A Two-Stage Stochastic Robust Optimization Method and System for Central Air Conditioning Energy Saving Optimization

Abstract

The present invention discloses a two-stage stochastic robust optimization method and system for central air-conditioning energy conservation optimization, including: dividing the central air-conditioning terminal load into objective thermal comfort load and subjective thermal comfort load; constructing a two-stage stochastic robust optimization scheduling model; in the first stage, with an hour as the time scale, aiming at minimizing the operating energy consumption of the central air-conditioning on the hour time scale, determining the optimal equipment operating parameters that satisfy the objective thermal comfort load and the corresponding subjective thermal comfort load according to the environmental temperature and personnel information; in the second stage, with a minute as the time scale, aiming at minimizing the change value of the operating energy consumption of the central air-conditioning on the minute time scale, determining the adjustment amount of the equipment operating parameters according to the transition probability distribution obeyed by the subjective thermal comfort load, thereby controlling the operation of the central air-conditioning. Optimizing and adjusting the operating parameters of the central air-conditioning according to different time scales of random factors realizes the uncertain energy conservation optimization of the central air-conditioning under the condition of meeting the user's thermal comfort requirements.

Description

Technical Field

[0001] The present invention relates to the technical field of central air-conditioning energy-saving optimization, and in particular to a central air-conditioning energy-saving optimization method and system based on a two-stage random robust optimization. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] Central air conditioners are widely used in large buildings to create a comfortable indoor thermal environment. However, they also become major energy consumers. This is due in part to the fact that their internal equipment operates according to preset settings throughout the day, outputting a constant power output to meet load demands. In reality, the load on central air conditioner terminals fluctuates randomly, influenced by ambient temperature and the flow of people within the building. Therefore, while ensuring indoor thermal comfort, adjusting the operating state of central air conditioner equipment based on these random factors and implementing energy-saving optimization with the goal of minimizing energy consumption can effectively alleviate this problem.

[0004] Random factors affecting the terminal load of central air conditioners have different realization times. From the perspective of building heat transfer, central air conditioners need to follow the slow changes in the external ambient temperature over a long time scale (hours) and operate relatively stably to compensate for the building's heat dissipation power, thereby maintaining the temperature in the area at a certain thermal comfort value.

[0005] From the perspective of human flow, central air conditioning needs to follow the random changes in crowd density, personnel activities and human subjective perception of temperature in the area on a shorter time scale (minutes), and operate relatively flexibly to meet users' additional physical thermal comfort needs.

[0006] Based on the different time scales of random factors, taking into account the thermal inertia of air, the external ambient temperature changes little over a period of time, and its uncertainty can be represented by a simple uncertainty set; random factors such as pedestrian flow have strong dynamic regularity, and their uncertainty can be represented by a Markov chain; in addition, central air conditioning in large buildings is easy to adjust and operate, and a large margin is left in the design process, making it suitable for implementing a two-stage optimization strategy in a shorter time domain; however, the current energy-saving optimization strategy for central air conditioning in large buildings does not take into account the different time scales of random factors, nor does it take into account both parts of the random factors. Summary of the Invention

[0007] In order to solve the above problems, the present invention proposes a two-stage stochastic robust optimization method and system for central air-conditioning energy conservation, which optimizes and adjusts the operating parameters of central air-conditioning equipment according to different time scales of random factors, thereby realizing uncertain energy conservation optimization of central air-conditioning while meeting users' thermal comfort needs.

