Air conditioner load response potential evaluation method comprehensively considering user thermal comfort and response intention

Abstract

The invention provides an air conditioner load response potential evaluation method comprehensively considering user thermal comfort and response intention, and belongs to the technical field of power system optimization scheduling. Firstly, starting from a power grid user side, the initial adjustable margin of the air conditioner temperature is obtained based on the thermal comfort degree of a user; considering the influence of the electricity price on the willingness of users to participate in power grid demand response, and obtaining a dynamic adjustable interval of the air conditioner temperature; then, the power utilization satisfaction degree of the user is considered, the dynamic adjustable interval of the air conditioner temperature is corrected, and the dynamic adjustable interval is closer to the temperature adjustable interval expected by the user; and finally, the air conditioner approximate aggregation model and the air conditioner aggregation response potential evaluation model are combined to obtain the power adjustable range and the response potential of air conditioner load aggregation. According to the method, the willingness of the user participating in the power grid demand response is quantified and comprehensively fused into the response potential evaluation model, and a good balance can be achieved between the maximum mining of the response potential and the maximum degree of satisfaction of the user participating in the demand response.

Description

Technical Field

[0001] The present invention belongs to the technical field of optimal dispatching of power systems, and relates to an evaluation method for the flexible load response potential of the user demand side of a power system, and particularly relates to an evaluation method for the response potential of an air-conditioning cluster load on the demand side of a power system. Background Art

[0002] Due to the intermittency and randomness of renewable energy, the power system faces huge pressure on the balance between power supply and demand. It is difficult to achieve the safe and economic operation of the system only relying on the regulation ability of the power generation side. In contrast, the regulation potential on the user demand side is objective and the cost is low, which is worthy of further research.

[0003] In recent years, with the rapid economic development and the continuous improvement of people's living standards, the power load has been increasing, especially the air-conditioning load. In economically developed areas such as Shanghai, Jiangsu, and Zhejiang, the highest proportion of air-conditioning load exceeds 50% and shows an increasing trend year by year, and has become an important factor affecting the peak-valley difference of the system. The rapid development of two-way communication technology and advanced measurement systems in smart grids provides technical support for load monitoring and control at the user end. The air-conditioning load has the characteristics of strong controllability, large dispatching potential, fast response speed, and diverse control methods. Efficiently managing the air-conditioning load helps to ensure the balance between power supply and demand and promote the consumption of renewable energy. Evaluating the controllable potential of the air-conditioning load is an important prerequisite for achieving this goal.

[0004] From the existing research results, there are mainly two categories of evaluation methods for the air-conditioning load response potential on the user side: the analysis of the aggregated response potential of the air-conditioning load considering only the human thermal comfort and the analysis of the aggregated response potential of the air-conditioning load considering only the user willingness. However, when users actually participate in the demand response of the power grid, not only the influence of the user's own body temperature, that is, the influence of thermal comfort, but also the influence of time-of-use electricity price on the user's willingness to participate in the demand response of the power grid and the satisfaction of the user's participation in the demand response of the power grid should be considered. Some existing models do not comprehensively integrate factors such as user thermal comfort and user willingness into the process of excavating the air-conditioning response potential. Summary of the Invention

[0005] The purpose of the present invention is to provide a reasonable and effective evaluation method for the air-conditioning load response potential on the user side of the power grid. From the perspective of the user side of the power grid, based on the approximate aggregation model of air-conditioning power, factors such as user thermal comfort, user willingness to participate in the demand response of the power grid, and satisfaction are comprehensively considered, and an evaluation model for the aggregated response potential of the air-conditioning is established. Through the above air-conditioning response potential evaluation model, a better balance can be achieved between maximizing the excavation of the response potential and maximizing the satisfaction of users' participation in the demand response.

