Vehicle network interaction cooperative regulation and control method and system

Abstract

The invention belongs to the technical field of power grid control, and discloses a vehicle-network interaction cooperative regulation and control method and system, and the method comprises the steps: constructing a vehicle-network interaction architecture through employing a dual operation mode of a cloud side end interaction mode and a cloud-cloud interaction mode; the method comprises the following steps: obtaining power distribution network operation data and charging pile operation data based on a vehicle network interaction architecture, carrying out hierarchical index calculation through a hierarchical architecture of a transformer substation level, a line level and a district level, and carrying out power distribution network bearing capacity evaluation grade judgment according to a hierarchical index calculation result; and formulating and executing a charging pile cooperative control strategy according to the power distribution network bearing capacity evaluation grade. The problems that an existing vehicle network interaction system is high in response delay, lack of bearing capacity evaluation and insufficient in multi-resource collaboration are solved, and dynamic balance between distribution network safety and charging loads is achieved.

Description

Technical Field

[0001] This invention relates to the field of power grid control technology, and in particular to a vehicle-grid interactive and coordinated control method and system. Background Technology

[0002] With the deepening of the global energy transition, the number of electric vehicles (EVs) is experiencing explosive growth. The contradiction between the massive, high-concurrency, and extremely unevenly distributed charging demand and the limited power supply capacity of the power grid is becoming increasingly acute, and vehicle-to-grid (V2G) technology is seen as the key to breaking this deadlock. V2G transforms the originally "passively receiving power" electric vehicles into mobile, dispatchable, and tradable distributed energy storage resources through bidirectional energy and information flows, showing great potential in peak shaving, valley filling, absorbing new energy sources, and providing ancillary services. However, the current V2G information flow, business flow, and control flow are unclear, and it is difficult to achieve unified access and efficient integration of multi-source, multi-dimensional, and full-time domain data from vehicles, charging piles, and the grid. V2G lacks precise control methods for local bidirectional charging and discharging, and the grid side lacks dynamic optimization operation strategies for vehicles and the grid, making it difficult to support the high-frequency interaction needs of millions of charging piles and tens of millions of electric vehicles.

[0003] Existing vehicle-to-grid (V2G) technologies generally adopt a cloud platform-based architecture, relying on V2G cloud platforms or third-party operator platforms for data relay. On the one hand, the interface standards between platforms are not unified, and there are significant differences in data formats, protocols, and semantics, requiring repeated cleaning and conversion. On the other hand, provincial V2G platforms are not yet fully integrated with municipal power distribution stations, charging operators, and aggregators, requiring multi-level data aggregation and manual review. System response delays generally exceed 10 seconds, failing to meet real-time control requirements such as second-level overload suppression and millisecond-level voltage support for distribution areas. Meanwhile, the core of safe distribution network operation lies in the real-time quantification of the three-level power supply capacity of "station-line-distribution area." However, current V2G systems only perform simple statistics on the rated capacity or historical maximum load of charging piles, lacking online assessment of substation outgoing load rate, feeder voltage distribution, and reverse power exceeding limits in distribution areas. This makes it impossible to detect "bottleneck" locations in advance or quantify the adjustability margin of charging piles at different time scales. Furthermore, most existing systems only support "basic orderly charging," which guides vehicle charging during off-peak electricity price periods. They do not adequately consider the coordination of distributed photovoltaic, energy storage, and controllable loads. Moreover, they lack a dual-mode control mechanism for emergency situations (instantaneous overruns of distribution areas / feeders) and planned situations (day-ahead / intraday rolling optimization). In the face of sudden failures or extreme weather, the system cannot quickly switch to strategies such as "emergency throttling" or "V2G reverse support," and can only passively cut off loads, which seriously affects user experience and grid security.

[0004] The shortcomings of the aforementioned existing technologies are specifically manifested in the following ways: low data interaction efficiency, resulting in high control delays and inability to meet real-time control requirements; lack of carrying capacity assessment, making it impossible to quantify the three-level power supply capacity of the distribution network in real time, resulting in orderly charging strategies often being "one-size-fits-all," either overly conservative and wasting resources, or aggressive operation inducing safety hazards in the transformer area; and a single control strategy, lacking a dual-mode control mechanism for emergency and planned states, making it unable to quickly respond to emergencies. The traditional "cloud-edge" two-level architecture is no longer able to support the high-frequency interaction needs of millions of charging piles and tens of millions of electric vehicles.

[0005] Therefore, how to provide a vehicle-to-grid interactive control solution that integrates cloud-edge-device collaboration, dynamic load capacity assessment, and dual-mode regulation to achieve a dynamic balance between power distribution network safety and charging load has become an urgent problem to be solved. Summary of the Invention

[0006] This invention provides a vehicle-to-grid interactive collaborative control method and system to solve the problems of unresolved real-time load capacity assessment at the substation level and high control delay caused by reliance on data interaction with third-party operators in the prior art.

[0007] According to a first aspect of the present invention, a vehicle-to-everything (V2X) interactive collaborative control method is provided.

[0008] In one embodiment, the vehicle-to-grid (V2G) interactive collaborative control method includes: constructing a V2G interactive architecture by adopting a dual-operation mode of cloud-edge-device interaction and cloud-cloud interaction; the V2G interactive architecture includes a V2G collaborative carrying capacity assessment and optimization platform, a distribution area intelligent terminal, a V2G intelligent interactive terminal, a charging operator platform, and charging and discharging facilities; based on the V2G interactive architecture, acquiring distribution network operation data and charging pile operation data, and performing hierarchical index calculations through a substation-level, line-level, and distribution area-level hierarchical architecture, and determining the distribution network carrying capacity assessment level based on the results of the hierarchical index calculations; formulating and executing a charging pile collaborative control strategy based on the distribution network carrying capacity assessment level; the charging pile collaborative control strategy is used to control the charging piles by reducing their depreciation level according to the charging pile depreciation priority and depreciation power allocation principle when the distribution network carrying capacity assessment level is a limited level or an over-limit level.

[0009] In one embodiment, the cloud-to-cloud interaction mode is enabled in planned optimization scenarios to utilize the standard interface between the vehicle-to-grid collaborative carrying capacity assessment and optimization platform and the charging operator platform to implement batch policy distribution for charging and discharging facilities; the cloud-edge-device interaction mode is automatically enabled in emergency or abnormal scenarios to communicate with charging and discharging facilities through vehicle-to-grid intelligent interaction terminals based on the intelligent terminals in the distribution area to achieve real-time control with second-level response; among them, planned optimization scenarios include day-ahead and intraday peak-valley arbitrage scenarios and new energy consumption scenarios.

