources to obtain the total load regulation cost; then, combined with the model output result of the global optimal operation mode model of the distribution network in step S107 and the load resource scheduling information of multiple local area load resources in step S111, the target regulation power and actual regulation power of each load resource can be obtained, and the regulation accuracy of each load resource can be calculated and the regulation accuracies of all load resources can be summarized to obtain the total load regulation accuracy.

[0241] Step S503, obtain the comparison result of at least one of the load regulation cost and the load regulation accuracy with a preset threshold, and modify the load resource scheduling information based on the comparison result.

[0242] In this embodiment, the calculated index values are compared with preset standards or historical data to evaluate the effect of the distribution network operation state transition. According to the evaluation result, improvement suggestions are put forward and the load resource scheduling information is modified to optimize the operation strategy and control method of the distribution network.

[0243] In some embodiments, in step S113, controlling the operating state of the target load resource among the multiple local area load resources based on the load resource scheduling information includes:

[0244] Step S601, obtain the model output result of the global optimal operation mode model of the distribution network in step S107, and obtain information such as the optimal output of each distributed power source, the optimal voltage value of each node, and the optimal power flow distribution of each line. These data reflect the ideal operating state of the distribution network under different resources and constraints.

[0245] Step S603, obtain the load resource scheduling information of multiple local area load resources in step S111, including the division of each local area, the cooperation relationship between regions (such as power exchange strategy, load regulation strategy, etc.), and the distribution, characteristics and regulation potential of load resources in each local area.

[0246] Step S605: Obtain the real-time operation data of the distribution network, such as electrical quantity information of the current voltage, current, power, etc. at each node, as well as load resource management data (relevant information of specific load devices, load terminals, load groups, and load aggregators).

[0247] Step S607: Sort the load resources of different types and regions according to the evaluation results of the regulation potential of the load resources.

[0248] For example, for the electric vehicle charging load, if its charging time is relatively flexible and the charging power can be adjusted according to the grid demand, then it may have a higher priority in terms of regulation potential; for some industrial loads, if they have a large interruptible capacity during specific periods, they are also listed as load resources with high regulation potential.

[0249] Step S609: According to the power distribution characteristics of the load resources, ensure that the power distribution of different load resources is reasonably arranged in the dispatching strategy to meet the requirements of the overall power balance and optimal operation of the distribution network.

[0250] For example, during peak load periods, preferentially arrange load resources with large regulation potential and high power distribution flexibility for regulation, such as adjusting the air-conditioning load in large commercial buildings.

[0251] Step S611: Develop a dispatching strategy sequence according to the load demand, distributed power source access situation, and load resource characteristics in different periods.

[0252] For example, during peak load periods, preferentially dispatch load resources with large regulation potential to supply power to the load center, and at the same time adjust the status of tie switches to optimize the power flow distribution. This involves first calling the energy storage device to discharge, then adjusting the operating status of interruptible loads (such as some industrial equipment, large commercial lighting, etc.), and finally considering adjusting the charging power of electric vehicles, etc.

[0253] In this embodiment, corresponding dispatching strategy sequence templates can be developed for different scenarios (such as normal operation, fault recovery, large fluctuations in renewable energy, etc.) so as to make quick and effective dispatching decisions in different situations.

[0254] Step S613: Convert the developed dispatching strategy into specific control instructions and send the control instructions to the target load resources.

[0255] For example, if the dispatching strategy is to reduce the charging power of electric vehicles in a certain area during a certain period, then convert this strategy into a control instruction for the electric vehicle charging pile, specifying the reduced power value and the execution time range.

[0256] For different types of smart devices and load aggregators, targeted instruction format conversion is performed according to their communication protocols and interface requirements to ensure that control instructions can be accurately understood and executed.

[0257] Among them, the scheduling strategy can be expressed as: S = f(P load , P source , S switch )

[0258] Among them, S is the scheduling strategy, p load is the load, R source is the output of distributed power sources, and S switch is the switch state.

[0259] After that, through the communication network of the distribution automation master station, the control instructions are sent to smart devices (such as distributed power source controllers, switch controllers, etc.) and load aggregators in the distribution network. During the sending process, a reliable communication mechanism is adopted to ensure the accurate transmission of the instructions.

[0260] In this embodiment, a monitoring mechanism for instruction sending is established to track the sending status of instructions in real time. If it is found that the instruction sending fails or an abnormal situation occurs, retransmission or other remedial measures are taken in a timely manner.

