A cluster air conditioner and electric vehicle cooperative optimization method and system for a transformer area

Abstract

This invention discloses a method for the coordinated optimization of clustered air conditioning and electric vehicles in power distribution areas. The method includes the following steps: First, inputting relevant air conditioning load parameters to establish a clustered air conditioning load control model based on temperature difference; second, inputting relevant electric vehicle parameters to construct an electric vehicle charging and discharging model; then, combining the clustered air conditioning load control model and the electric vehicle charging and discharging model, introducing external spot price fluctuation factors, and constructing a coordinated optimization model for clustered air conditioning and electric vehicles with economic optimization as the objective, the objective function including electricity purchase cost, control cost, comfort cost, etc.; finally, solving the above model to obtain the clustered air conditioning control strategy and electric vehicle charging and discharging strategy at the power distribution area level. This invention reduces the electricity cost of power distribution areas and reduces transformer over-limit events by coordinating and optimizing the operation strategies of clustered air conditioning and electric vehicles within the power distribution area, thereby improving the efficiency of both the user side and the power system in the power distribution area.

Description

Technical Field

[0001] This invention belongs to the field of demand response, and specifically relates to a method and system for the coordinated optimization of cluster air conditioning and electric vehicles in a transformer substation. Background Technology

[0002] In power systems, distribution transformers (DVTs) are a crucial link in power distribution, and their energy utilization efficiency and load management effectiveness directly impact the stability and economy of the entire power system. In recent years, with the improvement of living standards and the growth of electric vehicle ownership, the proportion of air conditioning and electric vehicle loads in DVTs has gradually increased. Due to their time-sensitive and random characteristics, DVTs experience overload problems at certain times, leading to economic operational risks and posing challenges to load management. However, air conditioning and electric vehicles are also high-quality, adjustable resources, providing an important approach to solving DVT overload and economic operation problems.

[0003] Existing research generally uses ETP models for cluster air conditioning control, with the set temperature range as a constraint. However, the air conditioning load fluctuates significantly, which can easily cause load shocks to the transformer area. There is a lack of research on how to reduce air conditioning load fluctuations and effective smoothing control strategies. In addition, transformers may experience short-term power overruns due to load fluctuations within the transformer area, resulting in additional transformer economic losses. Existing research does not consider transformer overrun losses in economic costs, which deviates from actual operating requirements and should be taken into account during model optimization.

[0004] Furthermore, with the continuous maturation of the electricity market and the frequent fluctuations in external spot market prices, spot market price signals should be introduced into the load management at the distribution area level. By constructing a reasonable cluster air conditioning load control model and an electric vehicle charging and discharging model, a collaborative optimization method for cluster air conditioning and electric vehicles oriented towards the distribution area can be proposed, which has important practical significance and application value. Summary of the Invention

[0005] The purpose of this invention is to propose a method and system for the coordinated optimization of cluster air conditioning and electric vehicles in power distribution areas. This method introduces a cluster air conditioning load control model and an electric vehicle charging and discharging model, and combines external spot price fluctuation factors to construct a coordinated optimization model for cluster air conditioning and electric vehicles with the goal of economic optimization. This model can meet the load regulation needs of power distribution areas while coordinating with electric vehicles to reduce electricity costs and improve the economic and stable operation of the power system.

[0006] To achieve the above-mentioned objectives, the present invention adopts the following technical solution:

[0007] A method for coordinated optimization of clustered air conditioning and electric vehicles in a transformer substation area includes the following steps:

[0008] S1. Input relevant air conditioning load parameters, establish a cluster air conditioning load control model based on temperature difference, and ensure that the air conditioning is controlled within the comfort range.

[0009] S2. Input relevant parameters of the electric vehicle, construct an electric vehicle charging and discharging model that considers charging and discharging limits, focus on reasonable limitations of battery charging and discharging, and ensure that the electric vehicle's operation is within a comfortable range.