[0008] To achieve the above object, the present invention adopts the following technical solutions:

[0009] In a first aspect, the present invention provides a two-stage stochastic robust optimization method for central air-conditioning energy-saving optimization, including:

[0010] Dividing the terminal load of the central air-conditioning into an objective thermal comfort load based on ambient temperature and a subjective thermal comfort load based on personnel information;

[0011] Taking the minimization of the operating energy consumption of the central air-conditioning within a rolling time domain as the objective function, and constructing a two-stage stochastic robust optimization scheduling model;

[0012] Among them, in the first stage, with an hour as the time scale and aiming at minimizing the operating energy consumption of the central air-conditioning on the hour time scale, determining the optimal equipment operating parameters that satisfy the objective thermal comfort load and the corresponding subjective thermal comfort load according to the ambient temperature and personnel information;

[0013] In the second stage, with a minute as the time scale and aiming at minimizing the change value of the operating energy consumption of the central air-conditioning on the minute time scale, determining the equipment operating parameter adjustment amount according to the transition probability distribution followed by the subjective thermal comfort load;

[0014] Adjusting the optimal equipment operating parameters according to the equipment operating parameter adjustment amount, and controlling the operation of the central air-conditioning with the adjusted equipment operating parameters.

[0015] As an alternative implementation, the objective thermal comfort load is the cooling / heating load output by the central air-conditioning to compensate for building heat dissipation to meet the comfort requirements of the human body in the region under normal conditions based on the change of ambient temperature, and an uncertainty set of the objective thermal comfort load is constructed according to the uncertainty of the ambient temperature;

[0016] Objective thermal comfort load Q o And the thermal comfort temperature T under normal conditions in the region set , the external ambient temperature T out The relationship between them is:

[0017]

[0018] In the formula: σ bul , S bul Are the building heat transfer coefficient and heat dissipation area; λ is a correction coefficient; t i,0 Is the initial moment of the i-th hour.

[0019] As an alternative implementation, the subjective thermal comfort load is the load amount adjusted and changed on the basis of the objective thermal comfort load based on the uncertainties of the population density D, personnel activities B, and the subjective difference F in human body temperature;

[0020] Subjective thermal comfort load Q s The relationship with crowd density D, personnel activity B, and the subjective difference F in human body temperature is as follows:

[0021]

[0022]

[0023] Where: α1, α2, α3 are normalization ratio coefficients; ρ air is the air density; C air is the specific heat capacity; v in is the supply air flow rate; t i,j is the initial moment of the i-th hour and the j-th minute time scale, T set is the thermal comfort temperature.

[0024] As an alternative implementation, the transition probability distribution followed by the subjective thermal comfort load is:

[0025] In the next N t hours at time t i+h,j transfer the uncertainties of crowd density D, personnel activity B, and the subjective difference F in human body temperature to the subjective thermal comfort load, making it follow the joint probability distribution P s , and use the Markov chain to characterize the dynamic changes. Then the subjective thermal comfort load from time t i+h,0 The j-step transition probability of departure is:

[0026]

[0027] Where: a m is the possible value of the subjective thermal comfort load; is the predicted value of the subjective thermal comfort load at time t i+h,0 moment.

[0028] As an alternative implementation, the objective function for minimizing the operating energy consumption of the central air conditioner in the rolling time domain is:

[0029]

[0030] Where: are the operating parameters of the central air conditioner equipment at time t i+h,0 in the rolling time domain; is the adjustment amount of the operating parameters of the central air conditioner equipment at time t i+h,j in the rolling time domain; is the operating energy consumption determined at time t i+h,0 in the rolling time domain due to meeting the objective thermal comfort load and the corresponding subjective thermal comfort load; is the time in the rolling time domain i+h,j The change in operating energy consumption caused by adjusting the equipment operating parameters to meet the corresponding subjective thermal comfort load at the time.

[0031] As an optional implementation method, the process of controlling the central air conditioner includes:

[0032] The internal equipment of the central air conditioner includes fan coil units, chilled water pumps, refrigeration units, cooling water pumps and cooling towers, thereby constructing an operation model and energy consumption model of the internal equipment of the central air conditioner;

[0033] The operation model is constructed by the heat energy transfer process between internal devices through the heat transfer medium and the energy conservation theorem; the energy consumption model is constructed by the relationship between the electric energy consumed by the internal devices and their operation parameters.