[0261] After receiving the control instructions, the smart devices and load aggregators remotely and real-time control the load resources according to the instruction requirements. For example, the smart meter can adjust the operating state of the user's electrical equipment according to the instructions, and the load aggregator can coordinate the power output of multiple load groups it manages according to the instructions.

[0262] During the control process, feedback information is continuously collected, such as the actual adjustment situation of the load resources and the impact on the grid operation state. According to this feedback information, the scheduling strategy and control instructions are further optimized to achieve more accurate and efficient optimization management of the load resources.

[0263] It can be understood that before using the technical solutions of the various embodiments in the present disclosure, the types, usage scopes, usage scenarios, etc. of the personal information involved will be informed to the users in an appropriate manner and the authorization of the users will be obtained.

[0264] For example, when responding to the receipt of a user's active request, a prompt message is sent to the user to clearly prompt the user that the operation requested by the user will require obtaining and using the user's personal information. Thus, the user can autonomously choose whether to provide personal information to software or hardware such as an electronic device, application program, server, or storage medium that executes the operations of the technical solutions of the present disclosure according to the prompt message.

[0265] As an optional but non-limiting implementation manner, in response to receiving an active request from a user, the manner of sending a prompt message to the user may be, for example, in the form of a pop-up window, and the prompt message may be presented in text in the pop-up window. In addition, the pop-up window may also carry a selection control for the user to select "agree" or "disagree" to provide personal information to the electronic device.

[0266] It can be understood that the above notification and user authorization acquisition process is only illustrative and does not limit the implementation manner of the present disclosure. Other manners that comply with relevant laws and regulations can also be applied to the implementation manner of the present disclosure.

[0267] It should be noted that the method of the embodiment of the present disclosure can be executed by a single device, such as a computer or a server. The method of this embodiment can also be applied to a distributed scenario and completed by multiple devices cooperating with each other. In such a distributed scenario, one of the multiple devices can only execute one or more steps of the method of the embodiment of the present disclosure, and these multiple devices will interact with each other to complete the described method.

[0268] It should be noted that some embodiments of the present disclosure have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be executed in a different order from that in the above embodiments and still achieve the desired result. Additionally, the processes depicted in the drawings do not necessarily require the specific order or sequential order shown to achieve the desired result. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0269] Based on the same inventive concept, corresponding to the method of any of the above embodiments, the present disclosure further provides a hierarchical regulation device for grid load resources.

[0270] Referring to Figure 3 , the device includes:

[0271] An acquisition module 101, configured to: acquire distribution network operation data and hierarchical management data of load resources; wherein, based on load attributes, the load resources are hierarchically managed, and the data of the hierarchically managed load resources is acquired as the hierarchical management data;

[0272] A determination module 103, configured to: determine regulation potential information and power distribution characteristic information of the load resources based on the distribution network operation data and the hierarchical management data;

[0273] The model construction module 105 is configured to: construct a global optimal operation mode model of the distribution network, and construct the objective function and constraint conditions of the global optimal operation mode model of the distribution network based on the regulation potential information and the power distribution characteristic information;

[0274] The model solving module 107 is configured to: solve the global optimal operation mode model of the distribution network based on the objective function and the constraint conditions to obtain a model output result;

[0275] The partitioning module 109 is configured to: partition the load resources based on the hierarchical management data to obtain multiple local area load resources;

[0276] The generation module 111 is configured to: obtain the load resource information of multiple local area load resources, and generate load resource scheduling information for the multiple local area load resources according to the model output result and the load resource information;

[0277] The control module 113 is configured to: control the operation state of the target load resource among the multiple local area load resources based on the load resource scheduling information.

[0278] In some embodiments, the acquisition of the hierarchical management data includes:

[0279] Obtain the load device data of the load device;

[0280] Obtain the load terminal data of the load terminal, where the load terminal is used to manage at least one load device within the same preset range;

[0281] Obtain the load group data of the load group, where the load group is used to manage at least one load terminal of the same type;

[0282] Obtain the load aggregator data of the load aggregator, where the load aggregator is used to manage the load group;

[0283] Determine the hierarchical management data based on the load device data, the load terminal data, the load group data, and the load aggregator data.

[0284] In some embodiments, the model construction module 105 is further configured to:

[0285] Construct a regulation potential model based on the regulation potential information, and set the maximization of the utilization rate of the adjustable potential of the load resources as an objective function term of the objective function;

[0286] Construct a power distribution model based on the power distribution characteristic information, and set the power distribution balance information as the constraint condition.