[0010] S3. Introducing the S1 cluster air conditioning load control model and the S2 electric vehicle charging and discharging model, and considering external spot price fluctuations, a collaborative optimization model for cluster air conditioning and electric vehicles is constructed with the objective of economic optimization. The objective functions include: electricity purchase cost, control cost, comfort cost, and transformer over-limit loss cost.

[0011] S4. Solve the collaborative optimization model of cluster air conditioning and electric vehicles in S3 to obtain the cluster air conditioning control strategy and the electric vehicle charging and discharging strategy.

[0012] The cluster air conditioning load control model based on temperature difference is as follows:

[0013] Air conditioner load i during time period t as follows:

[0014]

[0015] In the formula: Let i be the base load of air conditioner i in time period t, where t∈T, T represents the total time period, and i∈I, where I is the total number of air conditioners in the area; It is the load that the air conditioner i controls during time period t.

[0016] To further improve the smoothness of air conditioning power output, a cluster air conditioning load control model based on temperature difference is established.

[0017]

[0018] In the formula: It is the controlled load of air conditioner i in time period t-1; β i It is the air conditioning temperature difference control coefficient, and its relationship with the air conditioning energy efficiency ratio and the room equivalent thermal resistance is: β i =1 / η i R i This reflects the sensitive characteristics of air conditioning temperature difference, where η i and R i These are the air conditioner energy efficiency ratio and the room's equivalent thermal resistance, respectively. This is the temperature setting value of the air conditioner i during time period t; T i set_min and T i set_max These are the lower and upper limits of the operating temperature of the air conditioner i.

[0019] The charging and discharging model for electric vehicles considering charging and discharging limits under the V2G access scenario is as follows:

[0020]

[0021] Where: SOC j,t The State of Charge (SOC) is the state of charge of the electric vehicle battery during time period t, where J is the total number of electric vehicles in the area. j,t-1 This refers to the state of charge (SOC) of the electric vehicle battery during time period t-1. and These are the charging and discharging power of electric vehicle j during time period t. This represents the maximum capacity of the electric vehicle's battery j, where Δt is the simulation step size.

[0022] The constraints include:

[0023] (1) SOC constraint: In actual charging process, it is necessary to set the maximum and minimum values ​​of the electric vehicle battery's state of charge to protect the battery and prevent overcharging and over-discharging from causing a decrease in battery life.

[0024]

[0025] In the formula: and These represent the minimum and maximum states of charge of electric vehicle battery j, respectively.

[0026] (2) Charge and discharge state constraints

[0027] We introduce binary variables μ1, μ2 ∈ {0, 1} to constrain the charging and discharging states of the electric vehicle, ensuring that they cannot charge and discharge simultaneously. μ1 = 1 represents the electric vehicle charging, and μ2 = 1 represents the electric vehicle discharging.

[0028]

[0029] In the formula: This is the maximum charging power of electric vehicle j. It is the maximum discharge power of electric vehicle j, (3) Off-grid SOC limit,

[0030] The state of charge (SBC) of electric vehicles off-grid should meet users' travel needs.

[0031]

[0032] In the formula: This refers to the state of charge of electric vehicle j when it is off-grid (t=T). This is the state of charge (SOC) set for electric vehicle j when it is off-grid.

[0033] (4) Charge and discharge limits are imposed to take into account user preferences and avoid the adverse effects on the battery caused by excessive charging and discharging range of electric vehicles.

[0034]

[0035] In the formula: It is the maximum SOC limit of the cumulative charge and discharge during the optimal period of electric vehicle j.

[0036] The objective function of the cluster air conditioning and electric vehicle collaborative optimization model is:

[0037] The goal is to optimize the economic cost of the distribution area, including electricity purchase cost, regulation cost, comfort cost, and transformer over-limit loss cost.

[0038] F = C buy_e +C ope +C comfort +C overloss

[0039] Economic Item 1: The electricity purchase cost function is as follows:

[0040] Electricity purchase cost refers to the expense incurred by a distribution area when purchasing electricity from the grid, calculated based on the electricity price and the amount of electricity purchased.

