Cooperative scheduling method for electric automobile and resident air conditioner based on transformer area capacity constraint

Abstract

The invention provides an electric vehicle and resident air conditioner cooperative scheduling method based on transformer area capacity constraint, which comprises the following steps: S1, acquiring operating parameters, determining a transformer area real-time load rate, and entering S2 when the transformer area real-time load rate is greater than a safety threshold value; the parameters comprise load parameters, equipment state parameters and environment parameters; s2, an electric vehicle and resident air conditioner coordinated dispatching model is constructed, and the coordinated dispatching model comprises three stages, namely an air conditioner starting stage, an air conditioner steady-state stage and an electric vehicle charging stage; and S3, solving the electric vehicle and resident air conditioner collaborative scheduling model of the three stages by adopting a particle swarm optimization algorithm to obtain an optimal regulation and control parameter of each stage, and controlling the resident air conditioner and the electric vehicle to run based on the optimal regulation and control parameter, the optimal regulation and control parameters comprise air conditioner operation power, electric vehicle charging power and electric vehicle discharging power.

Description

TECHNICAL FIELD

[0001] The application relates to a power grid dispatching method, in particular to a power grid dispatching method based on transformer capacity constraint. BACKGROUND

[0002] With the improvement of the living standard of residents and the promotion of low-carbon travel, the popularity of electric vehicles (EV) and air conditioning equipment in families is rapidly increasing. This change significantly changes the temporal and spatial distribution characteristics of residential electricity load, especially the high overlap of EV charging load and air conditioning load in electricity time sequence. During the peak period of electricity consumption in summer and winter, residents will generally turn on air conditioning for cooling or heating and simultaneously charge electric vehicles after returning home in the evening, resulting in high concentration of electricity consumption in time and space, causing a sharp rise in short-time power demand, further intensifying the instantaneous load pressure of the regional power grid, and making the transformer load rapidly approach or even exceed its rated capacity in a specific period. If not effectively regulated, it is easy to cause problems such as continuous overload of distribution transformers, high temperature rise, accelerated insulation aging, and even cause the protection device to trip, directly affecting the continuity of power supply and the safety of power grid equipment.

[0003] In the prior art, the coordinated dispatching of electric vehicles and air conditioners is mostly focused on the macro goals of global economic optimization of the power grid, renewable energy consumption or peak shaving of the main network. This approach has the following defects:

[0004] Firstly, the macro dispatching model usually takes the distribution network as a whole or the feeder as an object, and does not fully consider the upper limit of the terminal transformer capacity. In the process of scheme implementation, due to the limited capacity of the transformer, transformer overload is still unavoidable, and the inherent bottleneck of the limited capacity of the distribution network at the end is not overcome. Secondly, the accuracy and adaptability of the existing coordinated control method are insufficient, and it mostly relies on the price signal to guide load shifting or simple time-sharing control, and lacks deep excavation of the flexibility of user-side resources. Especially in the use of electric vehicle V2H (Vehicle-to-Home) capability, the existing scheme fails to fully exert the active support potential of EV as a distributed energy storage unit, and fails to realize the dynamic complementation of EV discharging and air conditioner start-stop process. In addition, the control process often ignores the thermal comfort of individual users, weakening the willingness of users to participate in demand response; moreover, the transformer load fluctuation is sudden and random, especially under extreme weather conditions, the traditional dispatching method cannot timely respond to the risk of instantaneous transformer overload, limiting its applicability and effectiveness in real scenarios.

[0005] Therefore, in order to solve the above technical problems, it is necessary to propose a new technical means. SUMMARY

[0006] In view of this, the purpose of this invention is to provide a method for coordinated scheduling of electric vehicles and residential air conditioners based on transformer area capacity constraints, to ensure that the total load of the transformer area always operates within the safe capacity range, while prioritizing V2H discharge of electric vehicles to ensure the power demand of air conditioners during the rapid start-up phase, thereby improving user satisfaction and ultimately meeting the user's electric vehicle charging needs.

