CN111412584A - Group intelligent optimization method for dynamic hydraulic balance of chilled water pipe network of central air conditioner - Google Patents
Group intelligent optimization method for dynamic hydraulic balance of chilled water pipe network of central air conditioner Download PDFInfo
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- 230000008014 freezing Effects 0.000 claims description 2
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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
- F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
- F24F11/46—Improving electric energy efficiency or saving
- F24F11/47—Responding to energy costs
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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
- F24F11/50—Control or safety arrangements characterised by user interfaces or communication
- F24F11/56—Remote control
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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
- F24F11/80—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
- F24F11/84—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 valves
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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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- 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
- F24F11/88—Electrical aspects, e.g. circuits
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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
- F24F2140/00—Control inputs relating to system states
- F24F2140/60—Energy consumption
Abstract
The invention discloses a group intelligent optimization method for dynamic hydraulic balance of a chilled water pipe network of a central air conditioner, wherein intelligent computing nodes CPN are arranged in an air conditioning box, a bypass valve and water pump equipment in the chilled water pipe network, and all the intelligent computing nodes CPN are interconnected according to an actual physical topological connection relationship to form a network communication system of a group intelligent framework; corresponding distributed calculation is completed aiming at the adjustment task, and finally the dynamic hydraulic balance of the chilled water pipe network is realized by the minimum total system power; the air-conditioning box intelligent computing node CPN obtains the minimum supply and return water pressure difference and total flow of the system meeting the flow requirements of all air-conditioning boxes at the tail end of the system through information interaction computing, and calculates the opening degree of a water valve of a surface cooler of the air-conditioning box; and the intelligent computing node CPN of the water pump computes the optimal operation strategy of the parallel water pump system according to the obtained system supply and return water pressure difference and total flow demand, and finally achieves the purposes of optimizing the dynamic hydraulic balance of the chilled water pipe network, saving energy and reducing consumption.
Description
Technical Field
The invention belongs to the technical field of air conditioner refrigeration, and particularly relates to a group intelligent optimization method for dynamic hydraulic balance of a chilled water pipe network of a central air conditioner.
Background
The energy consumption of the central air-conditioning system has a considerable ratio in the energy consumption of buildings, so that the energy-saving optimization research on the central air-conditioning system is of great significance.
At present, in the design process of a central air-conditioning system, the terminal load is calculated according to the maximum load of a building; when equipment is selected, performance parameters are determined according to design flow, and in order to consider system allowance, designers artificially increase equipment safety factors; in addition, the models of the devices are discrete and discontinuously changed, and when the calculated performance parameters are between the two models, a designer often selects the device with a larger model, so that the selection of the device is overlarge; and when the system operates, the system almost works under partial load under the influence of the change of external factors such as meteorological conditions and the like. Therefore, in the operation process of the system, the flow demand of the user is often smaller than the design flow, so that the temperature of the terminal room is uneven, dynamic hydraulic imbalance of the system is caused, and the consequences of energy waste, user comfort reduction and the like are caused.
The existing method realizes the dynamic hydraulic balance of a pipe network system through a balance valve device basically, but the solution can cause the impedance of the pipe network to be increased, thereby increasing the energy consumption of a water pump and failing to achieve the purposes of energy conservation and consumption reduction completely.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a group intelligent optimization method for dynamic hydraulic balance of a chilled water pipe network of a central air conditioner, aiming at the defects in the prior art, and on the premise of ensuring the flow demand of each branch at the tail end, the opening degree of a surface cooler valve of each parallel branch air conditioning cabinet at the tail end is adjusted in real time, so that the impedance of the pipe network is minimized, and the aim of reducing the energy consumption of the system is fulfilled.
