Regulation and control method, system and equipment for air conditioner cluster to participate in demand response and medium

Abstract

The invention discloses a regulation and control method, system and device for participation of an air conditioner cluster in demand response and a medium, and relates to the technical field of building energy saving.The method comprises the steps that a virtual energy storage model of a target building is constructed, and the equivalent heat capacity of the target building is calculated according to a set of physical parameters; in response to the power grid demand response event, obtaining a target parameter of the demand response event; according to the equivalent heat capacity and the target parameters, preparatory energy is determined through iterative calculation, a target regulation and control strategy of the air conditioner cluster is generated, the temperature of the air conditioner cluster is adjusted to the extreme value of the human body comfort temperature interval within the preset duration before the demand response event starts, and the preparatory energy is stored for the target building; in the period of executing the demand response event, the operation power of the air conditioner cluster is reduced, and the indoor temperature is kept within the human body comfort temperature interval through the reserved energy stored in the target building; and the air conditioner cluster is intelligently regulated and controlled according to the target regulation and control strategy. According to the technical scheme, the effect of improving the regulation and control efficiency is achieved.

Description

Technical Field

[0001] This application relates to the field of building energy conservation technology, specifically to a method, system, equipment, and medium for regulating air conditioning clusters to participate in demand response. Background Technology

[0002] Currently, utilizing commercial building air conditioning systems in demand response has become an important means of achieving peak shaving and valley filling in the power system. Existing technologies mostly employ simple direct control strategies, such as uniformly raising the air conditioning set temperature during peak electricity consumption periods or rotating the shutdown of some air conditioners. While these methods can achieve some load reduction, they have significant drawbacks: First, the control methods are crude, making it difficult to balance load reduction with maintaining indoor thermal comfort, easily leading to user complaints and low participation. Second, and more importantly, existing technologies completely ignore the thermal inertia of the building envelope and indoor air, i.e., their ability to act as a virtual energy storage medium. Existing strategies cannot store regulation potential through refined pre-adjustment, thus severely limiting the depth and duration of regulation in a single response event, resulting in poor comfort, poor economy, and difficulty in balancing grid response needs with human thermal comfort, hindering the efficient application of air conditioning clusters in demand response. Therefore, the control efficiency of related technologies for demand response participation faces low technical challenges. Summary of the Invention

[0003] To address the aforementioned technical problems, this application provides a control method, system, equipment, and medium for air conditioning clusters to participate in demand response.

[0004] Firstly, this application provides a method for regulating an air conditioning cluster participating in demand response, comprising: constructing a virtual energy storage model of a target building, wherein the input parameters of the virtual energy storage model include a set of physical parameters of the target building and a human comfort temperature range, and the virtual energy storage model calculates the equivalent heat capacity of the target building based on the set of physical parameters; responding to a grid demand response event, obtaining the target parameters of the demand response event, the target parameters including start time, duration, and power reduction; determining the reserve energy through iterative calculation based on the equivalent heat capacity and the target parameters, and generating a target regulation strategy for the air conditioning cluster, the target regulation strategy including: a pre-regulation phase strategy, adjusting the temperature of the air conditioning cluster to the extreme value of the human comfort temperature range within a preset duration before the start of the demand response event, storing reserve energy for the target building; an event-phase strategy, reducing the operating power of the air conditioning cluster during the execution of the demand response event, and using the reserve energy stored in the target building to maintain the indoor temperature within the human comfort temperature range; and intelligently regulating the air conditioning cluster according to the target regulation strategy.

[0005] By adopting the above technical solutions, a virtual energy storage model is constructed to calculate the equivalent heat capacity of the target building, which can accurately assess the thermal characteristics of the target building. Based on the equivalent heat capacity and demand response target parameters, the reserve energy is iteratively determined and the target control strategy is generated, which can utilize the thermal inertia of the target building for fine-grained control. Storing reserve energy during the pre-regulation stage can reserve regulation potential. During the event stage, the reserve energy is used to maintain the indoor temperature, which can reduce the operating power of air conditioning, maintain the thermal comfort of indoor occupants while reducing the load, improve the control efficiency of air conditioning clusters in demand response, and take into account both grid response requirements and human thermal comfort.

[0006] Optionally, the reserve energy is determined through iterative calculation based on the equivalent heat capacity and target parameters, including: Step a) Calculating the current power gap in the event phase based on the temperature difference between the outdoor temperature and the preset initial temperature, the building's natural thermal power coefficient, and the preset air conditioning cluster energy efficiency ratio, and calculating the current power gap = current power gap × duration based on the duration of the demand response event, where the building's natural thermal power coefficient represents the power required for heat exchange between the building envelope and the external environment for every 1°C temperature difference between the indoor and outdoor areas of the target building; Step b) Estimate the actual temperature change of the target building based on the current power gap and the equivalent heat capacity of the target building, and thus obtain the expected indoor temperature; Step c) Calculate the deviation rate between the expected indoor temperature and the preset initial temperature. If the deviation rate is greater than the preset deviation rate threshold, the expected indoor temperature is updated to the new preset initial temperature, and steps a) to b) are repeated; if the deviation rate is less than or equal to the preset deviation rate threshold, the iteration is stopped, and the current power gap is determined as the reserve energy.

[0007] By adopting the above technical solution, the current energy gap is calculated based on the outdoor temperature, preset initial temperature, building natural thermal power coefficient, preset air conditioning cluster energy efficiency ratio, and duration of demand response events. Then, the actual temperature change and expected indoor temperature are estimated by combining the equivalent heat capacity of the target building. Through iterative calculation using the deviation rate, the reserve energy can be accurately determined, providing an accurate basis for storing reserve energy in the subsequent pre-regulation stage. This helps to better utilize the thermal inertia of the target building during demand response events, reducing the operating power of the air conditioning cluster while maintaining the indoor temperature within the human comfort range, improving the regulation efficiency of the air conditioning cluster in demand response, and balancing grid response needs with human thermal comfort.

[0008] Optionally, the current power gap in the event phase is calculated, including: calculating the current power gap according to the following formula: Pgap=K×(t_out-t_in0)-P_obj×COP; where Pgap is the current power gap, K is the building natural thermal power coefficient, t_out is the outdoor temperature, t_in0 is the preset initial temperature, P_obj is the target power of the air conditioning cluster after reduction according to the power reduction required by the demand response event, and COP is the preset energy efficiency ratio of the air conditioning cluster.

[0009] By adopting the above technical solution and using the above formula to calculate the current power gap during the event phase, and determining the reserve energy through iterative calculation, the influence of factors such as outdoor temperature, preset initial temperature, building natural thermal power coefficient, target power of air conditioning cluster and energy efficiency ratio on the power gap can be considered more accurately. This generates a target control strategy that includes pre-adjustment phase, event phase and post-event recovery phase strategies to intelligently control the air conditioning cluster, realize refined pre-adjustment to reserve adjustment potential, improve the adjustment depth and duration of a single response event, maintain indoor thermal comfort while reducing load, improve the control efficiency of air conditioning cluster in demand response, and take into account both grid response needs and human thermal comfort.

[0010] Optionally, based on the current energy gap and the equivalent heat capacity of the target building, the actual temperature change of the target building is estimated to obtain the expected indoor temperature, including: calculating the actual temperature change according to the following formula: Δt=Qgap / C_eq, Qgap=Pgap×T0, where Qgap represents the current energy gap, T0 is the duration, Δt is the actual temperature change, and C_eq is the equivalent heat capacity of the target building; calculating the expected indoor temperature according to the following formula: t_in1=t_in2-sign×Δt, where t_in1 is the expected indoor temperature, t_in2 is the daily indoor temperature of the target building before the demand response event, and sign is a sign factor, which is +1 for cooling and -1 for heating; the deviation rate is equal to the ratio between the absolute value of the difference between the expected indoor temperature and the preset initial temperature and the preset initial temperature; the preset deviation rate threshold is 0.5%-2%, and the preset initial temperature is the upper or lower limit of the human comfort temperature range.

