Air conditioner load and electric vehicle energy mutual aid regulation and control optimization method and system

Abstract

The invention provides an air conditioner load and electric vehicle energy mutual aid regulation optimization method and system, and the method comprises the steps: adjusting the charging behavior of an electric vehicle in a transformer area according to the power grid demand, and carrying out the mutual aid regulation of the electric vehicle; the electric energy consumption behaviors of respective air conditioning equipment of residents in the transformer area are adjusted, and mutual aid scheduling is conducted on air conditioning loads; and on the basis of electric vehicle mutual aid regulation and control and air conditioner load mutual aid scheduling, collaborative mutual aid optimization is carried out on the electric vehicle and the air conditioner equipment. Through coordinated optimization of the air conditioning load and electric vehicle charging and discharging, the peak-valley difference rate of the power grid is effectively reduced, balanced distribution of the load of the power grid and minimization of the peak-valley difference rate are realized, the stability of the power grid is improved, and the method has important theoretical and practical significance for promoting development of the smart power grid and improving the energy management level of a residential district.

Description

Technical Field

[0001] The present invention relates to the technical field of energy joint regulation, and in particular to a method and system for optimizing the mutual energy regulation of air-conditioning loads and electric vehicles, as well as a corresponding computer terminal and a computer-readable storage medium. Background Art

[0002] With the massive access of various distributed resources to the distribution network, the operation of the distribution network is developing in a distributed and interactive direction, showing a more flexible and changeable trend. However, distributed entities are characterized by large number, wide variety, and dispersed forms. If they are not effectively managed, the stability and reliability of the power system may be affected to a certain extent, causing a great impact on the power supply of power grids at all levels. Therefore, it is necessary to take effective management and control measures to coordinate and fully utilize different types of distributed resources to ensure the long-term safe and efficient operation of the power grid.

[0003] Electric vehicles and air conditioning loads are the two most important forms of distributed resources today, and they play a key role in improving the economic operation level of power grids. In recent years, my country's new energy vehicles and their supporting charging facilities have grown rapidly. As a key component of energy terminal electrification, electric vehicles have strong controllability, but disorderly charging in the absence of effective guidance will cause peak loads on top of peak loads. With the improvement of people's living standards and technological progress, air conditioning loads have become a type of load that accounts for a relatively high proportion of power peaks in power grids, causing a great impact on the power supply of power grids at all levels. Therefore, it is of great significance to optimize and control these two forms of distributed resources.

[0004] With the acceleration of urbanization and the improvement of residents' living standards, air conditioners and electric vehicles, as important symbols of modern life, have an increasingly prominent position in the energy consumption of residential areas. However, the sharp increase in air conditioning load and electric vehicle charging demand has brought unprecedented challenges to the stability and reliability of the power grid. Especially during peak hours, the sharp fluctuation of power grid load may lead to unstable power supply or even power outages, seriously affecting the quality of life of residents and the economic operation efficiency of the power grid. Considering that after returning to the residential area, electric vehicles will stay in the parking position and start charging, they can start to participate in the dispatch optimization of electric vehicles by the power grid; at the same time, after returning to the residential area, the residents start the air conditioner, and the total load begins to increase. At this level, there is a strong spatiotemporal coupling between the air conditioning load and the electric vehicle load. How to coordinate and optimize the two is of practical significance and is also a problem to be solved in this field.

[0005] At present, no description or report of similar technology to the present invention has been found, and similar information at home and abroad has not been collected yet. Summary of the invention

[0006] In view of the above-mentioned deficiencies in the prior art, the present invention provides a method and system for optimizing the mutual regulation of energy between air-conditioning load and electric vehicles, and also provides a corresponding computer terminal and computer-readable storage medium.

[0007] According to a first aspect of the present invention, there is provided a method for optimizing the mutual regulation of energy between air-conditioning load and electric vehicle, comprising:

[0008] Adjust the charging behavior of electric vehicles in the substation area according to the grid demand and conduct mutual regulation of electric vehicles;

[0009] Adjust the energy consumption of air-conditioning equipment of residents in the area and conduct mutual assistance scheduling of air-conditioning loads;

[0010] Based on the mutual assistance regulation of electric vehicles and the mutual assistance scheduling of air-conditioning loads, the coordinated mutual assistance between electric vehicles and air-conditioning equipment is optimized.

[0011] Preferably, adjusting the charging behavior of electric vehicles in the substation area according to the grid demand and performing mutual assistance regulation on the electric vehicles includes:

[0012] Providing an electric vehicle optimization scheduling model;

[0013] Based on the electric vehicle optimal dispatching model, evaluating the dispatchable capacity of electric vehicle load;

[0014] Based on the evaluation result of the dispatchable capacity of the electric vehicle load, considering V2G access, a real-time optimization dispatch model of electric vehicle energy based on V2G access is constructed;

[0015] Based on the real-time optimization and dispatching model of electric vehicle energy, the charging behavior of electric vehicles in the substation is adjusted according to the demand of the power grid.

[0016] Preferably, providing an electric vehicle optimization scheduling model includes:

[0017] An electric vehicle optimization dispatching model is constructed. The objective function of the optimization dispatching model is to minimize the total cost of electricity purchase in the substation area. Then:

[0018]

[0019] S T ={1,2,...,T / Δt} (1-4)

[0020] In the formula, is the electricity purchase cost of the aggregator after period t, S T is the set of discrete optimizable time periods in the optimizable scheduling period T, P buy (t) and p TOU(t) are the corresponding power purchase power and time-of-use electricity price of the aggregator in time period t, Δt is the discrete time step, formula (1-3) is the power balance constraint, and formula (1-4) is the safe operation constraint, where: is the non-electric vehicle resident load in the residential area, Charging load for electric vehicles, The maximum value of the power purchased by the aggregator.

[0021] Preferably, the evaluating of the dispatchable potential of electric vehicle load based on the electric vehicle optimal dispatch model includes:

[0022] For different optimizable periods of the electric vehicle optimization scheduling model, the dispatchable capacity of a single electric vehicle is determined according to the following constraints on the dispatchable capacity of electric vehicles:

[0023] User travel restrictions:

[0024] SOC target (j)≤SOC(j,t off )≤1 (1-5)

[0025] In the formula, SOC target is the battery charging target value, t off is the departure time, j is the vehicle serial number;

[0026] Battery life constraints:

[0027]

[0028] Where SOC(j,t) represents the energy storage of electric vehicle j at time t; P dis (j, t) represents the charge and discharge power of electric vehicle j after scheduling time t; SOC min (j) and SOC max (j) represent the lower and upper limits of the energy storage of electric vehicle j, SOC base (j) is the battery capacity of electric vehicle j, and η is the charge and discharge efficiency;

[0029] Battery maximum charge and discharge power constraints:

[0030] P dis,min (j,t)≤P dis (j,t)≤P dis,max (j,t) (1-7)

[0031] P ch,min (j,t)≤P dis (j,t)≤P ch,max (j,t) (1-8)

[0032] Where P ch,max (j), P ch,min (j) and P dis,max (j), P dis,min (j) represent the maximum and minimum charging and discharging power of electric vehicle j, respectively, where the specifications of the charging pile determine the maximum charging power and the maximum discharging power;

[0033] Time and object constraints:

[0034] t on ≤t≤t sch ≤t off (1-9)

[0035] Where, t on is the vehicle arrival time, t sch is the schedulable time;

[0036] Battery V2G discharge period capacity constraints:

[0037]

[0038] In the formula, SOC target (j) represents the charging target value of electric vehicle j, and SOC(j,t) represents the initial power; when equation (1-10) takes the equal sign, we get P dis The upper limit of (j,t) is:

[0039]

[0040] P up (j,t)=P o (j,t)-P dis (j,t) (1-12)

[0041] Where P up (j, t) represents the adjustable charging power of electric vehicle j during the scheduling interval t, P o (j, t) represents the charging power of the jth electric vehicle when it does not participate in the battery capacity scheduling within the scheduling interval t;

[0042] Battery charging period capacity constraints:

[0043]

[0044] When the equation (1-13) is equal, we get P dis The threshold value P of (j,t) dis,max (j, t); combined with the capacity constraint (1-10), the downward dispatchable capacity formula can be expressed as:

[0045] P down (j,t)=Pdis (j,t)-P0(j,t) (1-14)

[0046] Where P down (j, t) represents the adjustable charging power of electric vehicle j in the scheduling interval t;

[0047] Based on the dispatchable capacity of the single electric vehicle, the adjustable power P of the electric vehicle cluster in the interval is obtained. up,all and adjustable power P down,all for:

[0048]

[0049] Where N is the total number of electric vehicles;

[0050] If electric vehicle j cannot participate in the dispatch within the dispatch interval t, then:

[0051] P up (j,t)=P down (j,t)=0 (1-17).

[0052] Preferably, the evaluation result of the dispatchable potential of the electric vehicle load is based on the consideration of V2G access, and a real-time optimization dispatch model of electric vehicle energy based on V2G access is constructed, including:

[0053] The basic model for real-time optimization and dispatch of electric vehicle energy under V2G access is constructed as follows:

[0054]

[0055] In the formula, For aggregators k The electricity purchase cost within a dispatchable period is For the k Time-of-use electricity prices in different optimizable time periods within a dispatchable cycle, For the purchased power, For aggregators k Total V2G subsidy in a dispatchable period, Subsidy for V2G units, represents the discharge power of electric vehicle j in time period t;

[0056] The power balance constraint and power purchase power limit constraint during the charging process are established as follows:

[0057]

[0058] In the formula, For the k The predicted value of conventional residential load in a dispatchable period, and Each is the electric car j in the k The charge and discharge power within a dispatchable cycle;

[0059] The charging and discharging constraints of electric vehicles are established as follows:

[0060]

[0061] Soc min (j)≤Soc k (j,t)≤Soc max (j) t∈S T (k) (1-27)

[0062] In the formula, and are the upper limits of charging and discharging power of electric vehicle j, respectively. For non-dispatchable electric vehicles, that is, v k (j, t) = 0, which corresponds to and Both are 0. Formula (1-25) indicates that the electric vehicle cannot be charged and discharged at the same time. Formula (1-26) indicates the relationship between the SOC states of electric vehicle j in adjacent time periods. and is the charging and discharging efficiency coefficient of electric vehicle j, E EV (j) is the battery capacity of electric vehicle j, in formula (1-27), Soc min (j) and Soc max (j) are the lower and upper limits of the SOC of electric vehicle j, respectively;

[0063] The electric vehicle charging satisfaction rate constraint is established as:

[0064]

[0065] In the formula, Soc Tar (j,T / Δt) is the target SOC for the discrete time period T / Δt set by the owner of electric vehicle j, Soc k (j,k-1) is the starting value of the battery state of charge of electric vehicle j in the kth dispatchable period.

[0066] Preferably, the adjusting the power consumption behavior of the air-conditioning equipment of each resident in the substation area and performing mutual assistance scheduling on the air-conditioning load includes:

[0067] Construct a thermodynamic model of air conditioning load to simulate the temperature changes of the room under the influence of indoor and outdoor cold and heat sources;

[0068] Based on the thermodynamic model of the air conditioning load, the operating characteristics and control methods of the air conditioning are analyzed, and a mathematical model of the air conditioning load is constructed;

[0069] Based on the mathematical model of the air conditioning load, the control mode of the air conditioning is analyzed and the dispatchable potential of the air conditioning load is evaluated;

[0070] According to the evaluation result of the dispatchable potential of the air-conditioning load, the power consumption behavior of the air-conditioning equipment of each resident in the substation is adjusted.

[0071] Preferably, the analyzing the control mode of the air conditioner and evaluating the dispatchable potential of the air conditioner load includes:

[0072] The control method of the air conditioner includes: switch control and temperature control of a fixed-frequency air conditioner, and temperature control and frequency control of a variable-frequency air conditioner;

[0073] For the on / off control of fixed-frequency air conditioners, the schedulable potential of the on / off response can also be evaluated as follows:

[0074] The thermodynamic model of air conditioning load is shown in equation (2-1) to equation (2-3):

[0075]

[0076] τ=RC (2-3)

[0077] In the formula, is the indoor temperature at time t, is the indoor temperature after a time interval of Δt, is the outdoor temperature at time t, R is the equivalent thermal resistance, Q Δt is the cooling capacity of the air conditioner, λ is the proportional factor, Δt is the time interval, τ is the time constant, and C is the equivalent heat capacity;

[0078] From the above formula, we can deduce:

[0079]

[0080] Where P Δt is the cooling power of the air conditioner, COP is the energy efficiency ratio of the air conditioner;

[0081]

[0082]

[0083] Where: S(t k ) and S(t k-1 ) are t k Time and t k-1 The switch operation status of the air conditioning load, T(t k ) is tk The indoor temperature at the time, T max is the upper limit of air conditioner operating temperature, T min The lower limit of the air conditioner operating temperature. and They are the down-adjustable time and up-adjustable time under the fixed-frequency air conditioner switch control, T O is the outdoor temperature, T set0 is the set temperature under the fixed frequency air conditioner switch control, Q is the cooling capacity of the air conditioner, k t is the operating state variable of the air conditioner, k t =1 means the air conditioner is in operation, k t =0 means the air conditioner is in standby mode, T t is the current indoor temperature, It is the maximum down-adjustable time under the fixed frequency air conditioner switch control. It is the maximum adjustable time under the fixed frequency air conditioner switch control. and They are the adjustable capacity power and adjustable capacity power under the switch control of the fixed-frequency air conditioner, which are used to describe the dispatchable potential of the fixed-frequency air conditioner. COP is the energy efficiency ratio.