The invention adopts the following technical scheme:
a group intelligent optimization method for dynamic hydraulic balance of a chilled water pipe network of a central air conditioner is characterized in that intelligent computing nodes CPN are arranged in an air conditioning box, a bypass valve and water pump equipment in the chilled water pipe network, and all the intelligent computing nodes CPN are interconnected according to the actual physical topological connection relationship of the equipment to form a network communication system of a group intelligent framework; when one intelligent computing node CPN initiates an adjusting task, the rest intelligent computing nodes CPN cooperate with the initiating intelligent computing node CPN to perform information interaction with the respectively connected one-hop neighbor intelligent computing nodes CPN, corresponding distributed computation is completed aiming at the adjusting task, and the dynamic hydraulic balance of the chilled water pipe network is realized by the minimum total system power; establishing an optimization objective function, obtaining the minimum supply and return water pressure difference and total flow of the system meeting the flow demand of each air conditioning box at the tail end of the system through information interaction calculation by an intelligent calculation node CPN in the air conditioning box, and calculating the opening of a water valve of a surface cooler of the air conditioning box; an intelligent computing node CPN in the water pump equipment combines the impedance of a connecting pipe section in a parallel water pump according to the supply and return water pressure difference and the total flow demand of the system, utilizes an alternating direction multiplier method with a regular term to select the flow of the water pump as an optimization variable, takes the minimum total energy consumption of the parallel water pump system as an optimization target, takes the supply and return water pressure difference and the total flow demand as constraint conditions, calculates the optimal operation strategy of the parallel water pump system, and realizes the optimization of the dynamic hydraulic balance of the freezing water pipe network and energy conservation and consumption reduction according to the optimal operation strategy.
Specifically, the minimum water supply and return pressure difference and the total flow of the system which meet the flow demand of each air conditioning box at the tail end of the system are obtained by the intelligent computation node CPN in the air conditioning box through information interaction computation:
s101, receiving a hydraulic balance adjustment starting signal flag _ AHU (equal to 1) by an intelligent computing node CPN of an air conditioning box;
s102, assuming the opening openning of a water valve of a surface cooler of an air conditioning boxiCalculating the pressure drop of a branch where the air conditioning box is located as 1;
s103, judging whether the intelligent computing nodes CPN of the air-conditioning box meet the intelligent computing nodes CPN of which the number of the intelligent computing nodes CPN of the neighbor air-conditioning box is 1 and which do not have a neighbor bypass valve, and turning to the step S104 if the intelligent computing nodes CPN meet the conditions, or turning to the step S105 if the intelligent computing nodes CPN do not meet the conditions;
s104, setting flag to be 1, and calculating a forward iteration pressure difference variable H1And flow variable Q1Flag, H1、Q1Sending to the intelligent computing node CPN of the neighbor, and going to step S111;
s105, the intelligent computing node CPN of the air-conditioning box judges whether flag and H transmitted by the adjacent intelligent computing node CPN are received1、Q1If the variable and flag are 1, the step is switched to step S106, otherwise, the step continues to wait;
s106, the intelligent computing node CPN of the air-conditioning box judges the pressure drop H _ AHU _ self of the branch where the intelligent computing node CPN is locatediWhether or not it is greater than the neighbor's transmitted forward iteration pressure difference variable H1If it is greater than, let H1＝H_AHU_selfiOtherwise H1＝H1；
S107, updating the forward iteration flow variable Q1And differential pressure variable H1；
S108, judging whether the number of the intelligent computing nodes CPN of the neighbor air-conditioning box is 1 or not and whether the intelligent computing nodes CPN meet the requirement of the neighbor air-conditioning box or not, if so, turning to the step S109, and if not, setting flag and H to be equal to the number of the intelligent computing nodes CPN of the neighbor air-conditioning box, otherwise, turning to the step S1091、Q1The variable is sent to the intelligent computing node CPN of the neighbor, and the step S111 is carried out;
S109. minimum water supply and return pressure difference H of systemsr＝H1Total flow demand Qall＝Q1The intelligent computing node CPN of the water feeding pump is sent through the intelligent computing node CPN of the bypass valve to start a water pump optimization algorithm;
s110, calculating a reverse iteration pressure difference variable H2And flow variable Q2And setting flag to be 2, transmitting the flag to a neighbor intelligent computing node CPN, and according to Ki(openingi) The pressure drop of the control valve i is determined and the openning is determinediAnd outputting, and making flag _ AHU equal to 0;
s111, judging whether H transmitted by CPN of neighbor intelligent computing node is received2、Q2If the variable flag is 2, the step S112 is carried out if the condition is met, otherwise, the step S continues to wait;
s112, calculating a reverse iteration pressure difference variable H2And flow variable Q2Is prepared from H2、Q2Sending the flag variable to the intelligent computing node CPN of the neighbor according to Ki(openingi) The pressure drop of the control valve i is determined and the openning is determinediAnd outputs, and let flag _ AHU be 0.