[0007] This invention provides a method for coordinated scheduling of electric vehicles and residential air conditioning based on transformer area capacity constraints, comprising the following steps:

[0008] S1. Obtain operating parameters and determine the real-time load rate of the distribution area. If the real-time load rate of the distribution area is greater than the safety threshold, proceed to step S2.

[0009] The parameters include load parameters, equipment status parameters, and environmental parameters;

[0010] S2. Construct a coordinated scheduling model for electric vehicles and residential air conditioning. The coordinated scheduling model includes three stages: the air conditioning start-up period, the air conditioning steady-state period, and the electric vehicle charging period.

[0011] S3. The particle swarm optimization algorithm is used to solve the three-stage coordinated scheduling model of electric vehicles and residential air conditioning to obtain the optimal control parameters for each stage. The operation of residential air conditioning and electric vehicles is controlled based on the optimal control parameters, which include the air conditioning operating power, electric vehicle charging power and electric vehicle discharging power.

[0012] Furthermore, the construction of a coordinated scheduling model for electric vehicles and residential air conditioning during the air conditioning start-up period specifically includes:

[0013] The triggering conditions for the air conditioner startup period are: the change in the operating power of air conditioner j is greater than the set value and the real-time load rate of the substation is greater than 0.8;

[0014] The coordinated scheduling model for electric vehicles and residential air conditioning during the air conditioning start-up period is as follows:

[0015] ;

[0016] in: , , These are the weighting coefficients; For the startup time window, This indicates a point in time within the prediction time domain. Indicates the prediction time step. express The indoor temperature of the air conditioner at all times. express Total load power of the transformer area at any given time; Indicates the safe power threshold of the transformer. This indicates the set temperature of air conditioner j. express The discharge power of electric vehicle i to the grid at any given time.

[0017] Furthermore, the specific steps in constructing a coordinated scheduling model for electric vehicles and residential air conditioning during the steady-state period include:

[0018] ;

[0019] in: , , These are the weighting coefficients. This refers to the steady-state time window for air conditioning. This indicates a point in time within the prediction time domain. Indicates the prediction time step. express The indoor temperature of the air conditioner at all times. express Total load power of the transformer area at any given time; Indicates the safe power threshold of the transformer. This indicates the set temperature of air conditioner j. express The discharge power of electric vehicle i to the grid at any given time. express The discharge power of electric vehicle i to the grid at any given time.

[0020] Furthermore, the construction of a coordinated scheduling model for electric vehicles and residential air conditioning during the electric vehicle charging period specifically includes:

[0021] The triggering condition for the electric vehicle charging period is: the real-time load rate of the transformer area is in the range (0.2, 0.8) and the electric vehicle has not reached the target charge level;

[0022] The coordinated scheduling model for electric vehicles and residential air conditioning during the electric vehicle charging period is as follows:

[0023] ;

[0024] in: This is the charging time window; express Electricity price at any time; , , Indicates the weighting coefficient. express The charging power of the air-conditioned electric vehicle at all times. Indicates at time The remaining battery power of electric vehicle j This represents the electricity demand of electric vehicle j.

[0025] Furthermore, it also includes constraints, including transformer capacity constraints, user thermal comfort and temperature dynamic constraints, electric vehicle charging demand, battery dynamics and power constraints, and system power balance constraints.