[0034] As an optional implementation method, the process of controlling the central air conditioner operation further includes:

[0035] Taking the operating parameter limit constraints and power balance constraints as constraints, the operation of the internal equipment of the central air conditioner is controlled according to the adjusted equipment operating parameters;

[0036] The operating parameter limit constraints include setting upper and lower limits for the number of operating units of fan coil units, chilled water pumps, refrigeration units, cooling water pumps and cooling towers;

[0037] The power balance constraint is: t i,j The central air conditioning terminal load at time t i,0 The objective thermal comfort load at each moment is related to t i,j The sum of subjective thermal comfort loads at all times.

[0038] In a second aspect, the present invention provides a central air conditioning energy-saving optimization system with a two-stage stochastic robust optimization, comprising:

[0039] a load division module configured to divide the terminal load of the central air conditioner into an objective thermal comfort load based on ambient temperature and a subjective thermal comfort load based on occupant information;

[0040] The model building module is configured to construct a two-stage stochastic robust optimization scheduling model with the objective function of minimizing the operating energy consumption of the central air conditioner in the rolling horizon;

[0041] In the first stage, the optimal equipment operating parameters that meet the objective thermal comfort load and the corresponding subjective thermal comfort load are determined based on the ambient temperature and occupant information, with the goal of minimizing the central air conditioning's operating energy consumption on an hourly time scale.

[0042] In the second stage, taking minutes as the time scale and aiming at minimizing the change value of the operating energy consumption of the central air conditioner on the minute time scale, the adjustment amount of the equipment operating parameters is determined according to the transition probability distribution followed by the subjective thermal comfort load.

[0043] A control module, configured to adjust the optimal equipment operating parameters according to the adjustment amount of the equipment operating parameters, and control the operation of the central air conditioner with the adjusted equipment operating parameters.

[0044] In a third aspect, the present invention provides an electronic device, including a memory, a processor, and computer instructions stored on the memory and running on the processor. When the computer instructions are run by the processor, the method described in the first aspect is completed.

[0045] In a fourth aspect, the present invention provides a computer-readable storage medium for storing computer instructions. When the computer instructions are executed by a processor, the method described in the first aspect is completed.

[0046] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0047] The present invention proposes a two-stage stochastic robust optimization method based on a rolling horizon to achieve energy-saving optimization of the central air conditioner in large buildings. Among them, in the first stage, the operating parameters of the internal equipment of the central air conditioner are optimized according to the environmental temperature in the next few hours and the personnel information at the initial moment of each hour, and in the second stage, the equipment operating parameters determined at the previous moment are optimized and adjusted at equal time intervals within each hour, so as to achieve the balance between human thermal comfort and the energy consumption of the central air conditioner.

[0048] The present invention proposes a central air conditioner energy-saving optimization method and system based on two-stage stochastic robustness, which optimizes and adjusts the operating parameters of the internal equipment of the central air conditioner according to different time scales of random factors, and realizes the uncertain energy-saving optimization of the central air conditioner in large buildings under the condition of meeting the user's thermal comfort requirements.

[0049] The present invention divides the terminal load of the central air conditioner into objective thermal comfort load and subjective thermal comfort load, which more accurately depicts the user's thermal comfort requirements; at the same time, an uncertainty set is used to characterize the uncertainty of the objective thermal comfort load and the subjective thermal comfort load on the hour time scale; a Markov chain is used to characterize the uncertainty of the subjective thermal comfort load on the minute time scale.

[0050] The advantages of the additional aspects of the present invention will be partially given in the following description, partially will become obvious from the following description, or will be understood through the practice of the present invention. Description of the Drawings

[0051] The accompanying drawings forming a part of this invention are used to provide a further understanding of the invention. The schematic embodiments and descriptions thereof of the invention are used to explain the invention and do not unduly limit the invention.

[0052] Figure 1 It is a flowchart of a two-stage stochastic robust optimization method for central air-conditioning energy-saving optimization provided in Embodiment 1 of the present invention;

[0053] Figure 2 It is a schematic diagram of the operation energy consumption model of the internal equipment of the central air conditioner provided in Embodiment 1 of the present invention. Detailed implementation manners

[0054] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0055] It should be noted that the following detailed descriptions are all exemplary and are intended to provide further descriptions of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0056] It should be noted that the terms used herein are only for describing specific implementation manners and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those clearly listed steps or units, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0057] In the case of no conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other.