[0041]

[0042] In the formula: C buy_e This refers to the cost of purchasing electricity from the grid, P t grid This refers to the amount of electricity purchased from the power grid by the t-station area during that time period. The electricity purchase price from the grid for the T-region during that time period.

[0043] Economic Item 2: The regulation cost function is as follows:

[0044]

[0045] Among them, C ope λ represents the operation and maintenance costs of electric vehicle charging and discharging and air conditioning load control. EV and λ ac These are the operation and maintenance cost coefficients for electric vehicle charging and discharging, and air conditioning load adjustment, respectively.

[0046] Economic Item 3: The comfort cost function is as follows:

[0047]

[0048] In the formula: C comfort γ represents the penalty cost incurred when indoor temperature exceeds the comfort range. comfort This represents the cost factor for the comfort temperature penalty.

[0049] Economic Item 4: The cost function for transformer over-limit losses is as follows:

[0050]

[0051] In the formula: C overloss This represents the cost of over-limit losses of transformers in the distribution area, μ. overloss It is the transformer over-limit loss cost coefficient, P grid_tar This is the rated power of the transformer.

[0052] The constraints of the cluster air conditioning and electric vehicle collaborative optimization model include: power balance constraints, grid power purchase constraints, and air conditioning load-related constraints.

[0053] (1) Power balance constraint

[0054]

[0055] In the formula: P t load It is the base load under the transformer area during time period t.

[0056] (2) Power purchase constraints

[0057] To ensure the safe and stable operation of the power grid, the maximum power purchase capacity of the distribution area and the power grid is constrained.

[0058] 0≤P t grid ≤P grid_max

[0059] In the formula: P grid_max This is the maximum permissible operating power of the transformer.

[0060] (3) Air conditioning load related constraints

[0061]

[0062] In the formula: and These are the minimum and maximum values ​​of the load control for air conditioning during period i and time t, respectively.

[0063] Solving the collaborative optimization model yields the cluster air conditioning control strategy and the electric vehicle charging and discharging strategy, including:

[0064] The CPLEX solver is used to solve this mixed integer programming problem, and the optimized results of the user subjects participating in load management are obtained. YALMIP is used as the optimization framework to transform the decision variables, objective function and constraints into a standard mathematical model, and the solver is used to optimize it, outputting the optimized results of the user subjects participating in load management.

[0065] A cluster air conditioning and electric vehicle collaborative optimization system for transformer substations, used to implement the aforementioned cluster air conditioning and electric vehicle collaborative optimization method for transformer substations, characterized in that the system comprises three modules.

[0066] The temperature difference module for cluster air conditioning load control is designed to control the cluster air conditioning load based on the temperature difference change value, so as to meet the requirements of comfort and smooth change of air conditioning load in the transformer area.

[0067] The electric vehicle charging and discharging limit module regulates the charging and discharging of electric vehicles and optimizes the charging and discharging strategy of the vehicle battery.

[0068] The collaborative optimization module is based on the cluster air conditioning load control temperature difference module and the electric vehicle charging and discharging limit module, while also taking into account external spot price fluctuations, to achieve optimal economic collaboration between cluster air conditioning and electric vehicles within the transformer area.

[0069] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the aforementioned method for coordinated optimization of cluster air conditioning and electric vehicles for transformer substations.

[0070] A computer-readable storage medium storing computer instructions that, when executed by a processor, implement the aforementioned method for coordinated optimization of clustered air conditioning and electric vehicles for power distribution areas.

