CN111737857A - Heating ventilation air-conditioning cluster coordination control method based on interaction capacity curve - Google Patents

Abstract

The invention relates to a heating, ventilating and air conditioning cluster coordination control method based on an interaction capacity curve, which comprises the following steps: 1) for an HVAC system of a commercial building, constructing a physical simulation model through physical modeling; 2) obtaining a commercial building interaction capacity curve according to the physical simulation model; 3) aggregating the interaction capacity curve of each commercial building HVAC and the reference power to form a total interaction capacity curve; 4) virtual out-of-inventory to get out-of-inventory GTA value

And sending the GTA value as a uniform coordination signal to each HVAC to complete coordination control. Compared with the prior artThe invention has the advantages of reducing control complexity, improving equipment safety, controlling fairness, having small overall comfort loss and the like.

Description

Heating ventilation air-conditioning cluster coordination control method based on interaction capacity curve

Technical Field

The invention relates to the field of heating, ventilation and air conditioning cluster control, in particular to a heating, ventilation and air conditioning cluster coordination control method based on an interaction capacity curve.

Background

Along with the tightening of conventional power generation construction, the stable increase of air conditioning load and the explosive increase of electric automobile load, the contradiction between seasonal peak load and periodic peak load of a power grid is increasingly prominent. Meanwhile, the power generation ratio of renewable energy sources such as wind and light is rapidly increasing, so that the power generation side adjustment capability is greatly reduced. To address this trend, it is desirable to utilize Demand Response (DR) technology to improve the ability of the load side to participate in the regulation of power system operation.

The air conditioning load becomes a very important demand side resource, on one hand, the air conditioning load in a load structure is higher and higher, and accounts for 30% -40% of the summer peak load of China, and even reaches 50% in an economically developed area; on the other hand, because the building has thermal inertia, the influence on the comfort level is small when the air conditioner load is regulated and controlled in a short time, so that the air conditioner load can participate in the power grid dispatching in a large scale, and the method is an effective mode for realizing the power grid load reduction in the peak period of power consumption in summer.

As an important component of air conditioning load, a commercial building heating, ventilation and air conditioning (HVAC) system is an important resource participating in auxiliary services such as power system peak shaving due to large load capacity and small influence on social production, and the existing HVAC regulation and control method mainly derives from a technical report issued by the national laboratory demand response center of lorens-berkeley in the united states in 2007. In domestic practice, a method for directly controlling HVAC main machines, water pumps, cooling towers and other equipment is mostly adopted, but the method has the following problems:

(1) many control methods are implemented with unknown effects on user comfort. For example, it is difficult to predict the degree of influence on the user after 1 refrigeration host is turned off;

(2) the control methods of different types of HVAC systems are not universal, joint control may need to be performed on all subsystems of the HVAC, otherwise, if only the operation parameters of the host are adjusted (such as the outlet water temperature of the host is increased) but the terminal temperature is not adjusted, the actual load requirement is not changed, and finally, the regulation effect cannot be achieved;

(3) the DR system needs to directly control devices such as an HVAC host, so that the system is easy to operate inefficiently and has control risks.

Disclosure of Invention

The invention aims to overcome the defects of the prior art and provide a heating, ventilating and air conditioning cluster coordination control method based on an interaction capacity curve.

The purpose of the invention can be realized by the following technical scheme:

a heating ventilation air-conditioning cluster coordination control method based on an interaction capacity curve comprises the following steps:

1) for an HVAC system of a commercial building, constructing a physical simulation model through physical modeling;

2) obtaining a commercial building interaction capacity curve according to the physical simulation model;

3) aggregating the interaction capacity curve of each commercial building HVAC and the reference power to form a total interaction capacity curve;

4) virtual out-of-inventory to get out-of-inventory GTA value

And sending the GTA value as a uniform coordination signal to each HVAC to complete coordination control.

In the step 1), a physical simulation model is constructed by a physical modeling method and by adopting, but not limited to, EnergyPlus as building energy consumption simulation software.

In the construction of a physical simulation model, two types of fixed model information and two types of disturbance information are input, the two types of fixed model information comprise three-dimensional BIM model information and HVAC system model information, the three-dimensional BIM model information comprises building appearance, orientation, structure, material and sunshade information, the HVAC system model information comprises information such as an HVAC host, a water or air loop and terminal equipment, and the two types of disturbance information are obtained through day-ahead prediction and comprise temperature and humidity prediction information and heat load prediction information.