[0287] In some embodiments, the model construction module 105 is further configured to:

[0288] Obtain the correlation between the power output of the power generation equipment and the load resource regulation amount information, determine the power generation cost based on the correlation between the power output of the power generation equipment and the load resource regulation amount information, and set the power generation cost as the objective function term of the objective function;

[0289] Obtain the distribution information of the load resources on the nodes and lines based on the hierarchical management data, determine the power flow distribution information of the distribution network based on the distribution information, and set the power flow distribution information as the constraint condition;

[0290] Determine the constraint condition based on at least one of the line capacity, node voltage, and load regulation information.

[0291] In some embodiments, the model construction module 105 is further configured to: generate a graph structure based on the distribution network operation data and the distribution network topology structure, where the nodes of the graph structure represent the equipment in the distribution network, and the edges of the graph structure represent the connection relationships between the equipment; extract features from the graph structure through a graph neural network, update the feature representations of the nodes through a message propagation mechanism, and dynamically adjust the decision variables through reinforcement learning.

[0292] In some embodiments, the model solving module 107 is further configured to: iteratively solve the distribution network global optimal operation mode model, narrow the feasible region by dynamically adding cut plane constraints, and output the distribution network operation information that meets the minimum operation cost as the model output result.

[0293] In some embodiments, the partitioning module 109 is further configured to:

[0294] Obtain at least one of the geographical distribution information, network topology structure association information, load characteristic information, and regulation potential information of the load resources based on the hierarchical management data;

[0295] Partition the load resources based on at least one of the geographical distribution information, network topology structure association information, load characteristic information, and regulation potential information of the load resources.

[0296] In some embodiments, the generation module 111 is further configured to:

[0297] Obtain at least one of the complementary information and regulation potential information of the local area load resources according to the load resource information;

[0298] Determine the load resource scheduling information based on at least one of the complementary information and the regulation potential information.

[0299] In some embodiments, before controlling the operating state of a target load resource among the load resources based on the load resource scheduling information, the apparatus is further configured to:

[0300] Generate at least one of a load regulation cost and a load regulation accuracy based on the load resource scheduling information;

[0301] Obtain a comparison result between at least one of the load regulation cost and the load regulation accuracy and a preset threshold, and modify the load resource scheduling information based on the comparison result.

[0302] For convenience of description, when describing the above apparatus, various modules are described separately according to their functions. Of course, when implementing the present disclosure, the functions of each module can be implemented in one or more pieces of software and / or hardware.

[0303] The apparatus in the above embodiments is used to implement the corresponding power grid load resource hierarchical regulation method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be elaborated herein.

[0304] Based on the same inventive concept, corresponding to the method in any of the above embodiments, the present disclosure further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the electronic device implements the power grid load resource hierarchical regulation method in any of the foregoing embodiments.

[0305] Figure 4 FIG. shows a more specific schematic hardware structure diagram of the electronic device provided in this embodiment. The device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. Among them, the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are communicatively connected to each other inside the device through the bus 1050.

[0306] The processor 1010 may be implemented in a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, etc., and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0307] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage devices, dynamic storage devices, etc. The memory 1020 can store the operating system and other application programs. When implementing the technical solutions provided in the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 1020 and called and executed by the processor 1010.

[0308] The input / output interface 1030 is used to connect to the input / output module to achieve information input and output. The input / output module can be configured as a component in the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Among them, the input device can include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device can include a display, a speaker, a vibrator, an indicator light, etc.

[0309] The communication interface 1040 is used to connect to the communication module (not shown in the figure) to achieve communication interaction between this device and other devices. Among them, the communication module can achieve communication through wired means (such as USB, network cable, etc.) or through wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0310] The bus 1050 includes a path for transmitting information between various components of the device (such as the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040).

[0311] It should be noted that although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040, and the bus 1050, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, those skilled in the art can understand that the above device may also only include the components necessary for implementing the solution of the embodiments of this specification and does not necessarily include all the components shown in the figure.

[0312] The electronic device in the above embodiment is used to implement the corresponding grid load resource hierarchical regulation method in any of the foregoing embodiments and has the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0313] Based on the same inventive concept, corresponding to the method in any of the above embodiments, the present disclosure also provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the grid load resource hierarchical regulation method as described in any of the foregoing embodiments.

[0314] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible by a computing device.

[0315] The computer instructions stored in the storage medium of the above embodiment are used to cause the computer to execute the grid load resource hierarchical regulation method described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0316] Those skilled in the art know that the embodiments of the present disclosure can be implemented as a system, method, or computer program product. Therefore, the present disclosure can be specifically implemented in the following forms: completely hardware, completely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software, generally referred to as "circuit", "module", or "system" in this article. In addition, in some embodiments, the present disclosure can also be implemented in the form of a computer program product in one or more computer-readable media, which contains computer-readable program code.