[0006] In order to achieve the above purpose, the technical solution of the present invention is:

[0007] A method for evaluating the response potential of air conditioning loads that comprehensively considers user thermal comfort and user willingness, the air conditioning load response potential evaluation process includes: the first step, performing equivalent approximate processing on the thermodynamic equivalent model of a single air conditioner to obtain an approximate aggregation model of the air conditioning cluster load; the second step, establishing a user thermal comfort model to obtain the initial interval of the air conditioning adjustable temperature; the third step, establishing a user willingness model, considering the impact of electricity prices and electricity satisfaction on the user's willingness to participate in grid demand response, and superimposing their impacts on the initial interval of the air conditioning adjustable temperature to obtain the dynamic adjustable interval of the air conditioning set temperature; the fourth step, combining the air conditioning approximate aggregation model and the dynamic adjustable interval of the air conditioning set temperature to obtain the power adjustable range and response potential of the air conditioning load aggregation. The innovation of the present invention is the third step, which is to quantify the impact of electricity prices and electricity satisfaction on the user's willingness to participate in grid demand response by establishing a model. It includes the following steps:

[0008] The first step is to establish an approximate aggregation model of air conditioning cluster load;

[0009] 1.1) For single air conditioners, the most common physical model is the equivalent thermal parameters (ETP) model. The air conditioning load stabilizes the indoor temperature of the system within the expected range through switching actions. The indoor temperature change process caused by the switching state of the air conditioner can be simulated by the first-order ETP model:

[0010]

[0011] Where: T out (t) and T in (t) are the outdoor temperature and indoor temperature at time t, respectively, ℃; C room is the equivalent heat capacity of the room, KW·h / ℃; R room is the equivalent thermal resistance of the room, ℃ / KW; Q ac is the cooling / heating power of the air conditioner, which is equal to the power consumption of the air conditioner P ac There is a relationship, Q ac =ηP ac , η is the energy efficiency ratio of the air conditioner; S(t) is a Boolean variable, indicating the on / off status of the air conditioner, "0" means the air conditioner is off, and "1" means the air conditioner is on. The specific expression is as follows:

[0012]

[0013] Where δ is the temperature dead zone width of the air conditioner, T set is the set value of the air conditioning temperature, and ε is the simulation time step in the discrete simulation environment.

[0014] By solving equations (1) and (2), the start-up period T of the air conditioner can be obtained. on and the shutdown period T off , and the expressions are as follows:

[0015]

[0016] Equations (3) and (4) can be further transformed into the following equations:

[0017]

[0018]

[0019] 1.2) For a single air conditioner, its power consumption P ac,i can be easily obtained through its physical model. However, for an air conditioner cluster participating in the grid demand response, how to accurately obtain the aggregated power after aggregating a large number of air conditioners is the most worthy of research. The aggregated power of N air conditioners at time t can be expressed as:

[0020]

[0021] where P AC (t) is the aggregated power of N air conditioners at time t, which is only related to the operating state of the air conditioners at time t.

[0022] In the steady state, the average power consumption of a single air conditioner is related to the duty cycle of its start-up period in the entire operating cycle. If P on,i represents the probability that air conditioner i is in the on state, it can be expressed in terms of the duty cycle as:

[0023]

[0024] When the number of air conditioners N participating in the demand response is N ≥ m, and each air conditioner operates independently, and the external temperature is constant within each hour of the scheduling period, according to the law of large numbers, the aggregated power of N air conditioners can be approximately expressed as:

[0025]

[0026] Substituting equations (5) and (6) into equation (8), we can get:

[0027]

[0028] According to the inequality transformation, equation (10) can be further transformed into:

[0029]

[0030] Substituting equations (5) and (6) into equation (11), we can get:

[0031]

[0032] According to Equations (9) and (12), the upper and lower bounds of the aggregated power of N air conditioners can be approximately obtained as Equations (13) and (14).

[0033]

[0034] Wherein, E(X) and E(Y) are the mathematical expectations of random variables X and Y respectively, and the calculation formulas are as follows:

[0035]

[0036] Furthermore, the estimated value of the aggregated power of the air-conditioning load can be represented by any value within the interval as:

[0037]

[0038] Wherein, P AC,es is the estimated value of the aggregated power of the air-conditioning load.

[0039] It can be obtained from Equations (13), (14), and (17) that when the air-conditioning parameters are determined, the dispatching center can obtain the aggregated power of the air-conditioning cluster load at the current moment only through the outdoor temperature T out at the current moment and the air-conditioning temperature setting value T set .

