A deployment control integrated comprehensive decision method, device, equipment and medium

Abstract

The application discloses a dispatching and control integrated comprehensive decision method and device, electronic equipment and a storage medium, and relates to the technical field of power grid dispatching control. The method comprises the following steps: acquiring multi-source heterogeneous data of a dispatching and control object; acquiring model data corresponding to the dispatching and control object by constructing an integrated model based on the multi-source heterogeneous data, wherein the integrated model is a coupled mathematical model constructed based on a power grid topology, energy flow, equipment characteristics and basic operation boundary conditions; determining constraint conditions of a multi-objective function based on the model data, constructing a multi-objective function with the minimum operation cost, the minimum network loss, the minimum voltage deviation, the maximum new energy consumption rate and the highest reliability as the target, solving the multi-objective function under the constraint conditions, and obtaining an integrated decision instruction set comprising a dispatching plan, a resource configuration scheme and a control strategy; and performing collaborative control based on the integrated decision instruction set. The application can improve the data consistency and collaborative execution reliability of dispatching, configuration and control decisions.

Description

Technical Field

[0001] This invention belongs to the field of integrated decision-making technology for allocation and control, and specifically relates to an integrated decision-making method, device, equipment and medium for allocation and control. Background Technology

[0002] With the development of new power systems and integrated energy systems, the operational correlation between power grids, distributed energy sources, loads, energy storage devices, and controllable energy-consuming devices is gradually strengthening. The high proportion of new energy integration, the widespread application of power electronic equipment, and the continuous increase in load-side response resources have led to the operational status of dispatchable and controlled objects exhibiting characteristics such as the coexistence of multi-source data, dynamic changes in operational boundaries, an increase in the number of controlled objects, and significant differences in dispatch cycles. The power grid operating parameters, load demand, distributed energy output, equipment status, environmental information, and historical operating data involved in dispatch operations typically have different data sources, collection cycles, and data formats, placing higher demands on the accuracy of dispatch, configuration, and control decisions.

[0003] In existing technologies, scheduling plans, resource allocation schemes, and control strategies are usually generated separately based on different data foundations, model calibers, and processing cycles. The correlation between power grid topology, energy flow, equipment characteristics, and constraints is insufficient, resulting in inconsistencies in the scheduling and control decision results during multi-objective comprehensive evaluation and collaborative control execution. Summary of the Invention

[0004] The purpose of this invention is to provide an integrated decision-making method, device, equipment, and medium for dispatching and control, so as to solve the problem of insufficient consistency in dispatching and control decision-making results caused by the separate generation of scheduling plans, resource allocation schemes, and control strategies in the prior art.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides an integrated decision-making method for allocation and control, comprising: Acquire multi-source heterogeneous data of the allocation control object; Based on the multi-source heterogeneous data, the model data corresponding to the dispatch control object is obtained through the constructed integrated model; the integrated model is a coupled mathematical model constructed based on power grid topology, energy flow, equipment characteristics and basic operating boundary conditions; Based on the model data, the constraints of the multi-objective function are determined. The multi-objective function is constructed with the objectives of minimizing operating cost, minimizing network loss, minimizing voltage deviation, maximizing renewable energy absorption rate, and maximizing reliability. The multi-objective function is solved under the constraints to obtain an integrated decision instruction set including scheduling plan, resource allocation scheme and control strategy. Collaborative control and execution are carried out based on the integrated decision instruction set.

[0006] The above scheme constructs an integrated model using multi-source heterogeneous data, and builds multi-objective functions and their constraints based on the model data output by the integrated model. This enables scheduling plans, resource allocation schemes, and control strategies to be formed from the same model data and the same optimization solution process, solving the problem of inconsistent decision-making basis for scheduling, allocation, and control. This achieves integrated generation and collaborative execution of scheduling and control decision results.

[0007] Furthermore, the multi-source heterogeneous data includes power grid operating parameters, load demand, distributed energy output, equipment status, environmental information, and historical operating data. The multi-source heterogeneous data is cleaned, standardized, and fused, and the cleaned, standardized, and fused multi-source heterogeneous data is used as the data foundation for constructing the integrated model.

[0008] The above-mentioned further solutions address the inconsistency in data caliber when multiple sources of data are directly used in modeling by cleaning, standardizing, and fusing data from different sources, formats, and time scales. This improves the stability of model data generation and the consistency of data in subsequent multi-objective function solutions.

