Collaborative optimization method and system for cluster air conditioners and electric vehicles in courts

Abstract

The invention discloses a transformer area-oriented cluster air conditioner and electric vehicle collaborative optimization method, which comprises the following steps of: firstly, inputting related parameters of an air conditioner load, and establishing a cluster air conditioner load control model based on a temperature difference; secondly, inputting relevant parameters of the electric vehicle, and constructing a charging and discharging model of the electric vehicle; then, a cluster air conditioner load control model and an electric vehicle charging and discharging model are combined, external spot price fluctuation factors are introduced, a cluster air conditioner and electric vehicle collaborative optimization model with economic optimization as a target is constructed, and target functions comprise electricity purchase cost, regulation and control cost, comfort level cost and the like; and finally, the model is solved, and a cluster air conditioner regulation and control strategy and an electric vehicle charging and discharging strategy of a transformer area level are obtained. According to the method, the operation strategy of the cluster air conditioner and the electric vehicle is optimized in a cooperative manner in the transformer area range, the power utilization cost of the transformer area is reduced, transformer out-of-limit events are reduced, and benefit improvement of a transformer area user side and a power system is achieved.

Description

Technical Field

[0001] The present invention belongs to the field of demand response, and specifically relates to a cluster air conditioner and electric vehicle collaborative optimization method and system for a substation. Background Art

[0002] In the power system, the substation is a key link in power distribution. Its energy utilization efficiency and the effectiveness of load management directly affect the stability and economy of the entire power system. In recent years, with the improvement of residents' living standards and the increase in the number of electric vehicles, the proportion of air conditioners and electric vehicles in the substation load has gradually increased. Due to their time-based and random characteristics, the substation has overload problems in some periods, which brings economic operation risks and challenges to the load management of the substation. However, air conditioners and electric vehicles are also high-quality adjustable resources, which provides an important way to solve the problems of overload and economic operation of the substation.

[0003] In existing research, cluster air conditioning control is generally based on the ETP model, with the set temperature range as a constraint. The air conditioning load fluctuates greatly, which can easily cause load shocks to the substation. There is little research on how to reduce air conditioning load fluctuations, and there is a lack of effective smoothing control strategies. In addition, due to load fluctuations within the substation, the transformer may have a short-term power over-limit problem, resulting in additional transformer economic losses. Existing research has not considered transformer over-limit losses in the economic cost, which deviates from the actual operating requirements and should be paid attention to when optimizing the model.

[0004] In addition, with the continuous maturity of the electricity market, external spot market prices fluctuate frequently. Spot market price signals should be introduced into the load management at the substation level. By constructing a reasonable cluster air-conditioning load control model and electric vehicle charging and discharging model, a cluster air-conditioning and electric vehicle collaborative optimization method for the substation is proposed, which has important practical significance and application value. Summary of the invention

[0005] The purpose of the present invention is to propose a cluster air-conditioning and electric vehicle collaborative optimization method and system for substations, introduce a cluster air-conditioning load control model and an electric vehicle charging and discharging model, and combine external spot price fluctuation factors to construct a cluster air-conditioning and electric vehicle collaborative optimization model with economic optimization as the goal. While meeting the load regulation needs of the substation, it coordinates with electric vehicles, reduces electricity costs, and improves the economic and stable operation of the power system.

[0006] In order to achieve the above-mentioned invention object, the present invention adopts the following technical scheme:

[0007] A cluster air conditioner and electric vehicle collaborative optimization method for a power station area includes the following steps:

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

[0009] S2. Input the parameters related to electric vehicles, construct an electric - vehicle charging and discharging model considering charging and discharging limits, pay attention to the reasonable limits of vehicle - battery charging and discharging, and ensure that the electric - vehicle regulation is within the comfort range.

[0010] S3. Introduce the S1 cluster air - conditioning load control model and the S2 electric - vehicle charging and discharging model, consider the external spot - price fluctuation, and construct a collaborative optimization model of cluster air - conditioners and electric vehicles with the goal of economic optimality. The objective function includes: power - purchase cost, regulation cost, comfort - level cost, and transformer - over - limit loss cost.

