CN111444627B - Comfortable area energy-saving optimization method based on indoor quality control model - Google Patents
Comfortable area energy-saving optimization method based on indoor quality control model Download PDFInfo
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Abstract
The invention discloses a comfort zone energy-saving optimization method based on an indoor quality control model, which is used for establishing a quality and energy balance model of an air-conditioning room; establishing an AHU mass and energy balance model; output variable water and CO to be modeled 2 The content is converted into a value of humidity and ppmV; determining the delivery temperature and flow constraint of the room and the AHU system according to the external environment volume flow of the room and the AHU system, the room input temperature, the room humidity and the pressure; dynamic constraint of the valve; temperature, humidity and air quality are constrained by time-varying conditions; adopting PMV-PPD index to set reasonable comfortable area; setting objective functions of a comfort zone optimization method and a set point tracking method based on a model, optimizing control variables, and giving temperature, humidity and CO required by the condition of meeting comfort level requirements and having minimum energy consumption 2 The content is as follows. The invention can greatly save energy consumption on the premise of meeting comfort.
Description
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 achieving environmental optimization, and many students have studied on modeling of the existing building environment.
However, most research has focused on individual equipment and variables such as air handling units, chiller units, humidity and air quality, lacking comprehensive angles, and models mostly based on conservation of energy, not taking into account conservation of mass. Meanwhile, the building system is single in the aspect of energy-saving strategy optimization methods, most of building systems are set point tracking methods, and the energy-saving effect is not ideal.
Disclosure of Invention
The invention aims to solve the technical problems in the prior art, and provides a comfort zone energy-saving optimization method based on an indoor quality control model, which is to build a comprehensive model combining energy and quality balance and a comfort zone optimization method based on the model. 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 quality and energy balance model of an air-conditioning room;
s2, establishing an AHU quality and energy balance model;
s3, modeling the steps S1 and S2 to output variable amounts of water and CO 2 The content is converted into a value of humidity and ppmV;
s4, according to the volume flow F of the room and the external environment of the AHU system a (h) Room input temperature T in (h) Room humidity R (h), pressure P (h) determines the delivery temperature and flow constraints of the room and AHU system; dynamic constraint of the valve; temperature, humidity and air quality are constrained by time-varying conditions;
s5, setting a reasonable comfort zone by adopting a PMV-PPD index;
s6, setting objective functions of a comfort zone optimization method and a set point tracking method based on a model by taking the steps S1 to S5 as constraint conditions, optimizing control variables, and giving the temperature, the humidity and the CO required by the condition of meeting comfort requirements and having minimum energy consumption 2 The content is as follows.
Specifically, in step S1, the mass dynamic model of the air-conditioning room is:
wherein V is the total volume of the building area; c (C) iz I component concentration C in room in Is the concentration of i in the inlet air; g i Is the component generation rate of each person; f (F) in For inlet volumetric flow, F o For outlet volumetric flow, p h For the number of people, i=1 denotes H 2 O, i=2 represents CO 2 。
Specifically, in step S1, the energy dynamic model of the air-conditioning room is:
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 ρ is air density under standard conditions; f (F) in Is the inlet volumetric flow rate; f (F) o For outlet volumetric flow; a is the wall area; t (T) a Is the external environment temperature; q is the gain of the heat gain of people in the building; p is the room occupancy, p=1 indicates that the space is occupied, p=0, i.e. the space is unoccupied, p h Is the number of people.
Specifically, in step S2, the mass dynamic model of the AHU is:
F o (h)+F a (h)＝F e (h)+F in (h)
m im (h)＝F in (h)C in (h)+F e (h)C iz (h)-F a (h)C ia (h)-F o (h)C iz (h)
wherein m is im Is the mass removal rate in the AHU, CO expressed when i=2 2 Mass removal rate; c (C) iz Is the concentration of i in the indoor space; c (C) ia Is the concentration of i in the ambient air.
