CN-121984988-A - Vehicle-network interaction electric carbon cooperative regulation and control method and related equipment

CN121984988ACN 121984988 ACN121984988 ACN 121984988ACN-121984988-A

Abstract

The disclosure provides a vehicle-network interaction electric carbon cooperative regulation and control method and related equipment. The method comprises the steps of obtaining vehicle network data, preprocessing the vehicle network data and aligning the vehicle network data in time and space to obtain a time and space unified data set, constructing a physical execution diagram based on the time and space unified data set, obtaining a pre-defined global strategy diagram, mapping each node in the global strategy diagram to a corresponding node in the physical execution diagram based on a preset node mapping function, generating state variables and control variables of the corresponding node in the physical execution diagram based on state variables and control variables of each node in the global strategy diagram, conducting joint prediction based on the physical execution diagram and the time and space unified data set, inputting a prediction result into the node state of the global strategy diagram, generating a regulation strategy set based on the global strategy diagram after node state update, and generating and issuing a regulation instruction based on the regulation strategy set.

Inventors

XU ZHIMIN
GAO YANG
HUANG CHAO
JIA ZIPEI
GU NAIYAN
GONG ZHENGWEI
WU YANYAN

Assignees

北京中电飞华通信有限公司
国网信息通信产业集团有限公司

Dates

Publication Date: 20260505
Application Date: 20251223

Claims (10)

1. A vehicle network interaction electric carbon cooperative regulation and control method comprises the following steps: Acquiring vehicle network data, preprocessing and space-time alignment are carried out on the vehicle network data to obtain a space-time unified data set, wherein the vehicle network data comprises at least one of vehicle data, charging equipment data, power distribution network data, traffic system data, meteorological data and carbon emission data; constructing a physical execution graph based on the space-time unified data set, wherein the physical execution graph takes at least one of a power distribution network node, a traffic system node, an energy storage node and a charging and converting equipment node as a graph node, and takes at least one of a physical and electric connection relationship, a traffic path relationship and an energy flow direction relationship as a graph edge; Acquiring a predefined global strategy diagram, wherein the global strategy diagram takes at least one of a dispatching center node, a vehicle group proxy node, a charging and replacing equipment proxy node and an energy storage proxy node as a graph node, and takes at least one of a strategy dependency relationship, a resource sharing relationship and a constraint coupling relationship as a graph edge; Mapping each node in the global strategy diagram to a corresponding node in the physical execution diagram based on a preset node mapping function, and generating a state variable and a control variable of the corresponding node in the physical execution diagram based on a state variable and a control variable of each node in the global strategy diagram; Based on the physical execution diagram and the space-time unified data set, carrying out joint prediction on at least one of power grid load, vehicle running flow, charging demand and carbon price, and inputting a prediction result into a node state of the global strategy diagram; Generating a regulation strategy set based on the global strategy graph updated by the node state, wherein the regulation strategy set comprises at least one regulation strategy; And generating a regulation command based on the regulation strategy set, and issuing the regulation command to at least one of a vehicle, charging and changing equipment, an energy storage system and a power distribution network.
2. The method of claim 1, wherein the mapping each node in the global policy map to a corresponding node in the physical execution map comprises at least one of: mapping the dispatching center node to the power distribution network node; Mapping the group agent node to the traffic system node; Mapping the charging and replacing equipment proxy node into the charging and replacing equipment node; And mapping the energy storage agent node to the energy storage node.
3. The method of claim 1, wherein the generating the state variables and control variables for the corresponding nodes in the physical execution graph based on the state variables and control variables for the nodes in the global policy graph comprises: setting mapping weights for each group of node mappings in the global policy map and the physical execution map based on the node mapping function; And multiplying the state variables and the control variables of all nodes in the global strategy diagram by the mapping weights element by element after being processed by the node mapping function to obtain the state variables and the control variables of the corresponding nodes in the physical execution diagram.
4. The method of claim 1, wherein the obtaining the regulatory policy set based on the global policy map after node state update comprises: constructing a first model based on the global policy map; and solving the first model by taking at least one of system operation cost, electric carbon transaction cost and equipment health as a combined optimization target and taking at least one of power flow constraint, voltage constraint, equipment capacity constraint, vehicle travel constraint and user charging preference constraint as constraint conditions to obtain the regulation strategy set.
5. The method of claim 4, further comprising: Constructing a digital twin deduction model based on the regulation strategy set and the physical execution diagram; carrying out space-time deduction on a preset disturbance scene based on the digital twin deduction model to obtain a deduction result; And calculating a key performance index according to the deduction result, and outputting the key performance index serving as a feedback signal to the first model and the node mapping function so as to update the parameters of the first model and the mapping weight of the node mapping function.
6. The method of any of claims 1-5, further comprising: Acquiring execution deviation data for executing the regulation operation based on the regulation instruction; executing to generate a carbon emission result based on the execution deviation data, a node power curve of each node in the physical execution diagram and a position marginal carbon intensity calculation result; Inputting the carbon emission result into the node state of each node in the global strategy diagram; and carrying out topology structure updating and parameter adjustment on the global policy map and the physical execution map.
7. A vehicle network interaction electric carbon cooperative regulation and control device, comprising: the first acquisition module is configured to acquire vehicle network data, and perform preprocessing and space-time alignment on the vehicle network data to obtain a space-time unified data set, wherein the vehicle network data comprises at least one of vehicle data, charging equipment data, power distribution network data, traffic system data, meteorological data and carbon emission data; The construction module is configured to construct a physical execution graph based on the space-time unified data set, wherein the physical execution graph takes at least one of a power distribution network node, a traffic system node, an energy storage node and a charging and changing equipment node as a graph node, and takes at least one of a physical electric connection relationship, a traffic path relationship and an energy flow direction relationship as a graph edge; the second acquisition module is configured to acquire a predefined global strategy diagram, wherein the global strategy diagram takes at least one of a dispatching center node, a vehicle group proxy node, a charging and replacing equipment proxy node and an energy storage proxy node as a diagram node, and takes at least one of a strategy dependency relationship, a resource sharing relationship and a constraint coupling relationship as a diagram edge; The mapping module is configured to map each node in the global strategy diagram to a corresponding node in the physical execution diagram based on a preset node mapping function, and generate a state variable and a control variable of the corresponding node in the physical execution diagram based on the state variable and the control variable of each node in the global strategy diagram; the prediction module is configured to perform joint prediction on at least one of power grid load, vehicle running flow, charging demand and carbon price based on the physical execution diagram and the space-time unified data set, and input a prediction result into a node state of the global strategy diagram; the generation module is configured to generate a regulation strategy set based on the global strategy graph after node state updating, wherein the regulation strategy set comprises at least one regulation strategy; and the issuing module is configured to generate a regulation command based on the regulation strategy set and issue the regulation command to at least one of a vehicle, a charging and replacing device, an energy storage system and a power distribution network.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the vehicle network interactive carbon co-regulation method according to any one of claims 1 to 6 when the computer program is executed.
9. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the vehicle network interactive carbon co-regulation method of any one of claims 1 to 6.
10. A computer program product comprising computer program instructions which, when run on a computer, cause the computer to perform the vehicle network interactive carbon co-regulation method of any one of claims 1 to 6.