[0010] In one embodiment, the vehicle-to-grid (V2G) interactive architecture includes the following information interaction paths: a first information interaction path, used for interaction between the V2G collaborative carrying capacity assessment and optimization platform and the smart terminal in the charging area, and the smart terminal in the charging area interacts with the charging and discharging facilities through the V2G intelligent interaction terminal; the V2G intelligent interaction terminal accesses the charging and discharging facility data through direct acquisition to realize protocol conversion and data transmission between the charging and discharging facilities and the smart terminal in the charging area; a second information interaction path, used for interaction between the V2G collaborative carrying capacity assessment and optimization platform and the charging operator platform, and the charging operator platform interacts with the charging and discharging facilities.

[0011] In one embodiment, obtaining distribution network operation data and charging / discharging facility operation data includes: obtaining substation-level data through interaction with the dispatch EMS system; substation-level data includes active power, reactive power, current, and bus voltage of substation outgoing lines; obtaining line-level data through interaction with the distribution automation system; line-level data includes voltage, current, and power of each node of the feeder; extracting transformer area-level data through interaction with the distribution cloud platform; transformer area-level data includes transformer load rate, reverse power, and three-phase imbalance; obtaining charging / discharging facility operation data based on cloud-edge-device architecture direct acquisition or forwarding by a third-party platform; charging / discharging facility operation data includes real-time power, SOC, and user travel demand of charging / discharging facilities; wherein, distribution network operation data includes substation-level data, line-level data, transformer area-level data, and static ledger data.

[0012] In one embodiment, the hierarchical index calculation through a substation-level, line-level, and transformer-level hierarchical architecture includes: at the substation level, measuring the substation's capacity to receive charging loads based on the main transformer load rate; at the line level, evaluating the line's carrying capacity using the feeder load rate and node voltage deviation; and at the transformer-level, measuring the transformer-level carrying capacity based on the distribution transformer load rate, reverse power margin, three-phase imbalance, and voltage limit exceedance indicators. The hierarchical index calculation is performed in parallel according to several time granularities, including second-level, minute-level, and hour-level time granularities.

[0013] In one embodiment, determining the distribution network carrying capacity assessment level based on the results of the hierarchical index calculation includes: classifying the distribution network carrying capacity assessment level into a sufficient level, a restricted level, and an over-limit level; based on the results of the hierarchical index calculation, if any level of index exceeds the limit at the same time, the carrying capacity assessment level of that level is marked as an over-limit level; if all indicators are within the restricted area, the carrying capacity assessment level of that level is marked as a restricted level; if all indicators are within the sufficient area, the carrying capacity assessment level of that level is marked as a sufficient level; wherein, the division of the sufficient area, restricted area, and over-limit area is determined based on the load rate threshold, voltage deviation threshold, reverse power load rate threshold, and three-phase current imbalance threshold.

[0014] In one embodiment, the load rate threshold includes a first load rate threshold and a second load rate threshold; when the load rate is not greater than the first load rate threshold, it is a sufficient region; when the load rate is between the first load rate threshold and the second load rate threshold, it is a restricted region; when the load rate is greater than the second load rate threshold, it is an over-limit region; the voltage deviation threshold includes a first voltage deviation threshold and a second voltage deviation threshold; when the voltage deviation is not greater than the first voltage deviation threshold, it is a sufficient region; when the voltage deviation is between the first voltage deviation threshold and the second voltage deviation threshold, it is a restricted region; when the voltage deviation is greater than the second voltage deviation threshold, it is an over-limit region; the reverse power load rate threshold includes a first reverse power load rate threshold and a second reverse power load rate threshold; when... The three-phase current imbalance thresholds are defined as follows: a sufficient region when the reverse power load rate is not greater than the first reverse power load rate threshold; a restricted region when the reverse power load rate is between the first and second reverse power load rate thresholds; and an over-limit region when the reverse power load rate is greater than the second reverse power load rate threshold. The three-phase current imbalance thresholds include the first and second three-phase current imbalance thresholds. The three-phase current imbalance thresholds are defined as follows: a sufficient region when the three-phase current imbalance is not greater than the first three-phase current imbalance threshold; a restricted region when the three-phase current imbalance is between the first and second three-phase current imbalance thresholds; and an over-limit region when the three-phase current imbalance is greater than the second three-phase current imbalance threshold.

[0015] In one embodiment, a collaborative control strategy for charging piles is formulated and executed based on the distribution network carrying capacity assessment level, including: when the distribution network carrying capacity assessment level is sufficient, maintaining the current dispatch strategy and sending instructions to the charging and discharging facilities to allow free charging or discharging; when the distribution network carrying capacity assessment level is limited, generating a power limit or time-based electricity price incentive strategy based on the charging and discharging facility de-rating priority and de-rating power allocation principle, and issuing it to the charging and discharging facilities; when the distribution network carrying capacity assessment level is over-limit, issuing a forced power limiting instruction or a vehicle-to-grid interaction reverse support instruction through the smart terminal of the distribution area, while blocking new charging requests in that area, and transmitting the over-limit event back to the grid dispatch platform; using the actual power change after the execution of the collaborative control strategy for charging piles as new measurement data, and re-determining the distribution network carrying capacity assessment level to form a closed-loop rolling assessment.

[0016] In one embodiment, the derating priority of charging and discharging facilities is determined according to the type of user demand. When the charging and discharging facilities in a distribution area need to be drated, the derating priority is given to charging and discharging facilities corresponding to non-rigid demand, followed by charging and discharging facilities corresponding to ordinary demand, and finally charging and discharging facilities corresponding to rigid demand. The types of users demanding the facilities include users with rigid demand, ordinary users, and users with non-rigid demand. Among them, users with rigid demand include special vehicle charging and discharging facilities, vehicles with a SOC lower than a first SOC threshold and a travel demand greater than a first travel threshold, and users with a historical response rate higher than a first response rate threshold. Users with non-rigid demand include commercial operating vehicles, vehicles with a SOC higher than a second SOC threshold, and users with a historical response rate lower than a second response rate threshold.

[0017] In one embodiment, the derating power allocation principle includes at least one of the following: equal derating principle, demand-based tiered derating principle, and dynamic bidding derating principle. The equal derating principle includes: uniformly derating all charging and discharging facilities, and the derating power of a single charging and discharging facility equals the total derating power divided by the number of charging and discharging facilities. The demand-based tiered derating principle includes: determining the derating power of a single charging and discharging facility based on the user's derating priority weight, and the derating power of a single charging and discharging facility equals the maximum charging power of that facility multiplied by a factor minus the product of the derating coefficient and the user's derating priority weight. The derating coefficient is determined based on the real-time status of the distribution network, and the user's derating priority weight is determined based on the type of user demand. The dynamic bidding derating principle includes: determining the derating power allocation based on the user's bidding price.

[0018] According to a second aspect of the present invention, a vehicle-to-everything (V2X) interactive and coordinated control system is provided.