[0058] Embodiment 1

[0059] This embodiment provides a two-stage stochastic robust optimization method for central air-conditioning energy-saving optimization, as Figure 1 shown, including:

[0060] Dividing the terminal load of the central air conditioner into an objective thermal comfort load based on the ambient temperature and a subjective thermal comfort load based on personnel information;

[0061] Taking the minimization of the operation energy consumption of the central air conditioner within the rolling time domain as the objective function, a two-stage stochastic robust optimization scheduling model is constructed;

[0062] In the first stage, the optimal equipment operating parameters that meet the objective thermal comfort load and the corresponding subjective thermal comfort load are determined based on the ambient temperature and occupant information, with the goal of minimizing the central air conditioning's operating energy consumption on an hourly time scale.

[0063] In the second stage, the time scale is minutes, and the goal is to minimize the change in the central air-conditioning's operating energy consumption on that time scale. The equipment operating parameter adjustments are determined based on the transfer probability distribution obeyed by the subjective thermal comfort load.

[0064] The optimal equipment operating parameters are adjusted according to the equipment operating parameter adjustment amount, and the operation of the central air conditioner is controlled with the adjusted equipment operating parameters.

[0065] In this embodiment, based on the thermal comfort needs of the human body under normal and random activities, the terminal load of the central air conditioner is divided into an objective thermal comfort load based on ambient temperature and a subjective thermal comfort load based on occupant information. Specifically:

[0066] (1) Objective thermal comfort load Q o , that is, in response to the slow changes in the external ambient temperature, the central air conditioner outputs the cooling / heating load to compensate for the heat dissipation of the building and meet the thermal comfort needs of the human body in the area under normal conditions;

[0067] (2) Subjective thermal comfort load Q s That is, in response to the population density, randomness of personnel activities and differences in human body temperature subjective perception in the area, the central air-conditioning adjusts the changing load based on the objective thermal comfort load in order to meet the personnel's additional personalized comfort needs for the thermal environment.

[0068] In this embodiment, the ambient temperature T out The time scale for uncertainty realization is based on hours (the following embodiment takes 1 hour as an example);

[0069] According to the heat dissipation structure and functional characteristics of large buildings, the objective thermal comfort load Q at the initial moment of each hour is obtained. o and the thermal comfort temperature T in the normal state in the area set , external ambient temperature T out The relationship between them is:

[0070]

[0071] Where: σ bul , S bul is the building heat transfer coefficient and heat dissipation area; λ is the correction coefficient; t i,0 is the initial time of the i-th hour.

[0072] In this embodiment, the time scale for realizing the uncertainties of crowd density D, personnel activity B, and subjective difference F in human body temperature is in minutes (hereinafter, this embodiment takes 15 minutes as an example);

[0073] According to the historical information of personnel flow in large buildings and the research on the differences in human thermal comfort, the joint probability distribution P s (D, B, F) and the subjective thermal comfort load Q at the initial moment of every 15·j minutes per hour s The relationship with D, B, and F is:

[0074]

[0075]

[0076] In the formula: The crowd density D is represented by the number of people per unit volume in the area; the personnel activity B is represented by the heat exchange power of the human body under behaviors such as sitting still, standing, walking, and exercising; the subjective difference F in human body temperature is represented by the evaluation scores of heat sensations such as hot, warm, slightly warm, moderate, slightly cool, cool, and cold; α1, α2, α3 are normalization proportionality coefficients; ρ air , C air are the air density and specific heat capacity; v in is the air supply flow rate; t i,j represents the initial moment of 15·j minutes in the i-th hour.