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

[0012] Among them, the cluster air - conditioning load control model based on temperature difference is as follows:

[0013] The load of air - conditioner i at time t is as follows:

[0014]

[0015] In the formula: is the basic load of air - conditioner i at time t, t ∈ T, T represents the total time period, i ∈ I, and I is the total number of air - conditioners in the distribution area; is the regulated load of air - conditioner i at time t.

[0016] To further improve the power smoothness of air - conditioners, establish a cluster air - conditioning load control model based on temperature difference.

[0017]

[0018] In the formula: is the regulated load of air - conditioner i at time t - 1; β i is the air - conditioner temperature - difference regulation coefficient, and its relationship with the air - conditioner energy - efficiency ratio and the room equivalent thermal resistance is: β i = 1 / η i R i , which reflects the sensitive characteristics of the air - conditioner temperature difference, where η i and R i are the air - conditioner energy - efficiency ratio and the room equivalent thermal resistance respectively; is the temperature - set value of air - conditioner i at time t; T i set_min and T i set_max are the lower and upper limits of the operating temperature of air - conditioner i respectively.

[0019] When building an electric vehicle charging and discharging model considering charging and discharging limits under V2G access, it is as follows:

[0020]

[0021] In the formula: SOC j,t is the state of charge of the battery of electric vehicle j at time t in period J, where J is the total number of electric vehicles in the distribution area; SOC j,t-1 is the state of charge of the battery of electric vehicle j at time t-1 in period J, and are the charging and discharging powers of electric vehicle j at time t respectively, is the maximum capacity of the battery of electric vehicle j, and Δt is the simulation step size,

[0022] Among them, the constraint conditions include:

[0023] (1) SOC limit constraint. In the actual charging process, the maximum and minimum values of the state of charge of the electric vehicle battery need to be set to protect the battery and prevent the decline of the battery life caused by overcharging and over-discharging of the battery,

[0024]

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

[0026] (2) Charging and discharging state constraint,

[0027] Introduce a binary variable μ 1 , μ 2 ∈{0,1} to constrain the charging and discharging state of the electric vehicle so that it cannot charge and discharge simultaneously. μ 1 =1 indicates that the electric vehicle is charging, and μ 2 =1 indicates that the electric vehicle is discharging,

[0028]

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

[0030] The state of charge of the electric vehicle when off-grid needs to meet the travel needs of users,

[0031]

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

[0033] (4) Charge and discharge limit constraints, taking into account user usage preferences and avoiding adverse effects on the battery due to excessive charge and discharge ranges of electric vehicles.

[0034]

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

[0036] Among them, the objective function of the collaborative optimization model of the cluster air conditioner and electric vehicle is:

[0037] Aiming at the optimal economic cost of the transformer substation area, including the 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] The electricity purchase cost refers to the cost generated by the transformer substation area purchasing electricity from the power grid side, which is calculated based on the electricity price and the electricity purchase quantity:

[0041]

[0042] In the formula: C buy_e refers to the cost generated by purchasing electricity from the power grid side, P t grid is the power of purchasing electricity from the power grid side of the transformer substation area at time t, is the electricity purchase price of the transformer substation area from the power grid side at time t,

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

[0044]

[0045] Among them, C ope represents the operation and maintenance cost of the charge and discharge of electric vehicles and the regulation of air conditioner loads, λ EV and λ ac are respectively the operation and maintenance cost coefficients of the charge and discharge of electric vehicles and the adjustment of air conditioner loads,

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

[0047]

[0048] In the formula: C comfortDenote the penalty cost generated by the indoor temperature exceeding the comfort range, γ comfort Denote the penalty cost coefficient of the comfort temperature,

[0049] Economic item 4: The cost function of transformer over-limit loss is as follows:

[0050]

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

[0052] Among them, the constraint conditions of the collaborative optimization model of cluster air conditioners and electric vehicles include: power balance constraint, power grid power purchase constraint, and air-conditioning load-related constraint,

[0053] (1) Power balance constraint

[0054]

[0055] In the formula: P t load is the basic load in the area at time period t,

[0056] (2) Power grid power purchase constraint

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

[0058] 0 ≤ P t grid ≤ P grid_max

[0059] In the formula: P grid_max is the maximum allowable operating power of the transformer,

[0060] (3) Air-conditioning load-related constraint

[0061]

[0062] In the formula: and are the minimum and maximum values of the load regulation of air conditioner i at time period t, respectively.

