CN111444627A - Comfort zone energy-saving optimization method based on indoor quality control model - Google Patents

Comfort zone energy-saving optimization method based on indoor quality control model Download PDF

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CN111444627A
CN111444627A CN202010275269.6A CN202010275269A CN111444627A CN 111444627 A CN111444627 A CN 111444627A CN 202010275269 A CN202010275269 A CN 202010275269A CN 111444627 A CN111444627 A CN 111444627A
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room
volume flow
temperature
humidity
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CN111444627B (en
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于军琪
张瑞
赵安军
刘奇特
解云飞
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Xian University of Architecture and Technology
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Abstract

The invention discloses a comfort zone energy-saving optimization method based on an indoor quality control model, which comprises the steps of establishing a quality and energy balance model of an air-conditioning room; establishing a mass and energy balance model of the AHU; output variables water and CO to be modeled2The content is converted into humidity and a ppmV value; determining the conveying temperature and flow constraint of the room and the AHU system according to the volume flow of the external environment of the room and the AHU system, the room input temperature, the room humidity and the pressure; valve dynamic constraint; the temperature, humidity and air quality adopt time-varying constraints; setting a reasonable comfort zone by adopting a PMV-PPD index; setting an objective function of a comfort zone optimization method and a set point tracking method based on a model, optimizing a control variable, and providing temperature, humidity and CO required when the comfort requirement is met and the energy consumption is minimum2And (4) content. The invention can greatly save energy consumption on the premise of meeting the requirement of comfort.

Description

Comfort zone energy-saving optimization method based on indoor quality control model
Technical Field
The invention belongs to the technical field of building energy conservation and building environment, and particularly relates to a comfort zone energy conservation optimization method based on an indoor quality control model.
Background
At present, accurate modeling of the environment is the basis for realizing environment optimization, and a plurality of scholars have studied on modeling of the existing building environment.
However, most of the research focuses on individual equipment and variables, such as air treatment devices, chiller, humidity and air quality, lacks comprehensive perspectives, and the models mostly dominate energy conservation, without considering mass conservation. Meanwhile, the building system is single in the aspect of an energy-saving strategy optimization method, mostly adopts a set point tracking method, and has an unsatisfactory energy-saving effect.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide a comfort zone energy-saving optimization method based on an indoor quality control model, which is to establish a comprehensive model combining energy and mass balance and establish a comfort zone optimization method based on the model, aiming at the defects in the prior art. The method has ideal energy-saving effect.
The invention adopts the following technical scheme:
a comfort zone energy-saving optimization method based on an indoor quality control model comprises the following steps:
s1, establishing a mass and energy balance model of the air-conditioning room;
s2, establishing an AHU mass and energy balance model;
s3, output variables of water and CO modeling the steps S1 and S22The content is converted into humidity and a ppmV value;
s4, according to the volume flow F of the room and the external environment of the AHU systema(h) Room input temperature Tin(h) Room humidity r (h), pressure p (h) determine the transport temperature and flow constraints of the room and AHU system; valve dynamic constraint; the temperature, humidity and air quality adopt time-varying constraints;
s5, setting a reasonable comfort zone by adopting a PMV-PPD index;
s6, setting the objective function of the model-based comfort zone optimization method and the set point tracking method by taking the steps S1 to S5 as constraint conditions, optimizing the control variables, and providing the temperature, the humidity and the CO which are required when the comfort requirement is met and the energy consumption is minimum2And (4) content.
Specifically, in step S1, the quality dynamic model of the air-conditioned room is:
Figure BDA0002444542240000023
Figure BDA0002444542240000021
wherein V is the total volume of the building area; cizConcentration of i component C in roominIs the concentration of i in the inlet air; giIs the component production rate for each person; finFor inlet volume flow, FoIs the outlet volume flow, phFor number of people, i ═ 1 denotes H2O, i ═ 2 denotes CO2。
Specifically, in step S1, the energy dynamic model of the air-conditioning room is:
Figure BDA0002444542240000022
wherein c is the air heat capacity under standard conditions; t is the room temperature; w is the wall heat transfer coefficient; m is the total mass of the building area; h is time, and rho is air density under standard conditions; finIs the inlet volume flow; foIs the outlet volume flow; a is the wall area; t isaIs the ambient temperature; q is the gain of the heat equalizing quantity of people in the building; p is the room occupancy, p 1 represents the space occupied, p 0 represents the space unoccupied, phIs the number of people.
