Flexible adjusting method for multi-area building air conditioning system

Abstract

The invention relates to a building energy management technology, and provides a flexible adjusting method for a multi-area building air conditioning system in order to solve the problems that cold load fluctuation characteristics and the adjustable and controllable capacity of the cold load fluctuation characteristics are insufficient, especially in some large public buildings with complex structures and diversified functions, and the flexible adjusting method comprises the steps that cold load data are collected, and a basic curve is preprocessed; the method comprises the following steps: establishing a typical change type structure model of a cold load, dividing the model into three basic change mechanisms, forming a diversified adjustment strategy space, defining set temperature and maximum acceptable temperature deviation of each building functional area, and establishing a regional-level room temperature dynamic response model according to building thermal inertia parameters; constructing a flexible adjusting capability index system and a judgment rule of the air conditioning system; generating a flexible adjustment strategy; and executing feedback and adaptive optimization to form an adaptive flexible optimization closed loop. The load response precision and the comfort guarantee capability are improved, and a technical path with a better physical basis and higher regulation and control efficiency is provided.

Description

Technical Field

[0001] This invention relates to a building energy management technology, and more particularly to a flexible adjustment method for a building air conditioning system based on a cooling load variation mechanism. Background Technology

[0002] With the rapid development of new energy technologies, the proportion of renewable energy sources such as solar energy in the global energy structure is gradually increasing. However, these energy sources are inherently intermittent and unstable, leading to significant differences between supply and demand. Adjusting demand to adapt to supply fluctuations and improving the flexibility of the power system has proven to be an effective strategy. In building energy consumption, air conditioning systems typically account for a large proportion, especially in medium and large buildings, where their energy consumption can exceed 40% of total energy consumption. Therefore, improving the energy efficiency and operational flexibility of building air conditioning systems has become an important research topic.

[0003] Regarding improving the flexibility of power systems, existing research has shown that demand response (DR) strategies can effectively adjust electricity load to match energy supply. DR achieves peak shaving and valley filling by balancing and regulating various electricity demands or shifting peak loads to off-peak periods. Currently, modeling and simulation research on the demand side of the power grid in the building sector has been widely carried out. For example, in a commercial building, a pre-cooling strategy achieved up to 80% peak load shifting without causing discomfort to users. Meanwhile, smart grid technology has also been applied to residential electricity scenarios, enabling real-time load adjustment and significantly alleviating supply-demand imbalances. Despite these advancements, existing research often rigidly classifies flexible loads into movable and scalable loads, which is not fully adaptable to real-time demand-side management in practical applications and limits the efficiency of flexible strategies.

[0004] For buildings with complex multi-regional structures and large-scale energy demands, their air conditioning systems have broad application prospects in improving energy flexibility. For example, peak shaving and valley filling of loads are often achieved by setting up water-based or ice-based chilled water storage systems. Furthermore, the chilled water delivery and distribution systems of such buildings often cover long distances, reaching several kilometers, and can also achieve a certain degree of energy storage when water temperature fluctuations are maintained within acceptable ranges. Research on water and ice-based chilled water storage has been extensive, and studies have shown that active thermal storage devices can significantly improve the operational flexibility of air conditioning systems. Simultaneously, various terminal devices can also activate the building's thermal inertia to achieve different degrees of passive energy storage, which is also one of the key focuses of this invention. For such complex building environments with frequent operational demands, accurately assessing their energy flexibility potential is crucial for achieving refined control and energy management.