[0009] Furthermore, based on the multi-source heterogeneous data, the model data corresponding to the dispatch control object is obtained through the constructed integrated model, including: determining the connection relationship between nodes, lines, branches, and switching equipment in the dispatch control object based on the grid operation parameters and switch or circuit breaker status in the multi-source heterogeneous data, to obtain grid topology data; determining the correspondence between active power, reactive power, load demand, distributed energy output, and line or branch network losses of the dispatch control object in each time period based on the load demand, distributed energy output, and grid operation parameters in the multi-source heterogeneous data, to obtain energy flow data; determining the operating status and equipment capacity boundary conditions of conventional units, distributed energy equipment, lines, branches, and switching equipment in the dispatch control object based on the equipment status and historical operation data in the multi-source heterogeneous data, to obtain equipment characteristic data; associating the grid topology data, energy flow data, and equipment characteristic data to form a coupling relationship expression between the grid topology, energy flow process, and equipment operating characteristics, and performing unified mapping in conjunction with basic operating boundary conditions to construct the integrated model, and outputting the model data corresponding to the dispatch control object through the integrated model.

[0010] The above-mentioned further solution generates power grid topology data, energy flow data, and equipment characteristic data separately, and performs correlation processing on the above data. This enables the model data to simultaneously reflect the connection relationship, power flow relationship, and equipment operating boundary of the dispatch and control objects, solving the problem that single model data cannot simultaneously support scheduling, configuration, and control decisions, thereby improving the completeness of the integrated model in expressing complex operating scenarios.

[0011] Furthermore, the multi-objective function includes an operating cost objective, a network loss objective, a voltage deviation objective, a renewable energy absorption rate objective, and a reliability objective. The operating cost objective is determined based on the active power output cost of conventional generating units, the active power output cost of distributed energy resources, and the system network loss cost. The network loss objective is determined based on the line or branch current and the line or branch resistance. The voltage deviation objective is determined based on the voltage amplitude and rated voltage of the load node. The renewable energy absorption rate objective is determined based on the actual active power output of wind turbines, the maximum usable active power output of wind turbines, the actual active power output of photovoltaic units, and the maximum usable active power output of photovoltaic units. The reliability objective is determined based on the load loss probability of the dispatching control object during the dispatch cycle.

[0012] The aforementioned further scheme incorporates operating costs, network losses, voltage deviations, renewable energy absorption rate, and reliability into a multi-objective function, solving the problem of a single evaluation dimension in dispatch control decision-making. This allows the solution to simultaneously reflect economic efficiency, power quality, renewable energy utilization, and power supply reliability.

[0013] Furthermore, the constraints include power balance constraints, voltage and frequency limit constraints, equipment capacity constraints, and switching operation frequency constraints; wherein, the power balance constraints are determined based on the output of conventional units, the output of distributed energy sources, the system load, and the system network loss; the voltage and frequency limit constraints are determined based on the node voltage limit and the system frequency limit; the equipment capacity constraints are determined based on the upper and lower limits of the output of conventional units and the upper and lower limits of the output of distributed energy sources; and the switching operation frequency constraints are determined based on the number of state changes of switches or circuit breakers within the scheduling cycle and the maximum allowed number of operations.

[0014] The above-mentioned further solutions address the problem of the disconnect between optimization results and actual operating boundaries by introducing constraints on power balance, voltage and frequency limits, equipment capacity, and the number of switching actions during the multi-objective function solution process. This improves the executability and operational safety of the integrated decision instruction set in actual power grids or integrated energy scenarios.

[0015] Furthermore, obtaining the integrated decision instruction set, which includes a scheduling plan, a resource allocation scheme, and a control strategy, includes: determining the output arrangement of conventional units and distributed energy sources within different time periods based on the solution results of the multi-objective function, thereby obtaining the scheduling plan; determining the operating modes of distributed energy equipment, lines, branches, and switching equipment in the dispatch control object based on the solution results of the multi-objective function, thereby obtaining the resource allocation scheme; determining the control parameters of the execution units in the dispatch control object based on the solution results of the multi-objective function, thereby obtaining the control strategy; and combining the scheduling plan, the resource allocation scheme, and the control strategy to form the integrated decision instruction set.

[0016] The above-mentioned further scheme solves the problem that optimization results are difficult to directly correspond to scheduling, configuration and control execution objects by converting the results of solving multi-objective functions into scheduling plans, resource allocation schemes and control strategies respectively, and further combining them into an integrated decision instruction set, thereby improving the completeness of decision output and the consistency of execution paths.

[0017] Furthermore, the coordinated control execution based on the integrated decision instruction set includes: splitting the integrated decision instruction set into scheduling sub-instructions, configuration sub-instructions, and control sub-instructions; determining the execution objects, execution times, and execution parameters corresponding to the scheduling sub-instructions, configuration sub-instructions, and control sub-instructions, respectively; issuing the scheduling sub-instructions, configuration sub-instructions, and control sub-instructions to the corresponding execution units according to region, voltage level, and equipment type, and obtaining the execution results returned by the execution units; performing closed-loop feedback and dynamic optimization based on the execution results and changes in the state of the controlled objects, and updating the integrated model and / or the integrated decision instruction set.