[0026] The transformer capacity constraint is:

[0027] ;

[0028] User thermal comfort and dynamic temperature constraints are:

[0029] ;

[0030] Electric vehicle charging demand, battery dynamics, and power constraints specifically include:

[0031] ;

[0032] System power balance constraints specifically include:

[0033] ;

[0034] in: Due to transformer capacity constraints; To ensure user thermal comfort and dynamic temperature constraints; For electric vehicle charging needs, battery dynamics and power constraints; For system power balance constraints; This indicates the allowable power margin for emergency overload of the transformer; This represents the minimum acceptable temperature for the i-th household; This indicates the highest acceptable temperature for the i-th household; Indicates the cooling / heating efficiency coefficient; Indicates the heat loss coefficient; This represents the outdoor temperature at time t; This represents the cooling / heating power of the i-th air conditioner at time t; This represents the discrete time step (in seconds) for the simulation or control. This parameter is related to the data acquisition cycle and determines the time resolution of the temperature dynamics model. Indicates the departure time set by the user; This represents the charging power of the j-th electric vehicle; This represents the discharge power of the j-th electric vehicle; This represents the total battery capacity of the j-th electric vehicle; Indicates the minimum safe charge level that the battery can withstand; Indicates the maximum safe charge capacity allowed by the battery; Let represent the battery charging efficiency of the j-th electric vehicle; This represents the battery discharge efficiency of the j-th electric vehicle. This indicates the maximum charging power of the electric vehicle; This indicates the maximum discharge power of the electric vehicle; express Active load of node l foundation at time t; express reactive load on node l foundation at time t; This represents the set of branches that begin at node l. This represents the set of branches ending at node l; This represents the active power injected into node l; This represents the reactive power injected at node l; This indicates the active power of branch il; This indicates the reactive power of branch il; This represents the current in branch il; This represents the voltage at node i; Indicates the resistance of branch il; Indicates the reactance of branch il; It represents the active power output from the external power grid connected to node l at time t; This represents the reactive power output from the external power grid connected to node l at time t; Represents the set of nodes in a distribution network; Second-order cone constraints are used to represent power flow calculations in distribution networks;

[0035] The particle swarm optimization algorithm was used to solve the total load power optimization model of the transformer area, and the optimal scheduling parameters were obtained. The optimal scheduling parameters include the air conditioning operating power. Electric vehicle charging power and electric vehicle discharge power .

[0036] Furthermore, the specific real-time load rate of the distribution area is determined as follows:

[0037] ;in: express Total load of the distribution area at any time Indicates the rated capacity of the transformer. The power factor.

[0038] The beneficial effects of this invention are as follows: By using the real-time load power of the distribution area as the triggering condition, an optimization model for different stages of air conditioning and electric vehicle charging is constructed, thereby achieving coordinated optimization of air conditioning power and electric vehicle power. This determines the corresponding optimal operating power of the air conditioner and the charging and discharging power of the electric vehicle, and controls the operation of the air conditioner and electric vehicle with this power. This effectively reduces peak loads on the power grid and significantly improves the user's immediate thermal comfort experience, resolving the contradiction between demand response and user satisfaction. The entire process is more in line with actual application scenarios, can effectively cope with the instantaneous fluctuations of the distribution area load, is highly practical, and is easy to promote. Attached Figure Description

[0039] The present invention will be further described below with reference to the accompanying drawings and embodiments:

[0040] Figure 1 This is a schematic diagram of the process of the present invention.

[0041] Figure 2 The diagram shows the air conditioner power at points 6-10, which is a specific example of the present invention.

[0042] Figure 3 The diagram shows the total power and room temperature changes at points 6-10, which are specific examples of the present invention.

[0043] Figure 4 This is a schematic diagram of the air conditioner power at points 6-10 after optimization by the present invention in a specific embodiment of the present invention.

[0044] Figure 5 This is a schematic diagram showing the changes in total power and room temperature at points 6-10 after optimization using the present invention in a specific example of the present invention.

[0045] Figure 6 The diagram shows the power output of 10-14 points after optimization according to the present invention, as a specific example of the present invention.

[0046] Figure 7 This is a schematic diagram showing the total power and room temperature changes at points 10-14 after optimization according to the present invention, as a specific example of the present invention.

[0047] Figure 8 The diagram shows the power output at points 14-18 after optimization according to the present invention, as a specific example of the present invention.

[0048] Figure 9 This is a schematic diagram showing the total power and room temperature changes at points 14-18 after optimization according to the present invention, as a specific example of the present invention.