Specifically, in step S2, the energy dynamic model of the AHU is:
Q l (h)＝m im C
Q s (h)＝F in (h)ρcT in (h)-ρc(F o (h)T(h)+F a (h)T a (h)-F e (h)T(h))
Q＝Q l +Q s
wherein Q is l Is the latent heat of increase/decrease in the AHU; c is condensationLatent heat, Q s For sensible heat, m im Is the mass removal rate in the AHU, CO expressed when i=2 2 Mass removal rate, Q is total energy, F in For inlet volume flow rate ρ is air density under standard conditions, c is air heat capacity under standard conditions, T in To input temperature for room, F o For outlet volumetric flow, T is room temperature, T a Is the outside ambient temperature, F a For the volume flow of the external environment, F e Is the volume flow of exhaust air.
Specifically, in step S3, the water content is converted into a humidity equation
CO in air 2 Is converted to ppmV value
Wherein C is 1z Is the concentration of water in the indoor space; c (C) 1s Is the saturation concentration of water; m is M C Is CO 2 Molecular weight; c (C) 2z Is indoor CO 2 Is a concentration of (2); p is the internal pressure of the building system; r is R u Is a universal gas constant.
Specifically, in step S4, the delivery temperature and flow constraints of the AHU system are:
T in,L ≤T in (h)≤T in,H
F o,L ≤F o (h)≤F o,H
F in,L ≤F in (h)≤F in,H
F e,L ≤F e (h)≤F e,H
F in,L ≤F a (h)≤F in,H
wherein T is in,L For inputting the minimum value of the temperature, T in,H Inputting the highest value of the temperature for the room, T in (h) To input temperature for room, F o,L Is the minimum value of the volume flow of the external environment, F o,H Is the highest value of the volume flow of the external environment, F o (h) For the volume flow of the external environment, F in,L Is the minimum of the room inlet volume flow, F in,H Is the highest value of the room inlet volume flow, F in (h) For room inlet volume flow, F e,L Is the lowest value of exhaust volume flow, F e,H Is the highest value of exhaust volume flow, F e (h) For volume flow of exhaust air F a (h) Is the volume flow of the external environment.
Specifically, in step S4, the dynamic constraint of the valve is:
wherein DeltaF in,H Delta F for room inlet volume flow variation o,H Delta F is the room outlet volume flow variation a,H Delta F is the change of the volume flow of the external environment e,H Is the highest value of exhaust volume flow, F in (h) For room inlet volume flowAmount of F o (h) For room outlet volume flow, F a (h) For the volume flow of the external environment, F e (h) Is the volume flow of exhaust air.
Specifically, in step S4, the temperature, humidity, and air quality are determined by time-varying constraints:
T L p(h)+(1-p(h))T s,L ≤T(h)≤T H p(h)+(1-p(h))T s,H
R L p(h)+(1-p(h))R s,L ≤R(h)≤R H p(h)+(1-p(h))R s,H
ppmV 2 (h)≤ppmV 2,H p(h)+(1-p(h))ppmV s,2
wherein T is L Is the lowest indoor temperature, T s,L T is the minimum indoor temperature under the strategy of set point H Is the highest value of indoor temperature, T s,H R is the indoor highest temperature under the strategy of the set point L R is the lowest indoor humidity value s,L R is the minimum indoor humidity under the strategy of set point H R is the highest value of indoor humidity s,H For maximum indoor humidity under setpoint strategy, ppmV 2 (h) Is indoor CO 2 Content of ppmV 2,H Is indoor CO 2 Maximum of ppmV s,2 For indoor CO under setpoint strategy 2 The content is as follows.
Specifically, in step S6, the objective function of the point tracking method is specifically:
wherein T is the indoor temperature, T s For the indoor temperature under the setpoint strategy, R (h) is the indoor humidity, R s (h) For indoor humidity under setpoint strategy, P (h) is indoor pressure, P s (h) For indoor pressure under setpoint strategy, ppmV 2 (h) Is indoor CO 2 Content of ppmV s,2 (h) For indoor CO under setpoint strategy 2 The content is as follows.
Compared with the prior art, the invention has at least the following beneficial effects:
the invention relates to a comfort zone energy-saving optimization method based on an indoor quality control model.