[0018] The aforementioned further solution addresses the issue of unclear execution objects and execution times for different types of instructions by classifying and splitting the integrated decision instruction set, matching execution objects, and distributing them hierarchically. This improves the reliability of coordinated execution of scheduling, configuration, and control instructions. Furthermore, by updating the integrated model and / or integrated decision instruction set through execution results and changes in object state, the system's adaptability to changes in operating state is enhanced.

[0019] In a second aspect, the present invention provides an integrated decision-making system for dispatch and control, comprising: The acquisition module is used to acquire multi-source heterogeneous data of the allocation and control object; The model processing module is used to obtain the model data corresponding to the dispatch control object based on the multi-source heterogeneous data and the constructed integrated model; the integrated model is a coupled mathematical model constructed based on power grid topology, energy flow, equipment characteristics and basic operating boundary conditions; The function processing module is used to determine the constraints of the multi-objective function based on the model data. The multi-objective function is constructed with the objectives of minimizing operating cost, minimizing network loss, minimizing voltage deviation, maximizing renewable energy absorption rate, and maximizing reliability. The multi-objective function is solved under the constraints to obtain an integrated decision instruction set including scheduling plan, resource allocation scheme and control strategy. The execution module is used to perform collaborative control execution based on the integrated decision instruction set.

[0020] The above system and the aforementioned method are based on the same inventive concept and can achieve the same or corresponding technical effects.

[0021] In a third aspect, the present invention provides an electronic device, including a processor and a memory, wherein the memory stores a computer program, and the processor executes the computer program to implement the aforementioned integrated decision-making method for dispatch and control.

[0022] The aforementioned electronic device executes computer programs stored in memory through a processor, enabling the aforementioned integrated decision-making method for dispatch and control to be implemented in a computing device, thereby improving the deployment adaptability of the method of the present invention in dispatch control platforms, integrated energy management platforms, or power grid operation management systems.

[0023] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned integrated decision-making method for dispatch and control.

[0024] The aforementioned computer-readable storage medium stores a computer program that implements the aforementioned integrated decision-making method for dispatch and control, enabling the method steps to be saved and invoked in program form, thereby improving the convenience of software deployment and system porting of the technical solution of the present invention. Attached Figure Description

[0025] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a schematic diagram of a comprehensive decision-making method integrating allocation and control provided in Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the integrated decision-making device for dispatching and control provided in Embodiment 2 of the present invention; Figure 3 This is a schematic diagram of the structure of an electronic device provided in Embodiment 3 of the present invention. Detailed Implementation

[0026] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0027] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. The terminology used in this invention is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.

[0028] Example 1 like Figure 1 As shown, an integrated decision-making method for allocation and control includes: S1. Obtain multi-source heterogeneous data of the allocation control object; S2. Based on the multi-source heterogeneous data, obtain the model data corresponding to the dispatch control object through the constructed integrated model; the integrated model is a coupled mathematical model constructed based on power grid topology, energy flow, equipment characteristics and basic operating boundary conditions; S3. Based on the model data, determine the constraints of the multi-objective function. Construct a multi-objective function with the objectives of minimizing operating cost, minimizing network loss, minimizing voltage deviation, maximizing renewable energy absorption rate, and maximizing reliability. Solve the multi-objective function under the constraints to obtain an integrated decision instruction set including scheduling plan, resource allocation scheme, and control strategy. S4. Perform collaborative control execution based on the integrated decision instruction set.

[0029] This scheme transforms multi-source heterogeneous data of the control and allocation objects into model data, and then determines constraints, constructs multi-objective functions, and generates an integrated decision instruction set based on the same model data. This ensures that scheduling plans, resource allocation schemes, and control strategies have a unified data source and model basis. Consequently, it reduces data discrepancies and execution coordination deviations when scheduling, allocation, and control are processed separately, improving the coordination and consistency of allocation and control decision results.

[0030] Specifically, the controllable objects can include conventional generating units, distributed energy equipment, lines, branches, switching equipment, load nodes, and controllable equipment related to the integrated energy system within the power grid. After acquisition, multi-source heterogeneous data enters the integrated model, which outputs model data. This model data further serves as the data foundation for constructing multi-objective functions, determining constraints, and generating the integrated decision instruction set. The integrated decision instruction set includes at least a scheduling plan, a resource allocation scheme, and a control strategy. The scheduling plan reflects the output arrangement within different time periods, the resource allocation scheme reflects the operating mode of equipment or lines, and the control strategy reflects the control parameters or actions required by the execution unit.

[0031] In one embodiment, the multi-source heterogeneous data includes power grid operating parameters, load demand, distributed energy output, equipment status, environmental information, and historical operating data. The multi-source heterogeneous data is cleaned, standardized, and fused, and the cleaned, standardized, and fused multi-source heterogeneous data is used as the data foundation for constructing the integrated model.