[0005] Thermal inertia refers to the ability of building materials to conduct and store heat. By fully utilizing the thermal inertia of buildings, peak loads on air conditioning systems can be effectively reduced, thereby lowering overall power consumption. Existing research shows that the thermal inertia of heat storage materials can provide a good buffering effect in regional temperature control, helping to reduce the cooling load demand of air conditioning systems in short periods. Furthermore, building thermal inertia can be combined with energy storage systems to jointly regulate heating or cooling loads during peak electricity periods, further improving system energy efficiency and flexibility. Another area of ​​research focuses on optimal control methods for air conditioning systems, exploring the potential for operational regulation from the perspective of system control parameters. Optimized control methods have attracted attention due to their good energy-saving effects, but these methods often overlook the dynamic characteristics of cooling load changes. In the research on cooling load-side regulation, some scholars have proposed direct load control methods based on load demand forecasting and power limitation strategies. This method predicts the building's cooling load demand and power thresholds, determines the number of air conditioning systems to start and stop in real time, and adjusts system power demand and cooling capacity distribution to quickly respond to the regulation needs of the smart grid. However, research on the characteristics and controllability of cooling load fluctuations remains insufficient, especially in large public buildings with complex structures and diverse functions. These buildings experience frequent changes in cooling load and uneven spatial distribution during the design and operation of their air conditioning systems, with more complex adjustment requirements. Therefore, there is an urgent need to conduct in-depth research on the diverse characteristics and variation patterns of their cooling loads to provide theoretical support and practical pathways for the flexible control of centralized air conditioning systems.

[0006] In summary, research on the characteristics and controllability of cooling load fluctuations remains insufficient, especially in large public buildings with complex structures and diverse functions. These buildings experience frequent and uneven spatial distribution of cooling loads during the design and operation of their air conditioning systems, and their regulation needs are more complex. Therefore, there is an urgent need to conduct more in-depth research on the diverse characteristics and variation patterns of their cooling loads, providing theoretical support and practical pathways for the flexible control of centralized air conditioning systems. Summary of the Invention

[0007] To address the shortcomings in the characteristics and controllability of cooling load fluctuations, especially in large public buildings with complex structures and diverse functions, a flexible adjustment method for air conditioning systems based on the fundamental change mechanism of cooling load is proposed. By establishing a structural model of typical cooling load change types and combining room temperature fluctuation tolerance for adjustment capability assessment and control strategy optimization, the air conditioning system can achieve adaptive flexible adjustment to multi-source fluctuations, thereby improving its load control accuracy and overall system operating efficiency.

[0008] The technical solution of this invention is as follows: A flexible adjustment method for a multi-zone building air conditioning system includes: Step 1: Cooling load data acquisition and basic curve preprocessing; Obtain historical operating data of cooling load for each functional area from the building energy consumption monitoring system. The data includes: cooling demand curves. q c (t) Indoor temperature T in (t) Air conditioning equipment status, outdoor weather data, and user-set temperature. T set (t) The above data were cleaned and denoised, and a clustering method was used to construct representative base load curves for different regions. q c,0 (t) , serving as a benchmark for flexible assessment and adjustment strategies; Step 2: Construct a basic cooling load variation mechanism model; to describe the adjustability of the cooling load, a model will be developed for... q c,0 (t) The adjustment is divided into three basic change mechanisms: incremental change: in the load curve q c,0 (t) A load pulse of Δq(t) is superimposed locally on the upper part, reflecting short-time fluctuation characteristics; translational change: for q c,0 (t) The overall time axis is shifted forward or backward by Δτ to simulate a time-shifted regulation strategy; discrete changes: the continuous load curve is resampled into multiple discrete steps through discrete order to simulate load jump characteristics; the three types of regulation mechanisms can be used independently or in combination to form a diversified regulation strategy space; Step 3: Construct a room temperature response model and adjustment constraints; define the set temperature for each building functional zone. T set and the deviation from the maximum acceptable temperature ΔT max A regional-level dynamic response model for room temperature was established based on building thermal inertia parameters. T in_actual (t) This regional-level room temperature dynamic response model is used to predict temperatures in areas including... Δq , Dt The deviation between actual indoor temperature and set value under different adjustment behaviors of discrete order provides a physical basis for the assessment of flexibility. Step 4: Construct an index system and judgment rules for the flexible adjustment capability of the air conditioning system; based on the regional indoor temperature response model in Step 3, use the capability boundary method under comfort constraints to jointly evaluate three indicators: adjustment ratio ζ, maximum temperature difference deviation, etc. ΔT max Maximum cumulative mismatch coefficientD max It outputs the capability boundary, including the translational time, ζmax, and maximum discrete order, to support policy formulation. Step 5: Generating flexible adjustment strategies; Based on the current building load status, target adjustment demand, electricity price or renewable energy output forecast for the current time period, the strategy library is called to match feasible adjustment schemes; Step 6: Execute feedback and adaptive optimization; During the adjustment process, continuously collect the actual load and room temperature trajectory of each area, compare them with the expected adjustment effect, and dynamically update the model indicators; Based on the feedback, correct the parameters of the regional indoor temperature response model to improve the accuracy and stability of the adjustment strategy in the next cycle, forming an adaptive flexible optimization closed loop.