[0011] By adopting the above technical solution, the actual temperature change can be accurately calculated using the equivalent heat capacity of the target building and the current energy gap, thereby obtaining the expected indoor temperature. At the same time, the deviation rate and the preset deviation rate threshold are determined. Combined with the preset initial temperature as the upper or lower limit of the human comfort temperature range, the iterative calculation can more accurately determine the reserve energy, thereby formulating a more reasonable target control strategy for the air conditioning cluster. This will better balance load reduction and maintaining the thermal comfort of indoor occupants, and improve the control efficiency and economy of the air conditioning cluster in demand response.

[0012] Optionally, the reserve energy satisfies the energy balance relationship: Q_target = COP×P1×T1 - K×ΔT_avg×T1; where Q_target is the reserve energy determined iteratively, COP is the preset energy efficiency ratio of the air conditioning cluster, P1 is the operating power of the air conditioning cluster in the pre-adjustment stage, T1 is the preset duration, K is the building natural thermal power coefficient, which represents the power of heat exchange between the building envelope and the external environment for every 1℃ temperature difference between the indoor and outdoor areas of the target building, ΔT_avg is the average indoor and outdoor temperature difference in the pre-adjustment stage, and P1 ≤ rated power of the air conditioning cluster × preset adjustment coefficient.

[0013] By adopting the above technical solutions, the energy balance relationship satisfied by the reserve energy is clarified. Combined with the preset energy efficiency ratio of the air conditioning cluster, the operating power of the air conditioning cluster in the pre-conditioning stage, the preset duration, the building's natural thermal power coefficient, and the average indoor and outdoor temperature difference in the pre-conditioning stage, the reserve energy can be accurately determined. This helps to store appropriate reserve energy for the target building in the pre-conditioning stage, better utilize the building's thermal inertia in demand response events, take into account both the grid response needs and human thermal comfort, and improve the regulation efficiency of the air conditioning cluster in demand response.

[0014] Optionally, the pre-adjustment phase strategy can be implemented in one of the following two ways: fix the preset duration T1, calculate the operating power P1 of the air conditioning cluster based on the energy balance relationship, and ensure that P1 does not exceed the maximum allowable power of the air conditioning cluster; fix the operating power P1 of the air conditioning cluster as the maximum allowable power, and calculate the required preset duration T1 based on the energy balance relationship.

[0015] By adopting the above technical solution, the reserve energy is determined iteratively based on the equivalent heat capacity and demand response target parameters, enabling the target building to store appropriate regulating energy. The operating power and preset duration of the pre-regulation phase are determined according to the energy balance relationship, ensuring that reserve energy is reasonably stored during the pre-regulation phase for use during events. Two execution methods for the pre-regulation phase strategy are provided, which can be flexibly selected according to different situations, improving the applicability of the method, taking into account the reserve energy requirements and the operating limitations of air conditioning equipment, and at the same time, the stored reserve energy can be used to maintain the indoor temperature within the human comfort range during events, improving user thermal comfort and enhancing the effect of grid demand response.

[0016] Optionally, a set of physical parameters of the target building includes building area, floor height and structural type. The virtual energy storage model calculates the volume of the target building based on a set of physical parameters, and obtains the equivalent heat capacity by combining air density, air specific heat capacity and building structure correction coefficient.

[0017] By adopting the above technical solution, the volume of the target building can be calculated using the building area, floor height, and structural type. The equivalent heat capacity can be obtained by combining air density, air specific heat capacity, and building structure correction coefficient. This can more accurately reflect the thermal characteristics of the target building, providing a more precise data basis for determining reserve energy and generating target control strategies. It helps to achieve refined control of air conditioning clusters, improve the control efficiency of air conditioning clusters in demand response, and achieve a better balance between load reduction and maintaining the thermal comfort of indoor occupants.

[0018] Optionally, the target control strategy also includes: a post-event recovery phase strategy, which controls the operating set temperature of the air conditioning cluster to gradually recover to the normal set temperature at a preset rate after the demand response event ends.

[0019] By adopting the above technical solutions, the target control strategy also includes a post-event recovery phase strategy, which can gradually restore the operating set temperature of the air conditioning cluster to the normal set temperature at a preset rate after the demand response event ends. This avoids sudden temperature changes from affecting the thermal comfort of indoor occupants, improves the thermal comfort of indoor occupants, and also enhances the control efficiency of the air conditioning cluster in demand response and the willingness of users to participate.

[0020] Optionally, the post-event recovery strategy includes: calculating recovery parameters based on the indoor temperature t_end, the target normal set temperature t_normal, and the equivalent heat capacity C_eq of the target building at the end of the demand response event, calculating the theoretical total energy required to restore the building temperature from t_end to t_normal: Qrecover=C_eq×|t_normal-t_end|; generating a recovery power curve, constrained by a preset maximum recovery power Precover_max, where the maximum recovery power is less than the rated power of the air conditioning cluster; generating a recovery power curve Precover(t) showing the change of air conditioning power over time during the recovery phase, with the goal of minimizing the total duration of the recovery phase while ensuring that the indoor temperature recovery rate does not exceed the preset rate Vmax; wherein, the recovery power curve must satisfy: ∫Precover(t)dt×COP=Qrecover, where COP is the preset energy efficiency ratio of the air conditioning cluster; and executing recovery by controlling the air conditioning cluster to operate according to the recovery power curve Precover(t) until the indoor temperature reaches t_normal.

[0021] By adopting the above technical solution, after the demand response event ends, the theoretical total energy replenishment can be calculated based on the indoor temperature, the target normal set temperature, and the equivalent heat capacity. With the maximum recovery power as a constraint, and under the premise of meeting the indoor temperature recovery rate requirements, a recovery power curve that minimizes the total duration of the recovery phase is generated. Then, the air conditioning cluster is controlled to operate according to this curve, which can accurately and efficiently restore the indoor temperature to the normal set temperature. This ensures the thermal comfort of indoor occupants and makes reasonable use of air conditioning power, thereby improving energy efficiency.

[0022] In a second aspect of this application, a control system for air conditioning clusters participating in demand response is also provided, used to execute the control method for air conditioning clusters participating in demand response as described in any of the preceding claims, comprising: a construction module for constructing a virtual energy storage model of a target building, wherein the input parameters of the virtual energy storage model include a set of physical parameters of the target building and a human comfort temperature range, and the virtual energy storage model calculates the equivalent heat capacity of the target building based on the set of physical parameters; an acquisition module for acquiring target parameters of the demand response event in response to a grid demand response event, the target parameters including start time, duration, and power reduction; a generation module for determining reserve energy through iterative calculation based on the equivalent heat capacity and target parameters, and generating a target control strategy for the air conditioning cluster, the target control strategy including: a pre-adjustment phase strategy, adjusting the temperature of the air conditioning cluster to the extreme value of the human comfort temperature range within a preset duration before the start of the demand response event, storing reserve energy for the target building; an event-phase strategy, reducing the operating power of the air conditioning cluster during the execution of the demand response event, and using the reserve energy stored in the target building to maintain the indoor temperature within the human comfort temperature range; and a control module for intelligently controlling the air conditioning cluster according to the target control strategy.

[0023] In a third aspect of this application, an electronic device is also provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor executes the program to implement the method steps of any of the above claims.

[0024] In a fourth aspect of this application, a computer-readable storage medium is also provided, which stores instructions that, when executed, perform the method steps of any of the above claims.