[0019] In one embodiment, the vehicle-to-grid (V2G) interactive and coordinated control system includes: an architecture construction unit, used to construct a V2G interactive architecture using a dual-operation mode of cloud-edge-device interaction and cloud-cloud interaction; the V2G interactive architecture includes a V2G collaborative carrying capacity assessment and optimization platform, a distribution area intelligent terminal, a V2G intelligent interactive terminal, a charging operator platform, and charging and discharging facilities; a carrying capacity assessment unit, used to acquire distribution network operation data and charging pile operation data based on the V2G interactive architecture, and to perform hierarchical index calculation through a substation-level, line-level, and distribution area-level hierarchical architecture, and to determine the distribution network carrying capacity assessment level based on the results of the hierarchical index calculation; and a strategy control unit, used to formulate and execute a charging pile collaborative control strategy based on the distribution network carrying capacity assessment level; the charging pile collaborative control strategy is used to perform de-rating control on charging piles according to the charging pile de-rating priority and de-rating power allocation principle when the distribution network carrying capacity assessment level is a limited level or an over-limit level.

[0020] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0021] (1) This invention constructs a vehicle-to-grid interactive architecture by adopting a dual operation mode of cloud-edge-device interaction mode and cloud-to-cloud interaction mode, which can select the corresponding collaborative control mode for different scenarios. Among them, the cloud-to-cloud interaction mode is suitable for planned optimization scenarios. It realizes the batch policy distribution of charging and discharging facilities through the standard interface between the vehicle-to-grid collaborative carrying capacity assessment and optimization platform and the charging operator platform, thereby improving the economical operating efficiency. The cloud-edge-device interaction mode is suitable for emergency or abnormal scenarios. Based on the intelligent terminal of the transformer area, it realizes real-time control with second-level response through communication between the vehicle-to-grid intelligent interaction terminal and the charging and discharging facilities, thereby ensuring the safe operation of the distribution network.

[0022] (2) This invention realizes dynamic assessment of the distribution network carrying capacity through hierarchical index calculation at the substation level, line level, and transformer area level, and divides the distribution network carrying capacity assessment level into sufficient level (green), limited level (yellow) and over-limit level (red), and outputs quantitative assessment results. Compared with the traditional scheme that limits the output of charging piles by static estimation based on rated capacity, resulting in long-term low equipment utilization, this invention dynamically adjusts the maximum charging and discharging power of charging and discharging facilities based on real-time margin assessment results, which can significantly improve the carrying capacity of the distribution network and the equipment utilization.

[0023] (3) This invention achieves hierarchical collaborative control through a three-level early warning system of sufficient level, limited level and over-limit level; when it is in the limited level (yellow warning), flexible derating control is activated, and the charging and discharging facilities corresponding to non-rigid demand are drated first according to the charging pile derating priority, so as to avoid a one-size-fits-all complete shutdown of charging; when it is in the over-limit level (red over-limit), the emergency control mode is triggered to issue a forced power limiting command or a vehicle-to-grid interactive reverse support command, which can effectively reduce the risk of overload in the transformer area; at the same time, users can participate in collaborative regulation according to their own preferences, realizing the transformation from passive acceptance to active participation.

[0024] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0025] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0026] Figure 1 This is a flowchart illustrating a vehicle-to-grid interactive and coordinated control method according to an exemplary embodiment;

[0027] Figure 2 This is a vehicle-to-grid (V2G) interaction architecture diagram in a vehicle-to-grid (V2G) interaction and collaborative control method according to an exemplary embodiment.

[0028] Figure 3This is a flowchart illustrating the distribution network carrying capacity assessment in a vehicle-to-grid interactive and coordinated control method according to an exemplary embodiment;

[0029] Figure 4 A schematic diagram of a vehicle-to-everything (V2X) interactive and coordinated control system is shown according to an exemplary embodiment. Detailed Implementation

[0030] The following description and accompanying drawings fully illustrate specific embodiments described herein to enable those skilled in the art to practice them. Some portions and features of certain embodiments may be included in or replace portions and features of other embodiments. The scope of the embodiments herein includes the entire scope of the claims and all available equivalents thereof. The various embodiments described herein are presented in a progressive manner, with each embodiment focusing on its differences from other embodiments; similar or identical parts between embodiments can be referred to interchangeably.

[0031] The modules in the apparatus or system of this application can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0032] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0033] Figures 1-2 An embodiment of a vehicle-to-grid interactive and coordinated control method of the present invention is shown.

[0034] In this optional embodiment, the vehicle-to-grid interactive collaborative control method includes:

[0035] Step S101: Adopt a dual operation mode of cloud-edge-device interaction mode and cloud-cloud interaction mode to construct a vehicle-network interaction architecture; the vehicle-network interaction architecture includes a vehicle-network collaborative carrying capacity assessment and optimization platform, a smart terminal in the substation area, a vehicle-network smart interaction terminal, a charging operator platform, and charging and discharging facilities;

[0036] Step S102: Based on the vehicle-to-grid interaction architecture, obtain the power distribution network operation data and charging pile operation data, and perform hierarchical index calculation through the substation level, line level and transformer area level hierarchical architecture. Based on the results of the hierarchical index calculation, determine the power distribution network carrying capacity assessment level.

[0037] Step S103: Formulate and execute a charging pile collaborative control strategy based on the distribution network carrying capacity assessment level; the charging pile collaborative control strategy is used to control the charging piles by reducing their ratings when the distribution network carrying capacity assessment level is a limited level or an over-limit level, based on the charging pile derating priority and derating power allocation principle.

[0038] In this optional embodiment, the cloud-to-cloud interaction mode is enabled in planned optimization scenarios, and is used to implement batch policy distribution for charging and discharging facilities by utilizing the standard interface between the vehicle-to-grid collaborative carrying capacity assessment and optimization platform and the charging operator platform; the cloud-edge-device interaction mode is automatically enabled in emergency or abnormal scenarios, and is used to communicate with charging and discharging facilities through vehicle-to-grid intelligent interaction terminals based on the intelligent terminals in the transformer area, so as to achieve real-time control with second-level response; among them, planned optimization scenarios include day-ahead and intraday peak-valley arbitrage scenarios and new energy consumption scenarios.

[0039] In this optional embodiment, the vehicle-to-grid (V2G) interactive architecture includes the following information interaction paths: a first information interaction path, used for interaction between the V2G collaborative carrying capacity assessment and optimization platform and the smart terminal in the charging area, and the smart terminal in the charging area interacts with the charging and discharging facilities through the V2G intelligent interaction terminal; the V2G intelligent interaction terminal accesses the charging and discharging facility data through direct acquisition to realize protocol conversion and data transmission between the charging and discharging facilities and the smart terminal in the charging area; a second information interaction path, used for interaction between the V2G collaborative carrying capacity assessment and optimization platform and the charging operator platform, and the charging operator platform interacts with the charging and discharging facilities.