[0032] Specifically, grid operating parameters may include at least one of voltage, current, active power, reactive power, frequency, line or branch network losses, and switch or circuit breaker status; load demand may include total system active load, total system reactive load, and load data corresponding to each load node; distributed energy output may include actual active output of wind turbines, maximum available active output of wind turbines, actual active output of photovoltaic units, maximum available active output of photovoltaic units, and output data of other distributed energy sources; equipment status may include operating status, capacity boundaries, and operational status of conventional units, distributed energy equipment, lines, branches, and switchgear; environmental information may include meteorological, temperature, solar radiation, or wind speed information that affects load demand or distributed energy output; historical operating data may include operating status data, load data, output data, and equipment status data within historical time periods.

[0033] Furthermore, data cleaning can include removing obvious outliers, eliminating duplicate data, and correcting missing fields; standardization can include unifying data units, time formats, object identifiers, and data field formats; and fusion can include associating data from different sources according to the allocation control object identifier, collection time, and data type. After the above processing, multi-source heterogeneous data can form a data foundation for building an integrated model, reducing time misalignment and object inconsistency issues between data from different sources.

[0034] This embodiment cleanses, standardizes, and fuses multi-source heterogeneous data, enabling data from different collection periods, formats, and object identifiers to participate in modeling on a unified data basis, thereby further improving the accuracy and traceability of model data generation.

[0035] In one embodiment, the step of obtaining model data corresponding to the dispatch control object based on the multi-source heterogeneous data and through the constructed integrated model includes: determining the connection relationships between nodes, lines, branches, and switching equipment in the dispatch control object based on the grid operation parameters and switch or circuit breaker status in the multi-source heterogeneous data to obtain grid topology data; determining the correspondence between active power, reactive power, load demand, distributed energy output, and line or branch network losses of the dispatch control object in each time period based on the load demand, distributed energy output, and grid operation parameters in the multi-source heterogeneous data to obtain energy flow data; determining the operating status and equipment capacity boundary conditions of conventional units, distributed energy equipment, lines, branches, and switching equipment in the dispatch control object based on the equipment status and historical operation data in the multi-source heterogeneous data to obtain equipment characteristic data; associating the grid topology data, energy flow data, and equipment characteristic data to form a coupling relationship expression between the grid topology, energy flow process, and equipment operating characteristics, and performing unified mapping with basic operating boundary conditions to construct the integrated model, and outputting the model data corresponding to the dispatch control object through the integrated model.

[0036] Specifically, power grid topology data is used to express the connection relationships between nodes, lines, branches, and switching equipment. Changes in the state of switches or circuit breakers will cause changes in the power grid topology data. Energy flow data is used to express the correspondence between conventional unit output, distributed energy output, load demand, active power, reactive power, and line or branch network losses in different time periods. Equipment characteristic data is used to express the operating status, capacity boundary conditions, and operational status of conventional units, distributed energy equipment, lines, branches, and switching equipment. By performing correlation processing on power grid topology data, energy flow data, and equipment characteristic data, a coupled expression reflecting the power grid topology, energy flow process, and equipment operating characteristics can be formed.

[0037] Furthermore, in practical implementation, the integrated model includes a spatiotemporally coupled topology model, an operational constraint model, a cost-benefit model, and a control response model. The spatiotemporally coupled topology model characterizes the distribution relationship between the power grid structure and energy and load at different time periods and node locations; the operational constraint model quantifies operational safety boundaries such as voltage, frequency, power flow, equipment capacity, and the number of switching actions; the cost-benefit model calculates indicators such as operating costs, system network losses, and renewable energy consumption; and the control response model describes the dynamic relationship between control commands and the responses of the executing objects. These models are not isolated from each other in implementation, but rather linked through power grid topology data, energy flow data, and equipment characteristic data, enabling the integrated model output to simultaneously reflect structural relationships, energy flow relationships, operational boundaries, and dynamic response characteristics.

[0038] For example, the control response model can be established based on system identification methods. For an execution unit or controllable device, the step response method can be used to obtain the device's response data to control inputs, and the transfer function and response delay time characterizing the device's dynamic characteristics can be determined based on the response data. The control response model can also be represented by state-space equations, where the state vector may include node voltage, line current, or internal device state, the input vector may include control commands, power setpoints, or switch state adjustment commands, and the output vector may include voltage, current, frequency, or power measurements. In this way, the integrated model can reflect both the static operating boundary and the dynamic response characteristics of the executed object to control commands.

[0039] This embodiment correlates and processes power grid topology data, energy flow data, and equipment characteristic data, and maps them together with basic operating boundary conditions. This transforms the model data from representing a single electrical quantity or a single equipment state into model data that can support collaborative decision-making for scheduling, configuration, and control, thereby improving the completeness of the basic data for subsequent construction and solution of multi-objective functions.