[0063] Among them, by solving the collaborative optimization model, the cluster air-conditioning regulation strategy and the electric vehicle charging and discharging strategy are obtained, including:

[0064] Solve this mixed-integer programming problem using the CPLEX solver to obtain the optimized results of the user entities participating in load management. Through YALMIP as an optimization framework, convert the decision variables, objective function, and constraints into a standard mathematical model, and optimize through the solver to output the optimized results of the user entities participating in load management.

[0065] A collaborative optimization system for cluster air conditioners and electric vehicles in a substation area, used to implement the collaborative optimization method for cluster air conditioners and electric vehicles in the substation area. It is characterized in that the system includes three modules.

[0066] The cluster air conditioner load control temperature difference module designs a cluster air conditioner load control mechanism related to the temperature difference change value to meet the comfort and smooth change requirements of the air conditioner load in the substation area.

[0067] The electric vehicle charging and discharging limit module regulates the charging and discharging of electric vehicles and reasonably optimizes the charging and discharging strategies of vehicle batteries.

[0068] The collaborative optimization module, based on the cluster air conditioner load control temperature difference module and the electric vehicle charging and discharging limit module, and considering the external spot price fluctuation at the same time, realizes the optimal economic collaboration between cluster air conditioners and electric vehicles within the substation area.

[0069] An electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the collaborative optimization method for cluster air conditioners and electric vehicles in the substation area.

[0070] A computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, they implement the collaborative optimization method for cluster air conditioners and electric vehicles in the substation area.

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

[0072] Due to the above technical solutions, for a collaborative optimization method and system for cluster air conditioners and electric vehicles in a substation area provided by the present invention, compared with the prior art, the beneficial effects of the present invention are that, taking two typical interactive resources, cluster air conditioners and electric vehicles in the substation area, as examples, through the collaborative optimization of cluster air conditioners and electric vehicles, it is not only possible to reasonably regulate the air conditioner load and balance the charging demand of electric vehicles, but also further improve the overall economic benefit of the substation area and reduce transformer over-limit behavior on the basis of considering electricity price fluctuations, taking into account the economic efficiency, reliability on the grid side and the comfort requirements on the user side, and achieving a win-win situation for the power system and users. The specific manifestations are as follows:

[0073] First, reduce the electricity cost in the substation area: Introduce the cost increment caused by transformer overload. Considering the external electricity price fluctuations, optimize the costs of electricity purchase, load regulation, and transformer overload. By coordinating the air-conditioning load and electric vehicle regulation strategies, optimize the operational economy of the substation area.

[0074] Second, improve the operation efficiency of equipment: Through a reasonable electric vehicle charging and discharging strategy, avoid overcharging and over-discharging of electric vehicles, extend the service life of the battery, and further reduce the risk of transformer overloading.

[0075] Third, smooth the regulation curve of the cluster air conditioners: Through a cluster air-conditioning load control model based on temperature difference, smooth the cluster air-conditioning load curve on the premise of ensuring the comfort of the air conditioners, and effectively reduce the impact of cluster air-conditioning regulation on the power grid.