[0040] In this optional embodiment, obtaining distribution network operation data and charging / discharging facility operation data includes: obtaining substation-level data through interaction with the dispatch EMS system; substation-level data includes active power, reactive power, current, and bus voltage of the substation outgoing lines; obtaining line-level data through interaction with the distribution automation system; line-level data includes voltage, current, and power of each node of the feeder; extracting transformer area-level data through interaction with the distribution cloud platform; transformer area-level data includes transformer load rate, reverse power, and three-phase imbalance; obtaining charging / discharging facility operation data based on cloud-edge-device architecture direct acquisition or forwarding by a third-party platform; charging / discharging facility operation data includes real-time power, SOC, and user travel demand of the charging / discharging facilities; wherein, distribution network operation data includes substation-level data, line-level data, transformer area-level data, and static ledger data.

[0041] In this optional embodiment, the hierarchical index calculation through the substation-level, line-level, and transformer-level hierarchical architecture includes: at the substation level, measuring the margin of the substation's ability to connect charging loads based on the main transformer load rate; at the line level, evaluating the line carrying capacity using the feeder load rate and node voltage deviation; at the transformer-level, measuring the transformer-level carrying capacity based on the distribution transformer load rate, reverse power margin, three-phase imbalance, and voltage over-limit index; the hierarchical index calculation is performed in parallel according to several time granularities, including second-level, minute-level, and hour-level time granularities.

[0042] In this optional embodiment, determining the distribution network carrying capacity assessment level based on the results of the hierarchical index calculation includes: dividing the distribution network carrying capacity assessment level into a sufficient level, a restricted level, and an over-limit level; based on the results of the hierarchical index calculation, if any level of index exceeds the limit at the same time, the carrying capacity assessment level of that level is marked as an over-limit level; if all indicators are within the restricted area, the carrying capacity assessment level of that level is marked as a restricted level; if all indicators are within the sufficient area, the carrying capacity assessment level of that level is marked as a sufficient level; wherein, the division of the sufficient area, restricted area, and over-limit area is determined based on the load rate threshold, voltage deviation threshold, reverse power load rate threshold, and three-phase current imbalance threshold.

[0043] In this optional embodiment, the load rate threshold includes a first load rate threshold and a second load rate threshold; when the load rate is not greater than the first load rate threshold, it is a sufficient region; when the load rate is between the first load rate threshold and the second load rate threshold, it is a restricted region; and when the load rate is greater than the second load rate threshold, it is an over-limit region. The voltage deviation threshold includes a first voltage deviation threshold and a second voltage deviation threshold; when the voltage deviation is not greater than the first voltage deviation threshold, it is a sufficient region; when the voltage deviation is between the first voltage deviation threshold and the second voltage deviation threshold, it is a restricted region; and when the voltage deviation is greater than the second voltage deviation threshold, it is an over-limit region. The reverse power load rate threshold includes a first reverse power load rate threshold and a second reverse power load rate threshold. The three-phase current imbalance thresholds include a first three-phase current imbalance threshold and a second three-phase current imbalance threshold. The three-phase current imbalance thresholds are: a sufficient region when the reverse power load rate is not greater than the first three-phase current imbalance threshold; a restricted region when the reverse power load rate is between the first and second three-phase current imbalance thresholds; and an over-limit region when the three-phase current imbalance is greater than the second three-phase current imbalance threshold.

[0044] In this optional embodiment, the charging pile collaborative control strategy is formulated and executed based on the distribution network carrying capacity assessment level, including: when the distribution network carrying capacity assessment level is sufficient, maintaining the current scheduling strategy and sending instructions to the charging and discharging facilities to allow free charging or discharging; when the distribution network carrying capacity assessment level is limited, generating a power upper limit or time-based electricity price incentive strategy based on the charging and discharging facility de-rating priority and de-rating power allocation principle, and issuing it to the charging and discharging facilities; when the distribution network carrying capacity assessment level is over-limit, issuing a forced power limiting instruction or a vehicle-to-grid interaction reverse support instruction through the intelligent terminal of the distribution area, while blocking new charging requests in the distribution area, and transmitting the over-limit event back to the grid dispatching platform; using the actual power change after the execution of the charging pile collaborative control strategy as new measurement data, and re-determining the distribution network carrying capacity assessment level to form a closed-loop rolling assessment.

[0045] In this optional embodiment, the derating priority of charging and discharging facilities is determined according to the type of user demand. When the charging and discharging facilities in a distribution area need to be drated, the charging and discharging facilities corresponding to non-rigid demand are drated first, followed by those corresponding to ordinary demand, and finally those corresponding to rigid demand. The types of users include users with rigid demand, ordinary users, and users with non-rigid demand. Among them, users with rigid demand include special vehicle charging and discharging facilities, vehicles with a SOC lower than a first SOC threshold and a travel demand greater than a first travel threshold, and users with a historical response rate higher than a first response rate threshold. Users with non-rigid demand include commercial operating vehicles, vehicles with a SOC higher than a second SOC threshold, and users with a historical response rate lower than a second response rate threshold.

[0046] In this optional embodiment, the derating power allocation principle includes at least one of the following: equal derating principle, demand-based tiered derating principle, and dynamic bidding derating principle. The equal derating principle includes: uniformly derating all charging and discharging facilities, and the derating power of a single charging and discharging facility equals the total derating power divided by the number of charging and discharging facilities. The demand-based tiered derating principle includes: determining the derating power of a single charging and discharging facility based on the user's derating priority weight, and the derating power of a single charging and discharging facility equals the maximum charging power of that facility multiplied by a factor minus the product of the derating coefficient and the user's derating priority weight. The derating coefficient is determined based on the real-time status of the distribution network, and the user's derating priority weight is determined based on the type of user requiring the derating power. The dynamic bidding derating principle includes: determining the derating power allocation based on the user's bidding price.

[0047] It should be noted that, in order to address the problems of high response latency, lack of capacity assessment, and insufficient multi-resource coordination in existing vehicle-to-grid (V2G) interaction systems, and to achieve a dynamic balance between distribution network security and charging load, this invention proposes a "cloud-edge-device + cloud-cloud" dual operation mode based on distribution network capacity assessment. This breaks through the bottleneck of existing technologies that rely on centralized cloud control and can solve the problems of V2G interaction response latency and security.

[0048] This invention mainly accomplishes the following:

[0049] A flexible interactive platform for vehicles, charging stations, and networks has been built, realizing a dual-mode control mechanism of cloud-edge-device and cloud-cloud interaction. Through local bidirectional charging and discharging at the edge and precise V2G guidance and control, a local autonomous control strategy for local charging and discharging at the edge has been achieved. A cloud-cloud and cloud-edge collaborative mechanism has been realized to support charging station operators in forming V2G guidance and incentive schemes for electric vehicles.

[0050] Based on the multi-entity resource regulation characteristics of flexible and adjustable loads such as power grids, electric vehicles, distributed photovoltaics, and energy storage, dynamic analysis and evaluation of distribution network carrying capacity are achieved. Vehicle-grid collaborative control, based on a cloud-edge-device architecture, enables dynamically optimized operation and control strategies for the orderly charging and discharging of electric vehicles according to different power grid operating scenarios.

