CN115451550B - Central air conditioning system total energy consumption base line optimizing algorithm based on optimal supply and demand balance - Google Patents
Central air conditioning system total energy consumption base line optimizing algorithm based on optimal supply and demand balance Download PDFInfo
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- 238000005265 energy consumption Methods 0.000 title claims abstract description 71
- 238000004378 air conditioning Methods 0.000 title claims abstract description 23
- 238000001816 cooling Methods 0.000 claims abstract description 86
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 83
- 238000012544 monitoring process Methods 0.000 claims abstract description 36
- 230000004888 barrier function Effects 0.000 claims abstract description 20
- 238000007710 freezing Methods 0.000 claims abstract description 16
- 230000008014 freezing Effects 0.000 claims abstract description 16
- 230000002411 adverse Effects 0.000 claims abstract description 11
- 238000004873 anchoring Methods 0.000 claims abstract description 11
- 230000007812 deficiency Effects 0.000 claims abstract description 9
- 238000000034 method Methods 0.000 claims description 14
- 239000000498 cooling water Substances 0.000 claims description 9
- 230000001419 dependent effect Effects 0.000 claims description 6
- 230000008569 process Effects 0.000 claims description 6
- 238000005057 refrigeration Methods 0.000 claims description 4
- 230000008859 change Effects 0.000 description 8
- 230000005611 electricity Effects 0.000 description 4
- 238000005070 sampling Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 238000010521 absorption reaction Methods 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 230000033228 biological regulation Effects 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 238000004134 energy conservation Methods 0.000 description 2
- 230000002349 favourable effect Effects 0.000 description 2
- 230000004927 fusion Effects 0.000 description 2
- 230000009467 reduction Effects 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
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- 229910052799 carbon Inorganic materials 0.000 description 1
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- 230000017525 heat dissipation Effects 0.000 description 1
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Classifications
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
- F24F11/63—Electronic processing
- F24F11/64—Electronic processing using pre-stored data
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/70—Control systems characterised by their outputs; Constructional details thereof
- F24F11/72—Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure
- F24F11/74—Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure for controlling air flow rate or air velocity
- F24F11/77—Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure for controlling air flow rate or air velocity by controlling the speed of ventilators
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/70—Control systems characterised by their outputs; Constructional details thereof
- F24F11/80—Control systems characterised by their outputs; Constructional details thereof for controlling the temperature of the supplied air
- F24F11/83—Control systems characterised by their outputs; Constructional details thereof for controlling the temperature of the supplied air by controlling the supply of heat-exchange fluids to heat-exchangers
- F24F11/85—Control systems characterised by their outputs; Constructional details thereof for controlling the temperature of the supplied air by controlling the supply of heat-exchange fluids to heat-exchangers using variable-flow pumps
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B30/00—Energy efficient heating, ventilation or air conditioning [HVAC]
- Y02B30/70—Efficient control or regulation technologies, e.g. for control of refrigerant flow, motor or heating
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- General Engineering & Computer Science (AREA)
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- Air Conditioning Control Device (AREA)
Abstract
The invention relates to the technical field of a total energy consumption algorithm of a central air conditioning system, in particular to the realization of automatic control of a total energy consumption base line optimizing algorithm of the central air conditioning system based on optimal balance of supply and demand, which comprises the following steps: establishing a least adverse bottom line algorithm of the frequency of the freezing pump; step two: establishing a cooling capacity experience deficiency monitoring algorithm as a basis for anchoring the current cooling capacity and the current cooling capacity; step three: establishing a cooling system and a safety barrier monitoring algorithm of the chiller unit as a basis for adjusting a bottom line at a cooling side; step four: based on the second step and the third step, a system total energy consumption base line optimizing algorithm is established, and optimal combinations of chilled water outlet temperature, chilled pump frequency, cooling pump frequency and cooling tower fan operation are iteratively searched in different temperature and humidity intervals, so that the sum of energy consumption under the current supply and demand conditions is the lowest; the invention realizes the self control of the total energy consumption base line optimizing algorithm of the central air conditioning system with the optimal balance of supply and demand based on energy balance by fusing the data of the supply side, the demand side and the dynamic ring.