[0084] For the temperature control of fixed-frequency air conditioners, the schedulable potential of temperature control response can also be evaluated as follows:

[0085] Set the new temperature setting value to T set1 , the dispatchable time period of air conditioning load at the new temperature for:

[0086]

[0087] Where, T set1 It is the set temperature under the temperature control of the fixed frequency air conditioner. It is the downward adjustment time under the temperature control of the fixed frequency air conditioner. It is the upward adjustment time under the temperature control of the fixed frequency air conditioner. The time that a fixed-frequency air conditioner can be adjusted down from the new set temperature. The time that a fixed-frequency air conditioner can be adjusted upward from the new set temperature;

[0088] The capacity power that can be adjusted up or down for the corresponding air conditioning load is:

[0089]

[0090] In the formula, and They are respectively the adjustable capacity power and the adjustable capacity power under the temperature control of the fixed-frequency air conditioner;

[0091] For the temperature control and frequency control of variable frequency air conditioners, the virtual energy storage modeling method can be used to evaluate the dispatchable potential of variable frequency air conditioners as follows:

[0092] A virtual energy storage model based on variable frequency air conditioning is established, which is used to simulate the conversion of electric power energy into heat energy:

[0093] The operating state of the virtual energy storage model and the consumed electric power are analyzed to obtain the SOC expression of the virtual energy storage model:

[0094]

[0095] In the formula, S SOC (t) and S SOC (t-1) are the battery charge states at the current time and the previous time period respectively; η es is the charge and discharge efficiency; P es (t-1) is the charge and discharge power in the previous period. When the power value is positive, it means that the battery is in the discharge state. When the power value is negative, it means that the battery is in the charge state. C es Indicates the battery capacity;

[0096] The constraints for establishing normal working situations are:

[0097] 0≤S SOC (t)≤100% (2-22)

[0098] P es,min ≤P es (t)≤P es,max (2-23)

[0099] Where P es,min and P es,max Respectively represent the maximum charging and discharging power of the virtual energy storage model;

[0100] Calculate the equivalent capacity C of the virtual energy storage model v for:

[0101]

[0102] In the formula, To increase the maximum amount of heat in the room, is the maximum heat reduction in the room, η AC is the energy efficiency ratio of the variable frequency air conditioner;

[0103] The thermal energy equivalent capacity of virtual energy storage is obtained as:

[0104]

[0105] Where, T set 、T max 、T min are the set temperature, upper limit temperature and lower limit temperature that can satisfy the needs of residents using variable frequency air conditioners in the area, C room is the equivalent heat capacity of the room;

[0106] Calculate the equivalent state of charge of the virtual energy storage model for:

[0107]

[0108] Where, T in is the initial ambient temperature;

[0109] Calculate the maximum charge and discharge power P of the virtual energy storage model v (t) is:

[0110] P AC0 -P AC (t)≤P v (t)≤P AC1 -P AC (t) (2-28)

[0111] Where P AC0 and P AC1 P is the working power of the air conditioner in standby operation and full load operation respectively; AC (t) is the real-time operating power of the air conditioner.

[0112] Preferably, the coordinated mutual assistance optimization between electric vehicles and air conditioning equipment based on the mutual assistance control method of electric vehicles and the mutual assistance scheduling of air conditioning loads includes:

[0113] Let L n,i is the initial daily load value of the power grid, i represents 1, 2, ..., N different optimizable time periods of the day, and the load of time period i for collaborative optimization is L i , EV charging load is L ch,i , EV discharge load is L dis,i , the air conditioning aggregate power is P ac,i , the total load in the area L i It is expressed as:

[0114]

[0115] Where P ch (i,j) is the charging power of the jth electric vehicle in time period i, P dis (i,j) is the discharge power of the jth electric vehicle in time period i, P ac,i is the power consumed by the air conditioner in period i;

[0116] The optimization goal is set to minimize the peak-to-valley difference rate of power grid load, then:

[0117]

[0118] Among them, min(F) is the objective function of minimizing the peak-to-valley difference rate of power grid load, max(L i ) is the peak load value in period i; min(L i ) is the load valley value in period i;

[0119] Set the following constraints:

[0120] The temperature range constraint T(t) of the air conditioning equipment is:

[0121] T set,min ≤T(t)≤T set,max (3-3)

[0122] Among them, T set,min and T set,max are the lower and upper limits of the ambient temperature respectively;

[0123] The actual working power constraint P of the aggregate air conditioning equipment AC，,i for:

[0124] P AC，,i ≤P AC,imax (3-4)

[0125] Among them, P AC,imax The initial operating power of the aggregated air-conditioning equipment before synergistic optimization;

[0126] The electric vehicle battery capacity constraint SOC(t) is:

[0127] SOC min ≤SOC(t)≤SOC max (3-5)

[0128] Among them, SOC min SOC is the lower limit of the battery's energy storage capacity during charging. max The upper limit of the energy storage capacity of the battery during charging;

[0129] The charging and discharging mode constraints of electric vehicles are:

[0130] min(P dis (t),P ch (t))=0 (3-6)

[0131] Among them, P dis (t) is the discharge power of the electric vehicle at time t, P ch(t) is the charging power of the electric vehicle at time t; the remaining power constraint when the electric vehicle leaves is:

[0132] SOC(j,t off )≥n (3-7)

[0133] Among them, SOC(j,t off ) represents the battery energy storage state SOC at the time when electric vehicle j leaves, and n is the battery energy storage charge state setting threshold.

[0134] According to a second aspect of the present invention, there is provided an energy mutual regulation and optimization system for air conditioning load and electric vehicle, comprising:

[0135] Electric vehicle mutual assistance control module, which is used to adjust the charging behavior of electric vehicles in the substation area according to the needs of the power grid and perform mutual assistance control on electric vehicles;

[0136] The air conditioning load mutual assistance scheduling module is used to adjust the energy consumption behavior of the air conditioning equipment of each resident in the substation area and perform mutual assistance scheduling of the air conditioning load;

[0137] The collaborative optimization module optimizes the collaborative optimization between electric vehicles and air-conditioning equipment based on the mutual regulation of electric vehicles and the mutual scheduling of air-conditioning loads.

[0138] Preferably, the electric vehicle mutual assistance control module comprises:

[0139] An electric vehicle load dispatchable capacity evaluation unit, the unit is used to provide an electric vehicle optimal dispatch model, and based on the electric vehicle optimal dispatch model, evaluate the electric vehicle load dispatchable capacity;

[0140] An electric vehicle charging behavior adjustment unit, based on the evaluation result of the dispatchable capacity of the electric vehicle load and considering V2G access, constructs an electric vehicle energy real-time optimization scheduling model based on V2G access; based on the electric vehicle energy real-time optimization scheduling model, adjusts the charging behavior of electric vehicles in the substation according to the power grid demand.

[0141] Preferably, the electric vehicle load dispatchable capacity evaluation unit provides an electric vehicle optimization dispatch model, including:

[0142] An electric vehicle optimization dispatching model is constructed. The objective function of the optimization dispatching model is to minimize the total cost of electricity purchase in the substation area. Then:

[0143]

[0144] S T ={1,2,...,T / Δt} (1-4)

[0145] In the formula, is the electricity purchase cost of the aggregator after period t, S T is the set of discrete optimizable time periods in the optimizable scheduling period T, P buy (t) and p TOU (t) are the corresponding power purchase power and time-of-use electricity price of the aggregator in time period t, Δt is the discrete time step, formula (1-3) is the power balance constraint, and formula (1-4) is the safe operation constraint, where: is the non-electric vehicle resident load in the residential area, Charging load for electric vehicles, The maximum value of the power purchased by the aggregator.

[0146] Preferably, the electric vehicle load dispatchable capacity evaluation unit evaluates the electric vehicle load dispatchable potential based on the electric vehicle optimization dispatch model, including:

[0147] For different optimizable periods of the electric vehicle optimization scheduling model, the dispatchable capacity of a single electric vehicle is determined according to the following constraints on the dispatchable capacity of electric vehicles:

[0148] User travel restrictions:

[0149] SOC target (j)≤SOC(j,t off )≤1 (1-5)

[0150] In the formula, SOC target is the battery charging target value, t off is the departure time, j is the vehicle serial number;

[0151] Battery life constraints:

[0152]

[0153] Where SOC(j,t) represents the energy storage of electric vehicle j at time t; P dis (j, t) represents the charge and discharge power of electric vehicle j after scheduling time t; SOC min (j) and SOC max (j) represent the lower and upper limits of the energy storage of electric vehicle j, SOC base (j) is the battery capacity of electric vehicle j, and η is the charge and discharge efficiency;

[0154] Battery maximum charge and discharge power constraints:

[0155] P dis,min (j,t)≤P dis (j,t)≤P dis,max(j,t) (1-7)

[0156] P ch,min (j,t)≤P dis (j,t)≤P ch,max (j,t) (1-8)

[0157] Where P ch,max (j), P ch,min (j) and P dis,max (j), P dis,min (j) represent the maximum and minimum charging and discharging power of electric vehicle j, respectively, where the specifications of the charging pile determine the maximum charging power and the maximum discharging power;

[0158] Time and object constraints:

[0159] t on ≤t≤t sch ≤t off (1-9)

[0160] Where, t on is the vehicle arrival time, t sch is the schedulable time;

[0161] Battery V2G discharge period capacity constraints:

[0162]

[0163] In the formula, SOC target (j) represents the charging target value of electric vehicle j, and SOC(j,t) represents the initial power; when equation (1-10) takes the equal sign, we get P dis The upper limit of (j,t) is:

[0164]

[0165] P up (j,t)=P o (j,t)-P dis (j,t) (1-12)

[0166] Where P up (j, t) represents the adjustable charging power of electric vehicle j during the scheduling interval t, P o (j, t) represents the charging power of the jth electric vehicle when it does not participate in the battery capacity scheduling within the scheduling interval t;

[0167] Battery charging period capacity constraints:

[0168]

[0169] When the equation (1-13) is equal, we get P dis The threshold value P of (j,t) dis,max (j, t); combined with the capacity constraint (1-10), the downward dispatchable capacity formula can be expressed as:

[0170] P down (j,t)=P dis (j,t)-P0(j,t) (1-14)

[0171] Where P down (j, t) represents the adjustable charging power of electric vehicle j in the scheduling interval t;

[0172] Based on the dispatchable capacity of the single electric vehicle, the adjustable power P of the electric vehicle cluster in the interval is obtained. up,all and adjustable power P down,all for:

[0173]

[0174] Where N is the total number of electric vehicles;

[0175] If electric vehicle j cannot participate in the dispatch within the dispatch interval t, then:

[0176] P up (j,t)=P down (j,t)=0 (1-17).

[0177] Preferably, in the electric vehicle charging behavior adjustment unit, based on the evaluation result of the electric vehicle load dispatchable potential, considering V2G access, a real-time optimization dispatch model of electric vehicle energy based on V2G access is constructed, including:

[0178] The basic model for real-time optimization and dispatch of electric vehicle energy under V2G access is constructed as follows:

[0179]

[0180] In the formula, For aggregators k The electricity purchase cost within a dispatchable period is For the k Time-of-use electricity prices in different optimizable time periods within a dispatchable cycle, For the purchased power, For aggregators k Total V2G subsidy in a dispatchable period, Subsidy for V2G units, represents the discharge power of electric vehicle j in time period t;

[0181] The power balance constraint and power purchase power limit constraint during the charging process are established as follows:

[0182]

[0183] In the formula, For the k The predicted value of conventional residential load in a dispatchable period, and Each is the electric car j in the k The charge and discharge power within a dispatchable cycle;

[0184] The charging and discharging constraints of electric vehicles are established as follows:

[0185]

[0186] Soc min (j)≤Soc k (j,t)≤Soc max (j) t∈S T (k) (1-27)

[0187] In the formula, and are the upper limits of charging and discharging power of electric vehicle j, respectively. For non-dispatchable electric vehicles, that is, v k (j, t) = 0, which corresponds to and Both are 0. Formula (1-25) indicates that the electric vehicle cannot be charged and discharged at the same time. Formula (1-26) indicates the relationship between the SOC states of electric vehicle j in adjacent time periods. and is the charging and discharging efficiency coefficient of electric vehicle j, E EV (j) is the battery capacity of electric vehicle j, in formula (1-27), Soc min (j) and Soc max (j) are the lower and upper limits of the SOC of electric vehicle j, respectively;

[0188] The electric vehicle charging satisfaction rate constraint is established as:

[0189]

[0190] In the formula, Soc Tar (j,T / Δt) is the discrete time period set by the owner of electric vehicle j T / Δt Target SOC,Soc k (j,k-1) is the number of electric vehicles j in the kThe starting value of the electric vehicle battery state of charge within a dispatchable period.