Further, according to the flow demand of each parallel air conditioning box at the tail end, the minimum water supply and return pressure difference of the system and the valve opening of each parallel branch are solved as follows:
min(Hsr)
Hbi+Ki(openingi)＝Hsr
wherein i is 1,2, …, n, Q _ AHUset iThe required flow of the ith air conditioning box at the tail end of the pipe network, HsrSupply and return water pressure difference for system, Ki(openingi) Opening the pressure drop of the regulation valve of the surface cooler of the ith air-conditioning boxiThe opening degree of an adjusting valve of a surface cooler of the ith air-conditioning box is n, the number of the air-conditioning boxes connected in parallel at the tail end of the system is HbiThe sum of the pressure drop of the water supply and return pipeline of the air-conditioning box i is subtracted by the surface cooler of the air-conditioning boxThe pressure drop across the throttle.
Specifically, by using an alternating direction multiplier method with a regular term, taking the flow of the water pump as an optimization variable, taking the total energy consumption of the parallel water pump system as an optimization target, and taking the supply-return water pressure difference and the total flow demand as constraint conditions, the optimal operation strategy of the parallel water pump system is calculated as follows:
s201, after receiving a parallel water pump optimization algorithm starting signal flag _ pump ═ 1, an intelligent computing node CPN of a water pump makes an initial iteration flow Q _ pump of the water pump i1, the initial value lambda is 100;
s202, judging whether the number of the CPNs of the neighbor water pump intelligent computing nodes is 1 and the CPNs of the neighbor water pump intelligent computing nodes are neighbors without bypass valves, if so, turning to the step S203, otherwise, turning to the step S204;
s203, reverse iteration flow variable Q3＝Q_pumpi kIf yes, sending the flag to the neighboring intelligent computing node CPN, and going to step S208;
s204, judging whether Q transmitted by the CPN of the neighbor intelligent computing node is received or not3If yes, the step is shifted to step S205, otherwise, the process continues to wait;
s205, judging whether the number of the intelligent computing nodes CPN of the neighbor water pump is 1 or not and whether the intelligent computing nodes CPN of the neighbor bypass valve exist or not, if so, turning to S206, otherwise, enabling a reverse iteration flow variable Q3＝Q3+Q_pumpi kIs mixing Q with3The flag variable is sent to an intelligent computing node CPN of the neighbor water pump, and the step S208 is carried out;
s206, judging Q3+Q_pump1 k-QallWhether the control precision requirement is met or the iteration frequency reaches the maximum value is judged, if yes, the algorithm iteration is finished, flag _ pump is set to be 0, and the intelligent computing nodes CPN of all the water pumps output water pump flow Q _ pumpi kSpeed ratio wiOtherwise, go to step S207;
s207, solving lambdak+1Let the forward iteration pressure difference variable H3＝Hsr+(S_pump_uLi+S_pump_uRi)×Qall 2And calculating Q _ pump by combining a standard distribution estimation algorithm1 k+1And λ'k+1Let the forward iterate the flow variable Q4＝Qall-Q_pump1 k +1And H is equal to 23、Q4、λk+1β, sending the flag variable to the neighbor CPN;
s208, judging whether H transmitted by CPN of neighbor intelligent computing node is received3、Q4、λ′k+1β, changing the flag variable, wherein the flag is 2, if yes, going to step S209, otherwise, continuing to wait;
s209, enabling a forward iteration pressure difference variable H3＝H3+(S_pump_uLi+S_pump_uRi)×Q4 2And calculating Q _ pump by combining with a distribution estimation algorithmi k+1Then let the forward iteration flow variable Q4＝Q4–Q_pumpi k+1；
S210, judging whether the number of the intelligent computing nodes CPN of the neighbor water pump is 1 and the intelligent computing nodes CPN without the neighbor bypass valve are met, if the number of the intelligent computing nodes CPN meets the judgment condition, turning to S203, and if not, turning to H3、Q4、λ′k+1β, and sending the flag variable to the neighboring intelligent computing node CPN, and going to step S204.
Further, by taking the flow of the water pump as an optimization variable, taking the minimum total energy consumption of the parallel water pump system as an optimization target and taking the pressure difference of supply and return water and the total flow demand as constraint conditions, an optimization model is established as follows:
min(Wtotal)
wherein i is 1,2, …, m, WtotalTotal power of the parallel water pump system, HiIs the head of the ith water pump, H _ siThe pump lift required to be provided when the impedance of a branch where the pump is located is not considered for the ith water pump; qallFor the total flow demand of the system, m is the number of water pumps connected in parallel, Q _ pumpiFor the branch flow of the water pump i, S _ pump _ self Li、S_pump_selfRiRespectively, the impedance of the branch in which the water pump i is located, HsrSupplying and returning water pressure difference for the system, wherein Sc is equivalent impedance record of the parallel connection cold machine and the auxiliary pipeline thereof, S _ pump _ u LjAnd S _ pump _ uRjRespectively, the impedance of the upper two side branches connected with the branch in which the water pump i is located, Q _ pumpkThe flow of the branch where the water pump k is located.