[0025] In summary, one or more technical solutions provided in this application have at least the following technical effects or advantages: 1. Constructing a virtual energy storage model to calculate the equivalent heat capacity of the target building can accurately assess the thermal characteristics of the target building; iteratively determining the reserve energy and generating the target control strategy based on the equivalent heat capacity and demand response target parameters can utilize the thermal inertia of the target building for fine-grained control; storing reserve energy during the pre-regulation stage can reserve regulation potential; using reserve energy to maintain indoor temperature during the event stage can reduce the operating power of air conditioning, maintain the thermal comfort of indoor occupants while reducing load, and improve the control efficiency of air conditioning clusters in demand response. 2. Based on outdoor temperature, preset initial temperature, building natural thermal power coefficient, preset air conditioning cluster energy efficiency ratio, and duration of demand response events, the current energy gap is calculated. Then, combined with the equivalent heat capacity of the target building, the actual temperature change and expected indoor temperature are estimated. Through iterative calculation using the deviation rate, the reserve energy can be accurately determined, providing an accurate basis for storing reserve energy in the subsequent pre-regulation stage. This helps to better utilize the thermal inertia of the target building during demand response events, reducing the operating power of the air conditioning cluster while maintaining the indoor temperature within the human comfort range, improving the regulation efficiency of the air conditioning cluster in demand response, and balancing grid response needs with human thermal comfort. 3. The target control strategy also includes a post-event recovery phase strategy, which can gradually restore the operating set temperature of the air conditioning cluster to the normal set temperature at a preset rate after the demand response event ends. This avoids sudden temperature changes from affecting the thermal comfort of indoor occupants, improves the thermal comfort of indoor occupants, and also enhances the control efficiency of the air conditioning cluster in demand response and the willingness of users to participate. Attached Figure Description

[0026] Figure 1 This is a flowchart of a control method for air conditioning clusters participating in demand response, provided in an embodiment of this application. Figure 2 This is a structural block diagram of a control system for air conditioning clusters participating in demand response, provided in an embodiment of this application. Figure 3 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application.

[0027] Explanation of reference numerals in the attached figures: 300 - Electronic device; 301 - Processor; 302 - Communication bus; 303 - User interface; 304 - Network interface; 305 - Memory. Detailed Implementation

[0028] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0029] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0030] In the description of the embodiments of this application, the term "multiple" means two or more. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0031] The following is in conjunction with the appendix Figure 1 -Appendix Figure 3 The embodiments of this application will be described.

[0032] This application provides a method for regulating air conditioning clusters to participate in demand response, referring to... Figure 1 , Figure 1 This is a flowchart of a control method for air conditioning clusters participating in demand response, provided in an embodiment of this application, including the following steps: Step S101: Construct a virtual energy storage model of the target building. The input parameters of the virtual energy storage model include a set of physical parameters of the target building and the human comfort temperature range. The virtual energy storage model calculates the equivalent heat capacity of the target building based on a set of physical parameters. Step S102: In response to the grid demand response event, obtain the target parameters of the demand response event, including start time, duration and power reduction required; Step S103: Based on the equivalent heat capacity and target parameters, determine the reserve energy through iterative calculation and generate the target control strategy for the air conditioning cluster. The target control strategy includes: a pre-adjustment phase strategy, which adjusts the temperature of the air conditioning cluster to the extreme value of the human comfort temperature range within a preset time before the start of the demand response event, and stores reserve energy for the target building; and an event phase strategy, which reduces the operating power of the air conditioning cluster during the execution of the demand response event, and uses the reserve energy stored in the target building to maintain the indoor temperature within the human comfort temperature range. Step S104: Perform intelligent control of the air conditioning cluster according to the target control strategy.

[0033] Through the above steps, a virtual energy storage model is constructed to calculate the equivalent heat capacity of the target building, which can accurately assess the thermal characteristics of the target building. Based on the equivalent heat capacity and demand response target parameters, the reserve energy is iteratively determined and the target control strategy is generated, which can utilize the thermal inertia of the target building for fine-grained control. The reserve energy is stored in the pre-regulation stage to reserve the control potential. In the event stage, the reserve energy is used to maintain the indoor temperature, which can reduce the operating power of air conditioning, maintain the thermal comfort of indoor occupants while reducing the load, improve the control efficiency of air conditioning clusters in demand response, and take into account both grid response needs and human thermal comfort.

[0034] This embodiment utilizes the thermal inertia of the building envelope and indoor air to construct a virtual energy storage model. Through a two-stage control strategy of "pre-regulation energy storage + in-event energy release," a balance between load reduction and thermal comfort is achieved. Specifically, a set of physical parameters of the target building (such as envelope material, area, indoor space volume, etc.) and the human comfort temperature range are input to calculate the equivalent heat capacity of the target building, thereby quantifying the building's virtual energy storage capacity. Upon receiving a grid demand response command, instead of immediately and abruptly cutting off power or adjusting the temperature, it determines the "reserve energy" that needs to be stored in the building in advance through iterative calculations based on event parameters (duration, required power reduction), thus generating a two-stage strategy of "pre-regulation" and "in-event regulation." In the pre-conditioning phase, before the response event begins, the air conditioning temperature is adjusted to the extreme value of the human comfort range (e.g., adjusting the temperature to the lower comfort limit in summer cooling, or the upper comfort limit in winter heating). This utilizes the building's thermal inertia to store reserve energy, thus "storing" load regulation potential in advance—essentially pre-cooling or pre-heating. In the event phase, when the response event is executed, the air conditioning operating power can be reduced according to the demand response event's requirements. The previously stored reserve energy is utilized, relying on the building's thermal inertia to maintain the indoor temperature within the comfort range, achieving load reduction. Then, the air conditioning cluster is uniformly scheduled according to the generated target control strategy. This embodiment is applicable to demand response control in summer or winter, and the aforementioned reserve energy can be reserve cooling or reserve heating. Related technologies employ a crude approach of "uniformly raising the temperature or rotating shutdowns," completely disregarding human thermal comfort needs and easily leading to user complaints. This application uses the human comfort temperature range as input parameters for a virtual energy storage model. During the pre-regulation phase, the temperature is adjusted only to the extreme value of the comfort range. During the event phase, the stored reserve energy is used to maintain the temperature within the comfort range, ensuring comfort from a regulatory logic perspective and solving the drawbacks of "one-size-fits-all" regulation. A key deficiency of related technologies is that they do not recognize the building as a "virtual energy storage medium," failing to pre-regulate and reserve potential, resulting in shallow regulation depth and short duration. This application constructs a virtual energy storage model to quantify the building's equivalent thermal capacity. By actively storing reserve energy during the pre-regulation phase, the building's thermal inertia is transformed into a dispatchable regulatory resource, breaking through the bottleneck of existing strategies' "no reserves, hard reduction" and solving the core problem of limited regulatory potential. Through this embodiment, the temperature adjustment during the pre-regulation phase does not exceed the human comfort range, and the temperature stability is maintained by the stored reserve energy during the event phase. This satisfies the load reduction requirements of grid demand response while avoiding user discomfort, increasing user participation. Since it addresses the core pain point of user complaints, the willingness and sustainability of commercial building owners or operators to participate in demand response projects will be greatly enhanced. At the same time, better response performance can also bring higher economic compensation, achieving a win-win situation for the power grid, users, and aggregators.By leveraging the reserve energy from the building's thermal inertia, power reduction can be supported for longer periods or achieve a greater load decrease without compromising comfort, significantly improving the control effect of a single demand response event. On the one hand, improved comfort reduces user complaint rates and the risk of user withdrawal, ensuring the long-term stable participation of air conditioning clusters in demand response. On the other hand, refined control reduces energy losses caused by frequent start-ups or large adjustments of air conditioning systems, balancing grid-side needs with user-side interests, and promoting the efficient application of air conditioning clusters in demand response.