[0077] In this embodiment, obtain the predicted values of T t , D, B, and F at the initial moments of the next N out hours, and respectively obtain the predicted value of the objective thermal comfort load and the predicted value of the subjective thermal comfort load

[0078] Transfer the uncertainty of T out to the objective thermal comfort load, and transfer the uncertainties of D, B, and F to the subjective thermal comfort load, so as to construct the corresponding uncertainty sets of the objective thermal comfort load and the subjective thermal comfort load:

[0079]

[0080]

[0081] In the formula: f(·) can be expressed as one of the structures such as a box uncertainty set, a polyhedron uncertainty set, and an ellipsoid uncertainty set; Γ o,max , Γ o,min are the upper and lower limits of the robust boundary value of the objective thermal comfort load uncertainty set; Γ s,max , Γ s,minare the upper and lower limits of the robust boundary value of the subjective thermal comfort load uncertainty set.

[0082] In the next N t hours at time t i+h,j the uncertainties of D, B, and F are transferred to the subjective thermal comfort load, making it follow the probability distribution P s so as to use the Markov chain to characterize its dynamic changes; therefore, the subjective thermal comfort load starting from time t i+h,0 has a j-step transition probability of:

[0083]

[0084] where: a m is a possible value of the subjective thermal comfort load; the subjective thermal comfort load follows the transition probability distribution P cs .

[0085] In this embodiment, as Figure 2 shown, an operating energy consumption model of the internal equipment of the central air conditioner is constructed, including fan coils, chilled water pumps, refrigeration units, cooling water pumps, and cooling towers, and satisfying the operating parameter limit constraints and power balance constraints;

[0086] Among them, (1) the operating model of the internal equipment of the central air conditioner is characterized by the heat energy transfer process between the internal equipment through the heat transfer medium and the law of conservation of energy:

[0087]

[0088] Q ch = ρ f C f v f (T f,h - T f,g )

[0089]

[0090] where: Q ac is the terminal load of the central air conditioner; Q ch , P ch,dis are the refrigerating capacity and energy consumption of the refrigeration unit; v w , T w are the air flow rate and temperature in the fan coil; T f,g , T f,h are the chilled water supply temperature and return water temperature; T c,g , T c,h are the cooling water supply temperature and return water temperature; v f , v care the flow rates of chilled water and cooling water; v ct , T ct are the flow rate and temperature of air in the cooling tower's heat dissipation fan; ρ f , C f are the density and specific heat capacity of chilled water; ρ c , C c are the density and specific heat capacity of cooling water; δ1, δ2, δ3, ζ1, ζ2, ζ3 are performance coefficients.

[0091] (2) The energy consumption model is characterized by the relationship between the electrical energy consumed by internal equipment and its operating parameters:

[0092] P dis = P w,dis + P f,dis + P ch,dis + P c,dis + P ct,dis

[0093]

[0094]

[0095]

[0096]

[0097]

[0098] In the formula: P dis is the energy consumption of the central air conditioner; P w,dis , P f,dis , P ch,dis , P c,dis , P ct,dis are the energy consumptions of fan coil units, chilled water pumps, refrigeration units, cooling water pumps, and cooling towers; ω w , v w,e are the speed ratio and rated flow rate of the fan coil unit; ω f , v f,e are the speed ratio and rated flow rate of the chilled water pump; Q ch,e is the rated refrigeration capacity of the refrigeration unit; v c,e , P c,e are the rated flow rate and rated power of the cooling water pump; v ct,e , P ct,e are the rated flow rate and rated power of the cooling tower's heat dissipation fan.

[0099] (3) The constraints on the operating parameter limits are:

[0100] k eq,min ≤ k eq ≤ k eq,max

[0101] Where: k eq = [N w , N f , N ch , N c , N ct , v w , v f , v c , v ct , T f,g , T f,h , T c,g , T c,h , ω w , ω f ; N w , N f , N ch , N c , N ct are the operating numbers of fan - coil units, chilled - water pumps, refrigeration units, cooling - water pumps, and cooling towers; k eq,max , k eq,min are the upper and lower limits of the operating parameter vectors of each component.