Specifically, in step S2, the quality dynamic model of the AHU is:
Fo(h)+Fa(h)＝Fe(h)+Fin(h)
mim(h)＝Fin(h)Cin(h)+Fe(h)Ciz(h)-Fa(h)Cia(h)-Fo(h)Ciz(h)
wherein m isimIs the mass removal rate in AHU, and when i is 2, it meansCO of2Mass removal rate; cizIs the concentration of i in the indoor space; ciaIs the concentration of i in the ambient air.
Specifically, in step S2, the energy dynamic model of the AHU is:
Ql(h)＝mimC
Qs(h)＝Fin(h)ρcTin(h)-ρc(Fo(h)T(h)+Fa(h)Ta(h)-Fe(h)T(h))
Q＝Ql+Qs
wherein Q islIs the latent heat of increase/decrease in AHU; c is the latent heat of condensation, QsFor sensible heat, mimIs the mass removal rate in AHU, and is expressed as CO when i is 22Mass removal rate, Q is total energy, FinFor inlet volume flow, ρ is air density under standard conditions, c is air heat capacity under standard conditions, TinTemperature is input into the room, FoFor outlet volume flow, T is the room temperature, TaIs the temperature of the external environment, FaIs the volume flow of the external environment, FeIs the volume flow of the exhaust air.
Specifically, in step S3, the water content is converted into the humidity equation of
Figure BDA0002444542240000031
Figure BDA0002444542240000032
CO in air2Is converted into the value of ppmV
Figure BDA0002444542240000033
Figure BDA0002444542240000034
Wherein, C1zIs the concentration of water in the indoor space; c1sIs the saturation concentration of water; mCIs CO2A molecular weight; c2zIs indoor CO2The concentration of (c); p is the internal pressure of the building system; ruIs the universal gas constant.
Specifically, in step S4, the delivery temperature and flow rate of the AHU system are constrained as follows:
Tin,L≤Tin(h)≤Tin,H
Fo,L≤Fo(h)≤Fo,H
Fin,L≤Fin(h)≤Fin,H
Fe,L≤Fe(h)≤Fe,H
Fin,L≤Fa(h)≤Fin,H
wherein, Tin,LFor the room, the lowest value of the temperature, Tin,HInputting the maximum value of the temperature, T, for the roomin(h) Temperature is input into the room, Fo,LIs the lowest value of the volume flow of the external environment, Fo,HIs the maximum volume flow value of the external environment, Fo(h) Is the volume flow of the external environment, Fin,LIs the lowest value of the room inlet volume flow, Fin,HIs the highest value of the room inlet volume flow, Fin(h) Is the room inlet volume flow, Fe,LIs the lowest value of exhaust volume flow, Fe,HIs the maximum value of the volume flow of the exhaust air, Fe(h) For volume flow of exhaust, Fa(h) Is the external environment volume flow.
Specifically, in step S4, the dynamic constraint of the valve is:
Figure BDA0002444542240000041
Figure BDA0002444542240000042
Figure BDA0002444542240000043
Figure BDA0002444542240000044
wherein, Δ Fin,HFor the room inlet volume flow variation, Δ Fo,HIs the room outlet volume flow variation, Δ Fa,HIs the volume flow change of the external environment, delta Fe,HIs the maximum value of the volume flow of the exhaust air, Fin(h) Is the room inlet volume flow, Fo(h) Is the room outlet volume flow, Fa(h) Is the volume flow of the external environment, Fe(h) Is the volume flow of the exhaust air.
Specifically, in step S4, the temperature, humidity and air quality are constrained to be time-varying as follows:
TLp(h)+(1-p(h))Ts,L≤T(h)≤THp(h)+(1-p(h))Ts,H
RLp(h)+(1-p(h))Rs,L≤R(h)≤RHp(h)+(1-p(h))Rs,H
ppmV2(h)≤ppmV2,Hp(h)+(1-p(h))ppmVs,2
wherein, TLIs the lowest value of the indoor temperature, Ts,LIs the minimum indoor temperature, T, under the set point strategyHIs the maximum value of the indoor temperature, Ts,HFor maximum indoor temperature under setpoint strategy, RLIs the lowest value of indoor humidity, Rs,LFor minimum indoor humidity under set point strategy, RHIs the highest value of indoor humidity, Rs,HFor maximum indoor humidity under set point strategy, ppmV2(h) Is indoor CO2Content, ppmV2,HIs indoor CO2Maximum content value, ppmVs,2For indoor CO under setpoint policy2And (4) content.