[0040] Step 2: Establish a user thermal comfort model;

[0041] In the process of air-conditioning dispatching, the user's thermal comfort experience is an important factor to be considered. The present invention introduces a thermal comfort model, which comprehensively considers environmental parameters and human parameters, and accurately depicts the user's thermal comfort experience by calculating the predicted mean vote (PMV).

[0042] The predicted mean vote PMV includes 4 independent environmental parameters (indoor temperature, mean radiant temperature, air flow velocity, air humidity) and 2 independent human parameters (human energy metabolic rate, clothing thermal resistance). Its calculation formula is:

[0043] I PMV =(0.303e -0.036M +0.028){(M-W)-3.05×10 -3 [5733-6.99(M-W)-p a -

[0044] 0.42M-W-58.15-1.7×10-5M5867-pa-0.0014M34-tin-3.96×10-8fcltcl+2734-tr+2734-fclhctcl-tin(18)

[0045] Where: I PMV is the value of the PMV function; M is the human energy metabolism rate, which is related to the degree of human activity; W is the mechanical work generated by human activities; p a is the water vapor pressure of the environment, which is a function of the relative humidity and the indoor temperature t in ; f cl is the clothing coefficient, which is a function of the clothing thermal resistance I cl ; is the mean radiant temperature; h c is the convective heat transfer coefficient, which is a function of the indoor temperature t in , the clothing surface temperature t cl and the air velocity v ar . The calculation formulas of t cl , f cl , p a , h c are shown in formulas (19)-(22).

[0046]

[0047]

[0048]

[0049]

[0050] When the PMV index is equal to 0, it means that the residents feel the most comfortable at this time; when the PMV index is greater than 0, it means that the residents feel hot, and the larger the PMV index, the hotter the residents feel; when the PMV index is less than 0, it means that the residents feel cold, and the smaller the PMV index, the colder the residents feel. ISO7730 stipulates that when the value of PMV is between -0.5 and 0.5, the user is in the best comfort state, and the corresponding indoor temperature is 24.8-27.3 °C, which is the initial adjustable margin [T min0 , T max0 .

[0051] Step 3: Establish a user willingness model;

[0052] The willingness of users to participate in the power grid demand response will be affected by electricity prices and electricity consumption satisfaction. The present invention quantifies the influence of them on the willingness of users to participate in the power grid demand response through the following two steps.

[0053] 3.1) Consider the impact of electricity price on user willingness;

[0054] When exploring the response potential of air conditioners, in addition to the user's own thermal comfort experience, the electricity price also affects the user's willingness to participate in the grid demand response. When the electricity cost is higher than the user's psychological expectation, the user is more sensitive to price and more expects to obtain subsidies by participating in the demand response to reduce the electricity cost. At this time, the user's willingness to participate in the demand response increases, and the adjustable margin of temperature will relatively increase; conversely, when the electricity cost is lower than the user's psychological expectation, the user is more sensitive to their own thermal comfort experience, and the willingness to participate in the grid demand response will decrease, and the adjustable margin of temperature will relatively decrease. The present invention adopts a user willingness impact factor μ i,t to reflect the impact of the electricity price on the user's willingness to participate in the demand response.

[0055]

[0056] In the formula: μ i,t represents the willingness degree of the end-user of the i-th air conditioner to participate in the demand response at time t. When μ i,t is positive, the user has a positive response willingness. When μ i,t is negative, the user has a negative response willingness, and the magnitude of its value reflects the strength of the willingness; P base is the user's expected electricity price, which can be defined according to different situations; P real is the current actual electricity price; P max is the maximum value of the time-of-use electricity price within the scheduling period.

[0057] The user's willingness to participate in the demand response will change dynamically with the electricity price, and its impact on the aggregated response potential of the air conditioner can be expressed by formulas (24)-(26).

[0058]

[0059] In the formula: and are respectively the upper and lower limits of the initial temperature adjustable margin of the i-th air conditioner terminal considering only the user's thermal comfort at time t; and are respectively the upper and lower limits of the temperature adjustable margin considering the electricity price on the basis of considering the user's thermal comfort; ΔT i,t is the temperature margin adjustment amount of the i-th air conditioner terminal caused by the user's willingness at time t.