[0071] Compared with the prior art, the advantages of the present invention are as follows:

[0072] By adopting the above technical solution, the present invention provides a method and system for the coordinated optimization of clustered air conditioning and electric vehicles in power distribution areas. Compared with the prior art, the beneficial effects of the present invention are as follows: taking clustered air conditioning and electric vehicles as typical interactive resources in power distribution areas, through the coordinated optimization of clustered air conditioning and electric vehicles, it can not only achieve reasonable regulation of air conditioning load and balance the charging demand of electric vehicles, but also further improve the overall economic efficiency of the power distribution area and reduce transformer over-limit behavior based on electricity price fluctuations. It takes into account the economic efficiency, reliability, and user comfort requirements of the power grid side, achieving a win-win situation for both the power system and users. Specifically, it is manifested as follows:

[0073] First, reduce the electricity cost of the distribution area: introduce the cost increase caused by transformer overload, optimize the costs of electricity purchase, load regulation and transformer overload in the case of external electricity price fluctuations, and optimize the economic operation of the distribution area by coordinating the control strategies of air conditioning load and electric vehicle.

[0074] Secondly, it improves equipment operating efficiency: By adopting a reasonable electric vehicle charging and discharging strategy, it avoids overcharging and discharging of electric vehicles, extends battery life, and further reduces the risk of transformer overload operation.

[0075] Third, smooth the cluster air conditioning control curve: By using a cluster air conditioning load control model based on temperature difference, the cluster air conditioning load curve is smoothed while ensuring air conditioning comfort, effectively reducing the impact of cluster air conditioning control on the power grid.

[0076] Fourth, it enhances grid flexibility: by coordinating the load distribution of air conditioners and electric vehicles in real time, it can quickly respond to changes in current power supply and demand, especially when electricity prices fluctuate. It can dynamically optimize charging and discharging and air conditioning control strategies, alleviate grid pressure during peak hours, and reduce local power shortages or redundancy. Attached Figure Description

[0077] Figure 1 Schematic diagram of power purchase load in the distribution area.

[0078] Figure 2 This is a flowchart of the method of the present invention.

[0079] Figure 3 Electric vehicle charging and discharging power graph

[0080] Figure 4 Operating power of air conditioners in the transformer substation (air conditioners 1-10),

[0081] Figure 5 Operating power of air conditioners in the transformer area (air conditioner 11-20),

[0082] Figure 6 Operating power of air conditioners in the transformer area (air conditioner 21-30). Detailed Implementation

[0083] The present invention will be further described below with reference to specific embodiments.

[0084] Example: A method for coordinated optimization of cluster air conditioning and electric vehicles in a transformer substation area, comprising the following steps:

[0085] S1: Taking a certain transformer substation as an example, 30 air conditioners and 8 electric vehicles were randomly selected from the substation. The average electricity price on the user side in this area fluctuates over time, lasting for 24 hours. The electricity prices for each time period are shown in Table 1. The time period is from 06:00 to 06:00 the next day, and the duration is 24 hours.

[0086] Table 1 24-hour dynamic electricity price

[0087]

[0088] The input air conditioning load parameters, as shown in Table 2, all follow a uniform distribution. Thirty air conditioners were randomly selected from the distribution area, with the equivalent heat capacity C of each air conditioner following a distribution between (0.17, 0.2). 2 The Laplace-Gaussian distribution has an equivalent thermal resistance that follows (5.49, 1). 2 A cluster air conditioning load control model based on temperature difference was established using the Laplace-Gaussian distribution. The initial indoor temperature distribution is within the range of [24, 26]℃.

[0089] Table 2 Air Conditioning Load Parameter Range

[0090] parameter Range of values parameter Range of values <![CDATA[P ac ]]> [1,3] <![CDATA[T set_max ]]> [26,28] η [2.6,2.9] <![CDATA[T set_min ]]> [24,26]

[0091] The cluster air conditioning load control model based on temperature difference is as follows:

[0092] Air conditioner load i during time period t as follows:

[0093]

[0094] In the formula: Let i be the base load of air conditioner i in time period t, where t∈T, T represents the total time period, and i∈I, where I is the total number of air conditioners in the area; It is the load that the air conditioner i controls during time period t.

[0095] To further improve the smoothness of air conditioning power output, a cluster air conditioning load control model based on temperature difference is established.