Specifically, in step S6, the objective function of the point tracking method is specifically:
Figure BDA0002444542240000051
wherein T is the indoor temperature, TsTo be provided withIndoor temperature under setpoint strategy, R (h) is indoor humidity, Rs(h) For indoor humidity under set point strategy, P (h) is indoor pressure, Ps(h) For indoor pressure under set point strategy, ppmV2(h) Is indoor CO2Content, ppmVs,2(h) For indoor CO under setpoint policy2And (4) content.
Compared with the prior art, the invention has at least the following beneficial effects:
the invention relates to a comfortable area energy-saving optimization method based on an indoor quality control model.
Furthermore, a comprehensive mathematical model of the building indoor and the air conditioning system is established, a foundation is provided for indoor optimization and control of the building, and the comfort zone optimization method based on the model can greatly save energy consumption under the condition of meeting the comfort degree.
Furthermore, the quality relation of each moment among all variables of the air-conditioning room is expressed through a quality dynamic model of the air-conditioning room, and the completeness and the accuracy of the model are improved.
Further, the basic energy input and output relation among the air-conditioning room variables at each moment is expressed through an energy dynamic model of the air-conditioning room.
Furthermore, the quality relation of each moment among all variables in the AHU is expressed through a quality dynamic model of the AHU, and the completeness and the accuracy of the model are improved.
Furthermore, the energy relation of each moment among all variables in the AHU is expressed through an energy dynamic model of the AHU.
Further, CO is converted into a common expression amount by converting the water content2The concentration is expressed as a more easily recognizable quantity, making the output more intuitive.
Further, the delivery temperature and flow through the AHU system are constrained to ensure performance and comfort of the equipment.
Furthermore, the valve is used for controlling the air supply quantity and plays an important role in the heat circulation of the system.
Furthermore, time-varying constraints rather than fixed values are adopted for three variables of temperature, humidity and air quality, so that energy consumption can be saved to a greater extent.
In conclusion, the comprehensive mathematical dynamic model of the building indoor and air conditioning system is established, a foundation is laid for indoor environment optimization control, and the comfort zone optimization method provided on the basis of the model can greatly save energy and consume energy on the premise of meeting the comfort.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
FIG. 1 is a simplified HVAC system schematic;
FIG. 2 is a graph of outdoor temperature and humidity for model validation;
FIG. 3 is a comparison graph of the model calculation result and the actual result, wherein (a) is a comparison graph of the indoor temperature and the model calculation temperature, and (b) is indoor ppmV2Calculating a temperature comparison graph with the model, (c) calculating a humidity comparison graph for the indoor humidity and the model;
FIG. 4 is a diagram of summer conditions, wherein (a) is outdoor temperature and (b) is outdoor humidity;
FIG. 5 is a graph showing the results of set point tracking strategy in summer, wherein (a) is a graph showing the change in indoor temperature, (b) is a graph showing the change in indoor humidity, (c) is a graph showing the change in indoor ppmV2 content, and (d) is a graph showing the results of energy consumption change;
FIG. 6 is a diagram showing the result of an optimization strategy for a comfort zone in summer, wherein (a) is a diagram showing the result of indoor temperature variation, (b) is a diagram showing indoor humidity variation, (c) is a diagram showing the variation of indoor ppmV2 content, and (d) is a diagram showing the result of energy consumption variation;
FIG. 7 is a diagram of winter conditions, where (a) is outdoor temperature and (b) is outdoor humidity;
FIG. 8 is a graph showing the results of the setpoint tracking strategy in winter conditions, wherein (a) is a graph showing the results of indoor temperature changes, (b) is a graph showing the results of indoor humidity changes, and (c) is indoor ppmV2The content change diagram, (d) is the energy consumption change result diagram;
FIG. 9 is a diagram showing the result of the optimization strategy of the comfort zone in winter, wherein (a) is a diagram showing the result of the change of the indoor temperature, (b) is a diagram showing the result of the change of the indoor humidity, and (c) is a diagram showing the result of the change of the ppmV2The content change diagram, (d) is the energy consumption change result diagram;
fig. 10 is a graph comparing energy consumption.
Detailed Description
The invention provides a comfort zone energy-saving optimization method based on an indoor quality control model.