In the step 2), the expression of the interaction capacity curve of the commercial building is as follows:

ΔP^DR＝f(ΔT_G；ξ)

wherein, Δ P^DRIs the average interactive power, Δ T, of the HVAC during the interactive period_GFor global temperature adjustment value, ξ is a disturbance variable that affects the interaction capability.

The disturbance variables influencing the interaction capacity comprise two types: the system comprises the following components of environment temperature, humidity and building thermal load, wherein the building thermal load comprises basic thermal load and random thermal load, and the random thermal load specifically comprises personnel and illumination in the building.

In the step 3), the total interaction capacity curve D^-1(ΔT_G) Is expressed as

d^-1(ΔT_G)＝f(ΔT_G；ξ)

Where the subscript M denotes the mth HVAC load, M being the total HVAC number.

In the step 4), the intersection point of the total interaction target power and the total interaction capacity curve is the output point.

When each HVAC responds to the GTA value

Then, its actual interaction power is equal to the decomposed interaction target power, i.e.

In the step 4), when the temperature adjustment values of the HVAC systems are equal, the overall comfort cost is the minimum.

The optimized target expression of the total comfort cost is as follows:

c＝α·ΔT_G ²

wherein α is a weight coefficient positively correlated with the thermal capacity of the building, c is a comfort cost function, P_reqIs the total interactive target power.

Compared with the prior art, the invention has the following advantages:

the invention first, the invention

As a uniform coordination signal, the device such as a host computer, a water pump and the like of each HVAC does not need to be directly controlled, so that the control complexity is obviously reduced, and the safety of the device is improved.

Second, HVAC response due to factors such as prediction error before day

The actual interactive power may not be equal to the target power, but the commercial building may be based on

The target power is calculated, which makes it possible to purposefully invoke standby interactive resources (such as energy storage, etc.) to reduce response bias.

Third, all HVAC responses in the present invention are the same

Control fairness is embodied and at the same time minimization of overall comfort loss is achieved.

Drawings

FIG. 1 is a simulation modeling process.

FIG. 2 is a HVAC interaction capability curve.

Fig. 3 is an aggregation and decomposition algorithm.

FIG. 4 is a graph of the effect of outdoor temperature prediction error on HVAC power, where graph (4a) is the outdoor temperature and graph (4b) is the power response.

FIG. 5 is a graph of the impact of the number of people prediction error on HVAC power.

FIG. 6 is a graph of the effect of lighting prediction error on HVAC power.

Fig. 7 is an interaction capacity prediction curve, where fig. 7a is a medium-sized office building HVAC interaction capacity prediction curve, fig. 7b is a hotel HVAC interaction capacity prediction curve, fig. 7c is a single retail store HVAC interaction capacity prediction curve, fig. 7d is a shopping center HVAC interaction capacity prediction curve, and fig. 7e is a total interaction capacity curve.

FIG. 8 is the aggregator response power for different GTAs, where Δ T is shown in FIG. 8a_cThe polymer quotient response power at-2 ℃ is shown in fig. (8b) as Δ T_cThe polymer quotient response power at-1.5 ℃ is shown in fig. (8c) as Δ T_cThe polymer quotient response power at-1 ℃ is shown in fig. (8d) as Δ T_cThe polymer quotient response power at-0.5 ℃ is given as Δ T in fig. (8e)_cThe polymer quotient response power at 0.5 ℃ is shown in fig. (8f) as Δ T_c(8g) polymer quotient response power at 1 ℃ Δ T_cThe polymer quotient response power at 1.5 ℃ is shown in fig. (8h) as Δ T_cFig. 8i is a partial enlarged view of fig. 8h, which is the polymer quotient response power at 2 ℃.

Fig. 9 is an internal temperature distribution diagram of different types of buildings, in which fig. 9a is an internal temperature distribution diagram of a medium-sized office building, fig. 9b is an internal temperature distribution diagram of a hotel, fig. 9c is an internal temperature distribution diagram of a single retail store, and fig. 9d is an internal temperature distribution diagram of a shopping mall.

Detailed Description

The invention is described in detail below with reference to the figures and specific embodiments.

Examples

The invention provides a heating, ventilating and air conditioning cluster coordination control method based on an interaction capacity curve, which can better control the comfort level and privacy data of a user and provide an efficient and universal method for aggregating and coordinating large-scale HVAC loads for aggregators.