[0009] Furthermore, the acceptableness of adjusting the regional-level room temperature dynamic response model in step 3 must meet the following requirements: | T in_actual (t) - T set (t) | ≤ ΔT max , ; Where t is the current time, and t1 and t2 are the start and end times of this time scale.

[0010] Furthermore, the regional indoor temperature response model in step 3 adopts various model forms, including steady-state building thermal models, unsteady-state building thermal models, or models implemented through commercial building energy consumption simulation software, in order to obtain the regional indoor temperature response to external disturbances and control inputs.

[0011] Furthermore, the definitions and calculations of the three indicators in step 4 are as follows; g This represents the ratio of the adjustable peak cooling load to the baseline value under current operating conditions and comfort constraints, reflecting the elastic boundary of the cooling load. ,in It is the average value of the base load; ΔT max The allowable drift range of the comfort temperature set by the user is a hard constraint on the adjustment boundary; D max The maximum or integral deviation between the actual room temperature trajectory and the target temperature trajectory after adjustment is used to determine the impact of the adjustment scheme on comfort. ,in To calculate the time period; The specific evaluation process is as follows: First, set a hard constraint ΔT_max for comfort. Within the feasible region that satisfies ΔT_max, take the upper bound of ζ as the flexible adjustment capability index of the region, denoted as ζmax. If there are multiple feasible solutions, the one with smaller Dmax is preferred to reduce the potential impact of time mismatch on comfort and operation. Finally, combine the three basic change forms to output the capability boundary, including the translation time, ζmax, and the maximum discrete order, to support strategy formulation.

[0012] Furthermore, feasible adjustment schemes in step 5 include: applying a shift mechanism to advance or delay cooling supply to accommodate peak-valley electricity price changes; applying an incremental mechanism to achieve short-term peak shaving or load filling; applying a discrete mechanism combined with equipment operation mode adjustments or the integration of a cold storage system to achieve load step switching; and simultaneously, according to the different regions, including g max , ΔT max The system prioritizes areas with greater thermal inertia or auxiliary energy regulation devices to achieve multi-zone coordinated regulation. The strategy generation module outputs regulation command parameters, including start / end time, invocation method, and target load curve, and sends them to the equipment group control module or building energy management platform for execution.

[0013] Preferably, the clustering method in step 1 can be the K-means method.

[0014] Optionally, this method can be embedded into a building energy management system, building control platform, or independent load aggregation control system to achieve linkage with power demand response platform, photovoltaic forecasting platform, and electricity price forecasting system.

[0015] The beneficial effects of this invention are as follows: This invention proposes a flexible adjustment method for air conditioning systems based on the fundamental change mechanism of cooling load. By introducing structured classification of cooling load (incremental, translational, and discrete) and building room temperature response modeling, it establishes a quantitative index system for flexibility, replacing existing experience-based load adjustment methods. This provides assessmentable and verifiable boundary conditions for the adjustment strategy. This method enables the quantitative identification of the flexibility potential of air conditioning systems and risk control of adjustment behavior, improving load response accuracy and comfort assurance capabilities. It provides a more physically grounded and efficient technical path for building air conditioning systems to participate in electricity demand response and renewable energy utilization. Attached Figure Description

[0016] Figure 1 This is an overall flowchart of the flexible adjustment method for the air conditioning system of the present invention; Figure 2 This is a schematic diagram of the basic cooling load change mechanism of the air conditioning system of the present invention; Figure 3This is a diagram showing the cooling capacity of three typical clustered areas in the terminal building, as presented in this invention. Figure 4 This is a model diagram of the energy consumption of an airport terminal building, as described in this invention. Figure 5 This is a simulation result diagram of the cooling curve increase / decrease transformation in the case of this invention; Figure 6 This is a simulation result of the translation transformation of the cooling curve in this invention case; Figure 7 This is a simulation result of the discrete transformation of the cooling curve in this invention. Figure 8 This is a diagram illustrating the flexible adjustment working conditions of the terminal building, a case study of this invention. Detailed Implementation