Furthermore, the distributed solution by using the alternating direction multiplier method with the regularization term specifically comprises:
wherein β is a penalty factor, mu is an algorithm parameter, and mu is more than m + 1.
Compared with the prior art, the invention has at least the following beneficial effects:
the invention relates to a swarm intelligence optimization method for dynamic hydraulic balance of a chilled water pipe network of a central air conditioner, which can enable the pipe network to meet the flow requirements of each branch at the tail end by only optimizing the frequency of a water pump in the chilled water pipe network of the central air conditioner and the opening of a surface cooler valve of an air conditioner box without adding a balance valve in the pipe network, thereby achieving the purposes of energy saving and consumption reduction; and in the second process, the optimal operation strategy of the parallel water pumps is obtained according to the minimum water supply and return pressure difference and the total flow of the system.
Furthermore, the dynamic hydraulic balance optimization problem of the chilled water pipe network of the central air conditioner is decoupled into two parts for solving, so that the algorithm complexity is reduced, and the optimization time is shortened.
Furthermore, according to the flow demand of each tail end branch, at least one air conditioner box surface cooler valve is always in a fully open state in the pipe network, the pressure difference of supply and return water required by a pipe network system can be minimized, and the energy consumption of the system is reduced.
Furthermore, the parallel water pump system is optimized according to the obtained minimum water supply and return pressure difference and the total flow demand, and the energy consumption of the system is further reduced.
Furthermore, the optimization method is completed by adopting distributed computation, algorithms in the intelligent computing nodes CPN of the same equipment are completely consistent, the time for developing the algorithms is saved, and the universality of the method is enhanced.
In conclusion, the invention completes the optimization calculation task of the dynamic hydraulic balance of the chilled water pipe network of the central air conditioner by the cooperative cooperation of the intelligent calculation nodes CPN of the air conditioning boxes and the intelligent calculation nodes CPN of the water pump and by adopting the idea of distributed asynchronous iterative calculation and utilizing less calculation resources. The distributed asynchronous iterative algorithm provided by the invention does not need to collect global information of the system, and can obtain an optimal solution only by performing information interaction on the result obtained by the calculation of each intelligent calculation node CPN and the neighbor intelligent calculation nodes CPN, so that the memory required by algorithm solution is reduced, the algorithm calculation speed is accelerated, the control topological connection of the whole hardware system is simplified, and the aims of improving the working efficiency, reducing the system energy consumption and saving energy are fulfilled.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
FIG. 1 is a schematic view of a chilled water pipe network of a central air conditioner according to the present invention;
FIG. 2 illustrates CPN topology connections of the group intelligence architecture of the present invention;
FIG. 3 is a communication process of the algorithm solved in step S1 according to the present invention;
FIG. 4 is a communication process of the algorithm solved in step S2 according to the present invention;
FIG. 5 is a diagram of the actual topology of a chilled water pipe network of a central air conditioner according to the present invention;
FIG. 6 shows the optimized pump flow-lift performance curve variation of the present invention;
fig. 7 shows the optimized flow-efficiency performance curve change of the water pump.
Detailed Description
Referring to fig. 1 and 2, the invention provides a group intelligent optimization method for dynamic hydraulic balance of a chilled water pipe network of a central air conditioner, wherein an intelligent computing node CPN is arranged in an air conditioning box, a bypass valve and water pump equipment in the chilled water pipe network, and all the intelligent computing nodes CPN are interconnected according to an actual physical topological connection relationship to form a network communication system of a group intelligent architecture; when one intelligent computing node CPN initiates an adjusting task, the other intelligent computing nodes CPN cooperate to initiate the intelligent computing node CPN to perform information interaction with the respectively connected one-hop neighbor intelligent computing node CPN, corresponding distributed computation is completed aiming at the adjusting task, and finally the dynamic hydraulic balance of the chilled water pipe network is realized by the minimum total system power; according to the principle of node flow balance and loop pressure drop balance of a pipe network, the air-conditioning box intelligent calculation node CPN obtains the system minimum supply and return water pressure difference and total flow which meet the flow demand of each air-conditioning box at the tail end of the system through information interaction calculation, and calculates the opening degree of a water valve of a surface cooler of the air-conditioning box; the intelligent calculation node CPN of the water pump combines the impedance of the internal connecting pipe section of the parallel water pump according to the obtained system supply and return water pressure difference and total flow demand, selects the water pump flow as an optimization variable by utilizing an Alternating Direction Multiplier Method (ADMM) with a regular term, takes the minimum total energy consumption of the parallel water pump system as an optimization target, takes the supply and return water pressure difference and the total flow demand as constraint conditions, calculates the optimal operation strategy of the parallel water pump system, and finally achieves the purposes of optimizing the dynamic hydraulic balance of the chilled water pipe network, saving energy and reducing consumption.