[0040] In one embodiment, the multi-objective function includes an operating cost objective, a network loss objective, a voltage deviation objective, a renewable energy absorption rate objective, and a reliability objective. The operating cost target is determined based on the active power output cost of conventional units, the active power output cost of distributed energy, and the system network loss cost; the network loss target is determined based on the line or branch current and the line or branch resistance; the voltage deviation target is determined based on the voltage amplitude of the load node and the rated voltage; the renewable energy absorption rate target is determined based on the actual active power output of wind turbine units, the maximum available active power output of wind turbine units, the actual active power output of photovoltaic units, and the maximum available active power output of photovoltaic units; the reliability target is determined based on the load loss probability of the dispatch control object during the dispatch cycle.

[0041] Specifically, the operating cost target item can be expressed as:

[0042] in, This represents the total number of time periods in the scheduling cycle. This refers to the number of conventional generator sets; For the first Unit active power output cost of a conventional generating unit; For the first Taiwan conventional units The amount of effort contributed during a given period; The quantity of distributed energy resources; For the first The unit active power output cost of Taiwan's distributed energy resources; For the first Taiwan's distributed energy sector The amount of effort contributed during a given period; The unit cost of system network loss; For the system in Total active network loss during the time period.

[0043] The network loss target can be represented as:

[0044] in, The total number of system lines or branches; For the first A line or branch in Current during a given period; For the first The resistance of a line or branch.

[0045] The voltage deviation target term can be expressed as:

[0046] in, This represents the total number of load nodes in the system. For the first Each load node Voltage amplitude over a given period; This is the rated voltage.

[0047] The target item for renewable energy consumption rate can be expressed as:

[0048] in, This refers to the number of wind turbine units; For the first Typhoon turbine units The actual active power output during the time period; For the first Typhoon turbine units The maximum available active power output during the time period; The number of photovoltaic units; For the first Taiwanese photovoltaic units The actual active power output during the time period; For the first Taiwanese photovoltaic units The maximum available active power output during a given time period.

[0049] The reliability objective can be expressed as:

[0050] in, Let be the system's failure probability during time period t. The reliability target is determined based on the failure probability of the dispatch control object within the dispatch cycle. The failure probability characterizes the system's power supply reliability risk; the lower the failure probability, the higher the power supply reliability of the dispatch control object.

[0051] Furthermore, the aforementioned multiple objective items can be normalized according to the objective direction, forming a multi-objective function during the solution process. In an optional embodiment, weights can be set for different objective items according to the operational scenario or scheduling requirements, and multiple candidate solutions can be optimized. This combined weighted optimization process can serve as a scheme selection method after solving the multi-objective function, used to determine an integrated decision instruction set that coordinates scheduling plans, resource allocation schemes, and control strategies among candidate schemes that meet the constraints. The aforementioned weights can be preset according to scheduling operation requirements or adjusted according to actual application scenarios.

[0052] This embodiment defines the data sources and calculation basis for each objective item in the multi-objective function, enabling the multi-objective function to simultaneously evaluate economic efficiency, network loss level, voltage quality, renewable energy utilization, and power supply reliability, thereby improving the completeness of dispatch control decision results across multiple evaluation dimensions.

[0053] In one embodiment, the constraints include power balance constraints, voltage and frequency limit constraints, equipment capacity constraints, and switching operation frequency constraints; wherein, the power balance constraints are determined based on the output of conventional units, the output of distributed energy sources, the system load, and the system network loss; the voltage and frequency limit constraints are determined based on node voltage limits and system frequency limits; the equipment capacity constraints are determined based on the upper and lower limits of the output of conventional units and the upper and lower limits of the output of distributed energy sources; and the switching operation frequency constraints are determined based on the number of state changes of switches or circuit breakers within the scheduling cycle and the maximum allowed number of operations.

[0054] Specifically, power balance constraints can include active power balance constraints and reactive power balance constraints. Active power balance constraints can be expressed as:

[0055] in, For the system in Total active load for a given period of time.

[0056] The reactive power balance constraint can be expressed as:

[0057] in, For the first Taiwan conventional units Unproductive output during a given period; For the first Taiwan's distributed energy sector Unproductive output during a given period; For the system in Total reactive load for the time period.

[0058] Voltage and frequency limits can be expressed as:

[0059]

[0060] in, and These are the lower and upper limits of the allowable node voltage, respectively. and These are the lower and upper limits of the system frequency, respectively.

[0061] Equipment capacity constraints can be expressed as:

[0062]

[0063]

[0064] in, and The first The lower and upper limits of active power output of conventional generating units; and The first Lower and upper limits of reactive power output of conventional generating units; and The first The lower and upper limits of active power output of Taiwan's distributed energy resources.

[0065] The constraint on the number of switching actions can be expressed as:

[0066] in, For the first A switch or circuit breaker in The status of a time period, 0 indicates open, 1 indicates closed; For the first The maximum number of permissible actions of a switch or circuit breaker within a scheduling cycle.