[0017] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are based on the technical solution of the present invention and provide detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0018] This invention structurally identifies and classifies the fundamental mechanisms of cooling load changes in building air conditioning systems during operation, and, combined with acceptable room temperature deviations, constructs flexible adjustment strategies to improve the system's flexible response level. The method is as follows: Figure 1 As shown. It is used to improve the overall system's energy efficiency, the air conditioning system's flexible load response capability, and its adaptability to dynamic changes over time. It can be widely used in the air conditioning systems of large, multi-area buildings such as commercial complexes, data centers, hospitals, and exhibition venues.

[0019] A flexible adjustment method for air conditioning systems based on a cooling load base change mechanism, the specific steps of which are as follows: Step 1: Cooling Load Data Acquisition and Basic Curve Preprocessing. Historical cooling load data for each functional area is obtained from the building energy consumption monitoring system. This data includes: cooling demand curves. q c (t) Indoor temperature T in (t) Air conditioning equipment status, outdoor weather data, and user-set temperature. T set (t) The above data was cleaned and denoised to remove outliers and the impact of holidays. Representative base load curves for different regions were then constructed using clustering methods. q c,0 (t) This serves as a benchmark for flexible assessment and adjustment strategies.

[0020] The clustering method in step 1 can be K-means.

[0021] Step 2: Construct a model of the basic cooling load variation mechanism. To describe the adjustability of the cooling load, this invention will... q c,0 (t) Regulation is divided into three basic change mechanisms, such as Figure 2 As shown, they are respectively: (a) Incremental changes: in the load curve q c,0 (t) A load pulse of Δq(t) is superimposed on the upper part to reflect the short-time fluctuation characteristics; (b) Translational changes: for q c,0 (t) The entire system is shifted forward or backward by Δτ on the time axis to simulate time-shifting adjustment strategies such as early power-on and delayed power-off. (c) Discrete change: The continuous load curve is resampled into multiple discrete steps by the discrete order to simulate the load jump characteristics caused by equipment group switching, cold storage system access, etc.

[0022] The above three types of regulatory mechanisms can be used independently or in combination to form a diverse regulatory strategy space.

[0023] Step 3: Construct a room temperature response model and adjustment constraints. Define the set temperature for each building functional zone. T set and the deviation from the maximum acceptable temperature ΔT max A regional-level dynamic response model for room temperature was established based on building thermal inertia parameters (wall heat capacity, building envelope, ventilation heat transfer coefficient, etc.). T in_actual (t) This model is used to predict different regulatory behaviors ( Δq , Dt The deviation pattern between the actual indoor temperature and the set value under discrete order provides a physical basis for the assessment of flexibility.

[0024] Whether the adjustment is acceptable must meet the following conditions: | T in_actual (t) - T set (t) | ≤ ΔT max , .

[0025] Where t is the current time, and t1 and t2 are the start and end times of this time scale.

[0026] The regional indoor temperature response model in step 3 is not limited in its form and can be implemented in any of the following ways: steady-state building thermal model, unsteady-state (transient / dynamic) building thermal model, or a model implemented using commercial building energy consumption simulation software. As long as the regional indoor temperature response to external disturbances and control inputs can be obtained, it can be used in this step.

[0027] Step 4: Construct an index system and judgment rules for the flexible adjustment capability of the air conditioning system. Based on the regional indoor temperature response model in Step 3, the capability boundary method under comfort constraints is used to jointly evaluate three indicators: ζ (adjustment ratio). ΔT max (Maximum temperature difference deviation) D max (Maximum cumulative mismatch coefficient), whose definition and calculation correspond to equations (1)–(3) respectively.

[0028] (1) g This represents the ratio of the adjustable peak cooling load to the baseline value under current operating conditions and comfort constraints, reflecting the elastic boundary of the cooling load. ,in It is the average value of the base load; (2) ΔT max The allowable drift range of the comfort temperature set by the user is a hard constraint on the adjustment boundary; (3) D max (Mismatch degree): The maximum or integral deviation between the actual room temperature trajectory and the target temperature trajectory after adjustment, used to determine the impact of the adjustment scheme on comfort. ,in To calculate the time period.