[0096]

[0097] In the formula: It is the controlled load of air conditioner i in time period t-1; β i It is the air conditioning temperature difference control coefficient, and its relationship with the air conditioning energy efficiency ratio and the room equivalent thermal resistance is: β i =1η i R i This reflects the sensitive characteristics of air conditioning temperature difference, where η i and R i These are the air conditioner energy efficiency ratio and the room's equivalent thermal resistance, respectively. This is the temperature setting value of the air conditioner i during time period t; T i set_min and T i set_max These are the lower and upper limits of the operating temperature of the air conditioner i.

[0098] S2: Input the relevant parameters of the electric vehicle as shown in Table 3, and construct a charging and discharging model of the electric vehicle considering charging and discharging limits:

[0099] The maximum charging power of existing electric vehicles is determined by the charging pile. Referring to existing charging piles on the market, a 21kW commercial charging pile was chosen, backward compatible with 16kW, 11kW, and 7kW charging powers. Eight randomly selected mid-to-high-end electric vehicles supporting reverse power supply have a maximum discharge power of 10kW. The State of Charge (SOC) of the electric vehicles connected to the charging pile follows a random distribution of (0.3, 0.6) upon grid connection.

[0100] Table 3 Parameters of Electric Vehicles

[0101]

[0102]

[0103] The charging and discharging model for electric vehicles considering charging and discharging limits under the V2G access scenario is as follows:

[0104]

[0105] Where: SOC j,t The State of Charge (SOC) is the state of charge of the electric vehicle battery during time period t, where J is the total number of electric vehicles in the area. j,t-1 This refers to the state of charge (SOC) of the electric vehicle battery during time period t-1. and These are the charging and discharging power of electric vehicle j during time period t. This represents the maximum capacity of the electric vehicle's battery j, where Δt is the simulation step size.

[0106] The constraints include:

[0107] (1) SOC constraint. In the actual charging process, it is necessary to set the maximum and minimum values ​​of the state of charge of the electric vehicle battery to protect the battery and prevent overcharging and over-discharging from causing a decrease in battery life.

[0108]

[0109] In the formula: and These represent the minimum and maximum states of charge of electric vehicle battery j, respectively.

[0110] (2) Charge and discharge state constraints.

[0111] We introduce binary variables μ1, μ2∈{0,1} to constrain the charging and discharging states of electric vehicles, ensuring that they cannot charge and discharge simultaneously. μ1=1 represents the electric vehicle charging, and μ2=1 represents the electric vehicle discharging.

[0112]

[0113] In the formula: This is the maximum charging power of electric vehicle j. It is the maximum discharge power of electric vehicle j.

[0114] (3) Off-grid SOC restrictions.

[0115] In real-world scenarios, the off-grid state of charge of electric vehicles needs to meet users' travel demands.

[0116]

[0117] In the formula: This refers to the state of charge of electric vehicle j when it is off-grid (t=T). It is the state of charge set for electric vehicle j when it is off-grid.

[0118] (4) Charge and discharge limits are imposed to take into account user preferences and avoid the adverse effects on the battery caused by excessive charging and discharging range of electric vehicles.

[0119]

[0120] In the formula: It is the maximum SOC limit of the cumulative charge and discharge during the optimal period of electric vehicle j.

[0121] S3: Introduce a cluster air conditioning load control model and an electric vehicle charging and discharging model, and consider external spot price fluctuations to construct a collaborative optimization model for cluster air conditioning and electric vehicles with the goal of economic optimization.

[0122] The specific parameters for the economic category are as follows:

[0123] The goal is to optimize the economic cost of the distribution area, including electricity purchase cost, regulation cost, comfort cost, and transformer over-limit loss cost. EV , λ ac γ comfort and μ overloss The coefficients take values ​​of 0.1, 0.3, 2 and 0.9 respectively.