[0191] Preferably, the air conditioning load mutual assistance scheduling module includes:

[0192] An air conditioning load model building unit, which is used to build a thermodynamic model of the air conditioning load to simulate the temperature change of the room under the action of indoor and outdoor cold and heat sources; based on the thermodynamic model of the air conditioning load, the operating characteristics and control methods of the air conditioner are analyzed to build a mathematical model of the air conditioning load;

[0193] An air conditioning equipment power consumption behavior adjustment unit analyzes the air conditioning control method based on the mathematical model of the air conditioning load and evaluates the dispatchable potential of the air conditioning load; according to the evaluation result of the dispatchable potential of the air conditioning load, the air conditioning equipment power consumption behavior of each resident in the substation is adjusted.

[0194] Preferably, the air conditioning equipment power consumption behavior adjustment unit analyzes the air conditioning control mode and evaluates the dispatchable potential of the air conditioning load, including:

[0195] The control method of the air conditioner includes: switch control and temperature control of a fixed-frequency air conditioner, and temperature control and frequency control of a variable-frequency air conditioner;

[0196] For the on / off control of fixed-frequency air conditioners, the schedulable potential of the on / off response can also be evaluated as follows:

[0197] The thermodynamic model of air conditioning load is shown in equation (2-1) to equation (2-3):

[0198]

[0199] τ=RC (2-3)

[0200] In the formula, is the indoor temperature at time t, is the indoor temperature after a time interval of Δt, is the outdoor temperature at time t, R is the equivalent thermal resistance, Q Δt is the cooling capacity of the air conditioner, λ is the proportional factor, Δt is the time interval, η is the time constant, and C is the equivalent heat capacity;

[0201] From the above formula, we can deduce:

[0202]

[0203] Where P Δt is the cooling power of the air conditioner, COP is the energy efficiency ratio of the air conditioner;

[0204]

[0205] Where: S(t k ) and S(t k-1 ) are t k Time and t k-1 The switch operation status of the air conditioning load, T(t k ) is t k The indoor temperature at the time, T max is the upper limit of air conditioner operating temperature, T min The lower limit of the air conditioner operating temperature. and They are the down-adjustable time and up-adjustable time under the fixed-frequency air conditioner switch control, T O is the outdoor temperature, T set0 is the set temperature under the fixed frequency air conditioner switch control, Q is the cooling capacity of the air conditioner, k t is the operating state variable of the air conditioner, k t =1 means the air conditioner is in operation, k t =0 means the air conditioner is in standby mode, T t is the current indoor temperature, It is the maximum down-adjustable time under the fixed frequency air conditioner switch control. It is the maximum adjustable time under the fixed frequency air conditioner switch control. and They are the adjustable capacity power and adjustable capacity power under the switch control of the fixed-frequency air conditioner, which are used to describe the dispatchable potential of the fixed-frequency air conditioner. COP is the energy efficiency ratio.

[0206] For the temperature control of fixed-frequency air conditioners, the schedulable potential of temperature control response can also be evaluated as follows:

[0207] Set the new temperature setting value to T set1 , the dispatchable time period of air conditioning load at the new temperature for:

[0208]

[0209]

[0210] Where, T set1 It is the set temperature under the temperature control of the fixed frequency air conditioner. It is the downward adjustment time under the temperature control of the fixed frequency air conditioner. It is the upward adjustment time under the temperature control of the fixed frequency air conditioner. The time that a fixed-frequency air conditioner can be adjusted down from the new set temperature. The time that a fixed-frequency air conditioner can be adjusted upward from the new set temperature;

[0211] The capacity power that can be adjusted up or down for the corresponding air conditioning load is:

[0212]

[0213] In the formula, and They are respectively the adjustable capacity power and the adjustable capacity power under the temperature control of the fixed-frequency air conditioner;

[0214] For the temperature control and frequency control of variable frequency air conditioners, the virtual energy storage modeling method can be used to evaluate the dispatchable potential of variable frequency air conditioners as follows:

[0215] A virtual energy storage model based on variable frequency air conditioning is established, which is used to simulate the conversion of electric power energy into heat energy:

[0216] The operating state of the virtual energy storage model and the consumed electric power are analyzed to obtain the SOC expression of the virtual energy storage model:

[0217]

[0218] In the formula, S SOC (t) and S SOC (t-1) are the battery charge states at the current time and the previous time period respectively; η es is the charge and discharge efficiency; P es (t-1) is the charge and discharge power in the previous period. When the power value is positive, it means that the battery is in the discharge state. When the power value is negative, it means that the battery is in the charge state. C es Indicates the battery capacity;

[0219] The constraints for establishing normal working situations are:

[0220] 0≤S SOC (t)≤100% (2-22)

[0221] P es,min ≤P es (t)≤P es,max (2-23)

[0222] Where P es,min and P es,max Respectively represent the maximum charging and discharging power of the virtual energy storage model;

[0223] Calculate the equivalent capacity C of the virtual energy storage model v for:

[0224]

[0225] In the formula, To increase the maximum amount of heat in the room, is the maximum heat reduction in the room, η AC is the energy efficiency ratio of the variable frequency air conditioner;

[0226] The thermal energy equivalent capacity of virtual energy storage is obtained as:

[0227]

[0228] Where, T set 、T max 、T min are the set temperature, upper limit temperature and lower limit temperature that can satisfy the needs of residents using variable frequency air conditioners in the area, C room is the equivalent heat capacity of the room;

[0229] Calculate the equivalent state of charge of the virtual energy storage model for:

[0230]

[0231] Where, T in is the initial ambient temperature;

[0232] Calculate the maximum charge and discharge power P of the virtual energy storage model v (t) is:

[0233] P AC0 -P AC (t)≤P v (t)≤P AC1 -P AC (t) (2-28)

[0234] Where P AC0 and P AC1 P is the working power of the air conditioner in standby operation and full load operation respectively; AC (t) is the real-time operating power of the air conditioner.

[0235] Preferably, in the collaborative optimization module, based on the electric vehicle mutual assistance control method and the air conditioning load mutual assistance scheduling, the collaborative optimization between the electric vehicle and the air conditioning equipment is performed, including:

[0236] Let L n,i is the initial daily load value of the power grid, i represents 1, 2, ..., N different optimizable time periods on the day, and the load of time period i for collaborative optimization is L i , EV charging load is L ch,i , EV discharge load is L dis,i , the air conditioning aggregate power is P ac,i , the total load in the area L i It is expressed as:

[0237]

[0238] Where P ch (i,j) is the charging power of the jth electric vehicle in time period i, P dis (i,j) is the discharge power of the jth electric vehicle in time period i, P ac,i is the power consumed by the air conditioner in period i;

[0239] The optimization goal is set to minimize the peak-to-valley difference rate of power grid load, then:

[0240]

[0241] Among them, min(F) is the objective function of minimizing the peak-to-valley difference rate of power grid load, max(L i ) is the peak load value in period i; min(L i ) is the load valley value in period i;

[0242] Set the following constraints:

[0243] The temperature range constraint T(t) of the air conditioning equipment is:

[0244] T set,min ≤T(t)≤T set,max (3-3)

[0245] Among them, T set,min and T set,max are the lower and upper limits of the ambient temperature respectively;

[0246] The actual working power constraint P of the aggregate air conditioning equipment AC，,i for:

[0247] P AC，,i ≤P AC,imax (3-4)

[0248] Among them, P AC,imax The initial operating power of the aggregated air-conditioning equipment before synergistic optimization;

[0249] The electric vehicle battery capacity constraint SOC(t) is:

[0250] SOC min ≤SOC(t)≤SOC max (3-5)

[0251] Among them, SOC min SOC is the lower limit of the battery's energy storage capacity during charging. max The upper limit of the energy storage capacity of the battery during charging;

[0252] The charging and discharging mode constraints of electric vehicles are:

[0253] min(P dis (t),P ch (t))=0 (3-6)

[0254] Among them, P dis (t) is the discharge power of the electric vehicle at time t, P ch (t) is the charging power of the electric vehicle at time t;

[0255] The remaining power constraint of the electric vehicle when leaving is:

[0256] SOC(j,t off )≥n (3-7)

[0257] Among them, SOC(j,t off ) represents the battery energy storage state SOC at the time when electric vehicle j leaves, and n is the battery energy storage charge state setting threshold.

[0258] According to a third aspect of the present invention, there is provided a computer terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, can be used to execute any one of the methods described above in the present invention, or to run the system described above in the present invention.

[0259] According to a fourth aspect of the present invention, there is provided a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, can be used to execute any of the methods described above in the present invention, or to run the system described above in the present invention.

[0260] Due to the adoption of the above technical solution, the present invention has at least one of the following beneficial effects compared with the prior art:

[0261] The method and system for optimizing the mutual energy regulation of air-conditioning loads and electric vehicles provided by the present invention can effectively reduce the peak-to-valley difference rate of the power grid and improve the stability of the power grid through the coordinated optimization of electric vehicles and air-conditioning loads, while taking into account user comfort and electricity costs, thereby achieving efficient management and optimal allocation of residential energy, and has important theoretical and practical significance for the development of smart grids and residential energy management.

[0262] The method and system for optimizing the mutual energy regulation of air-conditioning loads and electric vehicles provided by the present invention use electric vehicles as distributed energy storage to provide objective flexibility resources for the power system. Through orderly charging of electric vehicles, power return, and cascade utilization of retired batteries, the power system's ability to absorb part of the grid load is effectively improved, and it has considerable energy storage potential.

[0263] The method and system for optimizing the mutual energy regulation of air-conditioning loads and electric vehicles provided by the present invention can quickly respond to grid-side dispatch by clustering and flexibly dispatching a certain number of air-conditioning loads, thereby reducing load peaks to a certain extent, effectively improving the power supply capability, and reducing the cost of demand response.

[0264] The method and system for optimizing the mutual energy regulation of air-conditioning loads and electric vehicles provided by the present invention guide the linkage regulation and coordinated optimization of electric vehicles and air-conditioning loads by exploring the flexibility value of electric vehicle charging and discharging resources, and guide electric vehicles to perform staggered charging or even reverse discharge during peak air-conditioning load periods, thereby effectively reducing or even cutting global or local peak air-conditioning loads, and playing a key role in improving the power supply capability and the economic operation level of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0265] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0266] Figure 1 The figure is a working flow chart of a method for optimizing the mutual regulation of energy between air-conditioning load and electric vehicle in one embodiment of the present invention.

[0267] Figure 2 It is a working principle diagram of the energy mutual regulation and optimization method of air-conditioning load and electric vehicle in a preferred embodiment of the present invention.

[0268] Figure 3 Schematic diagram of the components of the energy mutual regulation and optimization system of air-conditioning load and electric vehicle in one embodiment of the present invention.

[0269] Figure 4 This is a distribution chart of arrival times of electric vehicles in a specific application example of the present invention.

[0270] Figure 5 This is a distribution chart of the departure time of electric vehicles in a specific application example of the present invention.

[0271] Figure 6 It is a curve diagram of total load data for four seasons in a specific application example of the present invention.

[0272] Figure 7 (a) to (d) are four-season load data curves of air conditioners and electric vehicles in a specific application example of the present invention.

[0273] Figure 8 (a) to (d) are four-season load condition diagrams in which only the air conditioning load is regulated in a specific application example of the present invention.

[0274] Fig. 9(a) to (d) are four-season load condition diagrams in which only electric vehicle load participates in regulation in a specific application example of the present invention.

[0275] Fig.10 (a) to (d) are four-season load condition diagrams of the coordinated optimization of electric vehicle and air conditioning load in a specific application example of the present invention. DETAILED DESCRIPTION

[0276] The following is a detailed description of the embodiments of the present invention: This embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation method and a specific operation process are given. It should be pointed out that for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention.