[0051] The present invention will now be described in detail with reference to specific embodiments.

[0052] I. Vehicle-to-Network Interaction Architecture (Step S101)

[0053] Specifically, this invention designs a vehicle-to-network (V2N) interaction architecture under a dual-mode "cloud-edge-device + cloud-cloud" operation (e.g., Figure 2 As shown in the diagram, this architecture enables "cloud-to-cloud interaction" in planned optimization scenarios (such as day-ahead / intraday peak-valley arbitrage and new energy consumption), utilizing the standard interface between the platform and the aggregator platform to achieve batch policy distribution for tens of thousands of charging piles, meeting the needs of economical scheduling; in emergency / abnormal scenarios, it automatically switches to "cloud-edge-device interaction", where the intelligent terminal in the distribution area communicates with the charging pile through the vehicle-to-network interaction terminal (direct procurement), meeting the needs of real-time control and second-level response. Among them, the planned optimization scenario, which combines day-ahead and intraday peak-valley arbitrage, mainly refers to arranging charging during low-price periods and high-price periods based on the electricity price curve and considering the safety constraints of grid equipment; the renewable energy consumption scenario refers to the platform issuing "consumption instructions" to the aggregator platform through the "cloud-to-cloud" interface during peak photovoltaic power generation periods. The aggregator platform prioritizes vehicles that are available and willing to participate during peak renewable energy generation periods and dynamically adjusts their charging plans to consume excess green electricity; the emergency or abnormal state scenario mainly refers to scenarios such as abnormal grid frequency, heavy overload of distribution areas, and voltage exceeding limits. This involves controlling the charging and discharging status of a large number of electric vehicles to achieve grid anomaly management. Figure 2 As shown, consider the following two interaction paths to achieve vehicle-to-grid interaction:

[0054] (1) Information interaction path 1 (first information interaction path)

[0055] The vehicle-to-grid (V2G) collaborative carrying capacity assessment and optimization platform interacts with the smart terminals in the charging substations. These smart terminals then exchange information with the charging piles (charging and discharging facilities) through the V2G smart interactive terminal. This path is based on a cloud-edge-device architecture, using a direct data acquisition method to access charging pile data. The main function of the V2G smart interactive terminal is protocol conversion between the charging piles and the charging substations, enabling data transmission between the charging piles and the smart terminals in the charging substations.

[0056] (2) Information interaction path 2 (second information interaction path)

[0057] The vehicle-to-grid (V2G) collaborative carrying capacity assessment and optimization platform interacts with the operator's platform, while the charging operator's platform interacts with the charging piles. In this interaction mode, the V2G collaborative carrying capacity assessment and optimization platform needs to interact with the charging operator's platform (such as the State Grid provincial V2G platform, TELD, Star Charge platform, etc.).

[0058] II. Assessment of the carrying capacity of power distribution network and vehicle-grid interaction (Step S102)

[0059] Specifically, this invention calculates the dynamic margin at three levels: "station-line-transformer area" and outputs quantitative results of "sufficient (green)-restricted (yellow)-exceeded limit (red)" in real time. Based on this, the maximum charging / discharging power of each vehicle is dynamically adjusted, and the carrying capacity of the distribution network is significantly improved.

[0060] Specifically, the dynamic assessment of vehicle-to-grid (V2G) carrying capacity requires the construction of a layered, multi-dimensional quantitative indicator system, covering a three-tiered architecture of "substation-line-distribution transformer" and three dimensions of safety, economy, and reliability. The distribution network carrying capacity assessment for each transformer substation is quantitatively evaluated according to three levels: "sufficient," "restricted," and "exceeding limits," corresponding to three alarm colors: "red," "yellow," and "green," respectively. The carrying capacity of the substation must consider the safety constraints of the upstream transformers and lines. The specific process for assessing the carrying capacity of the substation is as follows: Figure 3 As shown.

[0061] (1) Data Acquisition

[0062] The vehicle-to-grid (V2G) interactive capacity assessment and optimization platform interacts with external systems to obtain real-time measurement data of distribution network operation (distribution network operation data). It interacts with the dispatch EMS system to obtain active / reactive power, current, and bus voltage of substation outgoing lines; interacts with the distribution automation system to obtain voltage, current, and power at each node of the feeder; interacts with the distribution cloud platform to obtain data such as transformer load rate, reverse power, and three-phase imbalance; and obtains real-time power, SOC, and user travel demand of charging piles through direct acquisition or forwarding via third-party platforms via a "cloud-edge-device" architecture (cloud-edge-device interaction). Static ledger data, such as grid-side rated capacity and overload factor, is obtained from the distribution master station / cloud platform; ledger data on the charging pile side, such as rated power, quantity, and type, is obtained from the marketing system; and user-side data, such as vehicle battery capacity, charging rate, and rigid / non-rigid classification, is obtained from third-party platforms.

[0063] (2) Indicator Calculation (Hierarchical, Multidimensional, Multi-timescale)

[0064] Based on the substation-line-distribution area architecture, corresponding indicators are calculated for each level. At the substation level, the main transformer load rate is used to measure the margin of the substation's ability to handle charging loads. At the line level, feeder load rate and node voltage deviation are considered to assess line carrying capacity. At the distribution area level, distribution transformer load rate, reverse power margin, three-phase current imbalance, and voltage exceedance are used to measure distribution area carrying capacity (see Carrying Capacity Assessment Level Determination). All indicators are calculated in parallel at five time granularities: 1 second, 1 minute, 5 minutes, 15 minutes, and 1 hour, meeting the full-scale requirements from millisecond-level emergency control to day-ahead economic dispatch.

[0065] (3) Determination of bearing capacity assessment level

[0066] The distribution network carrying capacity assessment is divided into three levels: sufficient, restricted, and exceeded, corresponding to green, yellow, and red zones (sufficient area, restricted area, and exceeded-limit area), respectively. If any indicator at any level exceeds the limit at the same time, that level is marked as "red" (exceeded limit level); if all indicators are within the restricted area, it is marked as "yellow" (restricted level); and if all indicators are within the sufficient area, it is marked as "green" (sufficient level). The threshold settings in this embodiment are as follows:

[0067] ① Substation / line / transformer load rate: Load rate ≤ 80% (first load rate threshold) is green zone; load rate 80–100% is yellow zone; load rate > 100% (second load rate threshold) is red zone;

[0068] ② Voltage deviation: A voltage deviation ≤ 5% (first voltage deviation threshold) is in the green zone; a voltage deviation of 5–7% is in the yellow zone; a voltage deviation > 7% (second voltage deviation threshold) is in the red zone;

[0069] ③ Reverse power: A load rate ≤ 40% (first reverse power load rate threshold) is the green zone, a load rate of 40–80% is the yellow zone, and a load rate > 80% (second reverse power load rate threshold) is the red zone;

[0070] ④ Three-phase current imbalance: Three-phase current imbalance ≤ 2% (first three-phase current imbalance threshold) is the green zone, 2% < three-phase current imbalance ≤ 3% is the yellow zone, and three-phase current imbalance > 3% (second three-phase current imbalance threshold) is the red zone.