[0060] 3.2) Consider the impact of electricity consumption satisfaction on user willingness;

[0061] The electricity consumption satisfaction of users includes user electricity consumption comfort and economy. During the dispatching process, the flexible load of the air-conditioning cluster can be regarded as shiftable load and curtailable load. Then, the electricity consumption satisfaction of users' air conditioners can be expressed by the following formula.

[0062] w = αf ele + βf eco , α + β = 1#(27)

[0063] In the formula, f ele is the user's electricity consumption comfort, and f eco is the user's electricity consumption economy, which can be solved by formulas (28) and (29). α represents the weight of the user's electricity consumption comfort in the electricity consumption satisfaction, β represents the weight of the user's electricity consumption economy in the electricity consumption satisfaction, and α, β ∈ [0, 1].

[0064]

[0065] In the formula: |P AC,t -P AC0,t | represents the change in the air-conditioning load at time t before and after participating in the demand response. P AC0,t represents the aggregated power of the air-conditioning cluster before participating in the demand response, and P AC,t represents the aggregated power of the air-conditioning cluster after participating in the demand response; P real,t is the real-time electricity price. The electricity consumption comfort represents the impact of the electricity quantity change on users, while the economy is affected by both the electricity price and the electricity quantity change. It can be seen from this that the user's satisfaction is mainly affected by the electricity price and the change in electric energy. α and β reflect the focuses of different types of users. When α is greater than β, users pay more attention to the electricity consumption comfort, and vice versa for the economy. The magnitudes of α and β can be specifically adjusted according to different dispatching periods and different user groups.

[0066] P AC0,t , P AC,t , P real,t used in the calculation process of the user's electricity consumption satisfaction in the above formula can be obtained from historical data. The impact of the user's electricity consumption satisfaction on the air-conditioning aggregation response potential can be expressed by the following formula.

[0067]

[0068]

[0069] N = (αN ele + βN eco )#(32)

[0070]

[0071] In the formula: N eleDissatisfaction with the change in user electricity consumption, N eco Dissatisfaction with the real-time electricity price for users, and N is the comprehensive dissatisfaction with user electricity consumption; and are respectively the upper and lower limits of the adjustable margin of the air-conditioning temperature considering user satisfaction.

[0072] The willingness of users to participate in the power grid demand response will be affected by both electricity prices and electricity consumption satisfaction. First, according to the second step, an initial temperature adjustable range is obtained from the user's thermal comfort Then, according to 3.1), considering the influence of electricity prices on the willingness of users to participate in the demand response, the adjustable margin of the indoor temperature setting value where the air-conditioning terminal is located changes dynamically on the basis of the temperature adjustable margin of the user's thermal comfort, and Finally, according to 3.2), comprehensively considering the user's electricity consumption satisfaction, the adjustable dynamic range of the air-conditioning set temperature is further corrected to obtain In this way, a balance is achieved between maximizing the response potential and maximizing the satisfaction of users' participation in the demand response.

[0073] Step 4: Establish an evaluation model for the aggregated response potential of air conditioners;

[0074] Comprehensively considering the user's thermal comfort and willingness, and combining equations (13)(14)(15)(16)(17), it can be obtained that when the initial temperature setting value of the air conditioner is The aggregated power of the air conditioner The adjustable range is:

[0075]

[0076]

[0077] In the formula: are respectively the upper and lower limits of the aggregated power of the air conditioner, and also the adjustable range of the aggregated power when the air conditioner cluster participates in the demand response; is the initial aggregated power when the initial temperature setting value of the air conditioner is ; ξ is the controllability of the air-conditioning terminal. The more users with control equipment installed at the air-conditioning terminal, the higher the controllability of the air-conditioning terminal; is the response potential of the air-conditioning aggregation.

[0078] Equation (35) represents the adjustable range of the aggregated power of the air conditioner; equations (36)(37) are respectively the expressions of the lower and upper limits of the aggregated power of the air conditioner obtained by combining the approximate aggregation model of the air conditioner and the adjustable dynamic range of the air-conditioning set temperature; equation (38) is the expression of the initial aggregated power of the air conditioner; equation (39) is the expression of the load response potential of the air conditioner cluster.