[0277] In the prior art, with the acceleration of urbanization and the improvement of residents' living standards, air conditioners and electric vehicles, as important symbols of modern life, have an increasingly prominent position in the energy consumption of residential areas due to their electricity demand. Due to the strong spatiotemporal coupling characteristics between air conditioning loads and electric vehicle loads, how to mutually regulate and coordinately optimize the two is an urgent problem to be solved in this field. In response to the above problems, an embodiment of the present invention provides an energy mutual regulation and optimization method for air conditioning loads and electric vehicles. The method uses scheduling strategies and control technology designs to deeply analyze the characteristics of air conditioning loads and electric vehicle loads, obtain air conditioning equipment that cooperates with electric vehicle loads to participate in grid peak regulation, and effectively reduce the peak load of the grid through energy scheduling optimization, improve the operating efficiency and reliability of the grid, and take into account the user's comfort and electricity costs.

[0278] Specifically, Figure 1 As shown, the method for optimizing the mutual regulation of energy between air-conditioning load and electric vehicle provided in this embodiment may include the following operations:

[0279] S1, adjust the charging behavior of electric vehicles in the substation area according to the grid demand and conduct mutual regulation of electric vehicles;

[0280] S2, adjust the energy consumption behavior of the air-conditioning equipment of each resident in the area and perform mutual assistance dispatch of the air-conditioning load;

[0281] S3, based on the mutual assistance regulation of electric vehicles and the mutual assistance scheduling of air-conditioning loads, the coordinated mutual assistance between electric vehicles and air-conditioning equipment is optimized.

[0282] In this embodiment, the electric vehicles and air-conditioning equipment are coordinated and optimized, and the principle is as follows: the air-conditioning load can only occupy a larger load and play a role after the electric vehicles return to the residential area for charging, that is, the residents return home; the electric vehicles can only be stably charged after the residents return to the residential area, that is, after the air-conditioning load is started; considering the strong spatiotemporal coupling characteristics of the two, the air-conditioning load and the electric vehicle load can perform joint demand response, and the air-conditioning load can be cooled in advance during the off-peak period when the peak electricity consumption is about to come, so as to reduce the load during the peak period; the electric vehicle load is V2G-powered during the peak period, and the electric vehicles are charged during the off-peak period (mostly at night), and the air-conditioning load can cooperate with the electric vehicle load to increase the temperature and decrease the power.

[0283] In some preferred implementations, the above S1, adjusting the charging behavior of electric vehicles in the substation area according to the grid demand and performing mutual assistance regulation on the electric vehicles, may further include the following operations:

[0284] S11, providing a real-time optimization scheduling model for electric vehicle energy based on V2G access;

[0285] S14, based on the real-time optimization scheduling model of electric vehicle energy, adjusts the charging behavior of electric vehicles in the substation according to the power grid demand.

[0286] In some preferred implementations, the above S11 provides a real-time optimization scheduling model for electric vehicle energy based on V2G access, and may further include the following operations:

[0287] S111, providing an electric vehicle optimization scheduling model;

[0288] S112, based on the electric vehicle optimal dispatching model, evaluating the dispatchable capacity of electric vehicle load;

[0289] S113, based on the evaluation result of the dispatchable capacity of electric vehicle load, considering V2G access, construct a real-time optimization dispatch model of electric vehicle energy based on V2G access.

[0290] In some preferred implementations, the above S111, providing an electric vehicle optimization scheduling model, may further include the following operations:

[0291] An electric vehicle optimization dispatching model is constructed. The objective function of the optimization dispatching model is to minimize the total cost of electricity purchase in the substation area. Then:

[0292]

[0293] S T ={1,2,…,T / Δt} (1-4)

[0294] In the formula, is the electricity purchase cost of the aggregator after period t, S T is the set of discrete optimizable time periods in the optimizable scheduling period T, P buy (t) and p TOU (t) are the corresponding power purchase power and time-of-use electricity price of the aggregator in time period t, Δt is the discrete time step, formula (1-3) is the power balance constraint, and formula (1-4) is the safe operation constraint, where: is the non-electric vehicle resident load in the residential area, Charging load for electric vehicles, is the maximum value of the power purchased by the aggregator. In a specific application example, T is 24h. Δt Set to 15min, so S T Correspondingly, 96 optimizable time periods are included.

[0295] In some preferred implementations, the above S112, based on the electric vehicle optimization scheduling model, evaluates the dispatchable potential of the electric vehicle load, and may further include the following operations:

[0296] S1121, determining the dispatchable capacity of a single electric vehicle may further include:

[0297] For different optimizable periods of the electric vehicle optimization scheduling model, the dispatchable capacity of electric vehicles is determined according to the following constraints on the dispatchable capacity of electric vehicles:

[0298] (1) User travel constraints

[0299] Ensure the battery reaches the desired SOC target before the EV leaves the grid:

[0300] SOC target (j)≤SOC(j,t off )≤1 (1-5)

[0301] In the formula, SOC target is the battery charging target value, t off is the departure time, j is the vehicle serial number;

[0302] (2) Battery life constraints

[0303] After dispatch, the state of charge of the electric vehicle battery should fluctuate within a reasonable range:

[0304]

[0305] Where SOC(j,t) represents the energy storage of electric vehicle j at time t; P dis (j, t) represents the charge and discharge power of electric vehicle j after scheduling time t; SOCmin (j) and SOC max (j) represent the lower and upper limits of the energy storage of electric vehicle j, SOC base (j) is the battery capacity of electric vehicle j, and η is the charge and discharge efficiency;

[0306] (3) Battery maximum charge and discharge power constraints

[0307] The charging and discharging power of electric vehicle batteries must be limited within the specified range:

[0308] P dis,min (j,t)≤P dis (j,t)≤P dis,max (j,t) (1-7)

[0309] P ch,min (j,t)≤P dis (j,t)≤P ch,max (j,t) (1-8)

[0310] Where P ch,max (j), P ch,min (j) and P dis,max (j), P dis,min (j) represent the maximum and minimum charging and discharging powers of electric vehicle j respectively, where the specifications of the charging pile determine the maximum charging power and the maximum discharging power.

[0311] (4) Time and object constraints

[0312] Dispatching is only possible when electric vehicles are connected to the grid, so users need to control when electric vehicles enter and leave the grid:

[0313] t on ≤t≤t sch ≤t off (1-9)

[0314] Where, t on is the vehicle arrival time, t sch is the schedulable time;

[0315] (5) Battery V2G discharge period capacity constraints

[0316] In order to meet the travel constraints, the battery capacity upper limit threshold needs to meet the following constraints:

[0317]

[0318] In the formula, SOC target(j) represents the charging target value of electric vehicle j, and SOC(j,t) represents the initial power. Due to the discreteness of arrival and departure times, electric vehicles often do not start charging at the beginning of the scheduling interval. However, in order to maintain consistency with other variables, the charging and discharging power is equated with the entire scheduling cycle. In the evaluation of the dispatchable capacity of electric vehicles, the maximum potential capacity of electric vehicles that can be dispatched under the actual charging and discharging conditions is explored. Therefore, it is a dispatchable battery state calculated based on the actual state of charge change curve of the vehicle for a specific period of time for electric vehicles that meet the scheduling requirements. Therefore, during the calculation process, the actual state of charge change of the vehicle is not affected by the power change brought about by the simulated vehicle charging and discharging plan. As long as the actual SOC conditions are met, it can be ensured that the starting power requirements of the next trip are not affected. When equation (1-10) takes the equal sign, P can be obtained. dis The upper limit of (j,t) is:

[0319]

[0320] P up (j,t)=P o (j,t)-P dis (j,t) (1-12)

[0321] Where P up (j, t) represents the adjustable charging power of electric vehicle j during the scheduling interval t, P o (j, t) represents the charging power of the jth electric vehicle when it does not participate in the battery capacity scheduling within the scheduling interval t;

[0322] (6) Battery charging period capacity constraints

[0323] Battery capacity limitations are the main factor to consider when calculating the battery's downward dispatchable capacity:

[0324]

[0325] When the equation (1-13) is equal, we get P dis The threshold value P of (j,t) dis,max (j, t). Electric vehicles increase their charging power in the second charging period in response to the V2G demand of the power grid within their dispatchable range, but their charging power cannot be too large, otherwise the battery SOC will exceed the upper limit and damage the battery life. As shown in (5), combined with capacity constraints (1-10), the downward dispatchable capacity formula can be expressed as:

[0326] P down (j,t)=P dis (j,t)-P0(j,t) (1-14)

[0327] Where Pdown (j, t) represents the adjustable charging power of electric vehicle j in the scheduling interval t;

[0328] S1122, obtaining the dispatchable capacity of the electric vehicle cluster according to the dispatchable capacity of a single electric vehicle, may further include:

[0329] Based on the dispatchable capacity of a single electric vehicle, the adjustable power P of the electric vehicle cluster in the interval can be obtained: up,all and adjustable power P down,all for:

[0330]

[0331]

[0332] Where N is the total number of electric vehicles;

[0333] If electric vehicle j cannot participate in the dispatch within the dispatch interval t, then:

[0334] P up (j,t)=P down (j,t)=0 (1-17).

[0335] In some preferred implementations, the above S113, based on the evaluation result of the dispatchable potential of electric vehicle load, considering V2G access, constructing a real-time optimization dispatch model of electric vehicle energy based on V2G access, may further include the following operations:

[0336] The basic model for real-time optimization and dispatch of electric vehicle energy under V2G access is constructed as follows:

[0337]

[0338] In the formula, For aggregators k The electricity purchase cost within a dispatchable period is For the k Time-of-use electricity prices in different optimizable time periods within a dispatchable cycle, For the purchased power, For aggregators k Total V2G subsidy in a dispatchable period, Subsidy for V2G units, represents the discharge power of electric vehicle j in time period t;

[0339] The power balance constraint and power purchase power limit constraint during the charging process are established as follows:

[0340]

[0341] In the formula, For the k The predicted value of conventional residential load in a dispatchable period, and Each is the electric car j in the k The charge and discharge power within a dispatchable cycle.

[0342] The charging and discharging constraints of electric vehicles are established as follows:

[0343]

[0344] Soc min (j)≤Soc k (j,t)≤Soc max (j) t∈S T (k) (1-27)

[0345] In the formula, and are the upper limits of charging and discharging power of electric vehicle j, respectively. For non-dispatchable electric vehicles, that is, v k (j, t) = 0, which corresponds to and Both are 0. Formula (1-25) indicates that the electric vehicle cannot be charged and discharged at the same time. Formula (1-26) indicates the relationship between the SOC states of electric vehicle j in adjacent time periods. and is the charging and discharging efficiency coefficient of electric vehicle j, E EV (j) is the battery capacity of electric vehicle j, in formula (1-27), Soc min (j) and Soc max (j) are the lower and upper limits of the SOC of electric vehicle j, respectively;

[0346] The electric vehicle charging satisfaction rate constraint is established as:

[0347]

[0348] In the formula, Soc Tar (j,T / Δt) is the discrete time period set by the owner of electric vehicle j T / Δt Target SOC,Soc k (j,k-1) is the number of electric vehicles j in the k The starting value of the electric vehicle battery state of charge within a dispatchable period.

[0349] In some preferred implementations, the above S2, adjusting the power consumption behavior of the air-conditioning equipment of each resident in the substation area to perform mutual assistance scheduling of the air-conditioning load, may further include the following operations:

[0350] S21, construct a thermodynamic model of air conditioning load to simulate the temperature changes of the room under the influence of indoor and outdoor cold and heat sources;

[0351] S22, based on the thermodynamic model of air conditioning load, analyze the operating characteristics and control methods of air conditioning and construct a mathematical model of air conditioning load;

[0352] S23, based on the mathematical model of air conditioning load, analyze the control mode of air conditioning and evaluate the dispatchable potential of air conditioning load;

[0353] S24, according to the evaluation results of the dispatchable potential of air-conditioning load, adjust the power consumption behavior of the air-conditioning equipment of each resident in the substation.

[0354] In some preferred implementations, the above S23, analyzing the control mode of the air conditioner and evaluating the dispatchable potential of the air conditioner load, may further include:

[0355] The control methods of air conditioners include: switch control and temperature control of fixed-frequency air conditioners, and temperature control and frequency control of variable-frequency air conditioners.