Referring to fig. 1, the building system of the comfort zone energy-saving optimization method based on the indoor quality control model of the present invention includes an air-conditioned room and an AHU, and adopts a mixed ventilation mode, and the output variables are temperature, humidity, and CO2The method comprises the following specific steps:
s1, establishing a mass and energy balance model of the air-conditioning room
S101, quality dynamic model of air-conditioning room
Figure BDA0002444542240000071
Figure BDA0002444542240000081
Wherein V is the total volume of the building area, m3；CizConcentration of i component in room, g/m3；CinIs the concentration of i in the inlet air, g/m3；giIs the component production rate per person, g/h; i-1 represents H2O, i ═ 2 denotes CO2；
S102, energy dynamic model of air-conditioning room
Figure BDA0002444542240000082
Wherein c is the air heat capacity under standard conditions, kJ/(g.K); t is the room temperature, K; w is the wall heat transfer coefficient kJ/(h.K.m)2) (ii) a m is the total mass of the building area, g; h is time, rho is air density under standard conditions, g/m3；FinIs the inlet volume flow, m3/h；FoIs the outlet volume flow; a is the wall area, m2；TaIs the ambient temperature; q is the gain of the heat equalizing capacity of people in the building,kJ/h; p is the room occupancy, with p 1 representing the space occupied, and conversely p 0, i.e. the space is unoccupied, phIs the number of people;
s2, establishing a mass and energy balance model of an AHU (Air handling unit)
S201 and AHU quality dynamic model
Fo(h)+Fa(h)＝Fe(h)+Fin(h) (4)
mim(h)＝Fin(h)Cin(h)+Fe(h)Ciz(h)-Fa(h)Cia(h)-Fo(h)Ciz(h) (5)
Wherein m isimIs the mass removal rate in AHU, and is expressed as CO when i is 22Mass removal rate, here 0; cizIs the concentration of i in the room space, g/m3；CiaIs the concentration of i in the ambient air, g/m3；
S202, energy dynamic model of AHU
Ql(h)＝mimh (6)
Qs(h)＝Fin(h)ρcTin(h)-ρc(Fo(h)T(h)+Fa(h)Ta(h)-Fe(h)T(h)) (7)
Q＝Ql+Qs(8)
Wherein Q islIs the latent heat of increase/decrease in AHU, kJ/h; h is the latent heat of condensation, kJ/g;
s3, mixing the water and CO as the output variables of the steps S1 and S22Conversion of the content into humidity and ppmV values
S301, converting the water content into a humidity equation
Figure BDA0002444542240000091
Figure BDA0002444542240000092
S302CO in air2Is converted into the value of ppmV
Figure BDA0002444542240000093
Figure BDA0002444542240000094
Wherein, C1zIs the concentration of water in the room space, g/m3；C1sIs the saturation concentration of water, g/m3；MCIs CO2Molecular weight, g/mol; c2zIs indoor CO2Concentration of (2), g/m3(ii) a P is the building system internal pressure, atm; ruIs a general gas constant, atm · m3/(gmol·K)；
S4, analyzing the key freedom degree and necessary constraint condition of the room and AHU system
S401, key degree of freedom of room and AHU system
Ambient volume flow Fa(h) Room input temperature Tin(h) Room humidity r (h), pressure p (h);
s402, necessary constraints of room and AHU system
AHU delivery temperature and flow constraints; dynamic constraint of the valve; temperature, humidity and air quality employ time varying constraints.