Furthermore, a comprehensive mathematical model of the building indoor and air conditioning system is established, a foundation is provided for building indoor optimization and control, and the comfort zone optimization method based on the model can greatly save energy consumption under the condition of meeting comfort level.
Furthermore, the quality relation of each moment among all variables of the air-conditioning room is expressed through the quality dynamic model of the air-conditioning room, so that the perfection and accuracy of the model are improved.
Furthermore, the basic energy input and output relation at each moment between the air-conditioning room variables 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 the quality dynamic model of the AHU, so that the perfection and accuracy of the model are improved.
Further, the energy relation of each moment among the variables in the AHU is expressed through an energy dynamic model of the AHU.
Further, by converting the water content into a common expression level, CO is obtained 2 The concentration is expressed as a more easily recognizable quantity, making the output more intuitive.
Further, delivery temperature and flow constraints through the AHU system are to ensure performance and comfort of the equipment.
Furthermore, the valve is used for controlling the air supply quantity, and has an important effect on the thermal cycle of the system.
Furthermore, time-varying constraint rather than fixed value is adopted for the three variables of temperature, humidity and air quality, so that energy consumption can be saved to a greater extent.
In summary, the invention establishes the comprehensive mathematical dynamic model of the building indoor and air conditioning system, lays a foundation for optimizing and controlling the indoor environment, and provides the comfort zone optimizing method on the basis of the model, thereby greatly saving energy consumption on the premise of meeting comfort.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
FIG. 1 is a simplified HVAC system schematic;
FIG. 2 is a graph of model verification outdoor temperature, humidity;
FIG. 3 is a graph comparing the calculated results of the model with the actual results, wherein (a) is a graph comparing the indoor temperature with the calculated temperatures of the model, and (b) is indoor ppmV 2 Comparing the calculated temperature with a model, and (c) comparing the indoor humidity with the calculated humidity of the model;
FIG. 4 is a summer chart wherein (a) is the outdoor temperature and (b) is the outdoor humidity;
FIG. 5 is a graph showing the results of a setpoint tracking strategy under summer conditions, wherein (a) is an indoor temperature change graph, (b) is an indoor humidity change graph, (c) is an indoor ppmV2 content change graph, and (d) is an energy consumption change result graph;
FIG. 6 is a graph showing the results of a comfort zone optimization strategy under summer conditions, wherein (a) is a graph showing the results of indoor temperature changes, (b) is a graph showing the changes of indoor humidity, (c) is a graph showing the changes of indoor ppmV2 content, and (d) is a graph showing the results of energy consumption changes;
FIG. 7 is a winter condition chart wherein (a) is outdoor temperature and (b) is outdoor humidity;
FIG. 8 is a plot of setpoint tracking strategy results for winter conditions, wherein (a) is a plot of indoor temperature change results, (b) is a plot of indoor humidity change results, and (c) is indoor ppmV 2 A content change graph, (d) is an energy consumption change result graph;
FIG. 9 is a graph of comfort zone optimization strategy results for winter conditions, wherein (a) is a graph of indoor temperature change results, (b) is a graph of indoor humidity change results, and (c) is ppmV 2 A content change graph, (d) is an energy consumption change result graph;
fig. 10 is a graph of energy consumption versus graph.
Detailed Description
The invention provides a comfort zone energy-saving optimization method based on an indoor quality control model, which adopts the following steps.