[0035] In an optional embodiment, the reserve energy is determined through iterative calculation based on the equivalent heat capacity and target parameters, including: step a) calculating the current power gap in the event phase based on the temperature difference between the outdoor temperature and the preset initial temperature, the building's natural thermal power coefficient, and the preset air conditioning cluster energy efficiency ratio, and calculating the current power gap = current power gap × duration based on the duration of the demand response event, where the building's natural thermal power coefficient represents the power required for heat exchange between the building envelope and the external environment for every 1°C temperature difference between the indoor and outdoor areas of the target building; step b) estimating the actual temperature change of the target building based on the current power gap and the equivalent heat capacity of the target building, thereby obtaining the expected indoor temperature; step c) calculating the deviation rate between the expected indoor temperature and the preset initial temperature. If the deviation rate is greater than a preset deviation rate threshold, the expected indoor temperature is updated to a new preset initial temperature, and steps a) to b) are repeated; if the deviation rate is less than or equal to the preset deviation rate threshold, the iteration is stopped, and the current power gap is determined as the reserve energy.

[0036] In the above embodiments, the current energy gap is calculated based on the outdoor temperature, the preset initial temperature, the building's natural thermal power coefficient, the preset energy efficiency ratio of the air conditioning cluster, and the duration of the demand response event. Then, the actual temperature change and the expected indoor temperature are estimated by combining the equivalent heat capacity of the target building. Iterative calculations are performed using the deviation rate to accurately determine the reserve energy. This provides an accurate basis for storing reserve energy in the subsequent pre-regulation stage, which helps to better utilize the thermal inertia of the target building during the demand response event. While reducing the operating power of the air conditioning cluster, the indoor temperature is maintained within the human comfort temperature range, improving the regulation efficiency of the air conditioning cluster in demand response and taking into account both grid response needs and human thermal comfort.

[0037] This embodiment establishes a precise, closed-loop method for calculating reserve energy by quantifying the correlation between building heat exchange patterns and temperature changes. Specifically, based on the temperature difference between the outdoor temperature and the preset initial temperature, the building's natural thermal power coefficient (characterizing the building's heat exchange power under a 1℃ temperature difference between indoors and outdoors), and the energy efficiency ratio of the air conditioning cluster, the current gap power in a stage of the demand response event is calculated. For example, a certain value in the human comfort temperature range (such as the lower limit temperature in summer) can be used as the preset initial temperature for the first calculation. Then, combined with the duration of the event, the current gap energy is obtained: current gap energy = current gap power × duration. Based on the current gap energy and the equivalent heat capacity of the target building, the actual temperature change of the target building is estimated, and then the expected indoor temperature in a stage of the event is derived. The deviation rate between the expected indoor temperature and the preset initial temperature is calculated. If the deviation rate exceeds the preset deviation rate threshold, the expected indoor temperature is updated to the new preset initial temperature, and the "gap energy calculation - temperature estimation" process is repeated. If the deviation rate meets the requirements, the iteration stops, and the final current gap energy is determined as the reserve energy. For example, the deviation rate threshold can be 0.5% (or 1%, or other values). After several iterations, the assumed temperature and the calculated final temperature will tend to be consistent (deviation rate less than the threshold). At this point, the thermal balance model becomes self-consistent, and the calculated "gap energy" is an accurate and reliable reserve energy. This process essentially eliminates the impact of temperature changes on heat exchange power through multiple iterative corrections, ensuring that the calculated reserve energy accurately matches the load reduction targets of demand response and the constraints of the human comfort temperature range. Related technologies, lacking precise energy calculation methods, cannot quantify the energy storage potential of building thermal inertia and can only adopt a one-size-fits-all power reduction strategy. This embodiment, through precise iterative calculations, transforms the energy storage potential of building thermal inertia into quantifiable and controllable reserve energy, ensuring the scientific nature of "pre-adjusted energy storage" from a computational perspective and avoiding problems such as substandard comfort or insufficient adjustment potential caused by blind energy storage. Iterative calibration eliminates the impact of temperature changes on heat exchange power, avoiding errors caused by single calculations. This ensures that the final determined reserve energy not only meets the load reduction requirements of grid demand response but also strictly controls the indoor temperature within the human comfort range, achieving a precise balance between "energy storage" and "comfort." Precise reserve energy calculation avoids the waste of air conditioning energy caused by "excessive energy storage" and also avoids the occurrence of temperature exceeding the standard during events due to "insufficient energy storage," taking into account both economic efficiency and control effect, and promoting more efficient participation of air conditioning clusters in grid demand response.

[0038] In an optional embodiment, calculating the current power gap at a stage in the event includes: calculating the current power gap according to the following formula: Pgap=K×(t_out-t_in0)-P_obj×COP; where Pgap is the current power gap, K is the building's natural thermal power coefficient, t_out is the outdoor temperature, t_in0 is the preset initial temperature, P_obj is the target power of the air conditioning cluster after reduction according to the power reduction required by the demand response event, and COP is the preset energy efficiency ratio of the air conditioning cluster.

[0039] In the above embodiments, the current power gap during the event phase is calculated using the above formula, and the reserve energy is determined through iterative calculation. This allows for a more accurate consideration of the impact of factors such as outdoor temperature, preset initial temperature, building natural thermal power coefficient, target power of the air conditioning cluster, and energy efficiency ratio on the power gap. As a result, a target control strategy is generated that includes strategies for the pre-adjustment phase, the event phase, and the post-event recovery phase. This strategy enables intelligent control of the air conditioning cluster, achieving refined pre-adjustment to reserve adjustment potential, improving the adjustment depth and duration of a single response event, maintaining indoor thermal comfort while reducing load, improving the control efficiency of the air conditioning cluster in demand response, and balancing grid response needs with human thermal comfort.

[0040] In the above formula, K×(t_out-t_in0) represents the building's natural heat exchange power, obtained by multiplying the building's natural heat power coefficient K by the indoor-outdoor temperature difference (t_out−t_in0). It reflects the intensity of heat exchange between the building and the outside world through the building envelope when there is no air conditioning intervention. The building's natural heat power coefficient K is an inherent property of the building, with units of kW / °C. Pgap and P_obj are both in kW. P_obj represents the target operating power of the air conditioning cluster after power reduction during the response event. For example, if the normal power is 100kW and a reduction of 40kW is required, then P_obj is 60kW. W and COP represent the energy efficiency ratio, which is the efficiency of an air conditioner in converting electrical energy into cooling or heating. For example, COP=3 (or other values) means that consuming 1kW of electricity can produce 3kW of cooling. P_obj×COP represents the actual cooling or heating power of the air conditioner, which is obtained by multiplying the target operating power P_obj of the air conditioning cluster under the demand response event by the air conditioner's energy efficiency ratio COP. It reflects the actual heat regulation capacity that the air conditioner can provide after power reduction. Pgap represents the current power gap, which is the difference between the building's natural heat exchange power and the actual regulation power of the air conditioner. It represents the heat power gap that needs to be filled by the building's reserve energy. Related technologies lack accurate heat power calculation methods, making it impossible to determine the power gap that needs to be filled by the building's thermal inertia. This leads to blindly adjusting the air conditioner temperature or starting / stopping it. The above formula directly relates to the building's thermal characteristics, air conditioner operating parameters, and ambient temperature. The calculation results can accurately reflect the building's heat power gap under the demand response event, avoiding estimation bias and providing an accurate data foundation for subsequent iterative determination of reserve energy. The parameters K, COP, t_out in the formula can be set or obtained in real time according to the specific building, the specific air conditioning system, and the specific weather conditions. This means that, even under the same power grid commands, the system can calculate completely different, tailored power deficits and reserve energy for different buildings, thereby generating the most suitable control strategy for each building and achieving truly refined "one building, one policy" management. Whether it's summer cooling or winter heating, whether it's sunny or rainy (t_out variation), the formula can universally describe the system's thermal balance state by adjusting the positive or negative temperature difference and the air conditioning mode (cooling COP or heating COP). This makes the entire control method robust and able to cope with complex actual operating environments.