Generally, the equipment type selection of a chilled water pipe network of a central air-conditioning system is usually larger, so that when a central air-conditioning water pipe network system runs, the condition that the required flow at the tail end is smaller than the designed flow can occur, the temperature of a room at the tail end is uneven in temperature, and the hydraulic imbalance is caused. The existing method realizes the dynamic hydraulic balance of a pipe network system through a balance valve device basically, but the solution can cause the impedance of the pipe network to be increased, thereby increasing the energy consumption of a water pump and failing to achieve the purposes of energy conservation and consumption reduction completely. The invention provides a group intelligent optimization method for dynamic hydraulic balance of a chilled water pipe network of a central air conditioner, which is characterized in that an optimization objective function is established on the basis of knowing the flow demands of all branches at the tail end, and then the opening of a surface cooler valve of each air conditioner box and the rotating speed ratio of each water pump are obtained through solving, so that the dynamic hydraulic balance of the pipe network is realized.
Aiming at the schematic diagram of the chilled water pipe network of the central air conditioner shown in fig. 1, pipelines except a bypass pipe section in a loop formed by a branch where an air-conditioning box i is located and a bypass valve are integrated to be called a water supply and return pipeline of the air-conditioning box i, such as a red pipeline in fig. 1; the sum of the pressure drop of the water supply and return pipeline of the air-conditioning box i minus the pressure drop of the surface cooler regulating valve of the air-conditioning box is recorded as Hbi(ii) a Recording the equivalent impedance of the parallel connection cold machine and the auxiliary pipelines thereof shown in the blue part in the figure as Sc; recording the flow of the branch where the air-conditioning box i is as Q _ AHUiThe impedances are respectively denoted as S _ AHU _ self Li、S_AHU_selfRi(ii) a Recording the flow of the last branch connected with the branch where the air-conditioning box i is located as Q _ AHU _ uiThe branch impedances are respectively marked as S _ AHU _ u Li、S_AHU_uRi(ii) a Recording the flow of the next branch connected with the branch where the air-conditioning box i is positioned as Q _ AHU _ diThe branch impedances are respectively marked as S _ AHU _ d Li、S_AHU_dRi(ii) a Recording the flow of a branch where a water pump i is located as Q _ pumpiImpedance is respectively marked as S _ pump _ self Li、S_pump_selfRi(ii) a Recording the flow of the previous branch connected with the branch where the water pump i is positioned as Q _ pump _ uiThe branch impedances are respectively marked as S _ pump _ u Li、S_pump_uRi(ii) a Let the flow of the next branch connected to the branch in which the water pump i is located be Q _ pump _ diThe branch impedances are respectively marked as S _ pump _ d Li、S_pump_dRi。
Decoupling an optimization objective function into two parts, wherein the first part is to solve the minimum water supply and return pressure difference of the system and the valve opening of each parallel branch according to the flow demand of each parallel air conditioning box at the tail end, and the mathematical expression of the method is as follows:
min(Hsr)
Hbi+Ki(openingi)＝Hsr
wherein i is 1,2, …, n, Q _ AHUset iThe required flow of the ith air conditioning box at the tail end of the pipe network, HsrSupply and return water pressure difference for system, Ki(openingi) Opening the pressure drop of the regulation valve of the surface cooler of the ith air-conditioning boxiThe opening degree of the surface cooler regulating valve of the ith air-conditioning box is n, and the number of the air-conditioning boxes connected in parallel at the tail end of the system is n.
The second part is that under the condition that the pump lift and the flow of the water pump are known, the rotating speed ratio of the water pump can be obtained according to a pump lift-flow curve of the water pump and a similar law, further, the current efficiency of the water pump can be obtained according to an efficiency-flow curve of the water pump, and finally the power of the water pump is obtained. Therefore, the invention takes the flow of the water pump as an optimization variable, takes the minimum total energy consumption of the parallel water pump system as an optimization target, takes the pressure difference of supply and return water and the total flow demand as constraint conditions, and establishes an optimization model:
min(Wtotal)
wherein, WtotalTotal power of the parallel water pump system, HiIs the head of the ith water pump, H _ siThe pump lift required to be provided when the impedance of a branch where the pump is located is not considered for the ith water pump; qallM is the number of water pumps connected in parallel for the total flow demand of the system.