Description
Technical Field
The invention relates to the technical field of total energy consumption algorithms of central air conditioning systems, in particular to an implementation of automatic control of a total energy consumption base line optimizing algorithm of a central air conditioning system based on optimal balance of supply and demand.
Background
The typical central air conditioning system at present mainly comprises a supply side host computer room, a return transmission pipeline, a demand side fan coil pipe and the like, wherein the host computer room system mainly comprises a refrigerating unit, a cooling water circulation system, a chilled water circulation and the like, and mainly comprises equipment components such as the refrigerating unit, a chilled pump, a cooling tower, a water collector, a water separator, a regulating valve, a switching valve and the like. A typical mainframe room configuration is shown in fig. 1. The energy consumption of the whole central air conditioning system mainly comprises the electricity consumption of a water chilling unit, a circulating pump, a fan and the like at the host side, the electricity consumption of the fan inside the tail end fan coil and the like, wherein the electricity consumption at the host side accounts for a main part, so that the regulation and control of the electricity consumption data at the host side is a key for the energy-saving control of the central air conditioning system.
At present, no matter the manufacturer of the central air conditioning system or the energy-saving transformation service provider of a third party, the control modes of the host side can be classified into two types:
the first is a fuzzy matching control method, which realizes a fuzzy matching algorithm on a field automatic control system and operates the fuzzy matching algorithm, and mainly adjusts the starting number and the operating frequency of a circulating pump, the starting condition of a fan, the outlet water temperature of a water chilling unit and the like according to key parameters such as the temperature difference, the pressure difference and the flow of water supply and return in the system, so as to realize the self-adaptive adjustment operation of the system in a standard working condition boundary.
The second type is a manual experience control method, which sets the starting number and the running frequency of a circulation pump in a certain time period and the starting number and the water outlet temperature of a water chilling unit according to the factors of seasons, environment, management reality and the like, so as to realize the self-adaptive adjustment operation of the system in manual experience setting.
Both of the above approaches can achieve a degree of energy saving control, but have some drawbacks and limitations.
The whole central air conditioning system is known to be essentially energy supply and use, in principle, energy conservation is followed, the control means are to turn on and frequency modulation of a circulating pump, turn on and off of a fan and turn on and temperature adjustment of a water chilling unit, and the core is a non-time lag linear control algorithm for searching the best combination of the means based on energy supply and demand. Therefore, how to actively and dynamically cooperatively control the tail end side and the host side to be in the optimal energy balance working condition and find and control the optimal setting combination on the host side on the basis, so that the total energy consumption of the system is the lowest, is the key for evaluating the control logic.
The first mode is based on the temperature difference, pressure difference, flow and the like of backwater, the temperature difference, the pressure difference and the like are known to be feedback variables of the change of the tail end energy using superposition environment, and are very core control reference factors, but the feedback variables have obvious hysteresis, the feedback variables are generally transmitted to a host side for a delay of half an hour or even more than one hour, when the host side captures the change factors, the control action is triggered, the tail end state may have changed, and the control at the moment obviously cannot ensure that the host side and the tail end side are in the optimal energy balance operation condition;
the second type of mode is manual strategy control, which is simpler than the former first type of control mode, has thicker granularity, cannot be subjected to linear regulation control, has inferior control refinement effect as the first type, and cannot ensure that the host side and the tail end side are in the optimal energy balance operation condition.
Then, whether the demand is anchored based on the end-side demand change under different temperature and humidity environments can be found from the digital fusion angle based on the cross-boundary thinking, and then the optimal control combination setting with the lowest total energy consumption of the system is iteratively found at the host side according to the optimal balance, namely the implementation of the self-control of the central air conditioning system total energy consumption base line optimizing algorithm based on the optimal balance.