Referring to fig. 1, the present invention relates to a comfort zone energy-saving optimization method based on an indoor quality control model, wherein a building system comprises an air-conditioning room and an AHU, and adopts a mixed ventilation shapeThe output variable is temperature, humidity and CO 2 The content comprises the following specific steps:
s1, establishing a quality and energy balance model of an air-conditioning room
S101, quality dynamic model of air-conditioning room
Wherein V is the total volume of the building area, m 3 ；C iz I component concentration, g/m in room 3 ；C in Is the concentration of i in the inlet air, g/m 3 ；g i The component generation rate of each person, g/h; i=1 represents H 2 O, i=2 represents CO 2 ；
S102, energy dynamic model of air-conditioning room
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 ) The method comprises the steps of carrying out a first treatment on the surface of the m is the total mass of the building area, g; h is time, ρ is air density under standard conditions, g/m 3 ；F in For inlet volumetric flow, m 3 /h；F o For outlet volumetric flow; a is the wall area, m 2 ；T a Is the external environment temperature; q is the gain of the soaking quantity of people in the building, kJ/h; p is the room occupancy, p=1 indicates that the space is occupied, and p=0, i.e. the space is unoccupied, p h Is the number of people;
s2, establishing a mass and energy balance model of the AHU air treatment unit (Air handling unit, AHU)
S201, quality dynamic model of AHU
F o (h)+F a (h)＝F e (h)+F in (h) (4)
m im (h)＝F in (h)C in (h)+F e (h)C iz (h)-F a (h)C ia (h)-F o (h)C iz (h) (5)
Wherein m is im Is the mass removal rate in the AHU, CO expressed when i=2 2 Mass removal rate, here 0; c (C) iz Is the concentration of i, g/m in the indoor space 3 ；C ia Is the concentration of i, g/m in the outside air 3 ；
S202, energy dynamic model of AHU
Q l (h)＝m im h (6)
Q s (h)＝F in (h)ρcT in (h)-ρc(F o (h)T(h)+F a (h)T a (h)-F e (h)T(h)) (7)
Q＝Q l +Q s (8)
Wherein Q is l Is the latent heat of increase/decrease in AHU, kJ/h; h is latent heat of condensation, kJ/g;
s3, the output variables of the steps S1 and S2 are water and CO 2 Conversion of content to humidity and ppmV values
S301, converting the water content into a humidity equation
S302, CO in air 2 Is converted to ppmV value
Wherein C is 1z Is the concentration of water in indoor space, g/m 3 ；C 1s Is the saturated concentration of water, g/m 3 ；M C Is CO 2 Molecular weight, g/mol; c (C) 2z Is indoor CO 2 Concentration of g/m 3 The method comprises the steps of carrying out a first treatment on the surface of the P is the internal pressure of the building system, atm; r is R u Is a general gas constant, atm.m 3 /(gmol·K)；
S4, analyzing key degrees of freedom and necessary constraint conditions of the room and the AHU system
S401 key degrees of freedom of Room and AHU System
External environment volume flow F a (h) Room input temperature T in (h) Room humidity R (h), pressure P (h);
s402, necessary constraints of room and AHU systems
Delivery temperature and flow constraints for an AHU; dynamic restriction of the valve; temperature, humidity and air quality employ time-varying constraints.
S4021, delivery temperature and flow constraints for AHU
T in,L ≤T in (h)≤T in,H (13)
F o,L ≤F o (h)≤F o,H (14)
F in,L ≤F in (h)≤F in,H (15)
F e,L ≤F e (h)≤F e,H (16)
F in,L ≤F a (h)≤F in,H (17)
Wherein T is in,L For inputting the minimum value of the temperature, T in,H Inputting the highest value of the temperature for the room, T in (h) To input temperature for room, F o,L Is the minimum value of the volume flow of the external environment, F o,H Is the highest value of the volume flow of the external environment, F o (h) For the volume flow of the external environment, F in,L Is the minimum of the room inlet volume flow, F in,H Is the highest value of the room inlet volume flow, F in (h) Is thatRoom inlet volume flow, F e,L Is the lowest value of exhaust volume flow, F e,H Is the highest value of exhaust volume flow, F e (h) For volume flow of exhaust air F a (h) Is the volume flow of the external environment;
s4022, dynamic restriction of valve
Wherein DeltaF in,H Delta F for room inlet volume flow variation o,H Delta F is the room outlet volume flow variation a,H Delta F is the change of the volume flow of the external environment e,H Is the highest value of exhaust volume flow, F in (h) For room inlet volume flow, F o (h) For room outlet volume flow, F a (h) For the volume flow of the external environment, F e (h) Is the volume flow of exhaust air;
s4023, temperature, humidity and air quality adopt time-varying constraints
T L p(h)+(1-p(h))T s,L ≤T(h)≤T H p(h)+(1-p(h))T s,H (22)
R L p(h)+(1-p(h))R s,L ≤R(h)≤R H p(h)+(1-p(h))R s,H (23)
ppmV 2 (h)≤ppmV 2,H p(h)+(1-p(h))ppmV s,2 (24)
Wherein T is L Is the lowest indoor temperature, T s,L T is the minimum indoor temperature under the strategy of set point H Is the highest value of indoor temperature, T s,H R is the indoor highest temperature under the strategy of the set point L R is the lowest indoor humidity value s,L R is the minimum indoor humidity under the strategy of set point H R is the highest value of indoor humidity s,H For maximum indoor humidity under setpoint strategy, ppmV 2 (h) Is indoor CO 2 Content of ppmV 2,H Is indoor CO 2 Maximum of ppmV s,2 For indoor CO under setpoint strategy 2 The content is as follows;
s5, setting a reasonable comfort zone by adopting PMV-PPD index
A set of suitable temperatures, humidities, pressures, ventilation rates are selected based on existing experience and research to verify whether a reasonable comfort zone is achieved by PMV-PPD index.