[0076] Fourth, enhance the flexibility of the power grid: By coordinating the load distribution of air conditioners and electric vehicles in real time, quickly respond to the current power supply and demand changes. Especially when the electricity price fluctuates, dynamically optimize the charging and discharging and air-conditioning regulation strategies, relieve the power grid pressure during peak hours, and reduce local power shortages or redundancies. Description of the Drawings

[0077] Figure 1 Schematic diagram of the electricity purchase load in the substation area,

[0078] Figure 2 is the flowchart of the method of the present invention,

[0079] Figure 3 Power diagram of electric vehicle charging and discharging,

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

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

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

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

[0084] Embodiment: A collaborative optimization method for cluster air conditioners and electric vehicles facing the substation area, including the following steps,

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

[0086] Table 1 Dynamic electricity price for 24 hours

[0087]

[0088] The input air-conditioning load-related parameters all follow a uniform distribution as shown in Table 2. 30 air conditioners in the power distribution area are randomly selected. Among them, the equivalent heat capacity C of the air conditioner follows a Laplace-Gaussian distribution in the range of (0.17, 0.2 2 ) and the equivalent thermal resistance follows a Laplace-Gaussian distribution in the range of (5.49, 1 2 ). A cluster air-conditioning load control model based on the temperature difference is established. The initial indoor temperature distribution is in the range of [24, 26] °C.

[0089] Table 2 Range of air-conditioning load parameters

[0090] Parameter Value range Parameter Value range <![CDATA[P ac > [1,3] <![CDATA[T set_max > [26,28] η [2.6,2.9] <![CDATA[T set_min > [24,26]

[0091] Among them, the cluster air-conditioning load control model based on the temperature difference is as follows:

[0092] The load of air conditioner i at time t is as follows:

[0093]

[0094] In the formula: is the basic load of air conditioner i at time t, t ∈ T, T represents the total time period, i ∈ I, and I is the total number of air conditioners in the power distribution area; is the regulated load of air conditioner i at time t.

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

[0096]

[0097] In the formula: is the regulated load of air conditioner i at time t - 1; β i is the air-conditioning temperature difference regulation coefficient, and its relationship with the air-conditioning energy efficiency ratio and the room equivalent thermal resistance is: β i = 1η i R i , which reflects the sensitive characteristics of the air-conditioning temperature difference. Among them, η i and R i are the air-conditioning energy efficiency ratio and the room equivalent thermal resistance respectively; is the temperature set value of air conditioner i at time t; T i set_min and T i set_max are the lower and upper limits of the operating temperature of air conditioner i respectively.

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

[0099] The maximum charging power of existing electric vehicles is determined by the charging pile. Referring to the existing charging piles on the market, it is decided to adopt a 21kW commercial charging pile, which can be downward compatible with charging powers of 16kW, 11kW, and 7kW. Eight randomly selected mid - to - high - end electric vehicles that support reverse power supply have a maximum discharge power of 10kW each. When the electric vehicles connected to the charging pile are connected to the grid, the SOC follows a random distribution of (0.3, 0.6).

[0100] Table 3 Parameters of electric vehicles

[0101]

[0102]

[0103] Construct an electric vehicle charging and discharging model considering charging and discharging limits under the condition of V2G access as follows:

[0104]

[0105] In the formula: SOC j,t is the state of charge of the battery of electric vehicle j at time period t, J is the total number of electric vehicles in the distribution area; SOC j,t-1 is the state of charge of the battery of electric vehicle j at time period t - 1, and are the charging and discharging powers of electric vehicle j at time period t respectively, is the maximum capacity of the battery of electric vehicle j, Δt is the simulation step size,

[0106] The constraint conditions include:

[0107] (1) SOC limit constraint. During 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 the battery life from decreasing due to over - charging and over - discharging.

[0108]

[0109] In the formula: and are the minimum and maximum state of charge of the battery of electric vehicle j respectively.

[0110] (2) Charging and discharging state constraint.

[0111] Introduce a binary variable μ 1 , μ 2 ∈{0, 1} to constrain the charging and discharging state of the electric vehicle so that it cannot charge and discharge simultaneously, μ 1= 1 indicates electric vehicle charging, μ 2 = 1 indicates electric vehicle discharging.

[0112]

[0113] Where: is the maximum charging power of electric vehicle j, is the maximum discharging power of electric vehicle j.

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

[0115] In actual life, the state of charge of an electric vehicle when off-grid needs to meet the travel needs of users.

[0116]

[0117] Where: is the state of charge of electric vehicle j when off-grid (t = T), is the set state of charge of electric vehicle j when off-grid.