[0317] Any combination of one or more computer-readable media can be adopted. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive examples) of the computer-readable storage medium can include, for example: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, the computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0318] A computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, in which computer-readable program code is carried. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0319] The program code contained on a computer-readable medium can be transmitted using any appropriate medium, including but not limited to wireless, wire, optical fiber cable, RF, etc., or any suitable combination of the above.

[0320] The computer program code for performing the operations of the present disclosure can be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., by connecting through the Internet using an Internet service provider).

[0321] It should be understood that each block of the flowchart and / or block diagram, and the combinations of blocks 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, or other programmable data processing device, thereby producing a machine. These computer program instructions, when executed by a computer or other programmable data processing device, produce a device for implementing the functions / operations specified in the blocks of the flowchart and / or block diagram.

[0322] These computer program instructions can also be stored in a computer-readable medium that can cause a computer or other programmable data processing device to operate in a specific manner. In this way, the instructions stored in the computer-readable medium produce a product that includes an instruction device for implementing the functions / operations specified in the blocks of the flowchart and / or block diagram.

[0323] Computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device, so that a series of operation steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby enabling the instructions executed on the computer or other programmable apparatus to provide a process for implementing the functions / operations specified in the blocks of the flowchart and / or block diagram.

[0324] In addition, although the operations of the method of the present disclosure are depicted in a specific order in the drawings, this is not required or implied to perform these operations in that specific order, or that all of the illustrated operations must be performed to achieve the desired result. On the contrary, the steps depicted in the flowchart may be changed in their order of execution. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step for execution, and / or one step may be decomposed into multiple steps for execution.

[0325] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present application. Wherein, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the above-mentioned module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, as well as the combination of blocks in the block diagram or flowchart, may be implemented by a dedicated hardware-based system for performing the specified functions or operations, or may be implemented by a combination of dedicated hardware and computer instructions.

[0326] It should be noted that although several modules or units of the devices for action execution are mentioned in the above detailed description, such a division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of the two or more modules or units described above may be embodied in one module or unit. Conversely, the features and functions of one module or unit described above may be further divided and embodied by multiple modules or units.

[0327] Those of ordinary skill in the art should understand that any discussion of the above embodiments is merely exemplary and is not intended to imply that the scope of the present disclosure (including the claims) is limited to these examples; under the concept of the present disclosure, the technical features in the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations in different aspects of the embodiments of the present disclosure as described above, and they are not provided in detail for the sake of brevity.

[0328] In addition, for the sake of simplicity of explanation and discussion, and in order not to make the embodiments of the present disclosure difficult to understand, the well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the accompanying drawings. In addition, the devices may be shown in block diagram form in order to avoid making the embodiments of the present disclosure difficult to understand, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform on which the embodiments of the present disclosure are to be implemented (i.e., these details should be fully within the understanding of those skilled in the art). In the case where specific details (such as circuits) are set forth to describe the exemplary embodiments of the present disclosure, it will be apparent to those skilled in the art that the embodiments of the present disclosure may be implemented without these specific details or with variations of these specific details. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0329] Although the present disclosure has been described in connection with specific embodiments of the present disclosure, many alternatives, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art based on the foregoing description. For example, other memory architectures (such as dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0330] The embodiments of the present disclosure are intended to cover all such alternatives, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present disclosure shall be included within the protection scope of the present disclosure.

Claims

1. A method for hierarchical control of power grid load resources, characterized in that: include: Obtaining distribution network operation data and hierarchical management data of load resources; wherein the load resources are hierarchically managed based on load attributes, and data of the load resources after hierarchical management are obtained as the hierarchical management data; Determining the regulation potential information and power distribution characteristic information of the load resource based on the distribution network operation data and the hierarchical management data; Constructing a global optimal operation mode model of the distribution network, and constructing the objective function and constraint conditions of the global optimal operation mode model of the distribution network based on the regulation potential information and the power distribution characteristic information, including: constructing a regulation potential model based on the regulation potential information, setting the adjustable potential utilization rate of the load resources and the power generation cost that maximizes the load resources as the objective function terms of the objective function; constructing a power distribution model based on the power distribution characteristic information, setting at least one of the line capacity, node voltage, load regulation information, power distribution balance information, and power flow distribution information as the constraint conditions; Solving the global optimal operation mode model of the distribution network based on the objective function and the constraint conditions to obtain a model output result; Dividing the load resources based on the hierarchical management data to obtain multiple local area load resources; Obtaining load resource information of the plurality of local area load resources, and generating load resource scheduling information for the plurality of local area load resources according to the model output result and the load resource information; The operating state of a target load resource among the plurality of local area load resources is controlled based on the load resource scheduling information.