[0049] Figure 10 Load curves for air conditioners and electric vehicles.

[0050] Figure 11 To optimize the change curve of the objective function before and after. Detailed Implementation

[0051] The present invention will be further described in detail below:

[0052] This invention provides a method for coordinated scheduling of electric vehicles and residential air conditioning based on transformer area capacity constraints, comprising the following steps:

[0053] S1. Obtain operating parameters and determine the real-time load rate of the distribution area. If the real-time load rate of the distribution area is greater than the safety threshold, proceed to step S2.

[0054] Specifically, determining the real-time load rate of the transformer area is as follows:

[0055] ;in: express Total load of the distribution area at any time Indicates the rated capacity of the transformer. The power factor is used to trigger a coordinated scheduling process when the real-time load rate exceeds a safety threshold.

[0056] The parameters include load parameters, equipment status parameters, and environmental parameters;

[0057] S2. Construct a coordinated scheduling model for electric vehicles and residential air conditioning. The coordinated scheduling model includes three stages: the air conditioning start-up period, the air conditioning steady-state period, and the electric vehicle charging period.

[0058] S3. The particle swarm optimization algorithm is used to solve the three-stage coordinated scheduling model of electric vehicles and residential air conditioning to obtain the optimal control parameters for each stage. The operation of residential air conditioning and electric vehicles is controlled based on the optimal control parameters, which include the air conditioning operating power, electric vehicle charging power and electric vehicle discharging power.

[0059] Specifically: Constructing a coordinated scheduling model for electric vehicles and residential air conditioning during the air conditioning start-up period includes:

[0060] When an air conditioner starts up, the compressor needs to overcome static resistance, which typically generates a starting current 2-3 times the rated power. This causes a rapid increase in load in a short period. If the load in the distribution area is already at a high level at this time, it can easily lead to transformer overload. Therefore, the core objective of this stage is to use EV discharge to quickly offset the starting power surge of the air conditioner, ensuring that the transformer does not overload, while simultaneously accelerating the temperature rise to the set value and improving user comfort.

[0061] Therefore, the triggering conditions for the air conditioner startup period are: the change in the operating power of air conditioner j is greater than the set value and the real-time load rate of the substation is greater than 0.8.

[0062] The coordinated scheduling model for electric vehicles and residential air conditioning during the air conditioning start-up period is as follows:

[0063] ;

[0064] in: , , These are the weighting coefficients; For the startup time window, This indicates a point in time within the prediction time domain. Indicates the prediction time step. express The indoor temperature of the air conditioner at all times. express Total load power of the transformer area at any given time; Indicates the safe power threshold of the transformer. This indicates the set temperature of air conditioner j. express The discharge power of electric vehicle i to the grid is monitored at all times; during this process, electric vehicle discharge is prioritized to effectively smooth out load fluctuations caused by air conditioning startup, while accelerating the room temperature to reach the set value.

[0065] After the air conditioner enters a stable operating state (compressor starts up, power fluctuates around rated power), the system switches to this stage for multi-objective optimization. At this time, load fluctuations decrease, the system operates relatively smoothly, and instantaneous response is not required. However, while ensuring transformer safety, user comfort and EV power demand must be considered. Therefore:

[0066] The specific steps involved in constructing a coordinated scheduling model for electric vehicles and residential air conditioning during the steady-state period of air conditioning include:

[0067] ;

[0068] in: , , These are the weighting coefficients. This refers to the steady-state time window for air conditioning. This indicates a point in time within the prediction time domain. Indicates the prediction time step. express The indoor temperature of the air conditioner at all times. express Total load power of the transformer area at any given time; Indicates the safe power threshold of the transformer. This indicates the set temperature of air conditioner j. express The discharge power of electric vehicle i to the grid at any given time. express The discharge power of electric vehicle i to the grid at any given time. In the model: the first term minimizes the deviation between the total load and the safety threshold to ensure the safe operation of the transformer; the second term minimizes the deviation between the indoor temperature and the set temperature to ensure that air conditioning control does not affect the user experience; the third term minimizes the deviation between the electric vehicle's SOC and the target charge to take into account the electric vehicle's charge demand.