[0029] The evaluation process involves first setting a hard comfort constraint ΔT_max (±1.0℃ in the implementation case). Within the feasible region satisfying ΔT_max, the upper bound of ζ is taken as the flexible adjustment capability index for that region, denoted as ζmax. If multiple feasible solutions exist, the one with the smaller Dmax is preferred to reduce the potential impact of time mismatch on comfort and operation. Finally, combining the three basic change forms, the capability boundary is output, including the shiftable time, ζmax, and the maximum discrete order, providing support for strategy development.

[0030] Step 5: Generating Flexible Adjustment Strategies. Based on the current building load status, target adjustment needs (such as peak shifting, off-peak charging, and synchronous photovoltaic power generation), and the electricity price or renewable energy output forecast for the current time period, the strategy library is invoked to match feasible adjustment schemes, including: (1) Apply the shift mechanism to advance or delay cooling supply to accommodate peak-valley electricity price changes; (2) Apply incremental mechanisms to achieve short-term peak shaving or load valley filling; (3) Apply discrete mechanisms in combination with equipment operation mode adjustment or cold storage system access to achieve load step switching; At the same time, according to each region g max , ΔT max Based on indicators such as these, priority is given to scheduling areas with greater thermal inertia or auxiliary energy regulation devices to achieve multi-zone coordinated regulation. The strategy generation module outputs regulation command parameters, including start / end time, invocation method, and target load curve, and sends them to the equipment group control module or building energy management platform (general ones, which will not be explained in detail here) for execution.

[0031] Step 6: Execution Feedback and Adaptive Optimization. During the adjustment process, the actual load and room temperature trajectory of each area are continuously collected and compared with the expected adjustment effect, and dynamically updated. g max , D max These indicators, along with feedback, are used to correct the parameters of the regional indoor temperature response model, improving the accuracy and stability of the adjustment strategy for the next cycle and forming an adaptive flexible optimization closed loop.

[0032] This method can be embedded into building energy management systems, building control platforms, or independent load aggregation control systems to achieve linkage with power demand response platforms, photovoltaic forecasting platforms, electricity price forecasting systems, etc.

[0033] Multiple embodiments: Step 1: Obtain historical operating data of cooling load for each functional area from the building energy consumption monitoring system, preprocess the data, and construct representative base load curves for different areas using clustering methods. q c,0 (t)， The clustering results are divided into three categories, such as Figure 3 As shown, this serves as a benchmark for flexible assessment and adjustment strategies.

[0034] Step 2: Consistent with Step 2 in the technical solution, it involves three common basic changes.

[0035] Step 3: Define the building's set temperature Tset The maximum acceptable temperature deviation is 26℃. ΔTmax A regional building energy consumption model was established using DeST software, with a temperature of 1℃. Figure 4 As shown, it is used to predict different regulatory behaviors ( Δq , Dt The deviation pattern between the actual indoor temperature and the set value under discrete order provides a physical basis for the assessment of flexibility.

[0036] Step 4: Construct an index system for the flexible adjustment capability of the air conditioning system. Through system simulation, estimate the system's performance under different regions and seasonal conditions. g – ΔT – D The response feature surface provides support for strategy development, and the results are as follows: Figure 5-7 As shown. The key parameters of the increase / decrease transformation were investigated, and the results indicate that the adjustment ratio of cooling capacity changes over a short period of time... g max The effect can exceed 50%. The results of shifting the cooling curve indicate that supplying cooling 1 hour earlier or later can ensure that the indoor temperature fluctuates within the range of 25-27℃. Simulation results of the discrete curve changes show that, under the supply of cooling through 6-interval to 4-interval discrete cooling curves, the indoor temperature in Class I areas can generally be maintained within the range of 25-28℃, while Class II and Class III areas can maintain a temperature within the range of 25-27℃.