[0067] Furthermore, the constraints are determined by the model data. The grid topology data in the model data determines the power balance relationship and power flow paths; the energy flow data determines the correspondence between load, output, and network losses; and the equipment characteristic data determines the operating boundaries of generating units, distributed energy devices, lines, branches, and switching equipment. Through these constraints, the solution results of the multi-objective function can fall within the actual allowable operating range of the dispatching and control object.

[0068] This embodiment incorporates power balance, voltage and frequency limits, equipment capacity, and the number of switching operations into the constraints, enabling the multi-objective function to be solved while meeting the safe operating boundaries, thereby reducing the risk of generating unexecutable or out-of-capacity decision instructions.

[0069] In one embodiment, obtaining an integrated decision instruction set including a scheduling plan, a resource allocation scheme, and a control strategy includes: determining the output arrangement of conventional units and distributed energy sources within different time periods based on the solution results of the multi-objective function, thereby obtaining the scheduling plan; determining the operating modes of distributed energy equipment, lines, branches, and switching equipment in the dispatch control object based on the solution results of the multi-objective function, thereby obtaining the resource allocation scheme; determining the control parameters of the execution units in the dispatch control object based on the solution results of the multi-objective function, thereby obtaining the control strategy; and combining the scheduling plan, resource allocation scheme, and control strategy to form the integrated decision instruction set.

[0070] Specifically, the solution results of the multi-objective function can include the output of conventional units, the output of distributed energy sources, the operating status of lines or branches, the status of switches or circuit breakers, and the control parameters of execution units within different time periods. Based on the above solution results, the system organizes the data related to the output time series into a scheduling plan, the data related to the equipment operating mode, the status of lines or branches, and the status of switches or circuit breakers into a resource allocation scheme, and the data related to voltage, frequency, reactive power regulation, switch actions, or other execution controls into a control strategy.

[0071] Furthermore, the integrated decision-making instruction set can be organized according to instruction type, execution object, execution time, and execution parameters. Instruction types can include scheduling, configuration, and control types; execution objects can include conventional generating units, distributed energy equipment, lines, branches, switching equipment, and other execution units; execution time can be the target period in the scheduling cycle or the real-time control time; execution parameters can include output setpoints, operating mode identifiers, switch status, control parameters, or control target values. By uniformly organizing the above information, the integrated decision-making instruction set can provide direct input for collaborative control execution.

[0072] This embodiment decomposes the solution of a multi-objective function into a scheduling plan, a resource allocation scheme, and a control strategy, and combines them into an integrated decision instruction set in a unified format. This creates a clear data mapping relationship between the optimization results and the execution control, thereby improving the executability of the decision results and the completeness of the output.

[0073] In one embodiment, the coordinated control execution based on the integrated decision instruction set includes: splitting the integrated decision instruction set into scheduling sub-instructions, configuration sub-instructions, and control sub-instructions; determining the execution objects, execution times, and execution parameters corresponding to the scheduling sub-instructions, configuration sub-instructions, and control sub-instructions, respectively; issuing the scheduling sub-instructions, configuration sub-instructions, and control sub-instructions to the corresponding execution units according to region, voltage level, and equipment type, and obtaining the execution results returned by the execution units; performing closed-loop feedback and dynamic optimization based on the execution results and changes in the state of the controlled objects, and updating the integrated model and / or the integrated decision instruction set.

[0074] Specifically, scheduling sub-instructions can correspond to the output arrangement of conventional generating units or distributed energy equipment; configuration sub-instructions can correspond to the adjustment of the operating mode of distributed energy equipment, lines, branches, and switching equipment; and control sub-instructions can correspond to the adjustment of control parameters of execution units. Before issuing sub-instructions, the system can parse each sub-instruction based on the execution object, execution time, and execution parameters. For multiple sub-instructions acting simultaneously on the same execution object or in adjacent execution periods, the instructions can be time-sequentially arranged, and conflict detection and resolution can be performed by combining the execution object, permission level, and equipment type.

[0075] Furthermore, hierarchical instruction delivery can be implemented based on region, voltage level, and equipment type. For example, for execution objects in different regions, corresponding sub-instructions are delivered to the execution units in the respective regions; for equipment at different voltage levels, sub-instructions are sent to the control units at the corresponding voltage levels; and for execution objects of different equipment types, sub-instructions are sent to the corresponding equipment control interfaces. After completing the action, the execution unit returns the execution result, which may include the output execution result, the operating mode adjustment result, the control parameter execution result, the execution completion status, and execution exception information.