S6, setting objective functions of a comfort zone optimization method and a set point tracking method based on the model, and optimizing the control variable.
S601, model-based comfort zone optimization method objective function
Wherein Q (h) is total energy consumption, dh is time integral, τ is a moment, and t is a period of time increased on the basis of the moment;
s602, setting an objective function of a point tracking method;
the method comprises the following steps:
wherein T is the indoor temperature, T s For the indoor temperature under the setpoint strategy, R (h) is the indoor humidity, R s (h) For indoor humidity under setpoint strategy, P (h) is indoor pressure, P s (h) For indoor pressure under setpoint strategy, ppmV 2 (h) Is indoor CO 2 Content of ppmV s,2 (h) For indoor CO under setpoint strategy 2 The content is as follows;
for the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, 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 invention, as 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 made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 2, specific validations include validating a modeled model and validating energy savings for a model-based comfort zone optimization method.
A laboratory was selected for model verification and the humidity and temperature of the day of 7 months were selected as inputs to the overall system, as shown in fig. 3. After the air conditioner in the building is started, the temperature is set at 20 ℃, the humidity is set at 60 percent, and the air conditioner is in ppmV 2 500 is set, the indoor personnel are 200 persons, and the volume is 3000m 3 The working time is 9:00-18:00 an experiment was performed.
FIG. 3 shows the actual indoor temperature, ppmV 2 And the indoor temperature, ppmV calculated from the humidity and the model 2 And humidity contrast. As can be seen from fig. 3a, when the air-conditioning temperature is set to 20 ℃, both curves fluctuate around the set value, and the difference is small, and the average relative error is 2.2%; whereas in FIG. 3b, ppmV 2 The model calculation and the actual value of (2) have larger difference, and the average relative error is 5.2 percent, which is mainly caused by personnel initiative; as can also be seen from the graph of humidity comparison, fig. 3c, the model calculation substantially matches the actual situation given the indoor environment, with an average relative error of 2.0%. From which the accuracy of the model presented herein can be demonstrated.
Verification of the comfort zone optimization method. Still a laboratory, the indoor personnel are 200 people, and the volume is 3000m 3 . Experiments were performed at work hours, i.e. 9:00-18:00. To demonstrate the general applicability of this strategy, outdoor temperature and humidity for 71 days each in winter and summer were chosen as external input conditions. To ensure real-time, the data is discretized into steps per hour, with a predicted time of one day, i.e., 24 hours.
The outdoor temperature and humidity conditions under summer conditions are shown in fig. 4 (a) (b), respectively.
Setting the idle time temperature to limit 10-30deg.C, setting the occupied time temperature set point to 23deg.C, keeping humidity at 50%, and pressure at 1atm, and CO 2 The value was kept at 800ppmV. The results are shown in FIG. 5.