[0037] Steps 5 and 6: Generating and Executing Flexible Adjustment Strategies, Feedback, and Adaptive Optimization. The airport in this case study uses a system combining centrifugal chillers and water storage for cooling, with the two storage tanks accounting for 30% of the total designed cooling capacity. The terminal's air conditioning system uses an independent temperature and humidity control system, with fresh air dehumidification handled by dual-source equipment. Considering the terminal's cooling capacity adjustment characteristics, this study assumes the indoor temperature fluctuates within the range of 25-27°C. The cooling demand curve is adjusted to advance or delay peak load demand while maintaining a constant total daily cooling capacity, thus avoiding peak electricity price periods. For the first peak electricity price period, 9:00-11:00, the adjustable proportion of cooling demand at 9:00 and 11:00 is reduced. g The cooling capacity will be supplied earlier, at 7:00 and 8:00. For the second peak electricity price period from 17:00 to 20:00, the adjustable proportion of cooling capacity from 17:00 to 18:00 will be selected. g The cooling supply will be provided from 15:00 to 16:00. The cooling supply from 19:00 to 20:00 will be provided from 21:00 to 22:00. Figure 8 The results show the cooling system adjusted according to electricity prices and the corresponding indoor temperature in the terminal building, including the maximum adjustment ratio. g At 50%, after adjusting the cooling capacity, the overall cooling capacity curve differed significantly from the cooling demand curve. However, the indoor temperature results showed that the indoor temperature in the three typical cluster areas remained within the range of 25-27℃, indicating that this adjustment could effectively meet the indoor temperature requirements. In this flexible cooling capacity adjustment mode, the operating cost was 40,000 yuan / day, a 34% reduction compared to the mode without cooling storage equipment, and a further 12% reduction compared to the operating cost under the basic operating strategy of a water-based cooling storage system.

[0038] Key technical points: 1. Modeling the structured variation mechanism of cooling load The air conditioning system load is divided into three basic change structures (incremental, translational, and discrete) from a time series perspective. A standardized method for representing cooling load regulation is established, which realizes unified modeling and mechanism identification of the adjustable characteristics of building loads in multiple regions.

[0039] 2. A method for quantifying flexibility based on room temperature response By combining the building's thermal inertia characteristics with the user's set comfort tolerance range, an adjustment capability assessment model is constructed using indicators such as maximum adjustment ratio, temperature tolerance range, and degree of mismatch. This model enables the quantification and boundary determination of the adjustment space, significantly improving the safety and operability of air conditioning adjustment.

[0040] 3. Multi-regional flexible regulation and control strategy generation mechanism Based on the differences in regional flexibility, the optimal adjustment method (such as load shifting, discrete response control, and cold storage) is matched to generate zonal executable strategies and connect them to the building energy control system, thereby improving the adjustment efficiency and flexibility of the air conditioning system in scenarios such as load response, electricity price optimization, and renewable energy synergy.

[0041] The above-described embodiments are merely one implementation of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention should be determined by the appended claims.

Claims

1. A flexible adjustment method for a multi-zone building air conditioning system, characterized in that, include: Step 1: Cooling load data acquisition and basic curve preprocessing; Historical operating data of cooling load for each functional area is obtained from the building energy consumption monitoring system. This data includes: cooling demand curves. q c (t) Indoor temperature T in (t) Air conditioning equipment status, outdoor weather data, and user-set temperature. T set (t) The above data were cleaned and denoised, and a clustering method was used to construct representative base load curves for different regions. q c,0 (t) , serving as a benchmark for flexible assessment and adjustment strategies; Step 2: Construct a basic cooling load variation mechanism model; to describe the adjustability of the cooling load, a model will be developed for... q c,0 (t) The adjustment is divided into three basic change mechanisms: incremental change: in the load curve q c,0 (t) A load pulse of Δq(t) is superimposed locally on the upper part, reflecting short-time fluctuation characteristics; translational change: for q c,0 (t) The overall time axis is shifted forward or backward by Δτ to simulate a time-shifted regulation strategy; discrete changes: the continuous load curve is resampled into multiple discrete steps through discrete order to simulate load jump characteristics; the three types of regulation mechanisms can be used independently or in combination to form a diversified regulation strategy space; Step 3: Construct a room temperature response model and adjustment constraints; define the set temperature for each building functional zone. T set and the deviation from the maximum acceptable temperature ΔT max A regional-level dynamic response model for room temperature was established based on building thermal inertia parameters. T in_actual (t) This regional-level room temperature dynamic response model is used to predict temperatures in areas including... Δq , Δτ The deviation between actual indoor temperature and set value under different adjustment behaviors of discrete order provides a physical basis for the assessment of flexibility. Step 4: Construct an index system and judgment rules for the flexible adjustment capability of the air conditioning system; based on the regional indoor temperature response model in Step 3, use the capability boundary method under comfort constraints to jointly evaluate three indicators: adjustment ratio ζ, maximum temperature difference deviation, etc. Δ T max Maximum cumulative mismatch coefficient D max It outputs the capability boundary, including the translational time, ζmax, and maximum discrete order, to support policy formulation. Step 5: Generating flexible adjustment strategies; Based on the current building load status, target adjustment demand, electricity price or renewable energy output forecast for the current time period, the strategy library is called to match feasible adjustment schemes; Step 6: Execute feedback and adaptive optimization; During the adjustment process, continuously collect the actual load and room temperature trajectory of each area, compare them with the expected adjustment effect, and dynamically update the model indicators; Based on the feedback, correct the parameters of the regional indoor temperature response model to improve the accuracy and stability of the adjustment strategy in the next cycle, forming an adaptive flexible optimization closed loop.