[0118] (4) Charge and discharge limit constraints, taking into account user preferences and avoiding adverse effects on the battery due to excessive charge and discharge ranges of electric vehicles.

[0119]

[0120] Where: is the maximum limit of the SOC of the cumulative charge and discharge of electric vehicle j during the optimization period.

[0121] S3: Introduce a cluster air-conditioning load control model and an electric vehicle charge and discharge model, consider external spot price fluctuations, and construct a collaborative optimization model of cluster air-conditioning and electric vehicles with economic optimality as the goal.

[0122] Specific economic parameters are as follows:

[0123] With the goal of optimal economic cost in the distribution area, including power purchase cost, regulation cost, comfort cost, and transformer over-limit loss cost. λ EV 、λ ac 、γ comfort and μ overloss The coefficient values are 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 power purchase cost function is as follows:

[0126] The electricity purchase cost refers to the cost incurred by the substation area in purchasing electricity from the grid side, which is calculated based on the electricity price and the electricity purchase quantity:

[0127]

[0128] In the formula: C buy_e refers to the cost incurred by purchasing electricity from the grid side, P t grid is the electricity purchase power of the substation area from the grid side at time period t, is the electricity price at time period t, and T represents the total time period.

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

[0130]

[0131] Among them, C ope represents the operation and maintenance cost of electric vehicle charging / discharging and air-conditioning load regulation, λ EV and λ ac are respectively the operation and maintenance cost coefficients of electric vehicle charging / discharging and air-conditioning load adjustment, is the original load of air conditioner i at time period t.

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

[0133]

[0134] In the formula: C comfort represents the penalty cost generated when the indoor temperature exceeds the comfort range, and γ comfort represents the comfort temperature penalty cost coefficient.

[0135] Economic item 4: The transformer overlimit loss cost function is as follows:

[0136]

[0137] In the formula: C overloss represents the overlimit loss cost of the substation area transformer, and μ overloss is the transformer overlimit loss cost coefficient, P grid_tar is the rated power of the transformer.

[0138] S4: Considering that the constraint conditions of the collaborative optimization model of the cluster air conditioner and electric vehicle include, and solving according to the collaborative optimization model, the optimized result is obtained. Among them, the basic load under the substation area for each time period is shown in Table 4.

[0139] Table 4 Basic load under the substation area for each time period

[0140] Moment 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] Where: P t load is the basic load in the substation area during time period t,

[0144] (2) Grid power purchase constraint

[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] Where: P grid_max is the maximum operating power allowed by the transformer.

[0148] (3) Air conditioner load related constraint

[0149]

[0150] Where: and are respectively the maximum and minimum values of the load regulation of air conditioner i during time period t.

[0151] The optimized results obtained by solving are shown in Table 5. The total cost F is 6681.34 yuan, the power 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 overlimit penalty is 105.78 yuan, accounting for 1.59%.

[0152] Table 5 Optimized economic item results

[0153] Power purchase cost Operation and maintenance cost Comfort penalty Transformer overlimit penalty Cost / yuan 5242.1 835.96 497.5 105.78 Proportion of total cost 78.45％ 12.51％ 7.45％ 1.59％

[0154] The charging and discharging power of electric vehicles is as Figure 3 shown. It can be seen from the figure that in the earlier time period, that is, when electric vehicles first enter the grid, due to the low electricity price and the low state of charge (SOC) of themselves, electric vehicles tend to choose the charging mode. About 5 hours after charging is completed, that is, around 11:00 am, when the electricity price is at a high level, electric vehicles switch to the discharging mode, releasing electric energy to the grid to relieve the load pressure on the substation area transformer, thereby reducing the power purchase cost of the substation area and optimizing the power consumption structure of the substation area. In the subsequent time periods, electric vehicles will flexibly adjust their charging and discharging strategies according to the electricity price fluctuations: choosing to charge in the time periods with low electricity prices, and continuing to undertake the discharging task in the time periods with high electricity prices to achieve the goal of economic operation of the substation area.