Expressed as the sum of m independent functions without coupling variables:
wherein, Pi(Q_pumpi) Is the power of the i-th water pump, XiIs the strategy space of the ith water pump.
The objective function can be solved in a distributed manner by using the ADMM algorithm:
wherein β is a penalty factor, μ is an algorithm parameter, and μ > m +1 needs to be satisfied.
The algorithm is a distributed asynchronous solving algorithm and is mainly divided into two stages.
The first stage, the Q _ pump is paired by the first CPNiSum and update λ, λ' and Q _ pump1；
In the second stage, other nodes update Q _ pump according to lambdai；
And iterating in such a way until the maximum iteration times is reached or the iteration precision requirement is met, and stopping the algorithm.
Referring to fig. 3 and 4, the specific steps are as follows:
s1, obtaining the minimum water supply and return pressure difference and the total flow which meet the system requirements through information interaction solution according to the flow requirements of each parallel branch at the tail end of the pipe network system by taking node flow balance and loop pressure drop balance as the basis;
s101, receiving a hydraulic balance adjustment starting signal flag _ AHU (1) by an intelligent computing node CPN of an air conditioning box;
s102, assuming the opening openning of a water valve of a surface cooler of an air conditioning boxiCalculating the pressure drop of the branch where the air-conditioning box is located, i.e. 1
H_AHU_selfi＝(Ki(openingi)+S_AHU_selfLi+S_AHU_selfRi)×(Q_AHUi set)2
S103, judging whether the number of the intelligent computing nodes CPN of the air-conditioning box is 1 and whether the intelligent computing nodes CPN of the neighbor air-conditioning box meet the self requirement or not by the intelligent computing nodes CPN of the neighbor bypass valve, and turning to the step S104 if the intelligent computing nodes CPN of the air-conditioning box meet the requirement, or turning to the step S105 if the intelligent computing nodes CPN of the neighbor air-conditioning box do;
s104, setting flag to be 1, and calculating a forward iteration pressure difference variable H1And flow variable Q1Flag, H1、Q1Sending the information to a neighbor intelligent computing node CPN, and going to step S111;
H1＝H_AHU_selfi+(S_AHU_dLi+S_AHU_dRi)×(Q_AHUi set)2
Q1＝Q_AHUi set
s105, the CPN judges whether a flag and a H transmitted by the CPN are received or not1、Q1If the variable and flag are 1, the step is switched to step S106, otherwise, the step continues to wait;
s106, the intelligent calculation node CPN of the air conditioning box judges the pressure drop H _ AHU _ self of the branch where the intelligent calculation node CPN is locatediWhether or not it is greater than the neighbor's transmitted forward iteration pressure difference variable H1If it is greater than, let H1＝H_AHU_selfiOtherwise H1＝H1；
S107, updating the forward iteration flow variable Q1And differential pressure variable H1；
Q1＝Q1+Q_AHUi set
H1＝H1+(S_AHU_dLi+S_AHU_dRi)×(Q1)2；
S108, judging whether the number of the intelligent computing nodes CPN of the neighbor air-conditioning box is 1 or not and whether the intelligent computing nodes CPN of the neighbor bypass valve exist or not, if so, turning to the step S109, and if not, turning to the step S109, otherwise, turning to the step S, wherein the number of the intelligent computing nodes CPN of the neighbor air-conditioning box is 1 or not1、Q1The variable is sent to a neighbor intelligent computing node CPN, and the step S111 is carried out;
s109, making the minimum water supply and return pressure difference H of the systemsr＝H1Total flow demand Qall＝Q1The flag _ pump is equal to 1, and the intelligent computing node CPN of the water feeding pump is sent by the intelligent computing node CPN of the bypass valve so as to start the water pump optimization algorithm;
s110, calculating a reverse iteration pressure difference variable H2And flow variable Q2And setting flag to be 2, transmitting the flag to a neighbor intelligent computing node CPN, and according to Ki(openingi) The pressure drop of the control valve i is determined and the openning is determinediAnd outputting, and making flag _ AHU equal to 0;
H2＝Hsr-(S_AHU_dLi+S_AHU_dRi)×(Qall)2
Q2＝Qall-Q_AHUi set
Ki(openingi)＝H2-(S_AHU_selfLi+S_AHU_selfRi)×(Q_AHUi set)2
wherein, S _ validiIs the actual impedance of the i-th air conditioning box surface cooler regulating valve, S _ valve0The resistance when the ith air conditioner case surface cooler regulating valve is fully opened is shown, and R is the adjustable ratio of the regulating valve.