[0124] F = C buy_e +C ope +C comfort +C overloss

[0125] Economic Item 1: The electricity purchase cost function is as follows:

[0126] Electricity purchase cost refers to the expense incurred by a distribution area when purchasing electricity from the grid, calculated based on the electricity price and the amount of electricity purchased.

[0127]

[0128] In the formula: C buy_e This refers to the cost of purchasing electricity from the grid, Pt grid This refers to the amount of electricity purchased from the power grid by the t-station area during that time period. This is the electricity price for time period t, where T represents the total time period.

[0129] Economic Item 2: The regulation cost function is as follows:

[0130]

[0131] Among them, C ope λ represents the operation and maintenance costs of electric vehicle charging and discharging and air conditioning load control. EV and λ ac These are the operation and maintenance cost coefficients for electric vehicle charging and discharging, and air conditioning load adjustment, respectively. It is the original load of air conditioner i during time period t.

[0132] Economic Item 3: The comfort cost function is as follows:

[0133]

[0134] In the formula: C comfort γ represents the penalty cost incurred when indoor temperature exceeds the comfort range. comfort This represents the cost factor for the comfort temperature penalty.

[0135] Economic Item 4: The cost function for transformer over-limit losses is as follows:

[0136]

[0137] In the formula: C overloss This represents the cost of over-limit losses of transformers in the distribution area, μ. overloss It is the transformer over-limit loss cost coefficient, P grid_tar This is the rated power of the transformer.

[0138] S4: Considering the constraints of the collaborative optimization model for cluster air conditioning and electric vehicles, and solving the model to obtain the optimized results. The base load of each transformer area during each time period is shown in Table 4.

[0139] Table 4. Base load of each transformer area during each time period.

[0140] time Base load / kW Time / h Base load / kW Time / h Base load / kW 1 58 9 98 17 89 2 65 10 96 18 79 3 72 11 92 19 68 4 76 12 85 20 60 5 79 13 89 21 58 6 86 14 90 22 50 7 94 15 96 23 55 8 96 16 98 24 57

[0141] (1) Power balance constraint

[0142]

[0143] In the formula: P t load It is the base load under the transformer area during time period t.

[0144] (2) Power purchase constraints

[0145] To ensure the safe and stable operation of the power grid, reduce risks, optimize resource allocation, and improve economic efficiency.

[0146] 0≤P t grid ≤P grid_max

[0147] In the formula: P grid_max This is the maximum permissible operating power of the transformer.

[0148] (3) Air conditioning load related constraints

[0149]

[0150] In the formula: and These are the maximum and minimum values ​​of the load control for air conditioning during period i and time t, respectively.

[0151] The optimized results are shown in Table 5. The total cost F is 6681.34 yuan, the electricity purchase cost is 5242.1 yuan, accounting for 78.45%, the operation and maintenance cost is 835.96 yuan, accounting for 12.51%, the comfort penalty is 497.5 yuan, accounting for 7.45%, and the transformer over-limit penalty is 105.78 yuan, accounting for 1.59%.

[0152] Table 5 Results of the optimized economic items

[0153] Electricity purchase cost Operation and maintenance costs Comfort penalty Transformer over-limit penalty Cost / yuan 5242.1 835.96 497.5 105.78 Proportion of total cost 78.45％ 12.51％ 7.45％ 1.59％

[0154] Electric vehicle charging and discharging power such as Figure 3 As shown in the figure, in the early stages, when electric vehicles (EVs) are first connected to the grid, due to lower electricity prices and lower state of charge (SOC), EVs tend to choose charging mode. After about 5 hours of charging, around 11:00 AM, when electricity prices are higher, EVs switch to discharging mode, releasing energy back into the grid to reduce the load on the transformer in the distribution area, thereby lowering the area's electricity purchase cost and optimizing its power consumption structure. In subsequent periods, EVs flexibly adjust their charging and discharging strategies based on electricity price fluctuations: charging during periods of lower prices and continuing to discharge during periods of higher prices to achieve the economic operation goals of the distribution area.