[0076] Furthermore, when the execution result deviates from the target state corresponding to the integrated decision instruction set, or when changes in the state of the dispatch control object cause the original model data to no longer match the current operating state, the integrated model and / or integrated decision instruction set can be updated. In an optional embodiment, a multi-timescale coordination mechanism can be used for dynamic optimization, including global optimization of day-ahead scheduling, intraday rolling correction, and rapid adjustment of real-time control. When the aforementioned abnormal operating conditions such as faults, exceeding limits, or disturbances occur, an integrated emergency plan related to fault isolation, load transfer, or power restoration can also be generated based on changes in the state of the dispatch control object. The above-mentioned multi-timescale coordination mechanism and emergency handling can serve as a specific implementation method for closed-loop feedback and dynamic optimization.

[0077] This embodiment enables the integrated decision instruction set to be implemented in specific execution units by splitting sub-instructions into types, matching execution objects, and distributing them hierarchically. At the same time, it improves the closed-loop adaptability of the system in scenarios such as load fluctuations, changes in new energy output, and equipment status adjustments by updating the integrated model and / or integrated decision instruction set through execution results and status changes.

[0078] Example 2 like Figure 2 As shown, based on the same inventive concept as the above embodiments, the present invention also provides an integrated decision-making device for dispatch and control, comprising: The acquisition module is used to acquire multi-source heterogeneous data of the allocation and control object; The model processing module is used to obtain the model data corresponding to the dispatch control object based on the multi-source heterogeneous data and the constructed integrated model; the integrated model is a coupled mathematical model constructed based on power grid topology, energy flow, equipment characteristics and basic operating boundary conditions; The function processing module is used to determine the constraints of the multi-objective function based on the model data. The multi-objective function is constructed with the objectives of minimizing operating cost, minimizing network loss, minimizing voltage deviation, maximizing renewable energy absorption rate, and maximizing reliability. The multi-objective function is solved under the constraints to obtain an integrated decision instruction set including scheduling plan, resource allocation scheme and control strategy. The execution module is used to perform collaborative control execution based on the integrated decision instruction set.

[0079] Example 3 like Figure 3 As shown, the present invention also provides an electronic device 100 for realizing an integrated decision-making method for dispatch and control; The electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on at least one processor 102, and at least one communication bus 104.

[0080] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the integrated decision-making method for allocation and control in Embodiment 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101.

[0081] The memory 101 may primarily include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created based on the use of the electronic device 100 (such as audio data), etc. In addition, the memory 101 may include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0082] At least one processor 102 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 102 may be a microprocessor or any conventional processor. Processor 102 is the control center of electronic device 100, connecting various parts of electronic device 100 via various interfaces and lines.

[0083] The memory 101 in the electronic device 100 stores multiple instructions to implement an integrated decision-making method for dispatch and control, and the processor 102 can execute multiple instructions to achieve the following: Acquire multi-source heterogeneous data of the allocation control object; Based on the multi-source heterogeneous data, the model data corresponding to the dispatch control object is obtained through the constructed integrated model; the integrated model is a coupled mathematical model constructed based on power grid topology, energy flow, equipment characteristics and basic operating boundary conditions; Based on the model data, the constraints of the multi-objective function are determined. The multi-objective function is constructed with the objectives of minimizing operating cost, minimizing network loss, minimizing voltage deviation, maximizing renewable energy absorption rate, and maximizing reliability. The multi-objective function is solved under the constraints to obtain an integrated decision instruction set including scheduling plan, resource allocation scheme and control strategy. Collaborative control and execution are carried out based on the integrated decision instruction set.

[0084] Example 4 If the modules / units integrated in the electronic device 100 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, and read-only memory (ROM).

[0085] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0086] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0087] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0088] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0089] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0090] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A comprehensive decision-making method integrating allocation and control, characterized in that, include: Acquire multi-source heterogeneous data of the allocation control object; Based on the aforementioned multi-source heterogeneous data, the model data corresponding to the allocation and control object is obtained through the constructed integrated model; The integrated model is a coupled mathematical model constructed based on power grid topology, energy flow, equipment characteristics, and basic operating boundary conditions; Based on the model data, the constraints of the multi-objective function are determined. The multi-objective function is constructed with the objectives of minimizing operating cost, minimizing network loss, minimizing voltage deviation, maximizing renewable energy absorption rate, and maximizing reliability. The multi-objective function is solved under the constraints to obtain an integrated decision instruction set including scheduling plan, resource allocation scheme and control strategy. Collaborative control and execution are carried out based on the integrated decision instruction set.

2. The integrated decision-making method for allocation and control as described in claim 1, characterized in that, The multi-source heterogeneous data includes power grid operating parameters, load demand, distributed energy output, equipment status, environmental information, and historical operating data. The multi-source heterogeneous data is cleaned, standardized, and fused, and the cleaned, standardized, and fused multi-source heterogeneous data is used as the data foundation for constructing the integrated model.