Description

Vehicle-network interaction electric carbon cooperative regulation and control method and related equipment Technical Field The disclosure relates to the technical field of vehicle networks, in particular to a vehicle network interaction electric carbon cooperative regulation and control method and related equipment. Background With the deep promotion of new energy Internet of vehicles, electric power system intellectualization and 'double carbon' policies, the interaction among electric vehicles, charging and replacing stations and distribution networks is increasingly complex, and the cooperative regulation and control of Internet of vehicles and carbon emission become an important research direction in the field of energy Internet. In practical engineering application, massive vehicles, distributed energy sources and diversified regulation and control resources participate in the operation of the power distribution network in a highly dynamic mode, so that scheduling, load prediction, carbon accounting and control decision in the interaction process of the vehicle network face space-time complexity and uncertainty. In the related art, the vehicle network interaction system is mainly focused on the acquisition and processing of a single data stream, lacks of unified fusion and standardization of multi-source heterogeneous vehicle network data, and is difficult to provide a high-quality data base for complex space-time prediction and intelligent regulation. Meanwhile, the existing vehicle network interactive scheduling method mostly adopts a static physical model or rule base optimization method, is difficult to adapt to real-time regulation and control requirements under large-scale vehicle group coordination, strategy feedback and scene disturbance, and in the electric carbon coordination field, the current method often cannot track carbon emission distribution and dynamic evolution among vehicles, station ends and power distribution network nodes in a fine granularity mode, so that delay and inaccuracy exist in carbon emission accounting and carbon cost feedback. Disclosure of Invention In view of the above, the disclosure is directed to a vehicle-network interaction carbon cooperative control method and related devices, so as to solve or partially solve the above technical problems. Based on the above object, a first aspect of the present disclosure provides a vehicle-network interaction electric carbon cooperative regulation method, including: Acquiring vehicle network data, preprocessing and space-time alignment are carried out on the vehicle network data to obtain a space-time unified data set, wherein the vehicle network data comprises at least one of vehicle data, charging equipment data, power distribution network data, traffic system data, meteorological data and carbon emission data; constructing a physical execution graph based on the space-time unified data set, wherein the physical execution graph takes at least one of a power distribution network node, a traffic system node, an energy storage node and a charging and converting equipment node as a graph node, and takes at least one of a physical and electric connection relationship, a traffic path relationship and an energy flow direction relationship as a graph edge; Acquiring a predefined global strategy diagram, wherein the global strategy diagram takes at least one of a dispatching center node, a vehicle group proxy node, a charging and replacing equipment proxy node and an energy storage proxy node as a graph node, and takes at least one of a strategy dependency relationship, a resource sharing relationship and a constraint coupling relationship as a graph edge; Mapping each node in the global strategy diagram to a corresponding node in the physical execution diagram based on a preset node mapping function, and generating a state variable and a control variable of the corresponding node in the physical execution diagram based on a state variable and a control variable of each node in the global strategy diagram; Based on the physical execution diagram and the space-time unified data set, carrying out joint prediction on at least one of power grid load, vehicle running flow, charging demand and carbon price, and inputting a prediction result into a node state of the global strategy diagram; Generating a regulation strategy set based on the global strategy graph updated by the node state, wherein the regulation strategy set comprises at least one regulation strategy; And generating a regulation command based on the regulation strategy set, and issuing the regulation command to at least one of a vehicle, charging and changing equipment, an energy storage system and a power distribution network. In some embodiments, the mapping each node in the global policy map to a corresponding node in the physical execution map includes at least one of: mapping the dispatching center node to the power distribution network node; Mapping the group agent n