[0356] In some preferred implementations, in the above S23, for the on / off control of the fixed-frequency air conditioner, the schedulable potential of the on / off response may also be evaluated as follows:

[0357] The thermodynamic model of air conditioning load is shown in equations (2-1)-(2-3):

[0358]

[0359] τ=RC (2-3)

[0360] In the formula, is the indoor temperature at time t, is the indoor temperature after a time interval of Δt, is the outdoor temperature at time t, R is the equivalent thermal resistance, Q Δt is the cooling capacity of the air conditioner, λ is the proportional factor, Δt is the time interval, τ is the time constant, and C is the equivalent heat capacity;

[0361] From the above formula, we can deduce:

[0362]

[0363] Where P Δt is the cooling power of the air conditioner, COP is the energy efficiency ratio of the air conditioner;

[0364]

[0365] Where: S(t k ) and S(t k-1 ) are t k Time and t k-1 The switch operation status of the air conditioning load, T(t k ) is t k The indoor temperature at the time, T max is the upper limit of air conditioner operating temperature, T min The lower limit of the air conditioner operating temperature. and They are the down-adjustable time and up-adjustable time under the fixed-frequency air conditioner switch control, T O is the outdoor temperature, T set0 is the set temperature under the fixed frequency air conditioner switch control, Q is the cooling capacity of the air conditioner, k t is the operating state variable of the air conditioner, k t =1 means the air conditioner is in operation, k t =0 means the air conditioner is in standby mode, T t is the current indoor temperature, It is the maximum down-adjustable time under the fixed frequency air conditioner switch control. It is the maximum adjustable time under the fixed frequency air conditioner switch control. and They are the adjustable capacity power and adjustable capacity power under the switch control of the fixed-frequency air conditioner, which are used to describe the dispatchable potential of the fixed-frequency air conditioner. COP is the energy efficiency ratio.

[0366] In some preferred implementations, in the above S23, for the temperature control of the fixed-frequency air conditioner, the schedulable potential of the temperature control response may also be evaluated as follows:

[0367] Set the new temperature setting value to T set1 , the dispatchable time period of air conditioning load at the new temperature for:

[0368]

[0369] Where, T set1 It is the set temperature under the temperature control of the fixed frequency air conditioner. It is the downward adjustment time under the temperature control of the fixed frequency air conditioner. It is the upward adjustment time under the temperature control of the fixed frequency air conditioner. The time that a fixed-frequency air conditioner can be adjusted down from the new set temperature. The time that a fixed-frequency air conditioner can be adjusted upward from the new set temperature;

[0370] The capacity power that can be adjusted up or down for the corresponding air conditioning load is:

[0371]

[0372] In the formula, and They are respectively the capacity power that can be adjusted downward and the capacity power that can be adjusted upward under the temperature control of the fixed-frequency air conditioner.

[0373] In some preferred implementations, in the above S23, for the temperature control and frequency control of the variable frequency air conditioner, a virtual energy storage modeling method is used to evaluate the dispatchable potential of the variable frequency air conditioner as follows:

[0374] A virtual energy storage model based on variable frequency air conditioning is established, which is used to simulate the conversion of electric power energy into heat energy:

[0375] The operating status and consumed electric power of the virtual energy storage model are analyzed, and the SOC expression of the virtual energy storage model is obtained as follows:

[0376]

[0377] In the formula, S SOC (t) and S SOC (t-1) are the battery charge states at the current time and the previous time period respectively; η es is the charge and discharge efficiency; P es (t-1) is the charge and discharge power in the previous period. When the power value is positive, it means that the battery is in the discharge state. When the power value is negative, it means that the battery is in the charge state. C es Indicates the battery capacity;

[0378] The constraints for establishing normal working situations are:

[0379] 0≤S SOC (t)≤100% (2-22)

[0380] P es,min ≤P es (t)≤P es,max (2-23)

[0381] Where P es,min and P es,max Respectively represent the maximum charging and discharging power of the virtual energy storage model;

[0382] Calculating the state description index of the virtual energy storage model further includes:

[0383] Equivalent capacity refers to the sum of the maximum amount of heat that can be stored and the maximum amount of heat that can be released during the heat energy conversion process when air conditioning is used in a resident's home. It is used to describe the ability of a room as a heat energy storage unit.

[0384] The equivalent state of charge is used to indicate the ratio between the energy stored in the virtual energy storage system in real time and its maximum storage energy, that is, the maximum thermal energy capacity of the virtual energy storage system. It can help users and system controllers understand the current energy state of the virtual energy storage system, so as to better manage its charging and discharging process.

[0385] Maximum charge and discharge power;

[0386] Calculate the equivalent capacity C of the virtual energy storage model v The calculation formula is:

[0387]

[0388] In the formula, To increase the maximum amount of heat in the room, is the maximum heat reduction in the room, η AC is the energy efficiency ratio of the variable frequency air conditioner;

[0389] The thermal energy equivalent capacity of virtual energy storage is obtained as:

[0390]

[0391] Where, T set 、T max 、T min are the set temperature, upper limit temperature and lower limit temperature that can satisfy the needs of residents using variable frequency air conditioners in the area, C room is the equivalent heat capacity of the room;

[0392] Calculate the equivalent state of charge of the virtual energy storage model for:

[0393]

[0394] Where, T in is the initial ambient temperature;

[0395] Calculate the maximum charge and discharge power P of the virtual energy storage model v (t) is:

[0396] P AC0 -P AC (t)≤P v (t)≤P AC1 -P AC (t) (2-28)

[0397] Where P AC0 and P AC1 P is the working power of the air conditioner in standby operation and full load operation respectively; AC(t) is the real-time operating power of the air conditioner.

[0398] In some preferred embodiments, Figure 2 As shown, the above S3, based on the mutual assistance control method of electric vehicles and the mutual assistance scheduling of air-conditioning loads, performs coordinated mutual assistance optimization between electric vehicles and air-conditioning equipment, and can further include the following operations:

[0399] Let L n,i is the initial daily load value of the power grid, i represents 1, 2, ..., N different optimizable time periods on the day, and the load of time period i for collaborative optimization is L i , EV charging load is L ch,i , EV discharge load is L dis,i , the air conditioning aggregate power is P ac,i , the total load in the area L i It is expressed as:

[0400]

[0401] Where P ch (i,j) is the charging power of the jth electric vehicle in time period i, P dis (i,j) is the discharge power of the jth electric vehicle in time period i, P ac,i is the power consumption of the air conditioner in period i; the optimization goal is set to minimize the peak-to-valley difference rate of the power grid load, then:

[0402]

[0403] Among them, min(F) is the objective function of minimizing the peak-to-valley difference rate of power grid load, max(L i ) is the peak load value in period i; min(L i ) is the load valley value in period i;

[0404] Set the following constraints:

[0405] The temperature range constraint T(t) of the air conditioning equipment is:

[0406] T set,min ≤T(t)≤T set,max (3-3)

[0407] Among them, T set,min and T set,max are the lower and upper limits of the ambient temperature respectively.

[0408] The actual working power constraint P of the aggregate air conditioning equipment AC，,i for:

[0409] P AC，,i ≤P AC,imax (3-4)

[0410] Among them, P AC,imax It is the initial operating power of the aggregate air-conditioning equipment before synergistic optimization.

[0411] The electric vehicle battery capacity constraint SOC(t) is:

[0412] SOC min ≤SOC(t)≤SOC max (3-5)

[0413] Among them, SOC min SOC is the lower limit of the battery's energy storage capacity during charging. max It is the upper limit of the energy storage capacity of the battery during charging, taking into account the battery loss, and is preferably 0.2 and 0.9.

[0414] The charging and discharging mode constraints of electric vehicles are:

[0415] min(P dis (t),P ch (t))=0 (3-6)

[0416] Among them, P dis (t) is the discharge power of the electric vehicle at time t, P ch (t) is the charging power of the electric vehicle at time t; the remaining power constraint when the electric vehicle leaves is:

[0417] SOC(j,t off )≥n (3-7)

[0418] Among them, SOC(j,t off ) represents the battery energy storage state SOC at the time when electric vehicle j leaves, n is the battery energy storage charge state setting threshold, preferably 0.5.

[0419] In the coordinated optimization strategy of air-conditioning load and electric vehicle, the load optimization situation after the electric vehicle and the air-conditioning load respond separately is obtained through the electric vehicle mutual assistance control strategy and the air-conditioning load mutual assistance scheduling strategy. The advantages and disadvantages obtained by the optimization of their respective regulation are further complemented and coordinated, thereby realizing the energy mutual assistance regulation optimization of air-conditioning load and electric vehicle.

[0420] An embodiment of the present invention provides an energy mutual assistance control optimization system for air-conditioning loads and electric vehicles.

[0421] Specifically, Figure 3 As shown, the energy mutual regulation and optimization system of air-conditioning load and electric vehicle provided in this embodiment may include the following modules:

[0422] An electric vehicle mutual assistance control module, which is used to provide an electric vehicle mutual assistance control strategy, which is used to adjust the charging behavior of electric vehicles in the substation according to the power grid demand;

[0423] An air conditioning load mutual assistance scheduling module, which is used to provide an air conditioning load mutual assistance scheduling strategy, which is used to adjust the power consumption behavior of the air conditioning equipment of each resident in the substation area;

[0424] The collaborative mutual assistance optimization module is used to provide an air-conditioning load and electric vehicle collaborative mutual assistance optimization strategy. This strategy optimizes the collaborative mutual assistance between electric vehicles and air-conditioning equipment based on the electric vehicle mutual assistance control strategy and the air-conditioning load mutual assistance scheduling strategy.

[0425] The working contents of each functional module of the energy mutual regulation and optimization system for air-conditioning load and electric vehicle provided by the embodiment are further described in detail below.

[0426] In some preferred embodiments, the electric vehicle mutual assistance control module may further include:

[0427] An electric vehicle load dispatchable capacity evaluation unit, which is used to provide an electric vehicle optimal dispatch model, and evaluate the electric vehicle load dispatchable capacity based on the electric vehicle optimal dispatch model;

[0428] The electric vehicle charging behavior adjustment unit is based on the evaluation results of the dispatchable capacity of the electric vehicle load, takes V2G access into consideration, and builds a real-time optimization scheduling model for electric vehicle energy based on V2G access; based on the real-time optimization scheduling model for electric vehicle energy, the charging behavior of electric vehicles in the substation is adjusted according to the power grid demand.

[0429] In some preferred implementations, the electric vehicle load dispatchable capacity evaluation unit provides an electric vehicle optimization dispatch model, which may further include:

[0430] An electric vehicle optimization dispatching model is constructed. The objective function of the optimization dispatching model is to minimize the total cost of electricity purchase in the substation area. Then:

[0431]

[0432] S T ={1,2,...,T / Δt} (1-4)

[0433] In the formula, is the electricity purchase cost of the aggregator after period t, S T is the set of discrete optimizable time periods in the optimizable scheduling period T, P buy (t) and p TOU(t) are the corresponding power purchase power and time-of-use electricity price of the aggregator in time period t, Δt is the discrete time step, formula (1-3) is the power balance constraint, and formula (1-4) is the safe operation constraint, where: is the non-electric vehicle resident load in the residential area, Charging load for electric vehicles, The maximum value of the power purchased by the aggregator.

[0434] In some preferred implementations, the electric vehicle load dispatchable capacity evaluation unit may further include:

[0435] For different optimizable periods of the electric vehicle optimization scheduling model, the dispatchable capacity of a single electric vehicle is determined according to the following constraints on the dispatchable capacity of electric vehicles:

[0436] User travel restrictions:

[0437] SOC target (j)≤SOC(j,t off )≤1 (1-5)

[0438] In the formula, SOC target is the battery charging target value, t off is the departure time, j is the vehicle serial number;

[0439] Battery life constraints:

[0440]

[0441] Where SOC(j,t) represents the energy storage of electric vehicle j at time t; P dis (j, t) represents the charge and discharge power of electric vehicle j after scheduling time t; SOC min (j) and SOC max (j) represent the lower and upper limits of the energy storage of electric vehicle j, SOC base (j) is the battery capacity of electric vehicle j, and η is the charge and discharge efficiency;

[0442] Battery maximum charge and discharge power constraints:

[0443] P dis,min (j,t)≤P dis (j,t)≤P dis,max (j,t) (1-7)

[0444] P ch,min (j,t)≤P dis (j,t)≤P ch,max (j,t) (1-8)

[0445] Where P ch,max (j), P ch,min (j) and P dis,max (j), P dis,min (j) represent the maximum and minimum charging and discharging power of electric vehicle j, respectively, where the specifications of the charging pile determine the maximum charging power and the maximum discharging power;

[0446] Time and object constraints:

[0447] t on ≤t≤t sch ≤t off (1-9)

[0448] Where, t on is the vehicle arrival time, t sch is the schedulable time;

[0449] Battery V2G discharge period capacity constraints:

[0450]

[0451] In the formula, SOC target (j) represents the charging target value of electric vehicle j, and SOC(j,t) represents the initial power; when equation (1-10) takes the equal sign, we get P dis The upper limit of (j,t) is:

[0452]

[0453] P up (j,t)=P o (j,t)-P dis (j,t) (1-12)

[0454] Where P up (j, t) represents the adjustable charging power of electric vehicle j during the scheduling interval t, P o (j, t) represents the charging power of the jth electric vehicle when it does not participate in the battery capacity scheduling within the scheduling interval t;

[0455] Battery charging period capacity constraints:

[0456]

[0457] When the equation (1-13) is equal, we get P dis The threshold value P of (j,t) dis,max (j, t); combined with the capacity constraint (1-10), the downward dispatchable capacity formula can be expressed as:

[0458] Pdown (j,t)=P dis (j,t)-P0(j,t) (1-14)

[0459] Where P down (j, t) represents the adjustable charging power of electric vehicle j in the scheduling interval t;

[0460] Based on the dispatchable capacity of a single electric vehicle, the adjustable power P of the electric vehicle cluster in the interval is obtained. up,all and adjustable power P down,all for:

[0461]

[0462]

[0463] Where N is the total number of electric vehicles;

[0464] If electric vehicle j cannot participate in the dispatch within the dispatch interval t, then:

[0465] P up (j,t)=P down (j,t)=0 (1-17).