1. Existing HVAC modulation methods

In domestic practice, methods for directly controlling HVAC main engine, water pump, cooling tower and other equipment are mostly adopted, and table 1 shows part of regulation and control methods and empirical effects thereof

TABLE 1 typical modulation method for HVAC

The GTA method is that an Energy Management and Control System (EMCS) of HVAC sends a uniform temperature adjustment value to all terminal temperature zone controllers (such as fan coil, variable air volume control box, etc.). Compared to other methods, GTA has the following advantages:

(1) the influence of the GTA on the comfort of the user can be predicted and controlled, and each temperature area uniformly bears the regulation and control instruction. In addition, the GTA can adopt an absolute regulation method (namely, the set value is regulated to the same temperature) and a relative regulation method (namely, the existing set value is regulated to the same degree), thereby being suitable for commercial buildings with higher personalized requirements;

(2) the GTA is a general function of the EMCS, can be widely applied to various HVAC systems, and does not need to be specially modified in order to meet the interaction requirement of a power grid;

(3) when GTA is implemented, all subsystems of HVAC are still coordinated by the original EMCS, and the safety and the economical efficiency of the operation of the HVAC system are easier to be ensured.

Thus, the document Motegi N.introduction to Commercial Building control strategies and Techniques for Demand Response [ R ]. Lawrence Berkeley national laboratory (USA),2007 lists GTA as the preferred regulation method. The GTA approach, however, does not readily yield the power changes expected for HVAC versus other approaches. The present invention will focus on solving this problem. Without loss of generality, the invention develops research aiming at refrigeration scenes in summer.

2. Unified representation method for HVAC interaction capacity

2.1 HVAC interaction capability

Defining the HVAC interaction power as:

ΔP_t ^DR＝P_t ^DR-P_t ^base(1)

in the formula: p_t ^baseIs the reference power of the HVAC at t; p_t ^DRThe actual power after the response (hereinafter referred to as response power). It is specified that the interactive power is negative when the response power is lower than the reference power (down regulation).

Within the interaction time (generally 1-2 h), the delta P_t ^DRIs dynamically changing. The average value that can be taken in practice characterizes the interactive capacity of the HVAC during this period, namely:

in the formula: is the interaction period.

By Delta T_GIndicates the global temperature adjustment value (in c). The interactive capability of the HVAC is expressed as a function of:

ΔP^DR＝f(ΔT_G；ξ) (3)

in the formula: ξ represents the disturbance variable that affects the ability to interact.

The invention considers the following two types of disturbance variables: (1) ambient temperature and humidity; (2) and the heat load in the building comprises a basic heat load and a random heat load. In the latter case, the invention mainly considers personnel and lighting in the building, and the fluctuation characteristics of the two types of random heat loads have strong correlation with the building type.

2.2 physical modeling prediction method of HVAC interaction capacity

For HVAC systems of a particular commercial building, data regression, grey box (grey box) modeling, and physical modeling may be employed in order to obtain the prediction model described by equation (3).

The data regression method constructs a prediction model according to historical statistical data, and the model is not directly related to physical structures of buildings and HVAC and is a black box model. The difficulty with this approach is that for a particular HVAC, the number of external environmental and internal thermal load scenarios that affect its responsiveness is large, and it is difficult to obtain a sufficient number of samples in practice, so that prediction accuracy cannot be guaranteed.

The physical modeling method needs to establish a three-dimensional building envelope model of the building and a physical model of the HVAC, wherein the models belong to a white box model. The method can establish an accurate prediction model theoretically, but in practice, the modeling workload is huge for large buildings.

The gray box model is between the black box model and the white box model, and the representative gray box model is an Equivalent Thermal Parameter (ETP) model, i.e., the building thermal model is equivalent to a low-order RC model. But the method is only suitable for small buildings such as residential houses and the like. For large commercial buildings, the reduced order equivalence can lead to large errors.

In order to improve the prediction precision of the interaction capacity of the commercial buildings, the method adopts a physical modeling method, takes EnergyPlus as building energy consumption simulation software, and has stronger practicability after the evaluation of a performance verification project of the international energy organization. Two types of fixed model information and two types of disturbance information need to be input in the simulation, as shown in fig. 1.