S4021 and AHU transport temperature and flow restriction
Tin,L≤Tin(h)≤Tin,H(13)
Fo,L≤Fo(h)≤Fo,H(14)
Fin,L≤Fin(h)≤Fin,H(15)
Fe,L≤Fe(h)≤Fe,H(16)
Fin,L≤Fa(h)≤Fin,H(17)
Wherein, Tin,LFor the room, the lowest value of the temperature, Tin,HInputting the maximum value of the temperature, T, for the roomin(h) Temperature is input into the room, Fo,LIs the lowest value of the volume flow of the external environment, Fo,HIs the maximum volume flow value of the external environment, Fo(h) Is the volume flow of the external environment, Fin,LIs the lowest value of the room inlet volume flow, Fin,HIs the highest value of the room inlet volume flow, Fin(h) Is the room inlet volume flow, Fe,LIs the lowest value of exhaust volume flow, Fe,HIs the maximum value of the volume flow of the exhaust air, Fe(h) For volume flow of exhaust, Fa(h) The volume flow of the external environment;
s4022 dynamic constraint of valve
Figure BDA0002444542240000101
Figure BDA0002444542240000102
Figure BDA0002444542240000103
Figure BDA0002444542240000104
Wherein, Δ Fin,HFor the room inlet volume flow variation, Δ Fo,HIs the room outlet volume flow variation, Δ Fa,HIs the volume flow change of the external environment, delta Fe,HIs the maximum value of the volume flow of the exhaust air, Fin(h) Is the room inlet volume flow, Fo(h) Is the room outlet volume flow, Fa(h) Is the volume flow of the external environment, Fe(h) Is the volume flow of the exhaust air;
s4023, temperature, humidity and air quality Using time-varying constraints
TLp(h)+(1-p(h))Ts,L≤T(h)≤THp(h)+(1-p(h))Ts,H(22)
RLp(h)+(1-p(h))Rs,L≤R(h)≤RHp(h)+(1-p(h))Rs,H(23)
ppmV2(h)≤ppmV2,Hp(h)+(1-p(h))ppmVs,2(24)
Wherein, TLIs the lowest value of the indoor temperature, Ts,LIs the minimum indoor temperature, T, under the set point strategyHIs the maximum value of the indoor temperature, Ts,HFor maximum indoor temperature under setpoint strategy, RLIs the lowest value of indoor humidity, Rs,LFor minimum indoor humidity under set point strategy, RHIs the highest value of indoor humidity, Rs,HFor maximum indoor humidity under set point strategy, ppmV2(h) Is indoor CO2Content, ppmV2,HIs indoor CO2Maximum content value, ppmVs,2For indoor CO under setpoint policy2Content (c);
s5, setting reasonable comfort zone by adopting PMV-PPD index
And selecting a group of proper temperature, humidity, pressure and ventilation rate according to the existing experience and research, and further verifying whether a reasonable comfort zone is reached or not through a PMV-PPD index.
And S6, setting an objective function of a model-based comfort zone optimization method and a set point tracking method, and optimizing the control variables.
S601, comfort zone optimization method objective function based on model
Figure BDA0002444542240000111
Wherein Q (h) is total energy consumption, dh is time integral, τ is a certain time, and t is a period of time increased on the basis of the certain time;
s602, setting a target function of a point tracking method;
the method specifically comprises the following steps:
Figure BDA0002444542240000112
wherein T is the indoor temperature, TsFor indoor temperature under setpoint strategy, R (h) is indoor humidity, Rs(h) To be provided withIndoor humidity under setpoint strategy, P (h) is indoor pressure, Ps(h) For indoor pressure under set point strategy, ppmV2(h) Is indoor CO2Content, ppmVs,2(h) For indoor CO under setpoint policy2Content (c);
in order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 2, the detailed verification includes verification of the established model and verification of energy saving of the model-based comfort zone optimization method.
A laboratory was selected for model validation and humidity and temperature on a day of 7 months were selected as inputs to the overall system, as shown in figure 3. After the air conditioner in the building is turned on, the temperature is set at 20 ℃, the humidity is set at 60 percent, ppmV2Set as 500, 200 persons in the room, 3000m in volume3In working hours, namely 9:00-18:00 experiments were performed.
FIG. 3 shows the actual indoor temperature, ppmV2Indoor temperature and ppmV calculated by humidity and model2And a humidity contrast plot. As can be seen from fig. 3a, when the temperature of the air conditioner is set to 20 ℃, both curves fluctuate around the set value, the difference is small, and the average relative error is 2.2%; and in FIG. 3b, ppmV2The difference between the calculated model and the actual value is large, and the average relative error is 5.2 percent, mainly caused by the initiative of people; a comparison of humidity, FIG. 3c, can also be seen indoorsUnder the given condition of the environment, the model calculation is basically consistent with the actual condition, and the average relative error is 2.0 percent. From this the accuracy of the model proposed herein can be demonstrated.
Verification of the comfort zone optimization method. Still a laboratory with 200 persons in the laboratory and 3000m volume3. Experiments were performed at work hours, i.e., 9:00-18: 00. To demonstrate the general applicability of this strategy, outdoor temperature and humidity were chosen as external input conditions for 71 days each in winter and summer. To ensure real-time performance, the data is discretized into steps per hour, and the predicted time is one day, i.e., 24 hours.
The outdoor temperature and humidity conditions in summer are shown in fig. 4(a) (b), respectively.
Setting the idle time temperature to be limited to 10-30 ℃ when calculating the energy consumption time under the set point tracking method, wherein the occupied time temperature set point is 23 ℃, the humidity is kept at 50%, the pressure is 1atm, and CO is2The value was kept at 800 ppmV. The results are shown in FIG. 5.