[0466] In some preferred embodiments, the electric vehicle charging behavior adjustment unit may further include:

[0467] The basic model for real-time optimization and dispatch of electric vehicle energy under V2G access is constructed as follows:

[0468]

[0469] In the formula, For aggregators k The electricity purchase cost within a dispatchable period is For the k Time-of-use electricity prices in different optimizable time periods within a dispatchable cycle, For the purchased power, For aggregators k Total V2G subsidy in a dispatchable period, Subsidy for V2G units, represents the discharge power of electric vehicle j in time period t;

[0470] The power balance constraint and power purchase power limit constraint during the charging process are established as follows:

[0471]

[0472] In the formula, For the k The predicted value of conventional residential load in a dispatchable period, and Each is the electric car j in the k The charge and discharge power within a dispatchable cycle;

[0473] The charging and discharging constraints of electric vehicles are established as follows:

[0474]

[0475] Soc min (j)≤Soc k (j,t)≤Soc max (j) t∈S T (k) (1-27)

[0476] In the formula, and are the upper limits of charging and discharging power of electric vehicle j, respectively. For non-dispatchable electric vehicles, that is, v k (j, t) = 0, which corresponds to and Both are 0. Formula (1-25) indicates that the electric vehicle cannot be charged and discharged at the same time. Formula (1-26) indicates the relationship between the SOC states of electric vehicle j in adjacent time periods. and is the charging and discharging efficiency coefficient of electric vehicle j, E EV (j) is the battery capacity of electric vehicle j, in formula (1-27), Soc min (j) and Soc max (j) are the lower and upper limits of the SOC of electric vehicle j, respectively;

[0477] The electric vehicle charging satisfaction rate constraint is established as:

[0478]

[0479] In the formula, Soc Tar (j,T / Δt) is the discrete time period set by the owner of electric vehicle j T / Δt Target SOC,Soc k (j,k-1) is the number of electric vehicles j in the k The starting value of the electric vehicle battery state of charge within a dispatchable period.

[0480] In some preferred implementations, the air conditioning load mutual assistance scheduling module may further include:

[0481] Air conditioning load model building unit, which is used to build a thermodynamic model of air conditioning load, simulate the temperature change of the room under the action of indoor and outdoor cold and heat sources; based on the thermodynamic model of air conditioning load, analyze the operating characteristics and control methods of air conditioning, and build a mathematical model of air conditioning load;

[0482] The unit for adjusting the electric energy consumption behavior of air-conditioning equipment analyzes the control mode of the air-conditioning based on the mathematical model of the air-conditioning load and evaluates the dispatchable potential of the air-conditioning load; according to the evaluation results of the dispatchable potential of the air-conditioning load, the unit adjusts the electric energy consumption behavior of the air-conditioning equipment of each resident in the substation.

[0483] In some preferred implementations, the air conditioning equipment power consumption behavior adjustment unit analyzes the air conditioning control mode and evaluates the dispatchable potential of the air conditioning load, and may further include:

[0484] Air conditioning control methods, including: switch control and temperature control of fixed-frequency air conditioning, and temperature control and frequency control of variable-frequency air conditioning;

[0485] For the on / off control of fixed-frequency air conditioners, the schedulable potential of the on / off response can also be evaluated as follows:

[0486] The thermodynamic model of air conditioning load is shown in equation (2-1) to equation (2-3):

[0487]

[0488] τ=RC (2-3)

[0489] In the formula, is the indoor temperature at time t, is the indoor temperature after a time interval of Δt, is the outdoor temperature at time t, R is the equivalent thermal resistance, Q Δt is the cooling capacity of the air conditioner, λ is the proportional factor, Δt is the time interval, τ is the time constant, and C is the equivalent heat capacity;

[0490] From the above formula, we can deduce:

[0491]

[0492] Where P Δt is the cooling power of the air conditioner, COP is the energy efficiency ratio of the air conditioner;

[0493]

[0494] Where: S(t k ) and S(t k-1 ) are t k Time and t k-1The switch operation status of the air conditioning load, T(t k ) is t k The indoor temperature at the time, T max is the upper limit of air conditioner operating temperature, T min The lower limit of the air conditioner operating temperature. and They are the down-adjustable time and up-adjustable time under the fixed-frequency air conditioner switch control, T O is the outdoor temperature, T set0 is the set temperature under the fixed frequency air conditioner switch control, Q is the cooling capacity of the air conditioner, k t is the operating state variable of the air conditioner, k t =1 means the air conditioner is in operation, k t =0 means the air conditioner is in standby mode, T t is the current indoor temperature, It is the maximum down-adjustable time under the fixed frequency air conditioner switch control. It is the maximum adjustable time under the fixed frequency air conditioner switch control. and They are the adjustable capacity power and adjustable capacity power under the switch control of the fixed-frequency air conditioner, which are used to describe the dispatchable potential of the fixed-frequency air conditioner. COP is the energy efficiency ratio.

[0495] For the temperature control of fixed-frequency air conditioners, the schedulable potential of temperature control response can also be evaluated as follows:

[0496] Set the new temperature setting value to T set1 , the dispatchable time period of air conditioning load at the new temperature for:

[0497]

[0498] Where, T set1 It is the set temperature under the temperature control of the fixed frequency air conditioner. It is the downward adjustment time under the temperature control of the fixed frequency air conditioner. It is the upward adjustment time under the temperature control of the fixed frequency air conditioner. The time that a fixed-frequency air conditioner can be adjusted down from the new set temperature. The time that a fixed-frequency air conditioner can be adjusted upward from the new set temperature;

[0499] The capacity power that can be adjusted up or down for the corresponding air conditioning load is:

[0500]

[0501] In the formula, and They are respectively the adjustable capacity power and the adjustable capacity power under the temperature control of the fixed-frequency air conditioner;

[0502] For the temperature control and frequency control of variable frequency air conditioners, the virtual energy storage modeling method can be used to evaluate the dispatchable potential of variable frequency air conditioners as follows:

[0503] A virtual energy storage model based on variable frequency air conditioning is established, which is used to simulate the conversion of electric power energy into heat energy:

[0504] The operating status and consumed electric power of the virtual energy storage model are analyzed, and the SOC expression of the virtual energy storage model is obtained as follows:

[0505]

[0506] In the formula, S SOC (t) and S SOC (t-1) are the battery charge states at the current time and the previous time period respectively; η es is the charge and discharge efficiency; P es (t-1) is the charge and discharge power in the previous period. When the power value is positive, it means that the battery is in the discharge state. When the power value is negative, it means that the battery is in the charge state. C es Indicates the battery capacity;

[0507] The constraints for establishing normal working situations are:

[0508] 0≤S SOC (t)≤100% (2-22)

[0509] P es,min ≤P es (t)≤P es,max (2-23)

[0510] Where P es,min and P es,max Respectively represent the maximum charging and discharging power of the virtual energy storage model;

[0511] Calculate the equivalent capacity C of the virtual energy storage model v for:

[0512]

[0513] In the formula, To increase the maximum amount of heat in the room, is the maximum heat reduction in the room, η AC is the energy efficiency ratio of the variable frequency air conditioner;

[0514] The thermal energy equivalent capacity of virtual energy storage is obtained as:

[0515]

[0516] Where, T set 、T max 、T min are the set temperature, upper limit temperature and lower limit temperature that can satisfy the needs of residents using variable frequency air conditioners in the area, C room is the equivalent heat capacity of the room;

[0517] Calculate the equivalent state of charge of the virtual energy storage model for:

[0518]

[0519] Where, T in is the initial ambient temperature;

[0520] Calculate the maximum charge and discharge power P of the virtual energy storage model v (t) is:

[0521] P AC0 -P AC (t)≤P v (t)≤P AC1 -P AC (t) (2-28)

[0522] Where P AC0 and P AC1 P is the working power of the air conditioner in standby operation and full load operation respectively; AC (t) is the real-time operating power of the air conditioner.

[0523] In some preferred implementations, the above-mentioned collaborative optimization module can further include:

[0524] Let L n,i is the initial daily load value of the power grid, i represents 1, 2, ..., N different optimizable time periods on the day, and the load of time period i for collaborative optimization is L i , EV charging load is L ch,i , EV discharge load is L dis,i , the air conditioning aggregate power is P ac,i , the total load in the area L i It is expressed as:

[0525]

[0526] Where P ch (i,j) is the charging power of the jth electric vehicle in time period i, P dis (i,j) is the discharge power of the jth electric vehicle in time period i, Pac,i is the power consumed by the air conditioner in period i;

[0527] The optimization goal is set to minimize the peak-to-valley difference rate of power grid load, then:

[0528]

[0529] Among them, min(F) is the objective function of minimizing the peak-to-valley difference rate of power grid load, max(L i ) is the peak load value in period i; min(L i ) is the load valley value in period i;

[0530] Set the following constraints:

[0531] The temperature range constraint T(t) of the air conditioning equipment is:

[0532] T set,min ≤T(t)≤T set,max (3-3)

[0533] Among them, T set,min and T set,max are the lower and upper limits of the ambient temperature respectively;

[0534] The actual working power constraint P of the aggregate air conditioning equipment AC，,i for:

[0535] P AC，,i ≤P AC,imax (3-4)

[0536] Among them, P AC,imax The initial operating power of the aggregated air-conditioning equipment before synergistic optimization;

[0537] The electric vehicle battery capacity constraint SOC(t) is:

[0538] SOC min ≤SOC(t)≤SOC max (3-5)

[0539] Among them, SOC min SOC is the lower limit of the battery's energy storage capacity during charging. max The upper limit of the energy storage capacity of the battery during charging;

[0540] The charging and discharging mode constraints of electric vehicles are:

[0541] min(P dis (t),P ch (t))=0 (3-6)

[0542] Among them, P dis(t) is the discharge power of the electric vehicle at time t, P ch (t) is the charging power of the electric vehicle at time t;

[0543] The remaining power constraint of the electric vehicle when leaving is:

[0544] SOC(j,t off )≥n (3-7)

[0545] Among them, SOC(j,t off ) represents the battery energy storage state SOC at the time when electric vehicle j leaves, and n is the battery energy storage charge state setting threshold.

[0546] It should be noted that the steps in the method provided by the present invention can be implemented by using corresponding modules, devices, units, etc. in the system. Those skilled in the art can refer to the technical solution of the method to realize the composition of the system, that is, the embodiments in the method can be understood as preferred examples for constructing the system, which will not be elaborated here.

[0547] The technical solution provided by the above embodiment of the present invention is further described in detail below in conjunction with a specific application example.

[0548] This specific application example is analyzed in a substation in Shanghai. The load curves corresponding to typical days in the four seasons are shown in the following figure. There are 200 dispatchable air conditioners, the power of a single air conditioner is U(1900,2400)(W), the energy efficiency ratio is 3.0, and other key data are consistent with Chapter 4. The starting temperature of the building environment in the analysis substation is U(26~30)(℃), the set temperature of the air conditioner used by residents is 26℃, and there are 50 electric vehicles distributed in the substation. Other data are shown in Table 1, Table 2 and Table 3:

[0549] Table 1 Dispatchable air conditioning parameters

[0550]

[0551] Table 2 Electric vehicle parameters

[0552]

[0553]

[0554] Table 3 Time-of-use electricity prices

[0555]

[0556] pass Figure 4 and Figure 5It can be seen that vehicles begin to arrive at the residential area in a dispersed manner after four o'clock. The vast majority of electric vehicles return to the community between 16:00 and 22:00, and a small number of electric vehicles arrive at the residential area at other times; and starting at 6:00 in the morning, vehicles leave the community for other locations, while there are very few changes in vehicles at night. Most vehicles stay within a few hours at night, which is consistent with the travel trajectory and work and rest time of residents on a typical day.