[0102] (4) The power - balance constraint is: at time t i,j the central - air - conditioning terminal load at time t i,0 is the objective thermal - comfort load at time t i,j plus the subjective thermal - comfort load at time t:

[0103]

[0104] In this embodiment, taking the minimization of the operating energy consumption of the central - air - conditioning system within the rolling horizon N t as the objective function, a two - stage stochastic robust optimization scheduling model is constructed;

[0105] The objective function is:

[0106]

[0107]

[0108]

[0109] Where: is the first - stage decision vector, representing the operating parameters of the internal equipment of the central - air - conditioning system at time t i+h,0 within the rolling horizon; is the second - stage decision vector, representing the adjustment amount of the operating parameters of the internal equipment of the central - air - conditioning system at time t i+h,j within the rolling horizon; The energy consumption of the central air conditioner determined at time t within the rolling time domain i+h,0 for meeting the objective thermal comfort load and the corresponding subjective thermal comfort load in the next 1 hour. The energy consumption of the central air conditioner determined at time t within the rolling time domain i+h,j for the change in energy consumption caused by adjusting the equipment operation parameters to meet the corresponding subjective thermal comfort load in the next 15 minutes.

[0110] In the two-stage stochastic robust optimization scheduling model, in addition to the parameter adjustment limit constraints presented in the objective function, the constraint conditions also include the central air conditioner operation model, operation parameter limit constraints, power balance constraints, uncertainty set, and conditional probability expectation.

[0111] In the two-stage stochastic robust optimization scheduling model, max seeks to determine in the uncertainty set the objective thermal comfort load that realizes the uncertainty in 1 hour and the subjective thermal comfort load to maximize the total energy consumption of the central air conditioner within the rolling time domain, thereby ensuring the basic thermal comfort of users.

[0112] Based on max, min seeks to determine, in the first stage, according to the uncertainty set, the optimal equipment operation parameters at time t i+h,0 for meeting the objective thermal comfort load and the corresponding subjective thermal comfort load, so as to minimize the operation energy consumption of the central air conditioner in the (i + h)-th hour;

[0113] In the second stage, on a minute time scale, according to the transition probability distribution P cs determine the equipment operation parameter adjustment amount at time t i+h,j to minimize the energy consumption change value caused by adjusting the equipment operation parameters of the central air conditioner in the (i + h)-th hour, thereby ensuring the personalized thermal comfort of users with less energy consumption;

[0114] Finally, take the decision vector in the first hour of the rolling time domain as the optimization scheduling result within the current hour:

[0115]

[0116] Embodiment 2

[0117] This embodiment provides a two-stage stochastic robust optimization central air conditioner energy-saving optimization system, including:

[0118] A load division module, configured to divide the terminal load of the central air conditioner into an objective thermal comfort load based on the ambient temperature and a subjective thermal comfort load based on personnel information;

[0119] A model construction module, configured to construct a two-stage stochastic robust optimization scheduling model with the objective of minimizing the operating energy consumption of a central air conditioner within a rolling time domain;

[0120] In the first stage, with an hour as the time scale, aiming at minimizing the operating energy consumption of the central air conditioner at the hourly time scale, the optimal equipment operating parameters for meeting the objective thermal comfort load and the corresponding subjective thermal comfort load are determined according to the ambient temperature and personnel information;

[0121] In the second stage, with a minute as the time scale, aiming at minimizing the change value of the operating energy consumption of the central air conditioner at the minute time scale, the adjustment amount of the equipment operating parameters is determined according to the transition probability distribution followed by the subjective thermal comfort load;

[0122] A control module, configured to adjust the optimal equipment operating parameters according to the adjustment amount of the equipment operating parameters, and control the actions of the central air conditioner with the adjusted equipment operating parameters.

[0123] It should be noted here that the above modules correspond to the steps described in Embodiment 1. The examples and application scenarios implemented by the above modules and the corresponding steps are the same, but are not limited to the content disclosed in the above Embodiment 1. It should be noted that the above modules, as part of the system, can be executed in a computer system such as a set of computer-executable instructions.