3. The integrated decision-making method for allocation and control as described in claim 2, characterized in that, The process of obtaining model data corresponding to the allocation and control object based on the multi-source heterogeneous data and through the constructed integrated model includes: Based on the power grid operating parameters and switch or circuit breaker status in the multi-source heterogeneous data, the connection relationships between nodes, lines, branches and switching equipment in the dispatch control object are determined to obtain power grid topology data; Based on the load demand, distributed energy output and grid operation parameters in the multi-source heterogeneous data, the corresponding relationship between the active power, reactive power, load demand, distributed energy output and line or branch network loss of the dispatch control object in each time period is determined to obtain energy flow data. Based on the equipment status and historical operating data in the multi-source heterogeneous data, the operating status and equipment capacity boundary conditions of conventional units, distributed energy equipment, lines, branches and switchgear in the dispatch control object are determined, and equipment characteristic data are obtained. The power grid topology data, energy flow data, and equipment characteristic data are correlated and processed to form an expression of the coupling relationship between the power grid topology, energy flow process, and equipment operating characteristics. Combined with the basic operating boundary conditions, a unified mapping is performed to construct the integrated model. The model data corresponding to the dispatch and control object is output through the integrated model.

4. The integrated decision-making method for allocation and control as described in claim 1, characterized in that, The multi-objective function includes operating cost objective item, network loss objective item, voltage deviation objective item, new energy consumption rate objective item, and reliability objective item; The operating cost target is determined based on the active power output cost of conventional units, the active power output cost of distributed energy, and the system network loss cost; the network loss target is determined based on the line or branch current and the line or branch resistance; the voltage deviation target is determined based on the voltage amplitude of the load node and the rated voltage; the renewable energy absorption rate target is determined based on the actual active power output of wind turbine units, the maximum available active power output of wind turbine units, the actual active power output of photovoltaic units, and the maximum available active power output of photovoltaic units; the reliability target is determined based on the load loss probability of the dispatch control object during the dispatch cycle.

5. The integrated decision-making method for allocation and control as described in claim 1, characterized in that, The constraints include power balance constraints, voltage and frequency limit constraints, equipment capacity constraints, and switching action count constraints. The power balance constraint is determined based on the output of conventional generating units, the output of distributed energy sources, the system load, and the system network loss; the voltage and frequency limit constraint is determined based on the node voltage limit and the system frequency limit; the equipment capacity constraint is determined based on the upper and lower limits of the output of conventional generating units and the upper and lower limits of the output of distributed energy sources; the switch operation number constraint is determined based on the number of state changes of the switch or circuit breaker within the scheduling cycle and the maximum allowed number of operations.

6. The integrated decision-making method for allocation and control as described in claim 1, characterized in that, The obtained integrated decision instruction set, including scheduling plans, resource allocation schemes, and control strategies, includes: Based on the solution results of the multi-objective function, the output arrangement of conventional units and distributed energy sources in different time periods is determined, and the scheduling plan is obtained. Based on the solution results of the multi-objective function, the operating modes of distributed energy equipment, lines, branches and switching equipment in the control object are determined, and the resource allocation scheme is obtained; Based on the solution results of the multi-objective function, the control parameters of the execution unit in the control object are determined, and the control strategy is obtained; The scheduling plan, resource allocation scheme, and control strategy are combined to form the integrated decision instruction set.

7. The integrated decision-making method for allocation and control as described in claim 1, characterized in that, The coordinated control execution based on the integrated decision instruction set includes: The integrated decision instruction set is divided into scheduling sub-instructions, configuration sub-instructions, and control sub-instructions; Determine the execution object, execution time, and execution parameters corresponding to the scheduling sub-instruction, configuration sub-instruction, and control sub-instruction, respectively; The scheduling sub-instructions, configuration sub-instructions, and control sub-instructions are issued to the corresponding execution units according to the region, voltage level, and equipment type, and the execution results returned by the execution units are obtained. Based on the execution results and changes in the state of the control object, closed-loop feedback and dynamic optimization are performed to update the integrated model and / or the integrated decision instruction set.

8. A comprehensive decision-making device integrating dispatch and control, characterized in that, include: The acquisition module is used to acquire multi-source heterogeneous data of the allocation and control object; The model processing module is used to obtain the model data corresponding to the dispatch control object based on the multi-source heterogeneous data and the constructed integrated model; the integrated model is a coupled mathematical model constructed based on power grid topology, energy flow, equipment characteristics and basic operating boundary conditions; The function processing module is used to determine the constraints of the multi-objective function based on the model data. The multi-objective function is constructed with the objectives of minimizing operating cost, minimizing network loss, minimizing voltage deviation, maximizing renewable energy absorption rate, and maximizing reliability. The multi-objective function is solved under the constraints to obtain an integrated decision instruction set including scheduling plan, resource allocation scheme and control strategy. The execution module is used to perform collaborative control execution based on the integrated decision instruction set.

9. An electronic device, characterized in that, It includes a processor and a memory, the memory storing a computer program, and the processor executing the computer program to implement the integrated decision-making method for allocation and control as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the integrated decision-making method for allocation and control as described in any one of claims 1 to 7.

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