2. The flexible adjustment method for a multi-zone building air conditioning system according to claim 1, characterized in that, Whether the regional-level room temperature dynamic response model adjustment in step 3 is acceptable must meet the following requirements: | T in_actual (t) - T set (t) | ≤ ΔT max ， ； Where t is the current time, and t1 and t2 are the start and end times of this time scale.

3. The flexible adjustment method for a multi-zone building air conditioning system according to claim 1, characterized in that, The regional indoor temperature response model in step 3 can be in the form of a steady-state building thermal model, an unsteady-state building thermal model, or a model implemented through commercial building energy consumption simulation software, in order to obtain the regional indoor temperature response to external disturbances and control inputs.

4. The flexible adjustment method for a multi-zone building air conditioning system according to claim 1, characterized in that, The definitions and calculations of the three indicators in step 4 are as follows; ζ This represents the ratio of the adjustable peak cooling load to the baseline value under current operating conditions and comfort constraints, reflecting the elastic boundary of the cooling load. ,in It is the average value of the base load; ΔT max The allowable drift range of the comfort temperature set by the user is a hard constraint on the adjustment boundary; D max The maximum or integral deviation between the actual room temperature trajectory and the target temperature trajectory after adjustment is used to determine the impact of the adjustment scheme on comfort. ,in To calculate the time period; The specific evaluation process is as follows: First, set a hard constraint ΔT_max for comfort. Within the feasible region that satisfies ΔT_max, take the upper bound of ζ as the flexible adjustment capability index of the region, denoted as ζmax. If there are multiple feasible solutions, the one with smaller Dmax is preferred to reduce the potential impact of time mismatch on comfort and operation. Finally, combine the three basic change forms to output the capability boundary, including the translation time, ζmax, and the maximum discrete order, to support strategy formulation.

5. The flexible adjustment method for a multi-zone building air conditioning system according to claim 1, characterized in that, Feasible adjustment schemes in step 5 include: applying a shift mechanism to advance or delay cooling supply to accommodate peak-valley electricity price changes; applying an incremental mechanism to achieve short-term peak shaving or load filling; applying a discrete mechanism combined with equipment operation mode adjustments or the integration of a cold storage system to achieve load step switching; and simultaneously, based on the different regions, including ζ max , ΔT max The system prioritizes areas with greater thermal inertia or auxiliary energy regulation devices to achieve multi-zone coordinated regulation. The strategy generation module outputs regulation command parameters, including start / end time, invocation method, and target load curve, and sends them to the equipment group control module or building energy management platform for execution.

6. The flexible adjustment method for a multi-zone building air conditioning system according to claim 1, characterized in that, The clustering method in step 1 can be K-means.

7. The flexible adjustment method for a multi-zone building air conditioning system according to claim 1, characterized in that, This method can be embedded into building energy management systems, building control platforms, or independent load aggregation control systems to achieve linkage with power demand response platforms, photovoltaic forecasting platforms, and electricity price forecasting systems.

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