As shown in FIG. 5(a), it can be seen from the graph that the indoor temperature changes with the trend of the outdoor temperature in the idle time period from the early 0 point to the early 9 points and from the late 6 points to the late 0 point, but does not exceed the limit of 10-30 ℃, whereas the temperature is maintained at 23 ℃ in the occupied time period from the early 9 points to the late 6 points, mainly embodied in the temperature for maintaining the occupied time, and the humidity and CO are present2The same as the set value, the power consumption situation is shown in fig. 5 (d).
Under the optimization strategy of a comfortable area, the comfortable temperature range is 23-27 ℃, the comfortable humidity range is 40-70%, the temperature of the idle time is still 10-30 ℃, and the humidity is widened to 30-80%. With CO2The concentration was maintained within 800ppmV, and the results are shown in FIG. 6.
Fig. 6(a) is an indoor temperature variation graph of the comfort zone optimization strategy under summer working conditions, and it can be seen from the graph that the indoor temperature still varies with the variation trend of the external temperature during idle time, and the temperature varies between 23 ℃ and 27 ℃ during occupied time. The humidity is relatively high in summer, and it can be seen from fig. 6(b) that the humidity is changed within the set range during the occupied time, and is idleThe humidity of the time is not higher than 80%. CO22The content varies regularly with the occupation time and the idle time, but does not exceed the set value, namely 800ppmV2. Fig. 6(d) is a diagram of energy consumption results of the summer comfort zone optimization strategy, and it can be seen from the diagram that energy consumption will often be 0, which illustrates that the comfort zone optimization strategy greatly reduces energy usage.
The outdoor temperature and humidity conditions under winter conditions are shown in fig. 7(a) (b), respectively. It can be seen that the temperature often becomes below 0 c, and the humidity is not too low because the time period taken for the experiment is the frequent period of snowing.
The parameter settings of the winter condition setpoint tracking strategy are the same as those of the summer condition in the upper section, the results of which are shown in fig. 8.
It can be seen from fig. 8(a) that in the winter condition, the idle period system temperature is 10 ℃, and the occupied period indoor temperature is substantially maintained at 23 ℃. Humidity and CO2The content is always maintained at the set value, and under the condition, the energy consumption result is shown in fig. 8(d), the energy consumption is concentrated, and the difference between the occupied time and the idle time is small, because the outdoor temperature in the idle time in winter is generally lower than the minimum limit temperature of 10 ℃, and at the moment, a system consumes a large amount of energy in order to keep the temperature within the set range.
The basic parameters of the comfort zone optimization strategy under the winter condition are the same as those of the comfort zone optimization strategy under the upper summer condition, and the result is shown in fig. 9.
Fig. 9(a) is a temperature change diagram of a winter comfort zone strategy, and it can be known from the diagram that the temperature is 10 ℃ during the idle time in winter, the occupied time is changed between 23 ℃ and 27 ℃, but the change frequency is not frequent, because the outside temperature in winter is low, when the room temperature reaches 23 ℃, the room temperature can be maintained by slightly increasing the ventilation, and during the idle time, the temperature can not be guaranteed to be higher than or equal to 10 ℃ only by ventilation in most cases, and the energy consumption in winter is higher than that in summer due to the fact that the system is required to adjust. Meanwhile, as can be seen from fig. 9(b) and (c), the occupied time humidity is between 40% and 70%, the idle time does not exceed 80%, and the CO content is2The content occupation time is 800, and the idle time is kept atWithin the range. During the course of the day, the building system can maintain the indoor comfort zone requirements by reducing or increasing ventilation, as can be seen from fig. 9(d), in which case the energy consumption will often become 0, which will greatly reduce the energy usage.
As shown in fig. 10, compared to the conventional set point tracking method, the comfort zone optimization method can save energy by about 70% or more in both summer and winter conditions.
In conclusion, the comfort zone energy-saving optimization method based on the indoor quality control model establishes a model combining quality and energy balance for the indoor building and the air processing unit system, and verifies the model by taking the Yi Fu building laboratory of the university of science and technology of Xian building as an object, so that the model is reliable.