As shown in FIG. 5 (a), the indoor temperature in summer is shown as the graph, the idle time period is from 0 early to 9 early and from 6 late to 0 late, the indoor temperature changes along with the change trend of the outdoor temperature but does not exceed the limit of 10-30 ℃, the temperature is kept at 23 ℃ basically in the occupied time period, namely from 9 early to 6 late, and the humidity and CO are mainly represented by the temperature for keeping the occupied time 2 The energy consumption is the same as the set value as shown in fig. 5 (d).
Under the optimization strategy of the comfort zone, the temperature comfort range is selected to be 23-27 ℃, the humidity comfort range is selected to be 40-70%, the temperature in idle time is still 10-30 ℃, and the humidity is relaxed to be 30-80%. CO at the same time 2 Kept to within 800ppmV, the results of which are shown in fig. 6.
Fig. 6 (a) is a graph of indoor temperature change of the comfort zone optimization strategy under the summer working condition, and it can be seen from the graph that the indoor temperature still changes along with the change trend of the external temperature during the idle time, and the temperature changes between 23 ℃ and 27 ℃ during the occupied time. In summer the humidity is relatively high, and it can be seen from fig. 6 (b) that the humidity varies within a set range during the occupied time, whereas the humidity during the idle time is not higher than 80%. CO 2 The content varies regularly with the occupied time and the idle time, but does not exceed the set value, namely 800ppmV 2 . Drawing of the figure6 (d) is a graph of the energy consumption results of the comfort zone optimization strategy in summer, and as can be seen from the graph, the energy consumption is often 0, which indicates that the comfort zone optimization strategy greatly reduces the energy use.
The outdoor temperature and humidity conditions under winter conditions are shown in fig. 7 (a) (b), respectively. In the graph, the temperature is often lower than 0 ℃, and the time period taken by the experiment is a period of frequent snowing, so that the humidity is not too low.
The parameter setting of the winter condition set point tracking strategy is the same as the parameter of the summer condition in the upper section, and the result is shown in fig. 8.
As can be seen from fig. 8 (a), the system temperature is 10 ℃ during the idle period and the indoor temperature is maintained at substantially 23 ℃ during the occupied period under the winter condition. Humidity and CO 2 The content is always maintained at the set value, and under this condition, the energy consumption results are as 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 period in winter is generally lower than the minimum limit temperature of 10 ℃, and the system must consume a great amount of energy to keep the temperature in the set range.
The basic parameters of the comfort zone optimization strategy under the working condition of winter are the same as those of the comfort zone optimization strategy under the working condition of summer in the upper section, and the result is shown in figure 9.
Fig. 9 (a) is a diagram showing a temperature change of the winter comfort zone strategy, and shows that the temperature is 10 ℃ in the winter idle time and the occupied time is 23-27 ℃, but the change times are not frequent, because the outside temperature is lower in winter, the temperature can be kept by slightly increasing ventilation after the room temperature reaches 23 ℃, and the ventilation can not be ensured to be higher than or equal to 10 ℃ in most cases in the idle time, and the system is required to be regulated, so that the energy consumption in winter is higher than in summer. Meanwhile, as can be seen from fig. 9 (b) and (c), the humidity of the occupied time is between 40% and 70%, the idle time is not more than 80%, and the CO 2 The content was 800 in the occupied time, and the idle time was kept in range. Throughout the day, the building system can maintain indoor comfort zone requirements by reducing or increasing ventilation, which can be time consuming as seen in fig. 9 (d)Will often become 0, which will greatly reduce the energy usage.
As shown in FIG. 10, the comfort zone optimization method can save more than about 70% of energy, both in summer and winter conditions, as compared to the conventional set point tracking method.
In summary, 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 and air treatment unit systems of the building, and verifies the model by taking the Yifulou laboratory of the university of western architecture technology as an object, so that the model is proved to be reliable.