S111, judging whether H transmitted by CPN of neighbor intelligent computing node is received2、Q2If the variable flag is 2, the step S112 is carried out if the condition is met, otherwise, the step S continues to wait;
s112, calculating a reverse iteration pressure difference variable H2And flow variable Q2Is prepared from H2、Q2Sending the flag variable to a neighbor air conditioning box intelligent computing node CPN according to Ki(openingi) The pressure drop of the control valve i is determined and the openning is determinediAnd outputs, and let flag _ AHU be 0.
H2＝H2-(S_AHU_dLi+S_AHU_dRi)×(Q2)2
Q2＝Q2–Q_AHUi set
Ki(openingi)＝H2-(S_AHU_selfLi+S_AHU_selfRi)×(Q_AHUi set)2
S2, calculating the optimal operation strategy of the parallel water pump system by the CPN according to the system supply and return water pressure difference and the total flow solved by the step S1, combining the impedance of the internal connecting pipe sections of the parallel water pumps, using the ADMM algorithm with the regular term, the water pump flow as an optimization variable, the total energy consumption of the parallel water pump system as an optimization target, and the supply and return water pressure difference and the total flow demand as constraint conditions, so as to realize the optimal operation of the system.
S201, after the water pump intelligent computing node CPN receives a parallel water pump optimization algorithm starting signal flag _ pump ═ 1, enabling the water pump initial iteration flow Q _ pump i1, the initial value lambda is 100;
s202, judging whether the number of the CPNs of the neighbor water pump intelligent computing nodes is 1 and the CPNs of the neighbor water pump intelligent computing nodes are neighbors without bypass valves, if so, turning to the step S203, otherwise, turning to the step S204;
s203, reverse iteration flow variable Q3＝Q_pumpi kIf yes, sending the flag to the neighbor intelligent computing node CPN, and going to step S208;
s204, judging whether Q transmitted by the CPN of the neighbor intelligent computing node is received or not3If yes, the step is shifted to step S205, otherwise, the process continues to wait;
s205, judging whether the CPN number of the intelligent computing nodes of the neighbor water pump is 1 or not and whether a neighbor bypass valve exists or notThe intelligent computing node CPN goes to S206 if the judging condition is met, otherwise, the reverse iteration flow variable Q is enabled3＝Q3+Q_pumpi kIs mixing Q with3The flag variable is sent to a neighbor water pump intelligent computing node CPN, and the step S208 is carried out;
s206, judging Q3+Q_pump1 k-QallWhether the control precision requirement is met or the iteration frequency reaches the maximum value is judged, if yes, the algorithm iteration is finished, flag _ pump is set to be 0, and all the intelligent water pump computing nodes CPN output water pump flow Q _ pumpi kSpeed ratio wiOtherwise, go to step S207;
s207, solving lambdak+1Let the forward iteration pressure difference variable H3＝Hsr+(S_pump_uLi+S_pump_uRi)×Qall 2And calculating Q _ pump by combining a standard distribution estimation algorithm1 k+1And λ'k+1Let the forward iterate the flow variable Q4＝Qall-Q_pump1 k +1And H is equal to 23、Q4、λk+1β, sending the flag variable to a neighbor intelligent computing node CPN;
s208, judging whether H transmitted by CPN of neighbor intelligent computing node is received3、Q4、λ′k+1β, changing the flag variable, wherein the flag is 2, if yes, going to step S209, otherwise, continuing to wait;
s209, enabling a forward iteration pressure difference variable H3＝H3+(S_pump_uLi+S_pump_uRi)×Q4 2And calculating Q _ pump by combining with a distribution estimation algorithmi k+1Then let the forward iteration flow variable Q4＝Q4–Q_pumpi k+1；
S210, judging whether the number of the intelligent computing nodes CPN of the neighbor water pump is 1 and the intelligent computing nodes CPN without the neighbor bypass valve are met, if the number of the intelligent computing nodes CPN meets the judgment condition, turning to S203, and if not, turning to H3、Q4、λ′k+1β, and sending the flag variable to the neighbor intelligent computing node CPN, and going to the stepS204。
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. 5, a central air-conditioning chilled water pipe network system is taken as a research object, the pipe network consists of four water pumps, three coolers, 10 air-conditioning boxes and 1 bypass valve, and 18 intelligent computing nodes CPN are required to be equipped to form a group intelligent platform of the system. The system is subjected to static hydraulic balance adjustment before being put into operation, and the water flow of each surface cooler is 40.381m under rated working conditions3H is used as the reference value. In order to verify the effectiveness of the optimization method provided by the invention, under the condition of keeping the flow of other air-conditioning boxes unchanged, 5 working conditions of 80%, 60%, 40%, 20% and 0% of the rated flow of each air-conditioning box are respectively set for experimental analysis, and the results are shown in tables 1 and 2.