In order to reduce the technical complexity and cost, analyzing the key freedom degree in the strategy and the necessary constraint condition for ensuring the system to operate correctly; in order to verify the general feasibility of the optimization strategy based on the comfort zone, the traditional set point tracking and the comfort zone optimization strategies are respectively carried out on working conditions in summer and winter to optimize the energy consumption target, and the result shows that the comfort zone optimization strategy can greatly save the energy consumption and provide a certain range for the temperature compared with the traditional set point tracking method in which other degrees of freedom keep constant values under the condition of meeting the comfort requirement, the method for meeting the comfort range is provided by all the degrees of freedom of the comfort zone optimization, and the energy consumption saving is mainly embodied in two aspects:
firstly, the temperature, the humidity and the CO2 content fluctuate within a range in the whole day time, so that the operation frequency of the air conditioner can be reduced;
the second is that the variable ventilation rate in the comfort zone optimization method enables the system to enhance its control flexibility, i.e. the increase or decrease of temperature, humidity and CO2 content can be achieved by increasing or decreasing the ventilation, especially during the winter idle time, the ventilation can be decreased to bring the temperature to the set value, reducing the energy consumption.
And in summer, more comfortable areas are used, so that the operation times of the air conditioning system are reduced, and the use of energy is reduced. The ventilation rate as a key degree of freedom plays an important role in reducing energy consumption, and meanwhile, the energy consumption can be greatly reduced by setting the range of other key degrees of freedom instead of using the traditional fixed value. Thereby proving that a comfort zone optimization strategy based on this model is feasible and effective.
The above-mentioned contents are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modification made on the basis of the technical idea of the present invention falls within the protection scope of the claims of the present invention.

Claims (10)

1. A comfort zone energy-saving optimization method based on an indoor quality control model is characterized by comprising the following steps:
s1, establishing a mass and energy balance model of the air-conditioning room;
s2, establishing an AHU mass and energy balance model;
s3, output variables of water and CO modeling the steps S1 and S22The content is converted into humidity and a ppmV value;
s4, according to the volume flow F of the room and the external environment of the AHU systema(h) Room input temperature Tin(h) Room humidity r (h), pressure p (h) determine the transport temperature and flow constraints of the room and AHU system; valve dynamic constraint; the temperature, humidity and air quality adopt time-varying constraints;
s5, setting a reasonable comfort zone by adopting a PMV-PPD index;
s6, setting the objective function of the model-based comfort zone optimization method and the set point tracking method by taking the steps S1 to S5 as constraint conditions, optimizing the control variables, and providing the temperature, the humidity and the CO which are required when the comfort requirement is met and the energy consumption is minimum2And (4) content.
2. The comfort zone energy-saving optimization method based on indoor quality control model according to claim 1, wherein in step S1, the quality dynamic model of the air-conditioned room is:
Figure FDA0002444542230000011
Figure FDA0002444542230000012
wherein V is the total volume of the building area; cizConcentration of i component C in roominIs the concentration of i in the inlet air; giIs the component production rate for each person; finFor inlet volume flow, FoIs the outlet volume flow, phFor number of people, i ═ 1 denotes H2O, i ═ 2 denotes CO2。
3. The comfort zone energy-saving optimization method based on indoor quality control model according to claim 1, wherein in step S1, the energy dynamic model of the air-conditioned room is:
Figure FDA0002444542230000021
wherein c is the air heat capacity under standard conditions; t is the room temperature; w is the wall heat transfer coefficient; m is the total mass of the building area; h is time, and rho is air density under standard conditions; finIs the inlet volume flow; foIs the outlet volume flow; a is the wall area; t isaIs the ambient temperature; q is the gain of the heat equalizing quantity of people in the building; p is the room occupancy, p 1 represents the space occupied, p 0 represents the space unoccupied, phIs the number of people.
4. The comfort zone energy-saving optimization method based on indoor quality control model according to claim 1, wherein in step S2, the quality dynamic model of AHU is:
Fo(h)+Fa(h)＝Fe(h)+Fin(h)
mim(h)＝Fin(h)Cin(h)+Fe(h)Ciz(h)-Fa(h)Cia(h)-Fo(h)Ciz(h)
wherein m isimIs the mass removal rate in AHU, and is expressed as CO when i is 22Mass removal rate; cizIs the concentration of i in the indoor space; ciaIs the concentration of i in the ambient air.
5. The comfort zone energy-saving optimization method based on the indoor quality control model of claim 1, wherein in step S2, the energy dynamic model of the AHU is:
Ql(h)＝mimC
Qs(h)＝Fin(h)ρcTin(h)-ρc(Fo(h)T(h)+Fa(h)Ta(h)-Fe(h)T(h))
Q＝Ql+Qs
wherein Q islIs the latent heat of increase/decrease in AHU; c is the latent heat of condensation, QsFor sensible heat, mimIs the mass removal rate in AHU, and is expressed as CO when i is 22Mass removal rate, Q is total energy, FinFor inlet volume flow, ρ is air density under standard conditions, c is air heat capacity under standard conditions, TinTemperature is input into the room, FoFor outlet volume flow, T is the room temperature, TaIs the temperature of the external environment, FaIs the volume flow of the external environment, FeIs the volume flow of the exhaust air.