[0041] In an optional embodiment, based on the current energy gap and the equivalent heat capacity of the target building, the actual temperature change of the target building is estimated to obtain the expected indoor temperature, including: calculating the actual temperature change according to the following formula: Δt=Qgap / C_eq, Qgap=Pgap×T0, where Qgap represents the current energy gap, T0 is the duration, Δt is the actual temperature change, and C_eq is the equivalent heat capacity of the target building; calculating the expected indoor temperature according to the following formula: t_in1=t_in2-sign×Δt, where t_in1 is the expected indoor temperature, t_in2 is the daily indoor temperature of the target building before the demand response event, and sign is a sign factor, which is "+1" for cooling and "-1" for heating; the deviation rate is equal to the ratio between the absolute value of the difference between the expected indoor temperature and the preset initial temperature and the preset initial temperature; the preset deviation rate threshold is 0.5%-2%, and the preset initial temperature is the upper or lower limit of the human comfort temperature range.

[0042] In the above embodiments, the actual temperature change is accurately calculated by using the equivalent heat capacity of the target building and the current energy gap, thereby obtaining the expected indoor temperature. At the same time, the deviation rate and the preset deviation rate threshold are determined. Combined with the preset initial temperature as the upper or lower limit of the human comfort temperature range, the iterative calculation can more accurately determine the reserve energy, thereby formulating a more reasonable target control strategy for the air conditioning cluster, better balancing load reduction and maintaining the thermal comfort of indoor personnel, and improving the control efficiency and economy of the air conditioning cluster in demand response.

[0043] In the above formula, Qgap represents the energy difference that needs to be filled by the building's virtual energy storage, C_eq is the building's equivalent heat capacity calculated by the virtual energy storage model, characterizing the building's ability to accommodate changes in heat, and Δt is the actual temperature change of the building caused by the energy gap. This formula directly quantifies the correspondence between "energy gap" and "temperature fluctuation." Combining different cooling / heating scenarios, the expected indoor temperature is calculated using the formula t_in1=t_in2-sign×Δt, adapting to the control needs of different seasons. t_in2 is the target building's daily indoor temperature before the demand response event, i.e., the baseline indoor temperature before control, such as the daily setting of 24℃. Using relative deviation (the ratio of absolute value to the initial temperature) instead of absolute temperature difference makes the iteration termination condition adaptive to different temperature levels. The preset deviation rate threshold is 0.5%-2%, a trade-off engineering choice. A threshold that is too small (e.g., <0.5%) will lead to too many iterations, slow calculation convergence, and sacrifice of real-time performance; a threshold that is too large (e.g., >2%) will result in inaccurate results, affecting comfort assurance and the accuracy of energy calculation. The 0.5%-2% range achieves an optimal balance between accuracy and efficiency. The temperature calculation method based on thermodynamic formulas accurately predicts building temperature fluctuation trends during demand response events. Combined with sign factor adaptation for cooling / heating scenarios, it avoids scenario-specific errors in temperature calculation, ensuring minimal deviation between predicted and actual temperatures. Clear deviation rate calculation rules and a 0.5%-2% threshold range provide a quantitative criterion for iteration termination, avoiding both computational inefficiency caused by excessive iterations and deviations in prepared energy calculations due to premature iteration termination. This ensures that the final determined prepared energy accurately matches the dual objectives of "load reduction + comfort temperature." By calculating Δt and t_in1, the system can "pre-simulate" the adjusted temperature results during iterations and determine whether they deviate too far from the preset comfort starting point. This incorporates a comfort protection mechanism at the algorithm level, ensuring that the final generated strategy theoretically strictly limits temperature fluctuations within the allowable range.

[0044] In an optional embodiment, the reserve energy satisfies the energy balance relationship: Q_target = COP×P1×T1- K×ΔT_avg×T1; where Q_target is the reserve energy determined iteratively, COP is the preset energy efficiency ratio of the air conditioning cluster, P1 is the operating power of the air conditioning cluster in the pre-adjustment stage, T1 is the preset duration, K is the building natural thermal power coefficient, which represents the power of heat exchange between the building envelope and the external environment for every 1°C temperature difference between the indoor and outdoor areas of the target building, ΔT_avg is the average indoor and outdoor temperature difference in the pre-adjustment stage, and P1≤rated power of the air conditioning cluster×preset adjustment coefficient.

[0045] In the above embodiments, the energy balance relationship satisfied by the reserve energy is clearly defined. Combined with the preset energy efficiency ratio of the air conditioning cluster, the operating power of the air conditioning cluster in the pre-conditioning stage, the preset duration, the building's natural thermal power coefficient, and the average indoor and outdoor temperature difference in the pre-conditioning stage, the reserve energy can be accurately determined. This helps to store appropriate reserve energy for the target building in the pre-conditioning stage, better utilize the building's thermal inertia in demand response events, take into account both the grid response demand and human thermal comfort, and improve the regulation efficiency of the air conditioning cluster in demand response.

[0046] The above formula is the balance model of "total energy input to air conditioning - building heat loss = net energy storage (reserve energy)" in the pre-conditioning stage. Q_target is the reserve energy determined by iteration, which is the net available energy storage after deducting the building's natural heat loss from the effective energy storage of air conditioning. This ensures that the energy comes entirely from the active reserves in the pre-conditioning stage and can accurately match the load reduction requirements of demand response events. ΔT_avg is the average indoor and outdoor temperature difference in the pre-conditioning stage. For example, assuming the outdoor temperature is 35℃, the temperature before the start of the pre-conditioning stage is 24℃, the target temperature in the pre-conditioning stage is 23℃, and the average indoor temperature is 23.5℃, then ΔT_avg can be taken as 11.5℃. The formula has an additional constraint P1 ≤ rated power of the air conditioning cluster × preset adjustment coefficient. The purpose is to limit the upper limit of the operating power of the air conditioning in the pre-conditioning stage, avoid the air conditioning from overloading and causing equipment damage or a surge in energy consumption, and at the same time ensure the stability and controllability of the pre-conditioning process. The energy balance formula provides a clear physical calculation and verification basis for the reserve energy, ensuring that the reserve energy determined iteratively meets both the load reduction requirements of demand response and conforms to the objective laws of building heat exchange, avoiding the problems of "excessive energy storage" or "insufficient energy storage". The power constraint limits the upper limit of the air conditioning's operating power, preventing the air conditioning from operating under overload and reducing the risk of equipment failure. At the same time, by adjusting the preset adjustment coefficient, the energy storage efficiency and energy consumption cost can be balanced, improving the economic efficiency of the technical solution.

[0047] In one optional embodiment, the pre-adjustment phase strategy is executed in one of the following two ways: fixing the preset duration T1, calculating the operating power P1 of the air conditioning cluster based on the energy balance relationship, and ensuring that P1 does not exceed the maximum allowable power of the air conditioning cluster; fixing the operating power P1 of the air conditioning cluster as the maximum allowable power, and calculating the required preset duration T1 based on the energy balance relationship.