In order to reduce the technical complexity and cost, the key degree of freedom in the strategy and the necessary constraint conditions for ensuring the correct operation of the system are analyzed; in order to verify the general feasibility of the proposed comfort zone-based optimization strategy, two strategies of traditional set point tracking and comfort zone optimization are respectively carried out on summer and winter working conditions to optimize energy consumption targets, and the result shows that the comfort zone optimization strategy can save energy consumption to a greater extent under the condition of meeting comfort requirements, compared with the traditional set point tracking method which only gives a certain range to temperature and keeps other degrees of freedom constant, the method for meeting the comfort range is mainly given by all the degrees of freedom of the comfort zone optimization, and the energy consumption saving is mainly in two aspects:
firstly, the fluctuation of temperature, humidity and CO2 content in a range can reduce the running times of the air conditioner in the whole day;
secondly, the variable ventilation rate in the comfort zone optimization method enables the system to enhance the control flexibility, namely, the rise or the reduction of the temperature, the humidity and the CO2 content can be realized by increasing or reducing the ventilation, and especially, the idle time in winter can be reduced, so that the temperature reaches a set value and the energy consumption is reduced.
In summer, the air conditioner is more used in a comfortable area, and the running times of the air conditioner system are reduced, so that the energy consumption is reduced. The ventilation rate as a key degree of freedom plays an important role in reducing energy consumption, and the energy consumption can be greatly reduced by setting the range of other key degrees of freedom instead of using the traditional constant value. Thereby proving that comfort zone optimization strategies based on this model are viable and effective.
The above is only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited by this, and any modification made on the basis of the technical scheme according to the technical idea of the present invention falls within the protection scope of the claims of the present invention.
Claims (6)
1. The comfort zone energy-saving optimization method based on the indoor quality control model is characterized by comprising the following steps of:
s1, establishing a quality and energy balance model of an air-conditioning room, wherein a quality dynamic model of the air-conditioning room is as follows:
wherein V is the total volume of the building area; c (C) iz I component concentration C in room in Is the concentration of i in the inlet air; g i Is the component generation rate of each person; f (F) in For inlet volumetric flow, F o For outlet volumetric flow, p h For the number of people, i=1 denotes H 2 O, i=2 represents CO 2 ；
The energy dynamic model of the air-conditioning room is as follows:
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 ρ is air density under standard conditions; f (F) in Is the inlet volumetric flow rate; f (F) o For outlet volumetric flow; a is the wall area; t (T) a Is the external environmentA temperature; q is the gain of the heat gain of people in the building; p is the room occupancy, p=1 indicates that the space is occupied, p=0, i.e. the space is unoccupied, p h Is the number of people;
s2, establishing an AHU quality and energy balance model, wherein the AHU quality dynamic model is as follows:
F o (h)+F a (h)＝F e (h)+F in (h)
m im (h)＝F in (h)C in (h)+F e (h)C iz (h)-F a (h)C ia (h)-F o (h)C iz (h)
wherein m is im Is the mass removal rate in the AHU, CO expressed when i=2 2 Mass removal rate; c (C) iz Is the concentration of i in the indoor space; c (C) ia Is the concentration of i in the ambient air;
the energy dynamic model of the AHU is:
Q l (h)＝m im C
Q s (h)＝F in (h)ρcT in (h)-ρc(F o (h)T(h)+F a (h)T a (h)-F e (h)T(h))
Q＝Q l +Q s
wherein Q is l Is the latent heat of increase/decrease in the AHU; c is latent heat of condensation, Q s For sensible heat, m im Is the mass removal rate in the AHU, CO expressed when i=2 2 Mass removal rate, Q is total energy, F in For inlet volume flow rate ρ is air density under standard conditions, c is air heat capacity under standard conditions, T in To input temperature for room, F o For outlet volumetric flow, T is room temperature, T a Is the outside ambient temperature, F a For the volume flow of the external environment, F e Is the volume flow of exhaust air;
s3, modeling the steps S1 and S2 to output variable amounts of water and CO 2 The content is converted into a value of humidity and ppmV;
s4, according to the volume flow F of the room and the external environment of the AHU system a (h) Room input temperature T in (h) Room humidity R (h), pressure P (h) determines room anddelivery temperature and flow constraints for an AHU system; dynamic constraint of the valve; temperature, humidity and air quality are constrained by time-varying conditions;
s5, setting a reasonable comfort zone by adopting a PMV-PPD index;
s6, setting objective functions of a comfort zone optimization method and a set point tracking method based on a model by taking the steps S1 to S5 as constraint conditions, optimizing control variables, and giving the temperature, the humidity and the CO required by the condition of meeting comfort requirements and having minimum energy consumption 2 The content is as follows.