[0155] The operating power of the air conditioner in the transformer area is as follows Figure 4-6 As shown, Figure 4 Operating power of air conditioners in the transformer substation (air conditioners 1-10), Figure 5 Operating power of air conditioners in the transformer area (air conditioner 11-20), Figure 6The operating power of the air conditioner in the distribution area (air conditioner 21-30): By dynamically adjusting the operating power of the air conditioner, it is possible to ensure that the indoor temperature is within the set range while avoiding unnecessary large power fluctuations at the same time, thereby reducing the peak load of the distribution area.

[0156] The electricity purchase load of the distribution area is as follows Figure 1 As shown, during peak electricity consumption periods, the coordinated optimization mechanism of air conditioning and electric vehicles effectively reduced the amount of electricity purchased during peak hours, decreased the dependence of the distribution area on the power grid during peak hours, and reduced the demand for large amounts of high-priced electricity purchases, thereby effectively alleviating the pressure on transformers and achieving the goals of optimizing the power consumption structure and economical operation of the distribution area.

[0157] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for coordinated optimization of clustered air conditioning and electric vehicles in a transformer substation area, characterized in that, Includes the following steps: S1. Input relevant air conditioning load parameters, establish a cluster air conditioning load control model based on temperature difference, and ensure that the air conditioning is controlled within the comfort range. S2. Input relevant parameters of the electric vehicle, construct an electric vehicle charging and discharging model that considers charging and discharging limits, focus on reasonable limitations of battery charging and discharging, and ensure that the electric vehicle's operation is within a comfortable range. S3. Introducing the S1 cluster air conditioning load control model and the S2 electric vehicle charging and discharging model, and considering external spot price fluctuations, a collaborative optimization model for cluster air conditioning and electric vehicles is constructed with the objective of economic optimization. The objective functions include: electricity purchase cost, control cost, comfort cost, and transformer over-limit loss cost. S4. Solve the collaborative optimization model of cluster air conditioning and electric vehicles in S3 to obtain the cluster air conditioning control strategy and the electric vehicle charging and discharging strategy; The cluster air conditioning load control model based on temperature difference is as follows: Air conditioner load i during time period t as follows: In the formula: Let i be the base load of air conditioner i in time period t, where t∈T, T represents the total time period, and i∈I, where I is the total number of air conditioners in the area; It is the load that the air conditioner i controls during time period t. The charging and discharging model for electric vehicles considering charging and discharging limits under the V2G access scenario is as follows: Where: SOC j,t The State of Charge (SOC) is the state of charge of the electric vehicle battery during time period t, where J is the total number of electric vehicles in the area. j,t-1 This refers to the state of charge (SOC) of the electric vehicle battery during time period t-1. and These are the charging and discharging power of electric vehicle j during time period t. The maximum capacity of battery j in electric vehicle is given, Δt is the simulation step size, and the constraints include: (1) SOC constraint: In actual charging process, it is necessary to set the maximum and minimum values ​​of the electric vehicle battery's state of charge to protect the battery and prevent overcharging and over-discharging from causing a decrease in battery life. In the formula: and These represent the minimum and maximum states of charge of electric vehicle battery j, respectively. (2) Charge and discharge state constraints We introduce binary variables μ1, μ2 ∈ {0, 1} to constrain the charging and discharging states of the electric vehicle, ensuring that they cannot charge and discharge simultaneously. μ1 = 1 represents the electric vehicle charging, and μ2 = 1 represents the electric vehicle discharging. In the formula: This is the maximum charging power of electric vehicle j. It is the maximum discharge power of electric vehicle j. (3) Off-grid SOC restrictions The state of charge (SBC) of electric vehicles off-grid should meet users' travel needs. In the formula: The electric vehicle j is in its state of charge when it is off-grid, t = T. This is the state of charge (SOC) set for electric vehicle j when it is off-grid. (4) Charge and discharge limits are imposed to take into account user preferences and avoid adverse effects on the battery from excessive charge and discharge range. In the formula: It is the maximum SOC limit of the cumulative charge and discharge during the optimal period of electric vehicle j; The objective function of the collaborative optimization model for cluster air conditioning and electric vehicles is: The goal is to optimize the economic cost of the distribution area, including electricity purchase cost, regulation cost, comfort cost, and transformer over-limit loss cost. F=C buy_e +C ope +C comfort +C overloss Economic Item 1: The electricity purchase cost function is as follows: Electricity purchase cost refers to the expense incurred by a distribution area when purchasing electricity from the grid, calculated based on the electricity price and the amount of electricity purchased. In the formula: C buy_e This refers to the cost of purchasing electricity from the grid, P t grid This refers to the amount of electricity purchased from the power grid by the t-station area during that time period. The electricity purchase price from the grid for the T-region during that time period. Economic Item 2: The regulation cost function is as follows: Among them, C ope λ represents the operation and maintenance costs of electric vehicle charging and discharging and air conditioning load control. EV and λ ac These are the operation and maintenance cost coefficients for electric vehicle charging and discharging, and air conditioning load adjustment, respectively. Economic Item 3: The comfort cost function is as follows: In the formula: C comfort γ represents the penalty cost incurred when indoor temperature exceeds the comfort range. comfort This represents the cost factor for comfort penalty. Economic Item 4: The cost function for transformer over-limit losses is as follows: In the formula: C overloss This represents the cost of over-limit losses of transformers in the distribution area, μ. overloss It is the transformer over-limit loss cost coefficient, P grid_tar This is the rated power of the transformer; The constraints of the cluster air conditioning and electric vehicle collaborative optimization model include: power balance constraints, grid power purchase constraints, and air conditioning load-related constraints. (1) Power balance constraint In the formula: P t load It is the base load under the transformer area during time period t. (2) Power purchase constraints To ensure the safe and stable operation of the power grid, constraints are imposed on the maximum power purchase capacity of the distribution area and the power grid. 0≤P t grid ≤P grid_max In the formula: P grid_max This is the maximum permissible operating power of the transformer. (3) Air conditioning load related constraints In the formula: and These are the minimum and maximum values ​​of the load control for air conditioning during period i and time t, respectively.