[0069] The specific steps in constructing a coordinated scheduling model for electric vehicles and residential air conditioning during the electric vehicle charging period include:

[0070] Once the system load pressure eases, this phase begins to complete the charging task for electric vehicles. At this time, the transformer has remaining capacity and must charge the EVs in the most economical way possible, while ensuring grid safety, to ensure that users reach their target SOC before departure, and to avoid causing new overloads during the charging process.

[0071] The triggering condition for the electric vehicle charging period is: the real-time load rate of the transformer area is in the range (0.2, 0.8) and the electric vehicle has not reached the target charge level;

[0072] The coordinated scheduling model for electric vehicles and residential air conditioning during the electric vehicle charging period is as follows:

[0073] ;

[0074] in: This is the charging time window; express Electricity price at any time; , , Indicates the weighting coefficient. express The charging power of the air-conditioned electric vehicle at all times. Indicates at time The remaining battery power of electric vehicle j Let represent the electricity demand of electric vehicle j. In this model: the first term minimizes charging costs (combined with time-varying electricity prices) by increasing charging power during off-peak hours and decreasing charging power during peak hours, thus achieving charging economy; the second term minimizes the deviation between the electric vehicle's battery capacity and the target capacity, ensuring the user reaches the required capacity before departure; the third term minimizes fluctuations in charging power to avoid impacting the power grid. In setting the weighting coefficients, the weight of the first term can be appropriately increased during periods of significant electricity price differences to prioritize economic efficiency.

[0075] It also includes constraints, including transformer capacity constraints, user thermal comfort and temperature dynamic constraints, electric vehicle charging demand, battery dynamics and power constraints, and system power balance constraints.

[0076] The transformer capacity constraint is:

[0077] ;

[0078] User thermal comfort and dynamic temperature constraints are:

[0079] ;

[0080] Electric vehicle charging demand, battery dynamics, and power constraints specifically include:

[0081] ;

[0082] System power balance constraints specifically include:

[0083] ;

[0084] in: Due to transformer capacity constraints; To ensure user thermal comfort and dynamic temperature constraints; For electric vehicle charging needs, battery dynamics and power constraints; For system power balance constraints; This indicates the allowable power margin for emergency overload of the transformer; This represents the minimum acceptable temperature for the i-th household; This indicates the highest acceptable temperature for the i-th household; Indicates the cooling / heating efficiency coefficient; Indicates the heat loss coefficient; This represents the outdoor temperature at time t; This represents the cooling / heating power of the i-th air conditioner at time t; This represents the discrete time step (in seconds) for the simulation or control. This parameter is related to the data acquisition cycle and determines the time resolution of the temperature dynamics model. Indicates the departure time set by the user; This represents the charging power of the j-th electric vehicle; This represents the discharge power of the j-th electric vehicle; This represents the total battery capacity of the j-th electric vehicle; Indicates the minimum safe charge level that the battery can withstand; Indicates the maximum safe charge capacity allowed by the battery; Let represent the battery charging efficiency of the j-th electric vehicle; This represents the battery discharge efficiency of the j-th electric vehicle. This indicates the maximum charging power of the electric vehicle; This indicates the maximum discharge power of the electric vehicle; express Active load of node l foundation at time t; express reactive load on node l foundation at time t; This represents the set of branches that begin at node l. This represents the set of branches ending at node l; This represents the active power injected into node l; This represents the reactive power injected at node l; This indicates the active power of branch il; This indicates the reactive power of branch il; This represents the current in branch il; This represents the voltage at node i; Indicates the resistance of branch il; Indicates the reactance of branch il; It represents the active power output from the external power grid connected to node l at time t; This represents the reactive power output from the external power grid connected to node l at time t; Represents the set of nodes in a distribution network; Second-order cone constraints are used to represent power flow calculations in distribution networks;

[0085] By applying the above constraints and solving the problem using the particle swarm optimization algorithm, the optimal scheduling parameters, including the air conditioning operating power, are determined. Electric vehicle charging power and electric vehicle discharge power .