[0071] (4) Formulation of collaborative strategies (step S103)

[0072] Specifically, different collaborative optimization strategies are formulated based on different bearing capacity assessment levels:

[0073] ① If it is evaluated as the sufficient bearing capacity level, the current dispatching strategy is maintained, and the platform can send the instruction of "free charging / discharging" to the charging pile.

[0074] ② If it is evaluated as the restricted level, flexible derating is started. The strategy engine generates a "step-by-step" power upper limit or time-of-use electricity price incentive according to the charging pile derating priority and derating power distribution principle, and sends it to the charging pile in real time.

[0075] ③ If it is evaluated as over-limit, an emergency mode is triggered. The intelligent terminal of the distribution area sends the instruction of "forced power limit" or "V2G reverse support" within 1 s; at the same time, the platform automatically locks new charging requests in this distribution area and sends the over-limit event back to the power grid dispatching platform.

[0076] (5) Evaluation of strategy execution results

[0077] Specifically, the actual power change after the strategy execution is used as measurement data again to form a closed-loop rolling evaluation.

[0078] Through the above process, the present invention realizes the dynamic, quantitative and visual evaluation of the three-level power supply capacity of "substation-line-distribution area" from seconds to day-ahead, providing a timely, accurate and reliable safety boundary for vehicle-grid interaction.

[0079] III. Charging pile derating principle for vehicle-grid interaction in distribution network (step S103)

[0080] Specifically, when the evaluation and processing of the distribution network bearing capacity is at the restricted or over-limit level, the charging pile will be derated, and the corresponding derating strategy for the corresponding time period will be automatically generated and sent. The charging pile coordination strategy formulation principle is as follows:

[0081] (1) Charging pile derating priority (charging and discharging facility derating priority)

[0082] When the charging piles in the distribution area need to be derated, the charging piles of non-rigid demand users are derated first, followed by ordinary users, and finally rigid demand users. Rigid demand users generally refer to (ambulance / fire truck charging piles, private cars with SOC < 20% and travel demand > 50 km (low battery power does not meet long-distance demand), high credit score users (historical response rate > 90%); non-rigid demand users are derated first, generally referring to commercial operation vehicles (such as online car-hailing), vehicles with SOC > 80%, low credit score users (historical response rate < 60%); ordinary users are between rigid users and non-rigid users, generally referring to private cars, vehicles with 30% < SOC < 70%, medium credit score users (60% < historical response rate < 90%).

[0083] (2) Derating power distribution principle

[0084] The platform's vehicle-to-grid interaction strategy includes several power allocation principles, including equal derating, tiered derating on demand, and dynamic bidding derating. ① Equal derating means all charging piles are subject to a uniform derating, with the derating power per pile equal to the total derating power / N. ② Tiered derating on demand means the derating power per charging pile equals the maximum charging power of that pile * (1 - derating coefficient * user derating priority weight). The derating coefficient can be determined based on the real-time grid status, such as 0.2 for minor derating, 0.4 for moderate derating, and 0.6 for severe derating. User derating priority weights are: 0 for rigid users, 0.5 for ordinary users, and 1 for non-rigid users. ③ Dynamic bidding derating is determined by the bidding price.

[0085] Figure 4 An embodiment of a vehicle-to-everything (V2X) interactive and coordinated control system of the present invention is shown.

[0086] In this optional embodiment, the vehicle-to-grid interactive collaborative control system includes:

[0087] Architecture building unit 201 is used to build a vehicle-to-grid (V2G) interactive architecture by adopting a dual operating mode of cloud-edge-device interaction mode and cloud-to-cloud interaction mode. The V2G interactive architecture includes a V2G collaborative carrying capacity assessment and optimization platform, a smart terminal in the substation area, a smart V2G interactive terminal, a charging operator platform, and charging and discharging facilities.

[0088] The load-bearing capacity assessment unit 202 is used to acquire power distribution network operation data and charging pile operation data based on the vehicle-to-grid interaction architecture, and to perform hierarchical index calculation through a substation-level, line-level, and transformer-level hierarchical architecture, and to determine the power distribution network load-bearing capacity assessment level based on the results of the hierarchical index calculation.

[0089] The strategy control unit 203 is used to formulate and execute a charging pile collaborative control strategy based on the distribution network carrying capacity assessment level. The charging pile collaborative control strategy is used to control the charging piles according to the charging pile derating priority and derating power allocation principle when the distribution network carrying capacity assessment level is a limited level or an over-limit level.

[0090] In this optional embodiment, the load assessment unit 202, when acquiring distribution network operation data and charging / discharging facility operation data, includes: acquiring substation-level data through interaction with the dispatch EMS system; substation-level data includes active power, reactive power, current, and bus voltage of the substation outgoing lines; obtaining line-level data through interaction with the distribution automation system; line-level data includes voltage, current, and power of each node of the feeder; extracting transformer area-level data through interaction with the distribution cloud platform; transformer area-level data includes transformer load rate, reverse power, and three-phase imbalance; acquiring charging / discharging facility operation data based on cloud-edge-device architecture direct acquisition or forwarding by a third-party platform; charging / discharging facility operation data includes real-time power, SOC, and user travel demand of the charging / discharging facilities; wherein, the distribution network operation data includes substation-level data, line-level data, transformer area-level data, and static ledger data.

[0091] In this optional embodiment, when the load capacity assessment unit 202 performs hierarchical index calculations through a substation-level, line-level, and transformer-level hierarchical architecture, it includes: at the substation level, measuring the margin of the substation's ability to connect charging loads based on the main transformer load rate; at the line level, evaluating the line's load capacity using the feeder load rate and node voltage deviation; at the transformer-level, measuring the transformer-level load capacity based on the distribution transformer load rate, reverse power margin, three-phase imbalance, and voltage over-limit index; the hierarchical index calculations are performed in parallel according to several time granularities, including second-level, minute-level, and hour-level time granularities.

[0092] In this optional embodiment, when the strategy control unit 203 formulates and executes a charging pile collaborative control strategy based on the distribution network carrying capacity assessment level, it includes: when the distribution network carrying capacity assessment level is sufficient, maintaining the current scheduling strategy and sending instructions to the charging and discharging facilities to allow free charging or discharging; when the distribution network carrying capacity assessment level is limited, generating a power upper limit or time-based electricity price incentive strategy based on the charging and discharging facility de-rating priority and de-rating power allocation principle, and issuing it to the charging and discharging facilities; when the distribution network carrying capacity assessment level is over-limit, issuing a forced power limiting instruction or a vehicle-to-grid interaction reverse support instruction through the intelligent terminal of the distribution area, while blocking new charging requests in the distribution area, and transmitting the over-limit event back to the grid dispatching platform; using the actual power change after the execution of the charging pile collaborative control strategy as new measurement data, re-determining the distribution network carrying capacity assessment level to form a closed-loop rolling assessment.