2. The method for optimizing energy saving in a comfort zone based on an indoor quality control model according to claim 1, wherein in step S3, the water content is converted into a humidity equation
CO in air 2 Is converted to ppmV value
Wherein C is 1z Is the concentration of water in the indoor space; c (C) 1s Is the saturation concentration of water; m is M C Is CO 2 Molecular weight; c (C) 2z Is indoor CO 2 Is a concentration of (2); p is the internal pressure of the building system; r is R u Is a universal gas constant.
3. The comfort zone energy saving optimization method based on the indoor quality control model of claim 1, wherein in step S4, the delivery temperature and flow constraints of the AHU system are:
T in,L ≤T in (h)≤T in,H
F o,L ≤F o (h)≤F o,H
F in,L ≤F in (h)≤F in,H
F e,L ≤F e (h)≤F e,H
F in,L ≤F a (h)≤F in,H
wherein T is in,L For inputting the minimum value of the temperature, T in,H Inputting the highest value of the temperature for the room, T in (h) To input temperature for room, F o,L Is the minimum value of the volume flow of the external environment, F o,H Is the highest value of the volume flow of the external environment, F o (h) For the volume flow of the external environment, F in,L Is the minimum of the room inlet volume flow, F in,H Is the highest value of the room inlet volume flow, F in (h) For room inlet volume flow, F e,L Is the lowest value of exhaust volume flow, F e,H Is the highest value of exhaust volume flow, F e (h) For volume flow of exhaust air F a (h) Is the volume flow of the external environment.
4. The method for optimizing energy saving in a comfort zone based on an indoor quality control model according to claim 1, wherein in step S4, the dynamic constraint of the valve is:
wherein DeltaF in,H Delta F for room inlet volume flow variation o,H Delta F is the room outlet volume flow variation a,H Delta F is the change of the volume flow of the external environment e,H Is the highest value of exhaust volume flow, F in (h) For room inlet volume flow, F o (h) For room outlet volume flow, F a (h) For the volume flow of the external environment, F e (h) Is the volume flow of exhaust air.
5. The method for optimizing energy saving in a comfort zone based on an indoor quality control model according to claim 1, wherein in step S4, temperature, humidity and air quality are determined using time-varying constraints as follows:
T L p(h)+(1-p(h))T s,L ≤T(h)≤T H p(h)+(1-p(h))T s,H
R L p(h)+(1-p(h))R s,L ≤R(h)≤R H p(h)+(1-p(h))R s,H
ppmV 2 (h)≤ppmV 2,H p(h)+(1-p(h))ppmV s,2
wherein T is L Is the lowest indoor temperature, T s,L T is the minimum indoor temperature under the strategy of set point H Is the highest value of indoor temperature, T s,H R is the indoor highest temperature under the strategy of the set point L R is the lowest indoor humidity value s,L R is the minimum indoor humidity under the strategy of set point H R is the highest value of indoor humidity s,H For maximum indoor humidity under setpoint strategy, ppmV 2 (h) Is indoor CO 2 Content of ppmV 2,H Is indoor CO 2 Maximum of ppmV s,2 For indoor CO under setpoint strategy 2 The content is as follows.
6. The method for optimizing energy saving in a comfort zone based on an indoor quality control model according to claim 1, wherein in step S6, an objective function of the point tracking method is specifically:
wherein T is the indoor temperature, T s For the indoor temperature under the setpoint strategy, R (h) is the indoor humidity, R s (h) For indoor humidity under setpoint strategy, P (h) is indoor pressure, P s (h) For indoor pressure under setpoint strategy, ppmV 2 (h) Is indoor CO 2 Content of ppmV s,2 (h) For indoor CO under setpoint strategy 2 The content is as follows.
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