6. The comfort zone energy-saving optimization method based on the indoor quality control model according to claim 1, wherein in the step S3, the water content is converted into the humidity equation as
Figure FDA0002444542230000031
Figure FDA0002444542230000032
CO in air2Is converted into the value of ppmV
Figure FDA0002444542230000033
Figure FDA0002444542230000034
Wherein, C1zIs the concentration of water in the indoor space; c1sIs the saturation concentration of water; mCIs CO2A molecular weight; c2zIs indoor CO2The concentration of (c); p is the internal pressure of the building system; ruIs the universal gas constant.
7. The comfort zone energy-saving optimization method based on the indoor quality control model according to claim 1, wherein in step S4, the delivery temperature and flow constraints of the AHU system are:
Tin,L≤Tin(h)≤Tin,H
Fo,L≤Fo(h)≤Fo,H
Fin,L≤Fin(h)≤Fin,H
Fe,L≤Fe(h)≤Fe,H
Fin,L≤Fa(h)≤Fin,H
wherein, Tin,LFor the room, the lowest value of the temperature, Tin,HInputting the maximum value of the temperature, T, for the roomin(h) Temperature is input into the room, Fo,LIs the lowest value of the volume flow of the external environment, Fo,HIs the maximum volume flow value of the external environment, Fo(h) Is the volume flow of the external environment, Fin,LIs the lowest value of the room inlet volume flow, Fin,HIs the highest value of the room inlet volume flow, Fin(h) Is the room inlet volume flow, Fe,LIs the lowest value of exhaust volume flow, Fe,HIs the maximum value of the volume flow of the exhaust air, Fe(h) For volume flow of exhaust, Fa(h) Is the external environment volume flow.
8. The comfort zone energy-saving optimization method based on indoor quality control model according to claim 1, wherein in step S4, the dynamic constraint of the valve is:
Figure FDA0002444542230000041
Figure FDA0002444542230000042
Figure FDA0002444542230000043
Figure FDA0002444542230000044
wherein, Δ Fin,HFor the room inlet volume flow variation, Δ Fo,HIs the room outlet volume flow variation, Δ Fa,HIs the volume flow change of the external environment, delta Fe,HIs the maximum value of the volume flow of the exhaust air, Fin(h) Is the room inlet volume flow, Fo(h) Is the room outlet volume flow, Fa(h) Is the volume flow of the external environment, Fe(h) Is the volume flow of the exhaust air.
9. The comfort zone energy-saving optimization method based on the indoor quality control model according to claim 1, wherein in the step S4, the temperature, humidity and air quality adopt time-varying constraints as:
TLp(h)+(1-p(h))Ts,L≤T(h)≤THp(h)+(1-p(h))Ts,H
RLp(h)+(1-p(h))Rs,L≤R(h)≤RHp(h)+(1-p(h))Rs,H
ppmV2(h)≤ppmV2,Hp(h)+(1-p(h))ppmVs,2
wherein, TLIs the lowest value of the indoor temperature, Ts,LIs the minimum indoor temperature, T, under the set point strategyHIs the maximum value of the indoor temperature, Ts,HFor maximum indoor temperature under setpoint strategy, RLIs the lowest value of indoor humidity, Rs,LFor minimum indoor humidity under set point strategy, RHIs the highest value of indoor humidity, Rs,HFor maximum indoor humidity under set point strategy, ppmV2(h) Is indoor CO2Content, ppmV2,HIs indoor CO2Maximum content value, ppmVs,2For indoor CO under setpoint policy2And (4) content.
10. The comfort zone energy-saving optimization method based on indoor quality control model according to claim 1, wherein in step S6, the objective function of the point tracking method is specifically:
Figure FDA0002444542230000051
wherein T is the indoor temperature, TsFor indoor temperature under setpoint strategy, R (h) is indoor humidity, Rs(h) For indoor humidity under set point strategy, P (h) is indoor pressure, Ps(h) For indoor pressure under set point strategy, ppmV2(h) Is indoor CO2Content, ppmVs,2(h) For indoor CO under setpoint policy2And (4) content.
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