[0079] The effects and benefits of the present invention are as follows:

[0080] In view of the problems existing in the process of exploring the potential of air-conditioning cluster loads to participate in the power grid demand response, the present invention proposes a more reasonable evaluation method for the response potential of air-conditioning loads from the perspective of users. By modeling the user comfort and willingness, the willingness of users to participate in the power grid demand response is quantified and comprehensively incorporated into the response potential evaluation model, and the influence of the subjective factors of users themselves on the response potential evaluation is reasonably expressed mathematically, so that the response potential of air-conditioning aggregation can be obtained more reasonably and accurately. Description of the Drawings

[0081] Figure 1 is the flow chart for evaluating the response potential of air-conditioning aggregation considering both user thermal comfort and willingness;

[0082] Figure 2 is the aggregated power of air-conditioning loads on a typical summer day;

[0083] Figure 3 is the relationship between user willingness and electricity price;

[0084] Figure 4 is the adjustable range of air-conditioning temperature elasticity;

[0085] Figure 5 is the adjustable range of the aggregated power when air-conditioning participates in the demand response;

[0086] Figure 6 is the comparison chart of air-conditioning aggregation response potential, where Scenario 1 only considers user thermal comfort, Scenario 2 considers the influence of user thermal comfort and time-of-use electricity price, and Scenario 3 comprehensively considers user thermal comfort, time-of-use electricity price and electricity consumption satisfaction. Detailed Embodiment

[0087] The following uses an example to verify and describes in detail the specific embodiment of the present invention in combination with the technical solution and the drawings.

[0088] The schematic diagram of the example process is as Figure 1 shown. The parameters required for the example are shown in Table 1 and Table 2. Among them, Table 1 is the air-conditioning load parameters and Table 2 is the time-of-use electricity price. Assume that 10,000 air-conditioners are connected to a certain node of the distribution network. According to Figure 1 the process shown, the specific steps for evaluating the response potential of air-conditioning aggregation are as follows:

[0089] Step 1: Based on the basic physical model of a single air conditioner, an approximate aggregation model of the aggregated power of a large number of air conditioner loads is established. The upper and lower limits of the approximate aggregated power of the air conditioner cluster can be obtained from Equations (13) and (14), and the estimated value of the aggregated power of the air conditioner cluster can be obtained from Equation (17). Combining the outdoor temperature on a typical summer day and the air conditioner load parameters in Table 1, the range of the aggregated power of the air conditioner is obtained, as shown by the upper limit of the aggregated power and the lower limit of the aggregated power in Figure 2 ; if α is taken as 0.5 at this time, an approximate value of the aggregated power of the air conditioner cluster can be obtained, as shown by the predicted value of the aggregated power in Figure 2 . It can be seen from Figure 2 that the aggregated power of the air conditioner cluster is relatively low in the early morning and at night when the temperature is low; at noon when the temperature is the highest, the power of the air conditioner cluster load also reaches the maximum.

[0090] Step 2: Establish a user thermal comfort model, and obtain the comfortable interval of the human body's perceived temperature of air conditioner users, that is, the initial adjustable interval of the air conditioner set temperature, through Equations (18)-(22) and the ISO7730 standard as shown by the original adjustable temperature interval in Figure 4 .

[0091] Step 3: Establish a user willingness model, consider the influence of electricity price and electricity consumption satisfaction on the willingness of air conditioner users to participate in the power grid demand response respectively, quantify the user willingness and apply its influence to the adjustable interval of the air conditioner set temperature. 1) Consider the influence of electricity price on user willingness. The influence of the change in electricity price on user willingness can be obtained from Equation (23), and the relationship between user willingness and the change trend of electricity price is as shown in Figure 3 . It can be seen from Figure 3 that the willingness of users to participate in the power grid demand response is proportional to the change trend of electricity price, that is, when the electricity price is in the valley period, the willingness of users to participate in the demand response is low, and when the electricity price is in the peak period, the willingness of users to participate in the demand response is high. Based on the initial adjustable interval of the air conditioner set temperature that only considers user thermal comfort, the dynamic adjustable interval of the air conditioner set temperature considering time-of-use electricity price can be obtained from Equations (24)-(25) 2) Further consider the influence of user electricity consumption satisfaction on user willingness. The above-obtained dynamic adjustable interval of the air conditioner set temperature can be further corrected by Equations (27)-(34) to obtain the final dynamically adjustable interval of the air conditioner set temperature The above-obtained dynamic adjustable interval of the air conditioner set temperature and the final dynamically adjustable interval of the air conditioner set temperature are as shown in Figure 4 . It can be seen from Figure 4It can be seen that when the user's willingness is low, the adjustable range of the air conditioner's set temperature is generally low; when the user's willingness is high, the adjustable range of the air conditioner's set temperature is generally high.