[0086] To implement the above process in this invention, it is necessary to set up equipment in two main parts:

[0087] The intelligent management unit for the distribution area is used to perform calculations for the above-mentioned models of the present invention, as well as to issue scheduling instructions;

[0088] The user-side management unit, installed on the user side, includes smart meters (used to collect branch data of load power in the distribution area), air conditioning monitoring modules (used to detect indoor temperature, air conditioning operating status (including start-up, in operation, steady-state operation, low power operation, and air conditioning power data)), and electric vehicle charging stations (detecting the charge status and dispatchable power of electric vehicles). All of this data must be transmitted to the distribution area smart management unit.

[0089] Among them, the electric vehicle charging pile adopts the existing V2G charging pile, that is, bidirectional charging pile, which has the ability to flow electricity in both directions. It can charge electric vehicles and also support vehicles to discharge back to the home or the power grid (V2H / V2G). It collects the EV's state of charge and charging and discharging power data in real time and uploads them to the user-side gateway. At the same time, it receives the scheduling instructions relayed by the distribution area management unit through the gateway in real time and dynamically adjusts the charging and discharging power according to the instructions.

[0090] The invention will be further described in detail below with a specific example:

[0091] The IEEE 33-node power distribution system is used as the basic scenario. The simulation lasts for 24 hours with a time step of 2 hours, including 12 discrete scheduling periods, covering the typical peak power consumption window of approximately 8:00–17:30. Electric vehicles are always connected to the system for charging and discharging. It is assumed that the system will uniformly connect to the second type of adjustable load during the 7th time period (around 7:00). Before this, the distribution area only carries the basic load. This moment corresponds to the typical concentrated power-on instant of "employees arriving at work → turning on the air conditioning (always including plugging in the charging gun)," therefore the system will experience a significant step increase in load. The air conditioning side is modeled as a flexible refrigeration unit with hysteresis control, a minimum start-stop interval of 15 minutes, and power ramp-up rate constraints. The indoor thermal inertia equation is used to describe the dynamic process of room temperature gradually decreasing with cooling power.

[0092] The comparative analysis includes two operating modes: (1) the baseline mode without optimization strategy; and (2) the collaborative scheduling mode with optimization strategy.

[0093] During air conditioner startup, such as Figure 2 , Figure 3 Simulation results show that in the baseline operation mode, when the air conditioners are put into use at 6:00 AM and simultaneously connected to the grid with the electric vehicles, the total active load of the distribution transformer area rapidly rises to a high level of approximately 135kW, and then fluctuates around 120kW-130kW during subsequent periods such as 8:00, 9:00, and 10:00 AM. This causes the distribution transformer to continuously operate within the high-voltage danger range. Simultaneously, the state of charge (SOC) of the electric vehicles shows an upward trend in this mode, with the average SOC starting at 30%-40% and increasing to the required capacity later in the day. This result indicates that the baseline mode operation strategy is essentially "prioritizing immediate cooling and fast charging needs," without taking any proactive measures to suppress the power surge faced by the distribution transformer during peak hours.

[0094] In contrast, the optimization mode achieves significant performance improvements through intelligent coordination strategies. For example... Figure 4 , Figure 5As shown. Specifically, the optimization system achieves load management through coordinated control at two levels: First, when the indoor temperature of a certain air conditioning node approaches or falls below the set temperature, the system will proactively reduce or even completely shut down the air conditioning power of that node, effectively avoiding the unnecessary occupation of transformer capacity due to "overcooling"; second, electric vehicles no longer charge at full power upon connection, but dynamically allocate charging power based on the instantaneous available capacity of the transformer area. To avoid excessive load on the transformer area caused by large-scale startup, electric vehicles are first discharged to make up for the power gap, and some energy replenishment tasks are intelligently postponed to later periods with lower load. These coordinated measures enable the system to maintain a stable overall load at a low level of 80KW-90KW during the critical peak period from 7:00 to 10:00, successfully preventing further increases in load peaks and achieving the "peak shaving and valley filling" effect for the evening peak load.