[0124] In more embodiments, there is also provided:

[0125] An electronic device, including a memory, a processor, and computer instructions stored on the memory and running on the processor. When the computer instructions are run by the processor, the method described in Embodiment 1 is completed. For the sake of brevity, it will not be elaborated here.

[0126] It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general-purpose processors, digital signal processors DSP, application-specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA, or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0127] The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A part of the memory may also include a non-volatile random memory. For example, the memory may also store information about the device type.

[0128] A computer-readable storage medium, used to store computer instructions. When the computer instructions are executed by the processor, the method described in Embodiment 1 is completed.

[0129] The method in Embodiment 1 can be directly implemented by a hardware processor, or by a combination of hardware and software modules in the processor. The software module can be located in a mature storage medium in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, or electrically erasable programmable memory, registers, etc. This storage medium is located in the memory, and the processor reads the information in the memory and combines its hardware to complete the steps of the above method. To avoid repetition, it will not be described in detail here.

[0130] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with this embodiment can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0131] Although the specific implementation manners of the present invention have been described above in conjunction with the accompanying drawings, it is not a limitation on the protection scope of the present invention. Those skilled in the art should understand that, based on the technical solution of the present invention, various modifications or deformations that can be made by those skilled in the art without creative efforts are still within the protection scope of the present invention.

Claims

1. A two-stage stochastic robust optimization method for central air-conditioning energy-saving optimization, characterized in that Including: Dividing the terminal load of the central air conditioner into an objective thermal comfort load based on ambient temperature and a subjective thermal comfort load based on personnel information; Constructing a two-stage stochastic robust optimization scheduling model with the objective function of minimizing the operating energy consumption of the central air conditioner within a rolling time domain; Among them, in the first stage, with an hour as the time scale and the objective of minimizing the operating energy consumption of the central air conditioner at the hourly time scale, the optimal operating parameters of the equipment to meet the objective thermal comfort load and the corresponding subjective thermal comfort load are determined according to the ambient temperature and personnel information; In the second stage, with a minute as the time scale and the objective of minimizing the change value of the operating energy consumption of the central air conditioner at the minute time scale, the adjustment amount of the equipment operating parameters is determined according to the transition probability distribution followed by the subjective thermal comfort load; Adjusting the optimal equipment operating parameters according to the adjustment amount of the equipment operating parameters, and controlling the operation of the central air conditioner with the adjusted equipment operating parameters; The objective thermal comfort load is the cooling / heating load output by the central air conditioner to compensate for building heat dissipation to meet the comfort requirements of the human body in the region for the thermal environment under normal conditions based on the change of ambient temperature, and an uncertainty set of the objective thermal comfort load is constructed according to the uncertainty of the ambient temperature; Objective thermal comfort load The relationship with the thermal comfort temperature under normal conditions in the area , the external environmental temperature is as follows: In the formula: , are the building heat transfer coefficient and heat dissipation area; is the correction coefficient; is the initial moment of the hour.

2. The two-stage stochastic robust optimization method for central air-conditioning energy-saving optimization according to claim 1, characterized in that, The subjective thermal comfort load is a load quantity that is adjusted and varied on the basis of the objective thermal comfort load based on the uncertainty of the population density , human activities and the difference F in the subjective perception of human body temperature; Subjective thermal comfort load and population density , human activities The relationship with the subjective difference F in human body temperature is as follows: Wherein: , , are normalization scale factors; is the air density; is the specific heat capacity; is the supply air flow rate; is the initial moment of the i th hour and the j th minute time scale, is the thermal comfort temperature.

3. A two-stage stochastic robust optimization method for central air-conditioning energy-saving optimization according to claim 1, characterized in that, The transition probability distribution followed by the subjective thermal comfort load is: In the future within hours, transfer the uncertainties of crowd density , human activities and the subjective thermal comfort load due to the difference F in subjective perception of human body temperature, making it subject to a joint probability distribution . Using a Markov chain to characterize the dynamic changes, the subjective thermal comfort load starting from time has a step transition probability as follows: In the formula: represents the possible value of the subjective thermal comfort load.