Disclosure of Invention
Aiming at the defects of the prior art, the invention discloses an algorithm for optimizing the bottom line of the total energy consumption of a central air conditioning system based on optimal balance of supply and demand, which mainly solves the defects and limitations of the fuzzy matching control method and the manual experience control method of the traditional central air conditioning system, namely, the total energy consumption of a host system is greatly wasted because the supply and demand data are not coordinated and the feedback data or experience data are delayed by the self to identify the established rules, so that the optimal balance of the supply and demand cannot be realized in different time slices, and the total energy bias redundancy is sufficient. The invention aims to realize the automatic control of a central air conditioning system total energy consumption base line optimizing algorithm with optimal supply and demand balance based on energy balance mainly through the fusion of supply side, demand side and dynamic ring data.
The invention is realized by the following technical scheme:
the algorithm for optimizing the total energy consumption base line of the central air conditioning system based on the optimal balance of supply and demand is characterized by comprising the following steps:
step one: establishing a least adverse bottom line algorithm of the frequency of the freezing pump, wherein the algorithm result is used as a lower limit reference of the frequency of the freezing pump;
step two: establishing a cooling capacity experience deficiency monitoring algorithm as a basis for anchoring the current cooling capacity and the cooling capacity experience deficiency monitoring algorithm requires continuous dynamic monitoring of the process;
step three: establishing a cooling system and chiller unit safety barrier monitoring algorithm as a basis for adjusting a bottom line at a cooling side, wherein the algorithm requires continuous dynamic monitoring of a process;
step four: based on the second step and the third step, a system total energy consumption base line optimizing algorithm is established, and optimal combinations of chilled water outlet temperature, chilled pump frequency, cooling pump frequency and cooling tower fan operation are iteratively searched in different temperature and humidity intervals, so that the sum of energy consumption under the current supply and demand conditions is the lowest; the algorithm is dynamically adjusted along with the cooling capacity experience shortage monitoring algorithm, the cooling system and the safety barrier monitoring algorithm of the refrigerating unit.
Preferably, in the cryopump frequency least favorable ground line algorithm,
independent variable: a frequency; dependent variables: room temperature >26 ℃ and room-set temperature difference > =5deg.C threshold number;
principle of: on the premise that the outlet water temperature of chilled water meets the requirement, the water circulation pressure is insufficient due to the insufficient frequency of the chilled pump, and the indoor space temperature cannot meet the set requirement, the lowest adverse frequency base line value of the chilled pump can be anchored by quantitatively identifying the complaint possibility of the threshold number of the room temperature >26 ℃ and the room-set temperature difference > = 5 ℃ under the condition of a certain open probability (> = 80% >) and ensuring the lowest adverse space startup probability.
Preferably, in the cooling capacity experience deficiency monitoring algorithm,
independent variable: ambient temperature, ambient humidity, cryopump frequency, chilled water outlet temperature;
dependent variables: the room temperature is >26 ℃ and the room-temperature difference > = 5 ℃ and the threshold number of the manual touch temperature adjusting buttons;
principle of: on the premise that the frequency of the freezing pump meets the bottom line and above requirements, the cooling capacity is insufficient due to insufficient temperature or insufficient flow of the chilled water outlet, the indoor space temperature cannot reach the set requirements, and the user experience is affected, the cooling space of the starting machine can be monitored, the room temperature is more than 26 ℃ and the room-set temperature difference is more than the threshold number of 5 ℃, meanwhile, the human touch temperature control temperature adjustment signal data are combined for identification feedback, and the cooling capacity experience is insufficient and can change along with the change of the outdoor environment temperature and humidity.