[0092] Step 4: Combine the approximate aggregation model of air conditioner load and the dynamically adjustable range of air conditioner set temperature to establish an evaluation model for the aggregation response potential of air conditioner load. From equations (36) and (37), the upper and lower limits of the adjustable range of the aggregated power of air conditioner load in different scenarios can be obtained, and the results are as Figure 5 shown. From equation (39), the response potential of air conditioner load in different scenarios can be obtained, and the results are as Figure 6 shown, where Scenario 1 only considers the user's thermal comfort, Scenario 2 considers the influence of the user's thermal comfort and time-of-use electricity price, and Scenario 3 comprehensively considers the user's thermal comfort, time-of-use electricity price, and electricity consumption satisfaction. From Figure 5 and Figure 6 it can be seen that on the basis of considering the user's thermal comfort and then considering the user's willingness, the adjustable power range and response potential of the air conditioner cluster are further corrected, achieving a better balance between maximizing the response potential and maximizing the satisfaction of users' participation in demand response.

[0093] Table 1 Air conditioner load parameters

[0094]

[0095] Table 2 Time-of-use electricity price

[0096]

[0097] The above-described embodiments only represent the implementation manners of the present invention, but should not be construed as limiting the scope of the present invention. It should be noted that for those skilled in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention.

Claims

1. A method for evaluating the response potential of air conditioning loads by comprehensively considering user thermal comfort and response willingness, characterized in that: The air conditioning load response potential assessment process includes the following steps: The first step is to perform equivalent approximate processing on the thermodynamic equivalent model of the single air conditioner to obtain the approximate aggregation model of the air conditioner cluster load; The second step is to establish a user thermal comfort model to obtain the initial range of the air conditioner's adjustable temperature. The third step is to establish a user willingness model, which considers the impact of electricity price and electricity satisfaction on the user's willingness to participate in grid demand response, and superimposes their impact on the initial range of the air conditioner adjustable temperature to obtain the dynamic adjustable range of the air conditioner set temperature; Step 3.1, considering the impact of electricity prices on user willingness, the adjustable margin of the indoor temperature set value of the air-conditioning terminal is dynamically changed based on the adjustable margin of the user's thermal comfort temperature Step 3.2, considering the impact of electricity satisfaction on user willingness, correct the adjustable dynamic range of air conditioning set temperature, and obtain In the fourth step, the power adjustable range and response potential of air conditioning load aggregation are obtained by combining the air conditioning approximate aggregation model and the dynamically adjustable range of air conditioning set temperature.