2. The method for coordinated optimization of cluster air conditioning and electric vehicles for transformer substations according to claim 1, characterized in that, Solving the collaborative optimization model yields the cluster air conditioning control strategy and the electric vehicle charging and discharging strategy, including: The CPLEX solver is used to solve this mixed integer programming problem, and the optimized results of the user subjects participating in load management are obtained. YALMIP is used as the optimization framework to transform the decision variables, objective function and constraints into a standard mathematical model, and the solver is used to optimize it, outputting the optimized results of the user subjects participating in load management.

3. A cluster air conditioning and electric vehicle collaborative optimization system for transformer substations, used to implement the cluster air conditioning and electric vehicle collaborative optimization method for transformer substations as described in any one of claims 1-2, characterized in that, The system comprises three modules. The temperature difference module for cluster air conditioning load control is designed to control the cluster air conditioning load based on the temperature difference change value, so as to meet the requirements of comfort and smooth change of air conditioning load in the transformer area. The electric vehicle charging and discharging limit module regulates the charging and discharging of electric vehicles and optimizes the charging and discharging strategy of the vehicle battery. The collaborative optimization module is based on the cluster air conditioning load control temperature difference module and the electric vehicle charging and discharging limit module, while also taking into account external spot price fluctuations, to achieve optimal economic collaboration between cluster air conditioning and electric vehicles within the transformer area.

4. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, it implements the cluster air conditioning and electric vehicle collaborative optimization method for transformer substations as described in any one of claims 1 to 2 above.

5. A computer-readable storage medium storing computer instructions thereon, characterized in that: When the computer instruction is executed by the processor, it implements the cluster air conditioning and electric vehicle collaborative optimization method for the transformer substation area as described in any one of claims 1-2.

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