[0095] During periods of stable air conditioning performance, such as Figure 6 , Figure 7 Simulation results show that the air conditioner enters a steady-state period at 10:00 AM. Through the building's cooling storage function, its power is continuously adjusted, causing small fluctuations in air conditioner power. The remaining energy is used to charge electric vehicles, and the total active load of the transformer area remains at a high but safe level. Meanwhile, the state of charge (SOC) of electric vehicles shows a gradual upward trend under this mode.

[0096] During EV charging, such as Figure 8 , Figure 9 Simulation results show that the air conditioning power at 14 o'clock is stable and gradually decreases, the building's cold storage function is continuously released, and the remaining energy begins to increase to supply electric vehicle charging. The total active load of the transformer area remains at a high but safe level. At the same time, the charging power of electric vehicles begins to increase.

[0097] In summary, such as Figure 10 , Figure 11 This demonstrates that the collaborative scheduling method successfully transforms the traditional peak impact scenario of "household air conditioner + plug and charge" into a "time-segmented, gradual" controllable load process. While ensuring the comfort experience of residents, it significantly reduces the risk of distribution transformers exceeding limits during peak hours, providing an effective technical solution for flexible load management of distribution networks.

[0098] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for coordinated scheduling of electric vehicles and residential air conditioning based on transformer area capacity constraints, characterized in that: Includes the following steps: S1. Obtain operating parameters and determine the real-time load rate of the distribution area. If the real-time load rate of the distribution area is greater than the safety threshold, proceed to step S2. The parameters include load parameters, equipment status parameters, and environmental parameters; S2. Construct a coordinated scheduling model for electric vehicles and residential air conditioning. The coordinated scheduling model includes three stages: the air conditioning start-up period, the air conditioning steady-state period, and the electric vehicle charging period. S3. The particle swarm optimization algorithm is used to solve the three-stage coordinated scheduling model of electric vehicles and residential air conditioning to obtain the optimal control parameters for each stage. The operation of residential air conditioning and electric vehicles is controlled based on the optimal control parameters, which include the air conditioning operating power, electric vehicle charging power and electric vehicle discharging power.

2. The method for coordinated scheduling of electric vehicles and residential air conditioning based on transformer area capacity constraints according to claim 1, characterized in that: The specific steps in constructing a collaborative scheduling model for electric vehicles and residential air conditioning during the start-up phase include: The triggering conditions for the air conditioner startup period are: the change in the operating power of air conditioner j is greater than the set value and the real-time load rate of the substation is greater than 0.8; The coordinated scheduling model for electric vehicles and residential air conditioning during the air conditioning start-up period is as follows: ； in: , , These are the weighting coefficients; For the startup time window, This indicates a point in time within the prediction time domain. Indicates the prediction time step. express The indoor temperature of the air conditioner is always set. express Total load power of the transformer area at any given time; Indicates the safe power threshold of the transformer. This indicates the set temperature of air conditioner j. express The discharge power of electric vehicle i to the grid at any given time.

3. The method for coordinated scheduling of electric vehicles and residential air conditioning based on transformer area capacity constraints according to claim 1, characterized in that: The specific steps involved in constructing a coordinated scheduling model for electric vehicles and residential air conditioning during the steady-state period of air conditioning include: ； in: , , These are the weighting coefficients. This refers to the steady-state time window for air conditioning. This indicates a point in time within the prediction time domain. Indicates the prediction time step. express The indoor temperature of the air conditioner is always set. express Total load power of the transformer area at any given time; Indicates the safe power threshold of the transformer. This indicates the set temperature of air conditioner j. express The discharge power of electric vehicle i to the grid at any given time. express The discharge power of electric vehicle i to the grid at any given time.