Preferably, in the cooling system and chiller safety barrier monitoring algorithm, the principle is as follows:
based on national standards and safety barrier requirements, the following barrier boundaries are established:
1) Under the standard operation condition, the outlet water temperature of the cooling water is < = 35 ℃, the inlet and outlet temperature difference is < = 5 ℃, if the outlet water temperature of the cooling water is more than 35 ℃ or the inlet and outlet temperature difference is more than 5 ℃, the cooling pump frequency is set to be the highest frequency, and the fan is fully opened;
2) Chiller load > =90%, cooling pump frequency is set to be the highest frequency, and the fan is iteratively started until chiller load < =75% until the chiller is fully started.
Preferably, in the system total energy consumption floor optimizing algorithm,
total system energy consumption=water chiller energy consumption+freeze pump energy consumption+cooling tower fan energy consumption;
under the condition of a certain refrigerating demand, firstly, reducing the influence of the cooling side on the load of the water chilling unit due to insufficient cooling to the minimum, following the principle of raising the water outlet temperature of chilled water as much as possible, and anchoring an optimal control combination value of the iterative freezing side in a section where the user energy consumption experience (cannot cause user complaints) is optimal to the upper limit refrigerating system and the total energy consumption of the water chilling unit at the lower limit, namely, the frequency ffreeze of the iterative freezing pump, the energy consumption efreeze, the water outlet temperature tfreeze of chilled water and the energy consumption e machine of the water chilling unit, so that the e freeze+e machine is minimum, and anchoring the f freeze and the t freeze;
secondly, turning to a cooling system angle, under the condition that the refrigeration requirement is certain and the cooling load is also certain, anchoring is carried out through monitoring of the cooling system and a safety barrier of the water chilling unit, the water chilling unit load w machine and the energy consumption e machine are iterated, the cooling pump frequency f and the energy consumption e are carried out, and the fan switch is combined with on wind and the energy consumption e wind, so that the e machine +e and the e wind are minimum;
and finally, iteratively finding out the minimum value of the values of e freeze, e machine, e but e wind and total energy consumption under a certain temperature and humidity condition, and obtaining the optimal combined value of f freeze, t freeze, f but on wind. .
The invention has the following beneficial effects:
the energy conservation based central air conditioning system realizes energy supply and demand cooperation as required to be an optimal energy-saving mode, the patent breaks through the traditional energy-saving control mode of hysteresis fuzzy matching of the central air conditioner so as to solve the working condition of iterative optimal balance of time slices of different temperature and humidity changes, find the optimal setting combination of total energy consumption at the host side, and excavate an energy-saving space between fuzzy matching and optimal balance so as to realize the unification of energy consumption experience and energy-saving benefit and the realization of energy saving, emission reduction and carbon reduction peak reaching of a power-assisted building.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a diagram of a host machine room according to the present invention.
Fig. 2 is a schematic diagram of the present invention for implementing the optimal energy-saving AI control of the central air conditioning system.
FIG. 3 is a logic diagram of an algorithm of the present invention.
Detailed Description
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. 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. 1-3, the present invention provides an algorithm for optimizing a total energy consumption base line of a central air conditioning system based on an optimal balance of supply and demand, comprising:
1. algorithm target:
by identifying key correlation parameters of the system, a system total energy consumption base line optimizing control algorithm is established on the premise of meeting electromechanical characteristics, guaranteeing operation safety and meeting user base lines, and optimal energy-saving AI control of the central air conditioning system is achieved.
2. Algorithm logic:
combining algorithm: major algorithm related to AI control of central air conditioning system scene:
1) The least adverse bottom line algorithm (main algorithm) of the cryopump frequency;
2) A cold supply experience deficiency monitoring algorithm (auxiliary algorithm);
3) A cooling system and chiller safety barrier monitoring algorithm (auxiliary algorithm);
4) A system total energy consumption bottom line optimizing algorithm (main algorithm);
the main algorithm is a starting and entry algorithm, an auxiliary algorithm can be called, iteration adjustment parameters are carried out according to substitution results and optimal target values, and a model library is maintained. The auxiliary algorithm is only responsible for calculating and outputting a result according to the input variable, and is not responsible for adjusting parameters. On one hand, the method obtains a calculation result when being called by the main algorithm, and on the other hand, the method triggers the main algorithm to carry out timely linkage adjustment when independently monitoring and running.