[0093] This invention is not limited to the structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this invention is limited only by the appended claims.

Claims

1. A vehicle-to-grid (V2G) interactive and coordinated control method, characterized in that, The vehicle-to-grid interactive and collaborative control method includes: The vehicle-to-grid (V2G) interactive architecture is constructed by adopting a dual operation mode of cloud-edge-device interaction and cloud-to-cloud interaction. This V2G interactive architecture includes a V2G collaborative carrying capacity assessment and optimization platform, intelligent terminals in the charging area, intelligent V2G interactive terminals, charging operator platform, and charging and discharging facilities. Based on the vehicle-to-grid (V2G) interaction architecture, we acquire power distribution network operation data and charging pile operation data, and perform hierarchical index calculations through a substation-level, line-level, and transformer-level hierarchical architecture. Based on the results of the hierarchical index calculations, we determine the power distribution network carrying capacity assessment level. A collaborative control strategy for charging piles is formulated and implemented based on the carrying capacity assessment level of the distribution network. This collaborative control strategy is used to control the charging piles by reducing their depreciation rate according to the priority of depreciation and the principle of depreciation power allocation when the carrying capacity assessment level of the distribution network is limited or exceeds the limit.

2. The vehicle-to-grid interactive and coordinated control method according to claim 1, characterized in that, The cloud-to-cloud interaction mode is enabled in the planned optimization scenario and is used to implement batch policy distribution for charging and discharging facilities by utilizing the standard interface between the vehicle-to-grid collaborative carrying capacity assessment and optimization platform and the charging operator platform. The cloud-edge-device interaction mode is automatically activated in emergency or abnormal scenarios. It is used to communicate with charging and discharging facilities through vehicle-to-grid intelligent interaction terminals based on the intelligent terminals in the distribution area, so as to achieve real-time control with a response time of up to seconds. The planned optimization scenarios include day-ahead and intraday peak-valley arbitrage scenarios and new energy consumption scenarios.

3. The vehicle-to-grid interactive and coordinated control method according to claim 2, characterized in that, The vehicle-to-everything (V2X) interactive architecture includes the following information exchange paths: The first information interaction path is used to interact with the intelligent terminal in the distribution area based on the vehicle-to-grid collaborative carrying capacity assessment and optimization platform, and the intelligent terminal in the distribution area interacts with the charging and discharging facilities through the vehicle-to-grid intelligent interaction terminal; the vehicle-to-grid intelligent interaction terminal directly accesses the data of the charging and discharging facilities to realize the protocol conversion and data transmission between the charging and discharging facilities and the intelligent terminal in the distribution area. The second information exchange path is used for interaction between the vehicle-to-grid collaborative carrying capacity assessment and optimization platform and the charging operator platform, and for information exchange between the charging operator platform and the charging and discharging facilities.

4. The vehicle-to-grid interactive and coordinated control method according to claim 1, characterized in that, The acquisition of power distribution network operation data and charging / discharging facility operation data includes: By interacting with the dispatch EMS system, substation-level data is obtained; the substation-level data includes active power, reactive power, current and bus voltage of the substation outgoing lines. By interacting with the power distribution automation system, line-level data is obtained; the line-level data includes the voltage, current, and power of each node of the feeder. By interacting with the power distribution cloud platform, the transformer substation-level data is extracted; the transformer substation-level data includes the transformer substation load rate, reverse power, and three-phase imbalance. The charging and discharging facility operation data is obtained through direct acquisition or forwarding via a third-party platform based on a cloud-edge-device architecture; the charging and discharging facility operation data includes the real-time power, SOC, and user travel demand of the charging and discharging facility. The power distribution network operation data includes substation-level data, line-level data, transformer area-level data, and static ledger data.

5. The vehicle-to-grid interactive and coordinated control method according to claim 1, characterized in that, The hierarchical index calculation through the substation level, line level, and transformer area level includes: At the substation level, the margin by which the substation can connect to charging loads is measured based on the main transformer load rate. At the line level, the line carrying capacity is assessed using feeder load factor and node voltage deviation. At the transformer substation level, the substation's load-bearing capacity is measured based on the transformer load rate, reverse power margin, three-phase imbalance, and voltage over-limit indicators.

6. The vehicle-to-grid interactive and coordinated control method according to claim 5, characterized in that, The stratified index is calculated in parallel according to several time granularities, including second-level time granularity, minute-level time granularity, and hour-level time granularity.

7. The vehicle-to-grid interactive and coordinated control method according to claim 1, characterized in that, The determination of the power distribution network carrying capacity level based on the results of the stratified index calculation includes: The carrying capacity assessment level of the distribution network is divided into three levels: sufficient level, limited level, and over-limit level. Based on the results of the stratified index calculation, if any level index exceeds the limit at the same time, the bearing capacity assessment level of that level will be marked as the limit-exceeding level. If all indicators are within the restricted area, then the bearing capacity assessment level of that level is marked as restricted level; If all indicators are within the sufficient range, the bearing capacity assessment level of this level is marked as sufficient. The division of the sufficient region, restricted region and over-limit region is determined based on the load rate threshold, voltage deviation threshold, reverse power load rate threshold and three-phase current imbalance threshold.

8. The vehicle-to-grid interactive and coordinated control method according to claim 7, characterized in that, The load rate threshold includes a first load rate threshold and a second load rate threshold; when the load rate is not greater than the first load rate threshold, it is a sufficient region; when the load rate is between the first load rate threshold and the second load rate threshold, it is a restricted region; when the load rate is greater than the second load rate threshold, it is an over-limit region. The voltage deviation threshold includes a first voltage deviation threshold and a second voltage deviation threshold; when the voltage deviation is not greater than the first voltage deviation threshold, it is a sufficient region; when the voltage deviation is between the first voltage deviation threshold and the second voltage deviation threshold, it is a restricted region; when the voltage deviation is greater than the second voltage deviation threshold, it is an over-limit region.

9. The vehicle-to-grid interactive and coordinated control method according to claim 7, characterized in that, The reverse power load rate threshold includes a first reverse power load rate threshold and a second reverse power load rate threshold; when the reverse power load rate is not greater than the first reverse power load rate threshold, it is a sufficient region; when the reverse power load rate is between the first reverse power load rate threshold and the second reverse power load rate threshold, it is a restricted region; when the reverse power load rate is greater than the second reverse power load rate threshold, it is an over-limit region. The three-phase current imbalance threshold includes a first three-phase current imbalance threshold and a second three-phase current imbalance threshold. When the three-phase current imbalance is not greater than the first three-phase current imbalance threshold, it is a sufficient region. When the three-phase current imbalance is between the first three-phase current imbalance threshold and the second three-phase current imbalance threshold, it is a restricted region. When the three-phase current imbalance is greater than the second three-phase current imbalance threshold, it is an over-limit region.