[0155] The operating power of air conditioners in the substation area is as Figures 4 - 6 shown, Figure 4 The operating power of air conditioners in the substation area (Air conditioners 1 - 10), Figure 5 The operating power of air conditioners in the substation area (Air conditioners 11 - 20), Figure 6 The operating power of air conditioners in the substation area (Air conditioners 21 - 30); By dynamically adjusting the operating power of air conditioners, while ensuring that the indoor temperature is within the set range, it avoids unnecessary large power fluctuations at the same time period, and reduces the peak load of the substation area.

[0156] The power purchase load of the substation area is as Figure 1 shown. During the peak electricity consumption period, through the collaborative optimization mechanism of air conditioners and electric vehicles, the electricity purchase volume during peak hours is effectively reduced, the dependence of the substation area on the power grid during peak hours is reduced, the demand for a large amount of high - price electricity purchase is reduced, thus effectively alleviating the pressure on the transformer, and achieving the goal of optimizing the electricity consumption structure and economic operation of the substation area.

[0157] The above - mentioned is only the preferred implementation manner of the present invention. It should be pointed out that: for those of ordinary skill in the art of this technology, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A cluster air conditioner and electric vehicle collaborative optimization method for a power station, characterized in that: The following steps are involved: S1. Input air conditioning load related parameters, establish cluster air conditioning load control model based on temperature difference, and ensure that air conditioning control is within the comfort range. S2. Input the relevant parameters of electric vehicles, build an electric vehicle charging and discharging model that takes into account the charging and discharging limits, pay attention to the reasonable limits of the car battery charging and discharging, and ensure that the electric vehicle control is within the comfortable range. S3. Introducing the S1 cluster air conditioning load control model and the S2 electric vehicle charging and discharging model, considering the external spot price fluctuations, a cluster air conditioning and electric vehicle collaborative optimization model with economic optimization as the goal is constructed. The objective function includes: electricity purchase cost, regulation cost, comfort cost and transformer over-limit loss cost. S4. Solve the cluster air conditioning and electric vehicle collaborative optimization model in S3 to obtain the cluster air conditioning control strategy and electric vehicle charging and discharging strategy.

2. The method for coordinated optimization of cluster air conditioners and electric vehicles for a substation according to claim 1, characterized in that: The cluster air conditioning load control model based on temperature difference is as follows: The load of air conditioner i in period t as follows: Where: is the basic load of air conditioner i in time period t, t∈T, T represents the total time period, i∈I, I is the total number of air conditioners in the area; is the control load of air conditioner i in period t, In order to further improve the smoothness of air conditioning power, a cluster air conditioning load control model based on temperature difference is established. Where: is the control load of air conditioner i in period t-1; β i is the air conditioning temperature difference control coefficient, which is related to the air conditioning energy efficiency ratio and the room equivalent thermal resistance as follows: β i =1η i R i , reflecting the sensitive characteristics of air conditioning temperature difference, where η i and R i They are air conditioner energy efficiency ratio and room equivalent thermal resistance; is the temperature setting value of air conditioner i in period t; T i set_min and T i set_max are the lower and upper limits of the operating temperature of air conditioner i respectively.

3. The method for coordinated optimization of cluster air conditioners and electric vehicles for a substation according to claim 1, characterized in that: When building V2G access, the electric vehicle charging and discharging model considering the charging and discharging limit is: Where: SOC j,t is the state of charge of the electric vehicle battery in period j, J is the total number of electric vehicles in the area; SOC j,t-1 is the state of charge of the electric vehicle battery at time period t-1, and are the charging and discharging powers of electric vehicle j in period t, is the maximum capacity of the electric vehicle j battery, Δt is the simulation step length, The constraints include: (1) SOC limit constraint: During the actual charging process, it is necessary to set the maximum and minimum state of charge of the electric vehicle battery to protect the battery and prevent overcharging and over-discharging of the battery from causing a decrease in battery life. Where: and are the minimum and maximum state of charge of electric vehicle battery j, (2) Charge and discharge state constraints, The binary variables μ1, μ2∈{0,1} are introduced to constrain the charging and discharging states of electric vehicles so that they cannot be charged and discharged at the same time. μ1=1 means that the electric vehicle is charging, and μ2=1 means that the electric vehicle is discharging. Where: is the maximum charging power of electric vehicle j, is the maximum discharge power of electric vehicle j, (3) off-grid SOC limit, The charge state of off-grid electric vehicles should meet the travel needs of users. Where: is the charge state of electric vehicle j when it is off-grid (t = T), is the state of charge set for electric vehicle j when it is off-grid, (4) Charge and discharge limit constraints, taking into account user preferences and avoiding adverse effects on the battery due to excessive battery charge and discharge range, Where: It is the maximum limit of SOC of electric vehicle j during the optimization period of cumulative charging and discharging.