[0048] In the above embodiments, the reserve energy is determined iteratively based on the equivalent heat capacity and demand response target parameters, so that the target building stores appropriate regulating energy; the operating power and preset duration of the pre-regulation stage are determined according to the energy balance relationship, which can ensure that the reserve energy is reasonably stored during the pre-regulation stage for use in events; two execution methods of the pre-regulation stage strategy are provided, which can be flexibly selected according to different situations, improving the applicability of the method, taking into account the reserve energy demand and the operating limitations of air conditioning equipment, and at the same time, the stored reserve energy can be used to maintain the indoor temperature within the human comfort temperature range during events, improving user thermal comfort and enhancing the effect of grid demand response.

[0049] Based on the energy balance formula Q_target=COP×P1×T1-K×ΔT_avg×T1, two optional execution methods, "fixed duration power calculation" and "fixed power duration calculation," are used to transform the energy balance relationship into a directly implementable control scheme, adapting to demand response execution conditions under different scenarios. Method 1: Fixed duration T1, calculated power P1. First, determine the pre-adjustment duration T1 based on the time window of the grid demand response event and the actual building operation schedule, such as 1 hour (or 2 hours, or other duration) before the start of the response event. Then, substitute the T1 into the energy balance formula to calculate the required air conditioning operating power P1, ensuring that the calculated P1 does not exceed the maximum allowable power of the air conditioning cluster to avoid equipment overload. Method 2: Fixed power P1, calculated duration T1. First, set the air conditioning operating power P1 to its maximum allowable power (maximizing energy storage efficiency), then substitute the T1 into the energy balance formula to calculate the required pre-adjustment duration T1, ensuring that under this power, the pre-adjustment of duration T1 can store enough Q_target to meet the demand. This embodiment provides the most direct path to find feasible solutions under different constraints: when time resources are limited, method two is used to find the shortest time; when there is a requirement for smooth power consumption or the equipment needs to operate stably, method one is used to find the required power for a constant duration. This gives the system the ability to select the most suitable execution strategy based on the actual situation, realizing strategy optimization within a safety framework. Of course, in practical applications, reasonable selection can also be made based on the electricity price of the power grid at different times.

[0050] In an optional embodiment, a set of physical parameters of the target building includes building area, floor height and structural type. The virtual energy storage model calculates the volume of the target building based on the set of physical parameters and obtains the equivalent heat capacity by combining air density, air specific heat capacity and building structure correction coefficient.

[0051] In the above embodiments, the volume of the target building is calculated using the building area, floor height, and structural type. The equivalent heat capacity is obtained by combining air density, air specific heat capacity, and building structure correction coefficient. This can more accurately reflect the thermal characteristics of the target building, providing a more precise data foundation for subsequent determination of reserve energy and generation of target control strategies. This helps to achieve refined control of the air conditioning cluster, improve the control efficiency of the air conditioning cluster in demand response, and achieve a better balance between load reduction and maintaining the thermal comfort of indoor occupants.

[0052] The calculation of the virtual energy storage model consists of two steps: First, the indoor space volume of the target building is calculated based on the building area and floor height (volume = building area × floor height); second, the basic heat capacity of the indoor air is calculated by combining air density and specific heat capacity, and then a building structure correction factor is introduced (this factor is determined by the structure type and is used to characterize the thermal energy storage contribution of the building envelope), finally obtaining the building's equivalent heat capacity C_eq. Its core logic is: Equivalent heat capacity = (air density × volume × specific heat capacity) × building structure correction factor, which considers both the thermal energy storage capacity of the indoor air and the thermal inertia effect of the building envelope.

[0053] For example, the equivalent heat capacity can be calculated using the following formula based on a set of physical parameters: C_eq = ρ × V × c × k, where C_eq is the equivalent heat capacity, ρ is the air density (e.g., 1.2 kg / m³), V is the volume of the target building, c is the specific heat capacity of air (e.g., 1.005 kJ / (kg・℃), and k is the building structure correction factor. The volume of the target building is calculated based on a set of physical parameters, and the building structure correction factor is determined according to the structural type of the target building, which includes heavy structures and light structures. The building structure correction factor is used to quantify the contribution of the thermal inertia of the building envelope to the equivalent heat capacity. Heavy structures have higher thermal inertia and therefore a higher k value; light structures have lower thermal inertia and therefore a lower k value. For example, k is taken as 1.5 for heavy structures and 1.0 for light structures.

[0054] In an optional embodiment, the target control strategy further includes a post-event recovery phase strategy, which controls the operating set temperature of the air conditioning cluster to gradually recover to the normal set temperature at a preset rate after the demand response event ends.

[0055] In the above embodiments, the target control strategy also includes a post-event recovery phase strategy, which can gradually restore the operating set temperature of the air conditioning cluster to the normal set temperature at a preset rate after the demand response event ends, so as to avoid the impact of sudden temperature changes on the thermal comfort of indoor occupants, improve the thermal comfort of indoor occupants, and also improve the control efficiency of the air conditioning cluster in demand response and the willingness of users to participate.

[0056] This embodiment's target control strategy, based on the pre-adjustment phase strategy and the in-event phase strategy, adds a post-event recovery phase strategy, covering the entire lifecycle of demand response events. It constructs a complete three-stage control closed loop of "pre-energy storage - peak shaving operation - smooth recovery," clearly defining the rules for air conditioning temperature recovery after the demand response event, avoiding secondary load shocks caused by rapid recovery. After the event, instead of a crude operation of "resetting the temperature back to normal value all at once," the set temperature of the air conditioning cluster is gradually restored to the normal daily set temperature at a preset rate. This "preset rate" needs to match the building's thermal inertia characteristics and the air conditioning's operating capacity. For example, after the summer demand response ends, the temperature is gradually lowered from the upper comfort limit to the normal set value at a rate of 0.5℃ / 10 minutes to avoid a sudden increase in power caused by full-load operation of the air conditioning. Related technologies only focus on load reduction during demand response events, completely ignoring the air conditioning recovery process after the event, and generally adopt the operation of "immediately restoring the original temperature." This operation would cause a large number of air conditioners to run at full load simultaneously, creating a secondary load peak (i.e., "rebound load"), which would actually increase the peak-shaving pressure on the power grid and violate the original intention of demand response to "shaving peaks and filling valleys." At the same time, the sudden temperature change would also affect the thermal comfort of users. This embodiment, by gradually restoring the temperature after the event, can distribute the load recovery time of the air conditioners, avoid the sudden increase in load caused by a large number of air conditioners running at high power at the same time, prevent the formation of new electricity consumption peaks, truly achieve the demand response goal of "shaving peaks and filling valleys," and improve the stability of power grid operation. Compared with the rough recovery method of "sudden temperature change," the gradual temperature recovery can avoid large fluctuations in indoor temperature in a short period of time, maintain a smooth transition of perceived temperature, further improve the user's comfort experience, reduce user complaints, and enhance the enthusiasm of commercial buildings to participate in demand response. The gradual recovery operation mode can avoid the mechanical and current shocks caused by "instant full-load start-up" of core components such as air conditioner compressors, reduce the probability of equipment failure, extend the service life of equipment, and reduce the long-term operation and maintenance costs of building air conditioning systems.

[0057] In an optional embodiment, the post-event recovery strategy includes: calculating recovery parameters based on the indoor temperature t_end at the end of the demand response event, the target normal set temperature t_normal, and the equivalent heat capacity C_eq of the target building, calculating the theoretical total energy required to restore the building temperature from t_end to t_normal: Qrecover=C_eq×|t_normal-t_end|; generating a recovery power curve, constrained by a preset maximum recovery power Precover_max, the maximum recovery power being less than the rated power of the air conditioning cluster; generating a recovery power curve Precover(t) showing the change of air conditioning power over time during the recovery phase, with the goal of minimizing the total duration of the recovery phase, while ensuring that the indoor temperature recovery rate does not exceed the preset rate Vmax; wherein, the recovery power curve must satisfy: ∫Precover(t)dt×COP=Qrecover, COP being the preset energy efficiency ratio of the air conditioning cluster; and executing recovery by controlling the air conditioning cluster to operate according to the recovery power curve Precover(t) until the indoor temperature reaches t_normal.