2. The method according to claim 1, characterized in that The acquisition of the hierarchical management data includes: Obtain load device data of the load device; Acquiring load terminal data of a load terminal, wherein the load terminal is used to manage at least one load device within the same preset range; acquiring load group data of a load group, wherein the load group is used to manage at least one load terminal of the same type; Obtaining load aggregator data of a load aggregator, wherein the load aggregator is used to manage the load group; The hierarchical management data is determined based on the load equipment data, the load terminal data, the load group data, and the load aggregator data.

3. The method according to claim 1, characterized in that Also includes: Acquire the correlation between the output of the power generation equipment and the load resource regulation amount information, and determine the power generation cost based on the correlation between the output of the power generation equipment and the load resource regulation amount information; The distribution information of load resources on nodes and lines is acquired based on the hierarchical management data, and the power flow distribution information of the power distribution network is determined based on the distribution information.

4. The method according to claim 1, characterized in that: The constructing of the global optimal operation mode model of the distribution network includes: Generate a graph structure based on the distribution network operation data and the distribution network topology structure, wherein the nodes of the graph structure represent devices in the distribution network, and the edges of the graph structure represent connection relationships between the devices; Extract features from the graph structure through a graph neural network, update feature representations of nodes through a message propagation mechanism, and dynamically adjust decision variables through reinforcement learning; The step of solving the global optimal operation mode model of the distribution network based on the objective function and the constraint conditions to obtain a model output result includes: The global optimal operation mode model of the distribution network is solved iteratively, the feasible domain is reduced by dynamically adding cutting plane constraints, and the distribution network operation information that satisfies the minimum operation cost is output as the output result of the model.

5. The method according to claim 1, characterized in that The dividing the load resources based on the hierarchical management data includes: Acquire at least one of geographical distribution information of load resources, network topology association information, load characteristic information, and regulation potential information based on the hierarchical management data; Dividing the load resources based on at least one of geographical distribution information, network topology association information, load characteristic information, and regulation potential information of the load resources; The generating of load resource scheduling information for the plurality of local area load resources according to the model output result and the load resource information comprises: Acquire at least one of complementarity information and regulation potential information of the local area load resources according to the load resource information; The load resource scheduling information is determined based on at least one of the complementarity information and the regulation potential information.

6. The method according to claim 1, characterized in that Before controlling the operating state of the target load resource in the load resources based on the load resource scheduling information, the method further includes: generating at least one of a load regulation cost and a load regulation accuracy based on the load resource scheduling information; Obtain a comparison result between at least one of the load regulation cost and the load regulation accuracy and a preset threshold, and modify the load resource scheduling information based on the comparison result.

7. A hierarchical control device for power grid load resources, characterized in that: include: The acquisition module is configured to: acquire distribution network operation data and hierarchical management data of load resources; wherein the load resources are hierarchically managed based on load attributes, and the data of the load resources after hierarchical management are acquired as the hierarchical management data; A determination module is configured to: determine the regulation potential information and power distribution characteristic information of the load resource based on the distribution network operation data and the hierarchical management data; The model building module is configured to: build a global optimal operation mode model of the distribution network, and build the objective function and constraint conditions of the global optimal operation mode model of the distribution network based on the regulation potential information and the power distribution characteristic information, including: building a regulation potential model based on the regulation potential information, setting the adjustable potential utilization rate of the load resources and the power generation cost that maximizes the load resources as the objective function items of the objective function; building a power distribution model based on the power distribution characteristic information, setting at least one of the line capacity, node voltage, load regulation information, power distribution balance information, and power flow distribution information as the constraint conditions; A model solving module is configured to solve the global optimal operation mode model of the distribution network based on the objective function and the constraint conditions to obtain a model output result; A division module is configured to: divide the load resources based on the hierarchical management data to obtain a plurality of local area load resources; A generating module is configured to: obtain load resource information of a plurality of said local area load resources, and generate load resource scheduling information for the plurality of said local area load resources according to the output result of the model and the load resource information; The control module is configured to control the operating state of the target load resource among the plurality of local area load resources based on the load resource scheduling information.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method for hierarchical control of power grid load resources as described in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to enable the computer to execute the power grid load resource hierarchical control method described in any one of claims 1 to 6.

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