4. The method for coordinated optimization of cluster air conditioners and electric vehicles for a substation according to claim 1, characterized in that: The objective function of the cluster air conditioning and electric vehicle collaborative optimization model is: The goal is to optimize the economic cost of the substation, including power 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 cost function of purchasing electricity is as follows: The electricity purchase cost refers to the cost of purchasing electricity from the grid, which is calculated based on the electricity price and the amount of electricity purchased: Where: C buy_e Refers to the cost of purchasing electricity from the grid, P t grid is the power purchased from the grid by the station area during period t, is the price of electricity purchased from the grid in the period t, Economic Item 2: The regulation cost function is as follows: Among them, C ope represents the operation and maintenance cost of electric vehicle charging and discharging and air conditioning load regulation, λ EV and λ ac are the operation and maintenance cost coefficients of electric vehicle charging and discharging and air conditioning load adjustment, Economic Item 3: The comfort cost function is as follows: Where: C comfort represents the penalty cost caused by the indoor temperature exceeding the comfort range, γ comfort represents the comfort penalty cost coefficient, Economic Item 4: Transformer over-limit loss cost function is as follows: Where: C overloss represents the over-limit loss cost of the transformer in the substation area, μ overloss is the transformer over-limit loss cost coefficient, P grid _ tar is the rated power of the transformer.

5. The method for coordinated optimization of cluster air conditioners and electric vehicles for a substation according to claim 1, characterized in that: 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 constraints Where: P t load is the base load of the substation in period t, (2) Constraints on power purchases from the power grid In order to ensure the safe and stable operation of the power grid, the maximum power purchase power of the substation and the power grid is restricted. 0≤P t grid ≤P grid_max Where: P grid_max is the maximum operating power allowed by the transformer, (3) Air conditioning load related constraints Where: and are the minimum and maximum values ​​of load control of air conditioner i in period t.

6. The method for coordinated optimization of cluster air conditioners and electric vehicles for a substation according to claim 1, characterized in that: Solve the collaborative optimization model to obtain cluster air conditioning control strategy and electric vehicle charging and discharging strategy, including: The CPLEX solver is used to solve this mixed integer programming problem and obtain the optimized user subject results participating in load management. YALMIP is used as the optimization framework to transform the decision variables, objective function and constraints into a standard mathematical model, which is optimized through the solver and outputs the optimized user subject results participating in load management.

7. A cluster air conditioner and electric vehicle collaborative optimization system for a substation, used to implement the cluster air conditioner and electric vehicle collaborative optimization method for a substation as claimed in any one of claims 1 to 6, characterized in that: The system consists of three modules: Cluster air conditioning load control temperature difference module, design cluster air conditioning load control mechanism related to temperature difference change value, meet the comfort and smooth change requirements of air conditioning load in the substation; The electric vehicle charging and discharging limit module regulates the charging and discharging of electric vehicles and reasonably 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 taking into account external spot price fluctuations to achieve optimal economic coordination between cluster air-conditioning and electric vehicles within the area.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the cluster air conditioner and electric vehicle collaborative optimization method for the substation is implemented as described in any one of claims 1 to 7.

9. A computer-readable storage medium having computer instructions stored thereon, characterized in that: When the computer instruction is executed by the processor, the cluster air conditioner and electric vehicle collaborative optimization method for the substation as described in any one of claims 1 to 7 is implemented.

Images