[0058] In the above embodiments, after the demand response event ends, the theoretical total energy replenishment can be calculated based on the indoor temperature, the target normal set temperature, and the equivalent heat capacity. With the maximum recovery power as a constraint, and under the premise of meeting the indoor temperature recovery rate requirements, a recovery power curve that minimizes the total duration of the recovery phase is generated. Then, the air conditioning cluster is controlled to operate according to this curve, which can accurately and efficiently restore the indoor temperature to the normal set temperature, ensuring the thermal comfort of indoor occupants and making reasonable use of air conditioning power to improve energy efficiency.

[0059] The theoretical total energy required for the recovery phase is calculated using the formula Qrecover=C_eq×|t_normal-t_end|. The optimization objective is to minimize the total duration of the recovery phase. Dual constraints are set to generate a recovery power curve that reflects the air conditioning power over time. The power constraint is that the maximum recovery power Precover_max must be less than the rated power of the air conditioning cluster to prevent overload operation. The temperature rate constraint is that the indoor temperature recovery rate must not exceed the preset rate Vmax to prevent sudden temperature changes from affecting comfort. The recovery power curve must satisfy: ∫Precover(t)dt×COP=Qrecover, ensuring that the total input energy meets the theoretical energy replenishment requirements while balancing recovery efficiency and constraint requirements. The air conditioning cluster is controlled to operate strictly according to the generated recovery power curve, dynamically adjusting the air conditioning output power until the indoor temperature stably reaches the normal set temperature, completing the full-cycle control closed loop. The recovery power curve generated based on constraints can distribute the load recovery time of air conditioners, avoid a large number of air conditioners operating at high power at the same time, fundamentally eliminate the phenomenon of "second peak after response ends", truly achieve the demand response goal of "peak shaving and valley filling" of the power grid, and improve the dispatch stability of the power system. With the goal of "shortest total time", it ensures that temperature recovery will not take too long. At the same time, the constraint that the maximum recovery power is less than the rated power avoids mechanical and current impacts on the core components of air conditioners caused by instantaneous full load, extends the service life of equipment, and reduces operation and maintenance costs.

[0060] The following example illustrates how the reserve energy is determined in the embodiments of this application. Taking a summer cooling scenario as an example, it is assumed that the human comfort temperature range is 23-25°C (or other temperature range), the outdoor temperature is 35°C, the normal indoor temperature is 24°C, the normal operating power of the air conditioning cluster is 400kW, COP=3, the building's natural thermal power coefficient K=100 kW / °C, and the equivalent heat capacity of the target building is 8000 kWh / °C. 1) In the first iteration, based on the "demand response event" parameters (e.g., 300kW power needs to be reduced from 14:00 to 16:00), with a duration of 2 hours, calculate the current power gap Pgap_(t) and energy gap Qgap_(t) for the "event phase". Then, the air conditioning operating power during the event phase is 100kW, and Pgap_(t) = 100kW. kW / °C × (35-23)°C - 100kW × COP = 900kW, where 23°C corresponds to the aforementioned preset initial temperature, Qgap_(t) = 900kW × 2h = 1800kWh; actual cooling amount = 1800 / 8000 = 0.225 (°C), then the expected indoor temperature = 24 - 0.225 = 23.775 (°C), then the temperature deviation rate is: |23.775 - 23| / 23 = 3.36% > 1% (corresponding to the aforementioned preset deviation rate threshold); continue iterating; 2) In the second iteration, the new gap power Pgap_(t+1) = 100 kW / °C × (35-23.775)°C - 100kW × COP = 822.5kW, and the new gap energy Qgap_(t+1) = 822.5kW × 2h = 1645kWh; the new actual cooling amount = 1645 / 8000 = 0.205 (°C), and the new expected indoor temperature = 24 - 0.205 = 23.795 (°C). Therefore, the temperature deviation rate is: |23.795-23.775| / 23.775 = 0.084% < 1%; this shows that the gap power (822.5kW) calculated in the second iteration is feasible. This gap power is used as the target gap power, and the target gap energy = target gap power * 2h = 1645kWh, which is used as the aforementioned reserve energy.

[0061] This application's embodiments model the building envelope and indoor air thermal inertia as a "virtual energy storage system" to achieve refined and pre-controlled regulation of the office building's central air conditioning cluster. This enables the system to participate in grid demand response (DR) more efficiently and comfortably, thereby reducing load and improving economic benefits. Compared with related technologies, it has at least the following technical advantages: 1. Seamless adjustment to ensure comfort: By using the method of "pre-cooling and storing cold in advance + maintaining temperature by storing cold in the building during events", the indoor temperature is always controlled within the human thermal comfort range during the adjustment process, avoiding the deterioration of comfort caused by rough adjustment and reducing the risk of user complaints; 2. Enhanced regulation depth and duration: By utilizing the regulation potential of building virtual energy storage, the load reduction depth of a single demand response can be increased by 30%-50%, and the duration can be extended to several hours, which is far superior to traditional extensive regulation methods; 3. Maximize economic returns: Greater adjustment depth and longer duration enable building operators to obtain higher demand response subsidy revenue while avoiding additional operating costs caused by comfort issues; 4. Quantifiable and dispatchable: It transforms building thermal inertia into standardized virtual energy storage parameters, enabling precise quantification of the regulation potential of air conditioning clusters, facilitating integration with the power grid demand response platform, and supporting large-scale dispatch.

[0062] This application also provides a control system for air conditioning clusters participating in demand response, used to execute the control method for air conditioning clusters participating in demand response in any of the foregoing embodiments, such as... Figure 2 As shown, Figure 2 This is a structural block diagram of a control system for demand response involving an air conditioning cluster, as provided in an embodiment of this application. The system includes: The construction module is used to build a virtual energy storage model of the target building. The input parameters of the virtual energy storage model include a set of physical parameters of the target building and the human comfort temperature range. The virtual energy storage model calculates the equivalent heat capacity of the target building based on a set of physical parameters. The acquisition module is used to respond to grid demand response events and acquire the target parameters of the demand response events, including start time, duration and power reduction required. The generation module is used to determine the reserve energy through iterative calculation based on the equivalent heat capacity and target parameters, and generate the target control strategy for the air conditioning cluster. The target control strategy includes: a pre-adjustment phase strategy, which adjusts the temperature of the air conditioning cluster to the extreme value of the human comfort temperature range within a preset time before the start of the demand response event, and stores reserve energy for the target building; and an event phase strategy, which reduces the operating power of the air conditioning cluster during the execution of the demand response event, and uses the reserve energy stored in the target building to maintain the indoor temperature within the human comfort temperature range. The control module is used to intelligently control the air conditioning cluster according to the target control strategy.

[0063] It should be noted that the devices or systems provided in the above embodiments are only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiments belong to the same concept. Other device or system embodiments correspond to the aforementioned method embodiments. Other technical features are described in the previous embodiments and will not be repeated here.

[0064] This application also provides a computer-readable storage medium storing instructions that, when executed, perform the steps of any of the methods described above.

[0065] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard disk, magnetic disk, or optical disk.

[0066] This application also discloses an electronic device. For example... Figure 3 As shown, Figure 3 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application. The electronic device 300 may include: at least one processor 301, at least one network interface 304, a user interface 303, a memory 305, and at least one communication bus 302.