The combination step:
step one: and establishing a least adverse bottom line algorithm of the frequency of the freezing pump, wherein the algorithm result is used as a lower limit reference of the frequency of the freezing pump.
Step two: and establishing a cooling capacity experience deficiency monitoring algorithm as a basis for anchoring the current cooling capacity and the current cooling capacity. This algorithm requires continuous dynamic monitoring of the process.
Step three: and establishing a cooling system and water chiller safety barrier monitoring algorithm as a basis for adjusting a bottom line at a cooling side. This algorithm requires continuous dynamic monitoring of the process.
Step four: based on the second step and the third step, a system total energy consumption base line optimizing algorithm is established, and the optimal combination of the chilled water outlet temperature, the chilled pump frequency, the cooling pump frequency and the cooling tower fan operation is searched for in an iteration mode in different temperature and humidity intervals, so that the sum of energy consumption under the current supply and demand conditions is the lowest. The algorithm is dynamically adjusted along with the cooling capacity experience shortage monitoring algorithm, the cooling system and the safety barrier monitoring algorithm of the refrigerating unit.
Specifically, the least favorable bottom line algorithm for cryopump frequency is as follows:
(1) principle of algorithm
Independent variable: frequency of
Dependent variables: room temperature >26 ℃ and room-set temperature difference > =5 ℃ threshold number
Principle of: on the premise that the outlet water temperature of chilled water meets the requirement, the water circulation pressure is insufficient due to the insufficient frequency of the chilled pump, and the indoor space temperature cannot meet the set requirement, the lowest adverse frequency base line value of the chilled pump can be anchored by quantitatively identifying the complaint possibility of the threshold number of the room temperature >26 ℃ and the room-set temperature difference > = 5 ℃ under the condition of a certain open probability (> = 80% >) and ensuring the lowest adverse space startup probability.
(2) Sampling data
Insufficient cooling capacity experience monitoring algorithm:
(1) principle of algorithm
Independent variable: ambient temperature, ambient humidity, cryopump frequency, chilled water outlet temperature
Dependent variables: the room temperature is more than 26 ℃ and the room-set temperature difference is more than 5 ℃ and the threshold number of the manual touch temperature adjusting buttons is more than 5 DEG C
Principle of: on the premise that the frequency of the freezing pump meets the bottom line and the above requirements, the cooling capacity is insufficient due to insufficient temperature or insufficient flow of the chilled water outlet, and the indoor space temperature cannot meet the set requirements, so that the user experience is affected. The method can be used for monitoring the threshold number of the cooling space of the starting machine, wherein the room temperature is more than 26 ℃ and the room-temperature difference is more than 5 ℃, and meanwhile, the method is combined with human touch temperature control signal data to perform identification feedback. Insufficient cooling capacity experience can change along with the change of the temperature and humidity of the outdoor environment.
(2) Sampling data
Cooling system and chiller safety barrier monitoring algorithm:
(1) principle of algorithm
Based on national standards and safety barrier requirements, the following barrier boundaries are established:
1. under the standard operation condition, the outlet water temperature of the cooling water is < = 35 ℃, the inlet and outlet temperature difference is < = 5 ℃, and if the outlet water temperature of the cooling water is more than 35 ℃ or the inlet and outlet temperature difference is more than 5 ℃, the cooling pump frequency is set to be the highest frequency, and the fan is fully opened.
2. Chiller load > =90%, cooling pump frequency is set to be the highest frequency, and the fan is iteratively started until chiller load < =75% until the chiller is fully started.
(2) Sampling data
And (3) a system total energy consumption bottom line optimizing algorithm:
(1) principle of algorithm
Here, total system energy consumption=chiller energy consumption+cryopump energy consumption+cooling pump energy consumption+cooling tower fan energy consumption.