Two types of fixed models include building envelopes and HVAC system models. The former includes detailed information such as the shape, orientation, structure, material and sun-shading of a building; the latter includes information on the HVAC host, water or air circuit, and end equipment. With the progress of smart city construction, Building Information Modeling (BIM) technology is increasingly widely applied to modern buildings. In order to greatly reduce the workload of physical modeling, energy efficiency simulation data required by EnergyPlus can be extracted from BIM in an automatic or semi-automatic mode. For a built building, such models remain unchanged for a long time once established.

The two types of disturbance information comprise ambient temperature and humidity and internal heat load, and are obtained through day-ahead prediction. For commercial buildings with different functional attributes, the operation time, the change rule of personnel and illumination are different, so the prediction method and the prediction difficulty are different. The present invention does not discuss the environmental and load forecasting issues, to any extent.

2.3 HVAC interaction Capacity Curve

The disturbance variable ξ in the prediction equation (3) is first input, and then the different GTA values Δ T are set_GCalculating corresponding interaction power delta P by using the physical simulation model established by the method of FIG. 1^DREventually, an HVAC interaction capacity curve is formed as shown in figure 2. The curve is denoted as d^-1(ΔT_G) Obviously have d^-1(ΔT_G)＝f(ΔT_G；ξ)。

The interaction capability curve described above has two advantages. The intelligent building system has the advantages that firstly, the flexibility of any type of HVAC systems in various commercial buildings can be expressed uniformly and visually, secondly, the structures and key parameters of the buildings and the HVAC systems are effectively shielded, and the privacy of users is protected.

3. Aggregation and decomposition method of interactive capacity

3.1 aggregation and decomposition Algorithm

A load aggregator aggregates a plurality of commercial buildings and then participates in power grid interaction. The aggregator employs the following two algorithms.

(1) Aggregation algorithm

The aggregators collect the interaction capacity curve and the reference power of the HVAC of each commercial building and aggregate the interaction capacity curve and the reference power according to the formula (4) to form a total interaction capacity curve D^-1(ΔT_G) As shown in fig. 3 (b).

In the formula: the subscript M denotes the HVAC load, M being the total HVAC number.

FIG. 3(b) can show the aggregate interaction capabilities of aggregators to a scheduling visualization; meanwhile, in large-scale application, the algorithm also supports multi-stage aggregation from distribution network areas, regional power grids to provincial power grids.

(2) Decomposition algorithm

After receiving the total interactive target power issued by the power grid, the aggregator finishes virtual clearing by solving the intersection point of the total interactive capacity curve and records the checked GTA value as

As shown in FIG. 3 (c); then will be

And sent to each HVAC as a coordinating signal. In the ideal case, when the HVAC responds

Then, its actual interaction power should be equal to the decomposed interaction target, i.e.

As shown in fig. 3 (d). In this way, the total interactive power of all the HVAC units meets the total interactive objective of the grid.

3.2 demonstration of optimality

When Δ T_GFor smaller ranges, equation (3) can be linearly approximated as follows:

ΔP^DR＝-α·ΔT_G(5)

wherein the coefficient α is more than or equal to 0 and is positively correlated with the heat capacity of the building, and the larger the heat capacity is, the response is the same delta T_GGreater interactive power can be provided.

HVAC participates in grid interaction at the expense of loss of comfort. Defining the cost function as the following quadratic function:

c＝α·ΔT_G ²(6)

in the formula: the weighting factor alpha is used for distinguishing the comfort loss degree of buildings with different scales.

Solving the following optimization problem:

in the formula: m represents different HVAC; the objective function is to minimize the overall comfort cost; in constraint condition P_reqIs the total interactive target power.

The equation constraint of relaxation equation (7) can be obtained:

in the formula: λ is the lagrange multiplier.

By substituting equations (5) and (6) for equation (8), it can be deduced that the optimal solution should satisfy:

the above equation shows that the overall comfort penalty is minimal when the individual HVAC temperature adjustment values are equal.

4. Simulation analysis

4.1, design of working examples

4 types of typical commercial buildings such as medium-sized office buildings, hotels, single retail stores, shopping centers and the like are selected for simulation, and comparison of the various buildings is shown in table 2. The detailed parameters are set based on a commercial building reference model provided by the U.S. department of energy, the HVAC unit adopts the DOE2 model, and the internal thermal load is randomly set according to building characteristics. Selecting a summer typical day (7 months and 23 days) to carry out simulation, setting the interaction period to be 11:00-12:00 in the morning, and setting the simulation step length to be 10 minutes. The simulation software is EnergyPlus and matlab.