10. The vehicle-to-grid interactive and coordinated control method according to claim 1, characterized in that, The process of formulating and implementing a collaborative control strategy for charging piles based on the power distribution network carrying capacity assessment level includes: When the distribution network carrying capacity assessment level is sufficient, the current dispatch strategy is maintained, and instructions allowing free charging or discharging are sent to the charging and discharging facilities; When the carrying capacity assessment level of the distribution network is limited, a power ceiling or time-limited electricity price incentive strategy is generated based on the priority of derated facilities and the derated power allocation principle, and then distributed to the charging and discharging facilities. When the carrying capacity assessment level of the distribution network is over-limit level, a forced power limiting command or a vehicle-to-grid interactive reverse support command is issued through the smart terminal of the distribution area. At the same time, new charging requests in the distribution area are blocked, and the over-limit event is transmitted back to the grid dispatch platform. The actual power change after the implementation of the charging pile collaborative control strategy is used as new measurement data to re-evaluate the carrying capacity level of the distribution network, so as to form a closed-loop rolling evaluation.

11. The vehicle-to-grid interactive and coordinated control method according to claim 10, characterized in that, The priority of derating the charging and discharging facilities is determined according to the type of user demand. When the charging and discharging facilities in the distribution area need to be drated, the charging and discharging facilities corresponding to non-rigid demand are drated first, followed by the charging and discharging facilities corresponding to ordinary demand, and finally the charging and discharging facilities corresponding to rigid demand. The types of users with demand include users with rigid demand, ordinary users, and users with non-rigid demand. Among them, users with rigid demand include special vehicle charging and discharging facilities, vehicles with SOC below the first SOC threshold and travel demand greater than the first travel threshold, and users with historical response rates higher than the first response rate threshold. The non-essential users include commercial vehicles, vehicles with a SOC higher than the second SOC threshold, and users with a historical response rate lower than the second response rate threshold.

12. The vehicle-to-grid interactive and coordinated control method according to claim 10, characterized in that, The power reduction allocation principle includes at least one of the following: equal reduction principle, demand-based tiered reduction principle, and dynamic bidding reduction principle; The equal derating principle includes: uniformly derating all charging and discharging facilities, and the derating power of a single charging and discharging facility is equal to the total derating power divided by the number of charging and discharging facilities; The on-demand tiered derating principle includes: determining the derating power of a single charging and discharging facility based on the user's derating priority weight, and the derating power of a single charging and discharging facility is equal to the maximum charging power of the charging and discharging facility multiplied by one minus the product of the derating coefficient and the user's derating priority weight; wherein, the derating coefficient is determined based on the real-time status of the distribution network, and the user's derating priority weight is determined based on the type of user with demand. The dynamic bidding reduction principle includes: determining the reduction power allocation based on the user's bidding price.

13. A vehicle-to-everything (V2X) interactive and coordinated control system, characterized in that, The vehicle-to-grid interactive and collaborative control system includes: The architecture building unit is used to construct the vehicle-to-grid (V2G) interactive architecture using a dual operating mode of cloud-edge-device interaction and cloud-to-cloud interaction. The V2G interactive architecture includes a V2G collaborative carrying capacity assessment and optimization platform, a smart terminal in the substation area, a V2G smart interactive terminal, a charging operator platform, and charging and discharging facilities. The load-bearing capacity assessment unit is used to acquire power distribution network operation data and charging pile operation data based on the vehicle-to-grid interaction architecture, and to perform hierarchical index calculation through a substation-level, line-level, and transformer-level hierarchical architecture. Based on the results of the hierarchical index calculation, the load-bearing capacity assessment level of the power distribution network is determined. The strategy control unit is used to formulate and execute a collaborative control strategy for charging piles based on the distribution network carrying capacity assessment level. This collaborative control strategy is used to control the charging piles by reducing their debit rates according to the charging pile debit priority and debit power allocation principle when the distribution network carrying capacity assessment level is limited or exceeded.

14. The vehicle-to-grid interactive and coordinated control system according to claim 13, characterized in that, The load assessment unit, when acquiring distribution network operation data and charging / discharging facility operation data, includes: acquiring substation-level data through interaction with the dispatch EMS system; the substation-level data includes active power, reactive power, current, and bus voltage of the substation outgoing lines; obtaining line-level data through interaction with the distribution automation system; the line-level data includes voltage, current, and power of each node of the feeder; extracting transformer substation-level data through interaction with the distribution cloud platform; the transformer substation-level data includes transformer load rate, reverse power, and three-phase imbalance; and acquiring charging / discharging facility operation data based on cloud-edge-device architecture direct acquisition or forwarding by a third-party platform; the charging / discharging facility operation data includes real-time power, SOC, and user travel demand of the charging / discharging facilities; wherein, the distribution network operation data includes substation-level data, line-level data, transformer substation-level data, and static ledger data.

15. A vehicle-to-everything (V2X) interactive and coordinated control system according to claim 13, characterized in that, The load-bearing capacity assessment unit calculates hierarchical indicators using a substation-level, line-level, and transformer-level hierarchical architecture, including: at the substation level, measuring the substation's capacity to receive charging loads based on the main transformer load rate; at the line level, evaluating the line's load-bearing capacity using the feeder load rate and node voltage deviation; and at the transformer-level, measuring the transformer-level load-bearing capacity based on the distribution transformer load rate, reverse power margin, three-phase imbalance, and voltage limit exceedance indicators. The hierarchical indicator calculation is performed in parallel at several time granularities, including second-level, minute-level, and hour-level time granularities.

16. A vehicle-to-everything (V2X) interactive and coordinated control system according to claim 13, characterized in that, When the strategy control unit formulates and executes a collaborative control strategy for charging piles based on the distribution network carrying capacity assessment level, it includes: when the distribution network carrying capacity assessment level is sufficient, maintaining the current scheduling strategy and sending instructions to the charging and discharging facilities to allow free charging or discharging; when the distribution network carrying capacity assessment level is limited, generating a power upper limit or time-based electricity price incentive strategy based on the charging and discharging facility de-rating priority and de-rating power allocation principle, and issuing it to the charging and discharging facilities; when the distribution network carrying capacity assessment level is over-limit, issuing a forced power limiting instruction or a vehicle-to-grid interaction reverse support instruction through the intelligent terminal of the distribution area, while blocking new charging requests in that distribution area, and transmitting the over-limit event back to the grid dispatch platform; using the actual power change after the execution of the collaborative control strategy for charging piles as new measurement data, re-determining the distribution network carrying capacity assessment level to form a closed-loop rolling assessment.

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