Under a certain temperature and humidity condition, the refrigerating demand at the tail end side can be anchored through insufficient cooling capacity experience monitoring, and according to energy balance, the refrigerating supply at the host side can also be anchored. The refrigerating capacity at the host side is determined by the chilled water outlet temperature and the flow, the chilled water outlet temperature has positive correlation with the unit load, and the flow has positive correlation with the frequency of the chilled pump, so that the demand of the refrigerating capacity can be converted into the combined demand of the chilled water outlet temperature and the running frequency of the chilled pump.
The optimal combination value is found only at the chilled water outlet temperature and the chilled pump operating frequency, so that the minimum energy consumption of the chilled pump and the chiller under the current cooling capacity is ensured.
In addition, according to the electromechanical characteristics of the system, the increase of the outlet water temperature of the chilled water can obviously improve the COP value of the host, and the influence characteristic on energy consumption is more obvious, so that the principle is to try to improve the outlet water temperature of the chilled water and improve the water flow, namely the frequency of the chilled pump for optimizing.
It should be noted here that the energy consumption of the chiller essentially corresponds to the load parameter, and the load of the chiller is related to the chilled water outlet temperature on the one hand, but is affected by the heat dissipation effect of the condenser on the other hand, so that at the same chilled water outlet temperature, the chiller system performance is related and may change.
From the side of the cooling system, under the condition of a certain refrigeration requirement, the cooling load is also certain, the cooling absorption heat is determined by the water supply temperature and the flow of the cooling water, the water supply temperature of the cooling water has positive correlation with the fan of the cooling tower and the outdoor environment, the flow has positive correlation with the frequency of the cooling pump, and the superposition of the water supply temperature and the cooling pump affects the cooling absorption heat effect, and further the linkage affects the load of the water chilling unit.
On the whole, under the condition of a certain refrigeration demand, the influence of the cooling side on the load of the water chilling unit due to insufficient cooling can be reduced to the minimum, the principle of raising the water outlet temperature of the chilled water as much as possible is followed, the optimal control combination value of the iterative freezing side is anchored in the section where the energy consumption experience of the user (the user cannot be complained) is ensured to be optimal to the upper limit freezing system and the total energy consumption of the water chilling unit at the lower limit, namely the frequency ffreeze of the iterative freezing pump, the energy consumption efreeze, the water outlet temperature tfreeze of the chilled water and the energy consumption e machine of the water chilling unit are adopted, so that the e freeze+e machine is minimum, and the f freeze and the tfreeze are anchored.
Secondly, turning to the angle of a cooling system, under the condition that the refrigerating demand is certain and the cooling load is also certain, anchoring is carried out through monitoring of the cooling system and a safety barrier of the water chilling unit, the water chilling unit load w machine and the energy consumption e machine are iterated, the cooling pump frequency f and the energy consumption e are carried out, and the fan switch is combined with on wind and the energy consumption e wind, so that the e machine +e and the e wind are minimum.
Finally, we find out the minimum value of the values of e freeze +emachine +e but +ewind = total energy under a certain temperature and humidity condition, and obtain the optimal combination value of (f freeze, t freeze, f but, on wind).