TABLE 2 typical commercial building

4.2 Effect of disturbance variable prediction error

The performance of the physical modeling prediction method of the invention can be affected by the prediction error of the disturbance variable. The invention is based on simulation analysis by taking hotels as an example, and is limited to space.

The current day-ahead prediction error of the outdoor temperature can reach within +/-1.2 ℃. Fig. 4 shows the predicted and actual values of outdoor temperature and HVAC power.

FIG. 5 illustrates the effect of an error in the prediction of the number of people in a building on HVAC power. The results show that a human prediction error of 20% will result in an HVAC power prediction error of about 3%.

FIG. 6 shows the effect of building interior lighting prediction error on HVAC power. The results show that a lighting prediction error of 20% will result in an HVAC power prediction error of about 0.8%. It can be seen that the illumination has relatively little effect on HVAC power.

In conclusion, the simulation method based on physical modeling can better predict the HVAC power of the commercial building by combining the prediction technology of the environmental temperature and the internal heat load. This allows predicting the interactive capacity of the HVAC at a future date.

4.3 interaction Capacity prediction Curve

In the section, an interaction capacity prediction curve of the HVAC of 12 commercial buildings is formed according to day-ahead prediction data of various disturbance factors. Wherein, the outdoor temperature is the predicted value of figure 4; for the prediction error of personnel, the medium-sized office building is +/-10 percent (+/-represents a positive error or a negative error which is randomly selected, the same is applied below), and the rest types of buildings are +/-20 percent; for the lighting prediction error, the office building and shopping center are + -10%, and the rest of the building types are + -20%.

FIGS. 7a-7d show a representative interaction capability curve for each type of commercial building; based on the aggregation algorithm of equation (4), the total interaction capability prediction curve of fig. 7(e) is formed. Below each graph, the predicted HVAC reference load at the start of the interaction is also given.

As can be seen, at a smaller Δ T_GWithin range, the power of interaction Δ P^DRAnd Δ T_GThe linear relationship of equation (5) is approximately satisfied. For the shopping mall illustrated in the figure, when Δ T_GBelow-1 deg.C, no further power increase is observed due to insufficient HVAC cooling capacity.

4.4 actual power response situation within day

From fig. 7(b), the scheduling can visually see the up and down adjustment capability provided by the aggregator. Assuming the scheduling requirement is 200kW load reduction, the aggregator uses the decomposition algorithm proposed in section 3.1 to find Δ P^DRThe intersection of-200 kW with the total interaction prediction curve in fig. 7(b) was obtained

In response to the GTA signal, the respective HVAC system achieves the interaction goal. However, in practice, the actual delivered Δ T should take into account the limited resolution of HVAC temperature adjustment_GThe coordination signal should not exceed the HVAC adjustment resolution.

The interaction time period is set to be 11:00-12:00, the temperature regulation resolution of the HVAC is 0.5 ℃, and the delta T is given in figure 8_GThe actual response power of the polymer quotient at-2 ℃ to 2 ℃. The total target response power in the graph is Δ P^DR+1262.2kW, the latter being a predicted value of the reference power at the initial moment of interaction. As can be seen from the figure, the HVAC cluster can better track the target response power; with the GTA method, there is a delay of about 10 minutes in the power response of the HVAC; some degree of rebound of HVAC power occurs after the interaction is over.

4.5 comfort impact analysis

The method has the important advantages that the influence on the comfort of the user is predictable and controllable, and all temperature areas can uniformly bear the regulation and control instructions. FIG. 9 shows response periods in days at different Δ T_GUnder a set value, a representative building is selected from various buildings, and the temperature distribution condition of each internal temperature area is counted.

The average temperature inside the building is within the target delta T in general_GNearby; compared longitudinally, the influence degree of the room temperature is basically balanced no matter the scale of each building. But with | Δ T_GThe temperature of some spaces of some buildings does not reach the set delta T when the value is increased_G. The reason was analyzed as follows:

(1) as can be seen by comparing FIG. 7, the space temperature does not drop to the set point due to insufficient HVAC capacity in the shopping mall;

(2) the statistics in FIG. 9 are the average temperatures over the interaction period, the HVAC conditioning for space temperature has hysteresis due to thermal inertia of the building, and thus the average is below the set point;

(3) the different positions of the end temperature zones can cause different degrees of influence by outdoor condition changes. If the top space of the building is closer to the outdoor than the bottom space of the building, the outdoor temperature is sensed to be higher in summer, the required refrigerating capacity is larger, the refrigerating time is longer, and the temperature is not reduced to the specified temperature in the specified response time.