2. The air conditioning load response potential evaluation method according to claim 1, which comprehensively considers user thermal comfort and response willingness, is characterized in that: The first step is as follows: Step 1.1, the air conditioning load stabilizes the indoor temperature of the system within the expected range through switching action; the indoor temperature change process caused by the air conditioning switching state can be simulated by the first-order ETP model: Where: T out (t) and T in (t) are the outdoor temperature and indoor temperature at time t, respectively, ℃; C room is the equivalent heat capacity of the room, KW·h / ℃; R room is the equivalent thermal resistance of the room, ℃ / KW; Q ac is the cooling / heating power of the air conditioner, which is equal to the power consumption of the air conditioner P ac There is a relationship, Q ac =ηP ac , η is the energy efficiency ratio of the air conditioner; S(t) is a Boolean variable, indicating the on / off status of the air conditioner, "0" means the air conditioner is off, and "1" means the air conditioner is on. The specific expression is as follows: Where δ is the temperature dead zone width of the air conditioner, T set is the set value of the air conditioning temperature, ε is the simulation time step in the discrete simulation environment; By solving equations (1) and (2), we can get the air conditioner startup period T on and shutdown period T off , and then get the following formula: Step 1.2, power consumption of a single air conditioner P ac,i Through its physical model, the aggregate power of N air conditioners at time t is expressed as: Where P AC (t) is the aggregate power of N air conditioners at time t, which is only related to the operating status of the air conditioners at time t; In steady state, the average power consumption of a single air conditioner is related to the duty ratio of its power-on cycle to the entire operating cycle. on,i represents the probability that air conditioner i is in the on state, and its available duty cycle is expressed as: When the number of air conditioners participating in demand response is N ≥ m, and each air conditioner operates independently, and the outside temperature is constant within each hour of the scheduling cycle, the aggregate power of N air conditioners is approximately expressed as: Substituting equation (5) and equation (6) into equation (8), we obtain: According to the inequality transformation, equation (10) is further transformed into: Substituting equation (5) and equation (6) into equation (11), we can obtain: According to equations (9) and (12), the upper and lower bounds of the aggregate power of N air conditioners are approximately obtained as equations (13) and (14); Where E(X) and E(Y) are the mathematical expectations of random variables X and Y respectively; The available range of the estimated value of the aggregate power of the air conditioning load is Any value in is represented as: Where P AC,es An estimate of the aggregate power for the air conditioning load; From equations (13), (14), and (17), we can see that when the air conditioning parameters are determined, the dispatch center only needs to calculate the outdoor temperature T at the current moment. out and the air conditioning temperature setting value T set , obtain the aggregate power of the air conditioning cluster load at the current moment.

3. The air conditioning load response potential evaluation method according to claim 2, which comprehensively considers user thermal comfort and response willingness, is characterized in that: In step 1.2: by solving equations (1) and (2), the air conditioner startup period T is obtained. on and shutdown period T off , the expressions are as follows: Formulas (5) and (6) are obtained through formulas (3) and (4).

4. The air conditioning load response potential evaluation method according to claim 2, which comprehensively considers user thermal comfort and response willingness, is characterized in that: In step 1.2: the calculation formulas of E(X) and E(Y) are as follows:

5. The air conditioning load response potential evaluation method according to claim 2, which comprehensively considers user thermal comfort and response willingness, is characterized in that: The second step is as follows: The thermal comfort model is introduced to integrate environmental parameters and human parameters, and the predicted mean voting value (PMV) is calculated to accurately describe the user's thermal comfort experience. The predicted mean voting value PMV includes 4 independent environmental parameters and 2 independent human parameters; the environmental parameters include indoor temperature, average radiation temperature, air flow speed, and air humidity; the human parameters include human energy metabolism rate and clothing thermal resistance.

6. The air conditioning load response potential assessment method according to claim 5, which comprehensively considers user thermal comfort and response willingness, is characterized in that: The calculation formula of the predicted mean voting value PMV is: I PMV =(0.303e -0.036M +0.028){(MW)-3.05×10 -3 [5733-6.99(MW)-p a ]- 0.42MW-58.15-1.7×10-5M5867-pa-0.0014M34-tin-3.96×10-8fcltcl+2734-tr+2734-fclhctcl-tin(18) Where: I PMV is the value of the PMV function; M is the energy metabolism rate of the human body, which is related to the degree of human activity; W is the mechanical work generated by human activity; p a is the water vapor pressure of the environment and is the relative humidity and indoor temperature t in Function of cl is the clothing coefficient, which is the clothing thermal resistance I cl Function of is the mean radiation temperature; h c is the convective heat transfer coefficient, which is the indoor temperature t in , clothing surface temperature t cl and air velocity v ar Function of When the PMV index is equal to 0, it means that the residents feel the most comfortable at this time; when the PMV index is greater than 0, it means that the residents feel hotter, and the larger the PMV index, the hotter the residents feel; when the PMV index is less than 0, it means that the residents feel colder, and the smaller the PMV index, the colder the residents feel; when the PMV value is between -0.5 and 0.5, the user is in the best comfort state, and the corresponding indoor temperature is 24.8~27.3℃, which is defined as the initial adjustable margin of the air conditioning set temperature [T min0 ,T max0 ].