[0557] Figure 6 and Figure 7 (a) to (d) are the total load data diagrams for the four seasons and the air conditioning load and electric vehicle load diagrams respectively:

[0558] In the residential area analyzed in the example, the load curves of four typical days in spring, summer, autumn and winter in the four seasons of the year are as follows: Figure 7 As shown in (a) to (d) in the figure. From the analysis of the data in the figure, it can be seen that the load on typical days in summer and winter is obviously heavier than that in spring and autumn. This is because summer and winter are the peak seasons for power consumption, which require air conditioning cooling and heating. In addition, the load in summer and winter has obvious peak-to-valley fluctuations compared to spring and autumn, and the peak-to-valley difference is larger. Therefore, considering the regulation of air conditioning load in these two seasons can play a greater role. The load in all four seasons starts to increase after 16:00 in the evening, which is consistent with the objective phenomenon that residents in general residential areas return home after get off work and school in the evening to use household appliances, causing a short-term peak in the distribution network.

[0559] From the above analysis, we can see that the electric vehicle load is highly coupled with the travel trajectory of residents in the residential area. The load regulation can only be played after 16:00 when residents get home from get off work and before 7:00 when residents leave home for work. The air conditioning load in the cool seasons of spring and autumn is highly correlated with the outdoor temperature. At the same time, it is also closely related to the travel trajectory of residents in the residential area. During the period when residents gather at home, considering the daily air conditioning usage habits of different residents, the air conditioning load will also usher in a small peak period, which is coupled with the electric vehicle load to a certain extent. The air conditioning load in the summer load does not change much within a day, and it will increase to a certain extent during the daytime when the outdoor temperature is high. Due to the heating and heating reasons, the air conditioning load in the winter load has a curve roughly opposite to the summer load. The winter air conditioning load is lower when the temperature is higher during the day. The air conditioning consumes more power when the temperature is lower at night.

[0560] The following is a comparison between the optimization of air conditioning load and electric vehicle scheduling separately and the optimization of their mutual regulation:

[0561] (1) Only air conditioning load is involved in regulation, such as Figure 8 As shown in (a) to (d):

[0562] Through the analysis of the scheduling changes of air conditioning load participating in the regulation of loads throughout the year, it can be found that the changes of air conditioning load participating in the peak shaving and valley filling of the power grid can play a role in all weather periods. Among them, summer and winter are the seasons when air conditioning loads are used more frequently, and the overall proportion of air conditioning loads in these two seasons is larger, so air conditioning loads can play a greater role in this typical period. In the scenarios of the four seasons, the indoor temperature can be cooled / heated in advance by increasing the air conditioning load in a short period of time before 7 am and around 16 pm to relieve the load pressure when the morning peak period arrives. The peak load of the spring load during the evening peak period was reduced from 963.5kW to 899.6kW through regulation, and the peak-to-valley difference rate was reduced by 8.44%; the peak load of the summer load during the evening peak period was reduced from 1938kW to 1770kW, and the peak-to-valley difference rate was reduced by 12.85%; the autumn load was reduced from 701kW to 635kW, and the peak-to-valley difference rate was reduced by 10.74%; the winter load was reduced from 1678kW to 1478kW, and the peak-to-valley difference rate was reduced by 9.74%. But overall, relying solely on air-conditioning load scheduling cannot take into account scenarios where the air-conditioning load is not used much, such as spring and autumn, and it cannot play a good scheduling role for the period after residents return home, and the overall peak-to-valley rate reduction is not large.

[0563] Table 4 Optimization of peak-to-valley difference when only air conditioning load is involved in regulation

[0564]

[0565] (2) Only electric vehicles participate in regulation, such as Fig. 9 As shown in (a) to (d):

[0566] Through the analysis of the changes in the dispatch of electric vehicle loads participating in the regulation of loads throughout the year, it can be found that the changes in the electric vehicle loads participating in the peak-shaving and valley-filling of the power grid can only play a greater role between 16:00 in the afternoon and 7:00 the next day. This is because in residential areas, residents go out during the daytime, and there are not many electric vehicle loads remaining in the substation area, and the dispatchable capacity is limited. After 16:00 in the evening, when residents return to their homes, more electric vehicle loads can participate in the grid dispatch. Among them, the effect of electric vehicle loads on peak-shaving and valley-filling in summer and winter is not obvious. The reason is that the electric vehicle load accounts for a small proportion of the total load based on the large base power consumption in summer and winter, and plays a small role. In spring and autumn, the proportion of electric vehicle loads is larger and can play a greater role. In the scenario of the four seasons of the year, from 16:00 in the afternoon to 7:00 the next morning, the charging load of electric vehicles can be transferred from the peak period to the valley period by shifting the charging period of electric vehicles for orderly charging, so as to alleviate the load pressure when the morning peak period arrives. The peak load of the spring load during the evening peak period was reduced from 963.5kW to 899.6kW through regulation, and the peak-to-valley difference rate was reduced by 12.83%; the peak load of the summer load during the evening peak period was reduced from 1938kW to 1770kW, and the peak-to-valley difference rate was reduced by 5.49%; the autumn load was reduced from 701kW to 635kW, and the peak-to-valley difference rate was reduced by 15.67%; the winter load was reduced from 1678kW to 1478kW, and the peak-to-valley difference rate was reduced by 6.24%. However, overall, relying solely on electric vehicle load scheduling cannot take into account the summer and winter loads well, and cannot play a good scheduling role for the load during the daytime, and the overall peak-to-valley difference rate reduction is not large.

[0567] Table 5 Optimization of peak-to-valley difference when only air conditioning load is involved in regulation

[0568]

[0569] (3) Coordinated optimization of electric vehicles and air conditioning loads, such as Fig.10 As shown in (a) to (d):

[0570] After adopting the coordinated optimization scheduling scheme of air-conditioning load and electric vehicle, compared with the scenario where electric vehicles and air-conditioning loads are scheduled separately, the peak-valley load difference rate of the power grid in the four seasons is reduced to 20.17%, 17.38%, 23.56% and 22.03% respectively, making the optimized peak-valley difference rate all drop below 50%. After scheduling, the average scheduling subsidy for air-conditioning load is 180 yuan, while the average scheduling subsidy for electric vehicle charging is 165.2 yuan. When electric vehicles and air-conditioning loads are coordinated and optimized, compared with only scheduling electric vehicles or air-conditioning loads separately, the coordinated optimization scheduling can not only reduce the scheduling cost of the power system, but also minimize the peak-valley difference of the load, so that the stability of the power grid is improved. Before the electric vehicle completes charging and is ready to leave, the excess electric energy can be reversely supplied to the power grid, which can further reduce the load peak of the power system and make more full use of the dispatchable capacity of the power grid. In general, by coordinating and optimizing the charging and discharging of air-conditioning loads and electric vehicles, not only can the load peak of the power grid be reduced more effectively, which is conducive to the stable operation of the power grid, but also can reduce the electricity cost of users.

[0571] Table 6 Peak-valley difference optimization of electric vehicle and air conditioning load collaborative optimization

[0572]

[0573] The energy mutual regulation optimization method and system of air-conditioning load and electric vehicle provided in the above embodiment of the present invention analyzes the energy mutual regulation optimization of air-conditioning load and electric vehicle, establishes corresponding optimization models from the perspective of power grid and users, and proposes coordinated optimization strategies to achieve balanced distribution of power grid load and minimize the peak-to-valley difference rate. The effectiveness of the strategies and models proposed in the above embodiment of the present invention is verified through case analysis. The results show that the coordinated optimization of air-conditioning load and electric vehicle charging and discharging can not only effectively reduce the peak-to-valley difference rate of the power grid and improve the stability of the power grid, but also reduce the electricity cost of users. This has important theoretical and practical significance for promoting the development of smart grids and improving the energy management level of residential areas.

[0574] An embodiment of the present invention further provides a computer terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the processor can be used to execute any method of the above-mentioned embodiments of the present invention, or to run any system of the above-mentioned embodiments of the present invention.

[0575] Optionally, the memory is used to store programs; the memory may include volatile memory (English: volatile memory), such as random-access memory (English: random-access memory, abbreviated: RAM), such as static random-access memory (English: static random-access memory, abbreviated: SRAM), double data rate synchronous dynamic random access memory (English: Double Data Rate Synchronous Dynamic RandomAccess Memory, abbreviated: DDRSDRAM), etc.; the memory may also include non-volatile memory (English: non-volatile memory), such as flash memory (English: flash memory). The memory is used to store computer programs (such as applications, functional modules, etc. that implement the above method), computer instructions, etc., and the above computer programs, computer instructions, etc. can be partitioned and stored in one or more memories. And the above computer programs, computer instructions, data, etc. can be called by the processor.

[0576] The above-mentioned computer programs, computer instructions, etc. may be stored in one or more memories in partitions, and the above-mentioned computer programs, computer instructions, data, etc. may be called by a processor.

[0577] The processor is used to execute the computer program stored in the memory to implement the various steps of the method or various modules of the system involved in the above embodiments. For details, please refer to the relevant descriptions in the above method and system embodiments.

[0578] The processor and the memory may be independent structures or integrated structures. When the processor and the memory are independent structures, the memory and the processor may be coupled and connected via a bus.

[0579] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it can be used to execute any method of the above embodiments of the present invention, or to run any system of the above embodiments of the present invention.

[0580] The energy mutual regulation and optimization method and system of air-conditioning load and electric vehicle provided in the above embodiment of the present invention can effectively reduce the peak-to-valley difference rate of the power grid and improve the stability of the power grid through the coordinated optimization of electric vehicles and air-conditioning loads, while taking into account user comfort and electricity costs, and realizing efficient management and optimal allocation of residential energy, which has important theoretical and practical significance for the development of smart grids and residential energy management; using electric vehicles as distributed energy storage to provide objective flexibility resources for the power system, and effectively improving the power system's ability to absorb part of the power grid load through orderly charging, power return, and cascade utilization of retired batteries of electric vehicles, and having considerable energy storage potential; by controlling a certain number of air-conditioning load clusters and flexibly dispatching them, it can quickly respond to grid-side dispatching, reduce load peaks to a certain extent, effectively improve the power supply capacity, and reduce the cost of demand response; by tapping the flexibility value of electric vehicle charging and discharging resources, guiding the linkage regulation and coordinated optimization of electric vehicles and air-conditioning loads, and guiding electric vehicles to charge off-peak or even reversely discharge during the peak period of air-conditioning loads, thereby effectively reducing or even reducing the global or local peak air-conditioning load, and playing a key role in improving the power supply capacity and the economic operation level of the power grid.

[0581] All matters not covered in the above embodiments of the present invention are well known in the art.

[0582] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art may make various modifications or variations within the scope of the claims, which do not affect the essence of the present invention.

Claims

1. A method for optimizing the mutual regulation of energy between air conditioning load and electric vehicle, characterized in that: include: Adjust the charging behavior of electric vehicles in the substation area according to the grid demand and conduct mutual regulation of electric vehicles; Adjust the energy consumption of air-conditioning equipment of residents in the area and conduct mutual assistance scheduling of air-conditioning loads; Based on the mutual assistance regulation of electric vehicles and the mutual assistance scheduling of air-conditioning loads, the coordinated mutual assistance between electric vehicles and air-conditioning equipment is optimized.

2. The method for optimizing the mutual regulation of energy between air conditioning load and electric vehicle according to claim 1, characterized in that: The charging behavior of electric vehicles in the substation area is adjusted according to the demand of the power grid, and the electric vehicles are mutually assisted and regulated, including: Providing an electric vehicle optimization scheduling model; Based on the electric vehicle optimal dispatching model, evaluating the dispatchable capacity of electric vehicle load; Based on the evaluation result of the dispatchable capacity of the electric vehicle load, considering V2G access, a real-time optimization dispatch model of electric vehicle energy based on V2G access is constructed; Based on the real-time optimization and dispatching model of electric vehicle energy, the charging behavior of electric vehicles in the substation is adjusted according to the demand of the power grid.

3. The method for optimizing the mutual regulation of air-conditioning load and electric vehicle energy according to claim 2, characterized in that: The electric vehicle optimization scheduling model is provided, comprising: An electric vehicle optimization dispatching model is constructed. The objective function of the optimization dispatching model is to minimize the total cost of electricity purchase in the substation area. Then: S T ={1,2,…,T / Δt} (1-4) In the formula, is the electricity purchase cost of the aggregator after period t, S T is the set of discrete optimizable time periods in the optimizable scheduling period T, P buy (t) and p TOU (t) are the corresponding power purchase power and time-of-use electricity price of the aggregator in time period t, Δt is the discrete time step, formula (1-3) is the power balance constraint, and formula (1-4) is the safety operation constraint, where: is the non-electric vehicle resident load in the residential area, Charging load for electric vehicles, The maximum value of the power purchased by the aggregator.