5. Conclusion

According to the condition that the BIM technology is increasingly widely applied to modern buildings, the invention provides a physical modeling prediction method for the interaction capacity of commercial buildings, and the HVAC interaction capacity can be predicted by combining the prediction technology of the environmental temperature and the internal heat load.

The impact of the GTA method on user comfort is predictable compared to other regulatory methods. By utilizing the physical modeling method, the interaction capacity curve is established based on the GTA method, the same flexibility of HVAC of various commercial buildings can be represented uniformly, and the protection of the privacy of users is facilitated.

Based on an interaction capacity curve, the invention simulates the idea of bidding and clearing, and provides an aggregation and decomposition algorithm of interaction capacities of a plurality of commercial buildings, and the algorithm has the advantages of small calculated amount, high expandability and strong universality, and can minimize the loss of the overall comfort level.

Claims (10)

1. A heating ventilation air-conditioning cluster coordination control method based on an interaction capacity curve is characterized by comprising the following steps:

1) for an HVAC system of a commercial building, constructing a physical simulation model through physical modeling;

2) obtaining a commercial building interaction capacity curve according to the physical simulation model;

3) aggregating the interaction capacity curve of each commercial building HVAC and the reference power to form a total interaction capacity curve;

4) virtual out-of-inventory to get out-of-inventory GTA value

And sending the GTA value as a uniform coordination signal to each HVAC to complete coordination control.

2. The heating, ventilating and air conditioning cluster coordination control method based on the interaction capacity curve as claimed in claim 1, characterized in that in step 1), a physical simulation model is constructed by a physical modeling method and by using energy plus as building energy consumption simulation software.

3. The heating, ventilating and air-conditioning cluster coordination control method based on the interaction capacity curve as claimed in claim 2, characterized in that in the construction of the physical simulation model, two types of fixed model information and two types of disturbance information are input, the two types of fixed model information include three-dimensional BIM model information and HVAC system model information, the three-dimensional BIM model information includes building shape, orientation, structure, material and sun shading information, the HVAC system model information includes HVAC host, water or air loop and end equipment information, and the two types of disturbance information are obtained through day-ahead prediction and include temperature and humidity prediction information and heat load prediction information.

4. The heating, ventilating and air conditioning cluster coordination control method based on interaction capacity curve as claimed in claim 1, wherein in step 2), the expression of the interaction capacity curve of the commercial building is as follows:

ΔP^DR＝f(ΔT_G；ξ)

wherein, Δ P^DRIs the average interactive power, Δ T, of the HVAC during the interactive period_GFor global temperature adjustment value, ξ is a disturbance variable that affects the interaction capability.

5. The heating, ventilation and air conditioning cluster coordination control method based on interaction capacity curve as claimed in claim 4, wherein said disturbance variables affecting interaction capacity include two types: the system comprises the following components of environment temperature, humidity and building thermal load, wherein the building thermal load comprises basic thermal load and random thermal load, and the random thermal load specifically comprises personnel and illumination in the building.

6. The heating, ventilation and air conditioning cluster coordination control method based on interaction capacity curve according to claim 4, characterized in that in step 3), total interaction capacity curve D^-1(ΔT_G) Is expressed as

d^-1(ΔT_G)＝f(ΔT_G；ξ)

Where the subscript M denotes the mth HVAC load, M being the total HVAC number.

7. The heating, ventilating and air-conditioning cluster coordination control method based on interaction capacity curve as claimed in claim 6, wherein in step 4), the intersection point of the total interaction target power and the total interaction capacity curve is the out-counting point.

8. The HVAC cluster coordination control method based on interaction capacity curve of claim 7, wherein when each HVAC responds to GTA value Δ T_G ^*Then, its actual interaction power is equal to the decomposed interaction target power, i.e. d^-1(ΔT_G ^*)。

9. The HVAC cluster coordination control method based on the interaction capacity curve as claimed in claim 6, wherein in the step 4), when the temperature adjustment values of the HVAC systems are equal, the total comfort cost is minimum.

10. The heating, ventilating and air conditioning cluster coordination control method based on the interaction capacity curve as claimed in claim 9, wherein the optimization target expression of the total comfort cost is as follows:

c＝α·ΔT_G ²

wherein α is a weight coefficient positively correlated with the thermal capacity of the building, c is a comfort cost function, P_reqIs the total interactive target power.

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