[0067] The communication bus 302 is used to enable communication between these components.

[0068] The user interface 303 may include a display screen and a camera. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0069] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0070] The processor 301 may include one or more processing cores. The processor 301 connects to various parts of the electronic device (such as a server) using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory 305, and by calling data stored in memory 305. Optionally, the processor 301 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 301 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 301 and may be implemented as a separate chip.

[0071] The memory 305 may include random access memory (RAM) or read-only memory. Optionally, the memory 305 may include a non-transitory computer-readable storage medium. The memory 305 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 305 may also be at least one storage device located remotely from the aforementioned processor 301. (Refer to...) Figure 3 The memory 305, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for a control method of air conditioning clusters participating in demand response.

[0072] exist Figure 3 In the illustrated electronic device 300, the user interface 303 is mainly used to provide an input interface for the user and to acquire user input data; while the processor 301 can be used to call an application program stored in the memory 305 for a method of controlling an air conditioning cluster to participate in demand response. When executed by one or more processors 301, the electronic device 300 performs one or more of the methods described in the above embodiments. It should be noted that, for the foregoing method embodiments, for the sake of simplicity, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0073] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0074] In the various embodiments provided in this application, it should be understood that the disclosed apparatus or system can be implemented in other ways. For example, the apparatus or system embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between apparatuses or units may be electrical or other forms.

[0075] The above description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will be readily apparent to those skilled in the art upon consideration of the disclosure herein.

[0076] This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art that are not described in this disclosure.

Claims

1. A method for regulating air conditioning clusters in demand response, characterized in that, include: A virtual energy storage model of the target building is constructed, wherein the input parameters of the virtual energy storage model include a set of physical parameters of the target building and a human comfort temperature range, and the virtual energy storage model calculates the equivalent heat capacity of the target building based on the set of physical parameters. In response to a grid demand response event, the target parameters of the demand response event are obtained, including the start time, duration, and power reduction required. Based on the equivalent heat capacity and the target parameters, the reserve energy is determined through iterative calculation, and a target control strategy for the air conditioning cluster is generated. The target control strategy includes: The pre-conditioning phase strategy involves adjusting the temperature of the air conditioning cluster to the extreme value of the human comfort temperature range within a preset time period before the start of the demand response event, thereby storing the reserve energy for the target building. The event-phased strategy involves reducing the operating power of the air conditioning cluster during the execution of the demand response event, and utilizing the reserve energy stored in the target building to maintain the indoor temperature within the human comfort temperature range. The air conditioning cluster is intelligently controlled according to the target control strategy.

2. The method according to claim 1, characterized in that, Based on the equivalent heat capacity and the target parameters, the preliminary energy is determined through iterative calculation, including: Step a) Based on the temperature difference between the outdoor temperature and the preset initial temperature, the building's natural thermal power coefficient, and the preset air conditioning cluster energy efficiency ratio, calculate the current power gap in the stage of the event, and calculate the current power gap = the current power gap × the duration according to the duration of the demand response event, where the building's natural thermal power coefficient represents the power of heat exchange between the building envelope and the external environment for every 1°C temperature difference between the indoor and outdoor areas of the target building. Step b) Based on the current energy gap and the equivalent heat capacity of the target building, estimate the actual temperature change of the target building, and then obtain the expected indoor temperature; Step c) Calculate the deviation rate between the expected indoor temperature and the preset initial temperature. If the deviation rate is greater than the preset deviation rate threshold, update the expected indoor temperature to the new preset initial temperature and repeat steps a) to b). If the deviation rate is less than or equal to the preset deviation rate threshold, stop the iteration and determine the current gap energy as the reserve energy.

3. The method according to claim 2, characterized in that, Calculate the current gap power in the phase of the event, including: The current power gap is calculated using the following formula: Pgap = K × (t_out - t_in0) - P_obj × COP; where Pgap is the current power deficit, K is the building's natural thermal power coefficient, t_out is the outdoor temperature, t_in0 is the preset initial temperature, P_obj is the target power of the air conditioning cluster after power reduction according to the demand response event, and COP is the preset energy efficiency ratio of the air conditioning cluster.

4. The method according to claim 3, characterized in that, Based on the current energy deficit and the equivalent heat capacity of the target building, the actual temperature change of the target building is estimated, and the predicted indoor temperature is obtained, including: The actual temperature change is calculated using the following formula: Δt = Qgap / C_eq, Qgap = Pgap × T0, where Qgap represents the current energy gap, T0 is the duration, Δt is the actual temperature change, and C_eq is the equivalent heat capacity of the target building. The expected indoor temperature is calculated using the following formula: t_in1=t_in2-sign×Δt, where t_in1 is the expected indoor temperature, t_in2 is the daily indoor temperature of the target building before the demand response event, and sign is a sign factor, which is +1 when cooling and -1 when heating. The deviation rate is equal to the ratio of the absolute value of the difference between the expected indoor temperature and the preset initial temperature to the preset initial temperature; the preset deviation rate threshold is 0.5%-2%, and the preset initial temperature is the upper or lower limit of the human body comfort temperature range.

5. The method according to claim 1 or 2, characterized in that, The prepared energy satisfies the energy balance relationship: Q_target = COP×P1×T1 - K×ΔT_avg×T1; Where Q_target is the prepared energy determined iteratively, COP is the preset energy efficiency ratio of the air conditioning cluster, P1 is the operating power of the air conditioning cluster in the pre-adjustment stage, T1 is the preset duration, K is the building natural thermal power coefficient, which represents the power of heat exchange between the building envelope and the external environment for every 1°C temperature difference between the indoor and outdoor areas of the target building, ΔT_avg is the average indoor and outdoor temperature difference in the pre-adjustment stage, and P1 ≤ rated power of the air conditioning cluster × preset adjustment coefficient.

6. The method according to claim 5, characterized in that, The pre-adjustment phase strategy is implemented in one of the following two ways: The preset duration T1 is fixed, and the operating power P1 of the air conditioning cluster is calculated according to the energy balance relationship, ensuring that P1 does not exceed the maximum allowable power of the air conditioning cluster. The operating power P1 of the air conditioning cluster is fixed as the maximum allowable power, and the required preset duration T1 is calculated based on the energy balance relationship.

7. The method according to claim 1, characterized in that, The target building has a set of physical parameters including building area, floor height and structural type. The virtual energy storage model calculates the volume of the target building based on the set of physical parameters and obtains the equivalent heat capacity by combining air density, air specific heat capacity and building structure correction coefficient.

8. A control system for air conditioning clusters participating in demand response, characterized in that, A control method for performing the air conditioning cluster participation in demand response as described in any one of claims 1 to 7, comprising: A construction module is used to construct a virtual energy storage model of a target building. The input parameters of the virtual energy storage model include a set of physical parameters of the target building and a human comfort temperature range. The virtual energy storage model calculates the equivalent heat capacity of the target building based on the set of physical parameters. The acquisition module is used to respond to a power grid demand response event and acquire the target parameters of the demand response event, including the start time, duration, and power reduction required. A generation module is used to determine the reserve energy through iterative calculation based on the equivalent heat capacity and the target parameters, and generate a target control strategy for the air conditioning cluster. The target control strategy includes: a pre-adjustment phase strategy, which adjusts the temperature of the air conditioning cluster to the extreme value of the human comfort temperature range within a preset time before the start of the demand response event, and stores the reserve energy for the target building; and an event phase strategy, which reduces the operating power of the air conditioning cluster during the execution of the demand response event, and uses the reserve energy stored in the target building to maintain the indoor temperature within the human comfort temperature range. The control module is used to intelligently control the air conditioning cluster according to the target control strategy.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the method as described in any one of claims 1 to 7.

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