4. The method for coordinated scheduling of electric vehicles and residential air conditioning based on transformer area capacity constraints according to claim 1, characterized in that: The specific steps in constructing a coordinated scheduling model for electric vehicles and residential air conditioning during the electric vehicle charging period include: The triggering condition for the electric vehicle charging period is: the real-time load rate of the transformer area is in the range (0.2, 0.8) and the electric vehicle has not reached the target charge level; The coordinated scheduling model for electric vehicles and residential air conditioning during the electric vehicle charging period is as follows: ； in: This is the charging time window; express Electricity price at any time; , , Indicates the weighting coefficient. express The charging power of the air-conditioned electric vehicle at all times. Indicates at time The remaining battery power of electric vehicle j This represents the electricity demand of electric vehicle j.

5. The method for coordinated scheduling of electric vehicles and residential air conditioning based on transformer area capacity constraints according to any one of claims 2-4, characterized in that: It also includes constraints, including transformer capacity constraints, user thermal comfort and temperature dynamic constraints, electric vehicle charging demand, battery dynamics and power constraints, and system power balance constraints. The transformer capacity constraint is: ； User thermal comfort and dynamic temperature constraints are: ； Electric vehicle charging demand, battery dynamics, and power constraints specifically include: ； System power balance constraints specifically include: ； in: Due to transformer capacity constraints; To ensure user thermal comfort and dynamic temperature constraints; For electric vehicle charging needs, battery dynamics and power constraints; For system power balance constraints; This indicates the allowable power margin for emergency overload of the transformer; This represents the minimum acceptable temperature for the i-th household; This indicates the highest acceptable temperature for the i-th household; Indicates the cooling / heating efficiency coefficient; Indicates the heat loss coefficient; This represents the outdoor temperature at time t; This represents the cooling / heating power of the i-th air conditioner at time t; This represents the discrete time step (in seconds) for the simulation or control. This parameter is related to the data acquisition cycle and determines the time resolution of the temperature dynamics model. Indicates the departure time set by the user; This represents the charging power of the j-th electric vehicle; This represents the discharge power of the j-th electric vehicle; This represents the total battery capacity of the j-th electric vehicle; Indicates the minimum safe charge level that the battery can withstand; Indicates the maximum safe charge capacity allowed by the battery; Let represent the battery charging efficiency of the j-th electric vehicle; This represents the battery discharge efficiency of the j-th electric vehicle. This indicates the maximum charging power of the electric vehicle; This indicates the maximum discharge power of the electric vehicle; express Active load of node l foundation at time t; express reactive load on node l foundation at time t; This represents the set of branches that begin at node l. This represents the set of branches ending at node l; This represents the active power injected into node l; This represents the reactive power injected at node l; This indicates the active power of branch il; This indicates the reactive power of branch il; This represents the current in branch il; This represents the voltage at node i; Indicates the resistance of branch il; Indicates the reactance of branch il; It represents the active power output from the external power grid connected to node l at time t; This represents the reactive power output from the external power grid connected to node l at time t; Represents the set of nodes in a distribution network; Second-order cone constraints are used to represent power flow calculations in distribution networks; The particle swarm optimization algorithm was used to solve the total load power optimization model of the transformer area, and the optimal scheduling parameters were obtained. The optimal scheduling parameters include the air conditioning operating power. Electric vehicle charging power and electric vehicle discharge power .

6. The method for coordinated scheduling of electric vehicles and residential air conditioning based on transformer area capacity constraints according to claim 1, characterized in that: The specific details for determining the real-time load rate of the distribution area are as follows: ;in: express Total load of the distribution area at any time Indicates the rated capacity of the transformer. The power factor.

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