(2) Sampling data
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (1)
1. The algorithm for optimizing the total energy consumption base line of the central air conditioning system based on the optimal balance of supply and demand is characterized by comprising the following steps:
step one: establishing a least adverse bottom line algorithm of the frequency of the freezing pump, wherein the algorithm result is used as a lower limit reference of the frequency of the freezing pump;
step two: establishing a cooling capacity experience deficiency monitoring algorithm as a basis for anchoring the current cooling capacity and the cooling capacity experience deficiency monitoring algorithm requires continuous dynamic monitoring of the process;
step three: establishing a cooling system and chiller unit safety barrier monitoring algorithm as a basis for adjusting a bottom line at a cooling side, wherein the algorithm requires continuous dynamic monitoring of a process;
step four: based on the second step and the third step, a system total energy consumption base line optimizing algorithm is established, and optimal combinations of chilled water outlet temperature, chilled pump frequency, cooling pump frequency and cooling tower fan operation are iteratively searched in different temperature and humidity intervals, so that the sum of energy consumption under the current supply and demand conditions is the lowest; the algorithm is dynamically adjusted along with a cooling capacity experience shortage monitoring algorithm, a cooling system and a refrigerating unit safety barrier monitoring algorithm;
in the cryopump frequency least favored floor algorithm,
independent variable: a frequency; dependent variables: room temperature >26 ℃ and room-set temperature difference > =5deg.C threshold number;
principle of: on the premise that the outlet water temperature of chilled water meets the requirement, the water circulation pressure is insufficient due to the insufficient frequency of a chilled pump, and the indoor space temperature cannot reach the set requirement, the least adverse space startup probability is ensured under the condition that the certain startup probability > =80%, the quantitative identification of complaint probability is carried out on the threshold number of the room temperature >26 ℃ and the room-set temperature difference > =5 ℃, so that the least adverse frequency base line value of the chilled pump is anchored;
in the cooling experience deficiency monitoring algorithm,
independent variable: ambient temperature, ambient humidity, cryopump frequency, chilled water outlet temperature;
dependent variables: the room temperature is >26 ℃ and the room-temperature difference > = 5 ℃ and the threshold number of the manual touch temperature adjusting buttons;
principle of: on the premise that the frequency of the freezing pump meets the bottom line and above requirements, the cooling capacity is insufficient due to insufficient temperature or insufficient flow of the chilled water outlet, the indoor space temperature cannot meet the set requirements, and the user experience is affected;
in the cooling system and chiller safety barrier monitoring algorithm, the principle is as follows:
based on national standards and safety barrier requirements, the following barrier boundaries are established:
under the standard operation condition, the outlet water temperature of the cooling water is < = 35 ℃, the inlet and outlet temperature difference is < = 5 ℃, if the outlet water temperature of the cooling water is more than 35 ℃ or the inlet and outlet temperature difference is more than 5 ℃, the cooling pump frequency is set to be the highest frequency, and the fan is fully opened;
the load of the water chilling unit > =90%, the frequency of the cooling pump is set to be the highest frequency, and the fan is iteratively started until the load of the water chilling unit < =75% until the water chilling unit is fully started;
in the system total energy consumption floor optimizing algorithm,
total system energy consumption=water chiller energy consumption+freeze pump energy consumption+cooling tower fan energy consumption;
under the condition of a certain refrigerating demand, firstly reducing the influence of the cooling side on the load of the water chilling unit due to insufficient cooling to the minimum, following the principle of raising the water outlet temperature of chilled water as much as possible, and anchoring the optimal control combination value of the iterative freezing side in the interval of ensuring the optimal energy consumption of the user experience to the upper limit refrigerating system and the total energy consumption of the water chilling unit at the lower limit, namely, the frequency ffreeze of the iterative refrigerating pump, the energy consumption efreeze, the water outlet temperature tfreeze of chilled water and the energy consumption e machine of the water chilling unit, so that the e freeze+e machine is minimum, and anchoring the f freeze and the t freeze;
secondly, turning to a cooling system angle, under the condition that the refrigeration requirement is certain and the cooling load is also certain, anchoring is carried out through monitoring of the cooling system and a safety barrier of the water chilling unit, the water chilling unit load w machine and the energy consumption e machine are iterated, the cooling pump frequency f and the energy consumption e are carried out, and the fan switch is combined with on wind and the energy consumption e wind, so that the e machine +e and the e wind are minimum;
and finally, iteratively finding out the minimum value of the values of e freeze, e machine, e but e wind and total energy consumption under a certain temperature and humidity condition, and obtaining the optimal combined value of f freeze, t freeze, f but on wind.
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| CN106500280A (en) * | 2016-12-28 | 2017-03-15 | 珠海格力电器股份有限公司 | Control system and control method for cold station of central air conditioner |
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