4. The method for optimizing the mutual regulation of the energy of air conditioning load and electric vehicle according to claim 2, characterized in that: The method of evaluating the dispatchable potential of electric vehicle load based on the electric vehicle optimal dispatch model includes: For different optimizable periods of the electric vehicle optimization scheduling model, the dispatchable capacity of a single electric vehicle is determined according to the following constraints on the dispatchable capacity of electric vehicles: User travel restrictions: SOC target (j)≤SOC(j,t off )≤1 (1-5) In the formula, SOC target is the battery charging target value, t off is the departure time, j is the vehicle serial number; Battery life constraints: Where SOC(j, t) represents the energy storage of electric vehicle j at time t; P dis (j, t) represents the charge and discharge power of electric vehicle j after the scheduling time t; SOC min (j) and SOC max (j) represent the lower and upper limits of the energy storage of electric vehicle j, SOC base (j) is the battery capacity of electric vehicle j, and η is the charge and discharge efficiency; Battery maximum charge and discharge power constraints: P dis，min (j,t)≤P dis (j,t)≤P dis，max (j,t) (1-7) P ch，min (j,t)≤P dis (j,t)≤P ch，max (j,t) (1-8) Where P ch,max (j), P ch,min (j) and P dis，max (j), P dis，min (j) represent the maximum and minimum charging and discharging power of electric vehicle j, respectively, where the specifications of the charging pile determine the maximum charging power and the maximum discharging power; Time and object constraints: t on ≤t≤t sch ≤t off (1-9) In the formula, t on is the vehicle arrival time, t sch is the schedulable time; Battery V2G discharge period capacity constraints: In the formula, SOC target (j) represents the charging target value of electric vehicle j, and SOC(j, t) represents the initial power; when equation (1-10) takes the equal sign, we get P dis The upper limit of (j, t) is: P up (j,t)=P o (j,t)-P dis (j,t) (1-12) Where P up (j, t) represents the adjustable charging power of electric vehicle j during the scheduling interval t, P o (j, t) represents the charging power of the jth electric vehicle when it does not participate in the battery capacity scheduling within the scheduling interval t; Battery charging period capacity constraints: When the equation (1-13) is equal, we get P dis The threshold value P of (j, t) dis，max (j, t); combined with the capacity constraint (1-10), the downward dispatchable capacity formula can be expressed as: P down (j,t)=P dis (j,t)-P0(j,t) (1-14) Where P down (j, t) represents the adjustable charging power of electric vehicle j in the scheduling interval t; Based on the dispatchable capacity of the single electric vehicle, the adjustable power P of the electric vehicle cluster in the interval is obtained. up,all and adjustable power P down,all for: Where N is the total number of electric vehicles; If electric vehicles cannot participate in the dispatch within the dispatch interval t, then: P up (j,t)=P dwon (j,t)=0 (1-17)。 5. The method for optimizing the mutual regulation of energy between air conditioning load and electric vehicle according to claim 2, characterized in that: The evaluation result based on the dispatchable potential of the electric vehicle load is considered to construct a real-time optimization dispatch model of electric vehicle energy based on V2G access, including: The basic model for real-time optimization and dispatch of electric vehicle energy under V2G access is constructed as follows: In the formula, is the electricity purchase cost of the aggregator in the kth dispatchable period, is the time-of-use electricity price in different optimized time periods within the kth dispatchable period, For the purchased power, is the total V2G subsidy of the aggregator in the kth dispatchable period, Subsidy for V2G units, represents the discharge power of electric vehicle j in time period t; The power balance constraint and power purchase power limit constraint during the charging process are established as follows: In the formula, is the predicted value of conventional residential load in the kth dispatchable period, and Each is the charging and discharging power of electric vehicle j in the kth dispatchable period; The charging and discharging constraints of electric vehicles are established as follows: Soc min (j)≤Soc k (j,t)≤Soc max (j) t∈S T (k) (1-27) In the formula, and are the upper limits of charging and discharging power of electric vehicles, respectively. For non-dispatchable electric vehicles, that is, v k (j, t) = 0, which corresponds to and Both are 0. Formula (1-25) indicates that the electric vehicle cannot be charged and discharged at the same time. Formula (1-26) indicates the relationship between the SOC states of electric vehicle j in adjacent time periods. and is the charging and discharging efficiency coefficient of electric vehicle j, E EV (j) is the battery capacity of electric vehicle j, in formula (1-27), Soc min (j) and Soc max (j) are the lower and upper limits of the SOC of electric vehicle j, respectively; The electric vehicle charging satisfaction rate constraint is established as: In the formula, Soc Tar (j, T / Δt) is the target SOC for the discrete time period T / Δt set by the owner of electric vehicle j, Soc k (j, k-1) is the starting value of the battery state of charge of the electric vehicle j in the kth dispatchable period.

6. The method for optimizing the mutual regulation of energy between air conditioning load and electric vehicle according to claim 1, characterized in that: The adjusting of the power consumption behavior of the air conditioning equipment of each resident in the substation area and the mutual assistance dispatch of the air conditioning load include: Construct a thermodynamic model of air conditioning load to simulate the temperature changes of the room under the influence of indoor and outdoor cold and heat sources; Based on the thermodynamic model of the air conditioning load, the operating characteristics and control methods of the air conditioning are analyzed, and a mathematical model of the air conditioning load is constructed; Based on the mathematical model of the air conditioning load, the control mode of the air conditioning is analyzed and the dispatchable potential of the air conditioning load is evaluated; According to the evaluation result of the dispatchable potential of the air-conditioning load, the power consumption behavior of the air-conditioning equipment of each resident in the substation is adjusted.

7. The method for optimizing the mutual regulation of air-conditioning load and electric vehicle energy according to claim 6, characterized in that: The analysis of the air conditioning control mode and the evaluation of the dispatchable potential of the air conditioning load include: The control method of the air conditioner includes: switch control and temperature control of a fixed-frequency air conditioner, and temperature control and frequency control of a variable-frequency air conditioner; For the on / off control of fixed-frequency air conditioners, the schedulable potential of the on / off response can also be evaluated as follows: The thermodynamic model of air conditioning load is shown in equation (2-1) to equation (2-3): τ=RC (2-3) In the formula, is the indoor temperature at time t, is the indoor temperature after a time interval of Δt, is the outdoor temperature at time t, R is the equivalent thermal resistance, Q Δt is the cooling capacity of the air conditioner, λ is the proportional factor, Δt is the time interval, τ is the time constant, and C is the equivalent heat capacity; From the above formula, we can deduce: Where P Δt is the cooling power of the air conditioner, COP is the energy efficiency ratio of the air conditioner; Where: S(t k ) and S(t k-1 ) are t k Time and t k-1 The switch operation status of the air conditioning load, T(t k ) is t k The indoor temperature at the time, T max is the upper limit of air conditioner operating temperature, T min The lower limit of the air conditioner operating temperature. and They are respectively the down-adjustable time and the up-adjustable time under the fixed-frequency air conditioner switch control, T O is the outdoor temperature, T set0 is the set temperature under the fixed frequency air conditioner switch control, Q is the cooling capacity of the air conditioner, k t is the operating state variable of the air conditioner, k t =1 means the air conditioner is in operation, k t =0 means the air conditioner is in standby mode, T t is the current indoor temperature, It is the maximum down-adjustable time under the fixed frequency air conditioner switch control. It is the maximum adjustable time under the fixed frequency air conditioner switch control. and They are the adjustable capacity power and adjustable capacity power under the switch control of the fixed-frequency air conditioner, which are used to describe the dispatchable potential of the fixed-frequency air conditioner. COP is the energy efficiency ratio. For the temperature control of fixed-frequency air conditioners, the schedulable potential of temperature control response can also be evaluated as follows: Set the new temperature setting value to T set1 , the dispatchable time period of air conditioning load at the new temperature for: Where, T set1 It is the set temperature under the temperature control of the fixed frequency air conditioner. It is the downward adjustment time under the temperature control of the fixed frequency air conditioner. It is the upward adjustment time under the temperature control of the fixed frequency air conditioner. The time that a fixed-frequency air conditioner can be adjusted down from the new set temperature. The time that a fixed-frequency air conditioner can be adjusted upward from the new set temperature; The capacity power that can be adjusted up or down for the corresponding air conditioning load is: In the formula, and They are respectively the adjustable capacity power and the adjustable capacity power under the temperature control of the fixed-frequency air conditioner; For the temperature control and frequency control of variable frequency air conditioners, the virtual energy storage modeling method can be used to evaluate the dispatchable potential of variable frequency air conditioners as follows: A virtual energy storage model based on variable frequency air conditioning is established, which is used to simulate the conversion of electric power energy into heat energy: The operating state of the virtual energy storage model and the consumed electric power are analyzed to obtain the SOC expression of the virtual energy storage model: In the formula, S SOC (t) and S SOC (t-1) are the battery charge states at the current time and the previous time period respectively; η es is the charge and discharge efficiency; P es (t-1) is the charge and discharge power in the previous period. When the power value is positive, it means that the battery is in the discharge state. When the power value is negative, it means that the battery is in the charge state. C es Indicates battery capacity; The constraints for establishing normal working situations are: 0≤S SOC (t)≤100% (2-22) P es，min ≤P es (t)≤P es，max (2-23) Where P es，min and P es，max Respectively represent the maximum charging and discharging power of the virtual energy storage model; Calculate the equivalent capacity C of the virtual energy storage model v for: In the formula, To increase the maximum amount of heat in the room, is the maximum heat reduction in the room, η AC is the energy efficiency ratio of the variable frequency air conditioner; The thermal energy equivalent capacity of virtual energy storage is obtained as: Where, T set , T max , T min are the set temperature, upper limit temperature and lower limit temperature that can satisfy the needs of residents using variable frequency air conditioners in the area, C room is the equivalent heat capacity of the room; Calculate the equivalent state of charge of the virtual energy storage model for: Where, T in is the initial ambient temperature; Calculate the maximum charge and discharge power P of the virtual energy storage model v (t) is: P AC0 -P AC (t)≤P v (t)≤P AC1 -P AC (t) (2-28) Where P AC0 and P AC1 P is the working power of the air conditioner in standby operation and full load operation respectively; AC (t) is the real-time operating power of the air conditioner.

8. The method for optimizing the mutual regulation of energy between air conditioning load and electric vehicle according to claim 1, characterized in that: The method for mutually assisting electric vehicles and air conditioning loads is based on mutual assistance to optimize the coordination between electric vehicles and air conditioning equipment, including: Let L n，i is the initial daily load value of the power grid, i represents 1, 2, ..., N different optimizable time periods of the day, and the load of time period i for collaborative optimization is L i , EV charging load is L ch，i , EV discharge load is L dis，i , the air conditioning aggregate power is P ac，i , the total load in the area L i It is expressed as: Where P ch (i, j) is the charging power of the jth electric vehicle in time period i, P dis (i, j) is the discharge power of the jth electric vehicle in time period i, P ac，i is the power consumed by the air conditioner in period i; The optimization goal is set to minimize the peak-to-valley difference rate of power grid load, then: Among them, min(F) is the objective function of minimizing the peak-to-valley difference rate of power grid load, max(L i ) is the peak load value in period i; min(L i ) is the load valley value in period i; Set the following constraints: The temperature range constraint T(t) of the air conditioning equipment is: T set，min ≤T(t)≤T set，max (3-3) Among them, T set,min and T set,max are the lower and upper limits of the ambient temperature respectively; The actual working power constraint P of the aggregate air conditioning equipment AC，，i for: P AC，，i ≤P AC，imax (3-4) Among them, P AC，imax The initial operating power of the aggregated air-conditioning equipment before synergistic optimization; The electric vehicle battery capacity constraint SOC(t) is: SOC min ≤SOC(t)≤SOC max (3-5) Among them, SOC min SOC is the lower limit of the battery's energy storage capacity during charging. max The upper limit of the energy storage capacity of the battery during charging; The charging and discharging mode constraints of electric vehicles are: min(P dis (t),P ch (t))=0 (3-6) Among them, P dis (t) is the discharge power of the electric vehicle at time t, P ch (t) is the charging power of the electric vehicle at time t; The remaining power constraint of the electric vehicle when leaving is: SOC(j,t off )≥n (3-7) Among them, SOC(j,t off ) represents the battery energy storage state SOC at the time when electric vehicle j leaves, and n is the battery energy storage charge state setting threshold.

9. An energy mutual regulation and optimization system for air conditioning load and electric vehicle, characterized in that: include: Electric vehicle mutual assistance control module, which is used to adjust the charging behavior of electric vehicles in the substation area according to the needs of the power grid and perform mutual assistance control on electric vehicles; The air conditioning load mutual assistance scheduling module is used to adjust the energy consumption behavior of the air conditioning equipment of each resident in the substation area and perform mutual assistance scheduling of the air conditioning load; The collaborative optimization module optimizes the collaborative optimization between electric vehicles and air-conditioning equipment based on the mutual regulation of electric vehicles and the mutual scheduling of air-conditioning loads.

10. A computer terminal 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 computer program, it can be used to perform the method described in any one of claims 1 to 8, or to run the system described in claim 9.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it can be used to perform the method described in any one of claims 1 to 8, or to run the system described in claim 9.

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