7. The air conditioning load response potential assessment method according to claim 6, characterized in that: The cl 、f cl 、p a 、h c The calculation formulas are shown in equations (19)-(22); 8. The air conditioning load response potential assessment method according to claim 5, characterized in that: The third step is as follows: Step 3.1, consider the impact of electricity prices on user willingness; Adopt user willingness influence factor μ i,t To reflect the impact of electricity prices on users’ willingness to participate in demand response; Where: μ i,t represents the willingness of the i-th air conditioner terminal user to participate in demand response at time t, when μ i,t When μ is positive, the user is willing to respond positively. i,t When it is negative, the user has a negative response intention, and its value reflects the strength of the intention; P base is the user's expected electricity price, defined according to different situations; P real is the current actual electricity price; P max is the maximum value of the time-of-use electricity price within the dispatch period; The user's willingness to participate in demand response changes dynamically with the change of electricity price, and its impact on the air conditioning aggregation response potential can be expressed by equations (24)-(26); Where: and They are the upper and lower limits of the initial temperature adjustable margin of the i-th air-conditioning terminal at time t, considering only the thermal comfort of the user; and are the upper and lower limits of the temperature adjustable margin based on the consideration of the user's thermal comfort and the electricity price; ΔT i,t is the temperature margin adjustment amount of the i-th air-conditioning terminal due to the user's wishes at time t; Step 3.2, consider the impact of electricity satisfaction on user willingness; The user's electricity satisfaction includes the user's electricity comfort and economy. The flexible load of the air-conditioning cluster can be regarded as a transferable load and a reducible load during the scheduling process. The user's air-conditioning electricity satisfaction is expressed by the following formula: w=αf ele +βf eco ,α+β=1#(27) In the formula, f ele For the user's electricity comfort, f eco is the user's electricity economy, which can be solved by equations (28) and (29); α represents the weight of the user's electricity comfort level in electricity satisfaction, β represents the weight of the user's electricity economy level in electricity satisfaction, and the sizes of α and β are adjusted according to different scheduling periods and different user groups; The impact of user electricity satisfaction on the air conditioning aggregate response potential is expressed as follows; N=(αN ele +βN eco )#(32) Where: N ele is the user's dissatisfaction with the change in electricity consumption, N eco is the user's dissatisfaction with the real-time electricity price, and N is the user's comprehensive dissatisfaction with electricity consumption; and are the upper and lower limits of the adjustable margin of air conditioning temperature considering user satisfaction.

9. The air conditioning load response potential assessment method according to claim 8, which comprehensively considers user thermal comfort and response willingness, is characterized in that: In step 3.2: Said α,β∈[0,1]; The f ele 、f eco Obtained by the following formula: Where: |P AC,t -P AC0,t | represents the change in air conditioning load before and after participating in demand response at time t, P AC0,t represents the aggregate power of the air conditioning cluster before participating in demand response, P AC,t represents the aggregate power of the air conditioning cluster after participating in demand response; P real,t is the real-time electricity price; The P used in the calculation of user electricity satisfaction in the above formula is AC0,t , P AC,t , P real,t Obtained from historical data.

10. The air conditioning load response potential assessment method according to claim 8, characterized in that: The fourth step is as follows: Considering the user's thermal comfort and user willingness, combined with equations (13)(14)(15)(16)(17), it can be obtained that when the initial air conditioning temperature setting value is Air conditioning aggregate power The adjustable range is: Where: They are the upper and lower limits of the air conditioning aggregate power, and also the adjustable range of the aggregate power when the air conditioning cluster participates in demand response. The initial temperature setting value for the air conditioner is ξ is the controllability of the air-conditioning terminal. The more users with control devices installed on the air-conditioning terminal, the higher the controllability of the air-conditioning terminal. The responsive potential for air conditioning aggregation; Formula (35) represents the adjustable range of air conditioning aggregate power; Formula (36) and Formula (37) are respectively the lower and upper limit expressions of air conditioning aggregate power obtained by combining the air conditioning approximate aggregation model and the adjustable dynamic range of air conditioning set temperature; Formula (38) is the expression of the initial aggregate power of air conditioning; Formula (39) is the expression of the load response potential of the air conditioning cluster.

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