4. A two-stage stochastic robust optimization method for central air-conditioning energy-saving optimization according to any one of claims 1-3, characterized in that, The objective function of minimizing the operating energy consumption of the central air conditioner within a rolling time domain is: Wherein: is the operating parameter of the central air-conditioning equipment at the moment within the rolling time domain; is the adjustment amount of the operating parameter of the central air-conditioning equipment at the moment within the rolling time domain; is the operating energy consumption generated by meeting the objective thermal comfort load and the corresponding subjective thermal comfort load determined at the moment within the rolling time domain; is the change value of the operating energy consumption caused by adjusting the operating parameter of the equipment to meet the corresponding subjective thermal comfort load determined at the moment within the rolling time domain.

5. A central air-conditioning energy-saving optimization method based on two-stage stochastic robust optimization according to claim 1, characterized in that The process of controlling the operation of the central air conditioner includes: The internal equipment of the central air conditioner includes fan coils, chilled water pumps, refrigeration units, cooling water pumps and cooling towers, and the operation model and energy consumption model of the internal equipment of the central air conditioner are constructed accordingly; The operation model is constructed by the heat energy transfer process between internal equipment through heat transfer media and the law of conservation of energy; the energy consumption model is constructed by the relationship between the electrical energy consumed by internal equipment and its operating parameters.

6. A two-stage stochastic robust optimization method for central air-conditioning energy-saving optimization according to claim 5, characterized in that The process of controlling the operation of the central air conditioner also includes: Taking the operating parameter limit constraint and power balance constraint as constraint conditions, controlling the operation of the internal equipment of the central air conditioner according to the adjusted equipment operating parameters; The operating parameter limit constraint includes setting the upper and lower limits of the number of operating units of fan coils, chilled water pumps, refrigeration units, cooling water pumps and cooling towers; The power balance constraint is as follows: The central air-conditioning terminal load at time is the sum of the objective thermal comfort load and the subjective thermal comfort load at time 7. A central air-conditioning energy-saving optimization system based on two-stage stochastic robust optimization, characterized in that, Including: A load division module configured to divide the terminal load of the central air conditioner into an objective thermal comfort load based on ambient temperature and a subjective thermal comfort load based on personnel information; The objective thermal comfort load is the cooling / heating load output by the central air conditioner to compensate for building heat dissipation to meet the comfort requirements of the human body in the region for the thermal environment under normal conditions based on the change of ambient temperature, and an uncertainty set of the objective thermal comfort load is constructed according to the uncertainty of the ambient temperature; Objective thermal comfort load The relationship with the thermal comfort temperature under normal conditions in the area , the external environmental temperature is as follows: Wherein: , are the building heat transfer coefficient and the heat dissipation area; is the correction coefficient; is the initial moment of the th hour; A model construction module configured to construct a two-stage stochastic robust optimization scheduling model with the objective function of minimizing the operating energy consumption of the central air conditioner within a rolling time domain; Among them, in the first stage, with an hour as the time scale and the objective of minimizing the operating energy consumption of the central air conditioner at the hourly time scale, the optimal operating parameters of the equipment to meet the objective thermal comfort load and the corresponding subjective thermal comfort load are determined according to the ambient temperature and personnel information; In the second stage, taking the minute as the time scale and aiming at minimizing the change value of the operating energy consumption of the central air conditioner on the minute time scale, the adjustment amount of the equipment operating parameters is determined according to the transition probability distribution followed by the subjective thermal comfort load; A control module configured to adjust the optimal equipment operating parameters according to the adjustment amount of the equipment operating parameters and control the operation of the central air conditioner with the adjusted equipment operating parameters.

8. An electronic device, characterized in that, It includes a memory, a processor, and computer instructions stored on the memory and running on the processor. When the computer instructions are run by the processor, the method described in any one of claims 1-6 is completed.

9. A computer-readable storage medium, characterized in that, For storing computer instructions, when the computer instructions are executed by the processor, the method described in any one of claims 1-6 is completed.

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