TABLE 1 optimization strategy obtained by algorithm under test condition
As can be seen from the results of the optimization strategy shown in Table 1, the opening of the regulating valve of the surface cooler of the air-conditioning box above AHU-6 is the same as the system is subjected to static hydraulic balance regulation before the system is put into operation. Meanwhile, the opening degree of a certain branch regulating valve in the operation strategy obtained by the invention is fully opened, so that the strategy can ensure that the total impedance of a pipe network system is minimum, thereby achieving the purpose of saving energy of the system.
TABLE 2 flow results obtained by the Algorithm under test conditions
From table 2, it can be found that the flow rate of the air conditioning box obtained by the optimization method provided by the present invention is very similar to the flow rate set value, and the maximum relative error occurs in Q _ AHU6＝0m3In the case of/h, the maximum relative error is only 0.116%. Under other working conditions, except for a branch where the AHU-6 is located, the flow of other air-conditioning boxes in the result obtained by the optimization method provided by the invention is the same, and the relative error with the rated flow is 0.009%; and the flow of the branch where the AHU-6 is located also obtains a good control effect, and the maximum relative error is only 0.018%.
In order to verify the energy saving performance of the strategy obtained by the optimization method, the result obtained by the method is compared with the energy consumption under a certain actual working condition, and the comparison result is shown in table 3.
TABLE 3 comparison of actual operating conditions with the results of the algorithm optimization
As can be seen from table 3, if the operation strategy of the pipe network is not optimized during the operation process, although the dynamic hydraulic balance state can be achieved under the partial load condition, the opening degree of all the air conditioning box regulating valves may not be fully opened. Under the condition, the water pump needs to work at a higher frequency, and an extra lift is provided to overcome the resistance of the valve in the pipe network, so that the requirement of branch flow is met, and meanwhile, the requirement is one of the reasons for system energy waste. The distributed iterative optimization algorithm provided by the invention resets the opening of each air conditioning box regulating valve according to optimization calculation, and enables the opening of the regulating valve of the AHU-7 to be fully opened, thereby reducing the total impedance of a pipe network system and reducing the pressure difference of water supply and return of the system.
Furthermore, the method optimizes the operation strategy of the parallel water pump system according to the optimized water supply and return pressure difference, and finally obtains the operation strategy of starting the two water pumps. Meanwhile, in a pipe network system operating under an optimization strategy, the flow of each air conditioning box is very close to the set value, and the maximum relative error of the air conditioning box occurs in an AHU-1 branch and is only 0.015%.
Referring to fig. 6 and 7, the variation of the performance curves and the operating points of the water pumps before and after the optimization are shown, respectively, wherein the operating conditions of the two optimized water pumps are similar, so that the performance curves and the operating points are approximately coincident; it can be known from the figure that, in the original strategy, because the impedance of the pipe network is large, three water pumps which are started need to work at the working condition points with large lift and small flow, and the efficiency and the power of the water pumps are low at the moment. In the operation strategy of the algorithm optimization provided by the invention, because the impedance of a pipe network is reduced and one water pump is more closed, the flow rate of a single water pump is increased, the lift is reduced, the efficiency is increased, the total power of the system is reduced by 13.941kW, and the energy saving rate is about 29.634%. Therefore, the energy-saving effect of the distributed iterative optimization algorithm provided by the invention is very considerable.
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 (6)
1. A group intelligent optimization method for dynamic hydraulic balance of a chilled water pipe network of a central air conditioner is characterized in that intelligent computing nodes CPN are arranged in an air conditioning box, a bypass valve and water pump equipment in the chilled water pipe network, and all the intelligent computing nodes CPN are interconnected according to the actual physical topological connection relationship of the equipment to form a network communication system of a group intelligent framework; when one intelligent computing node CPN initiates an adjusting task, the rest intelligent computing nodes CPN cooperate with the initiating intelligent computing node CPN to perform information interaction with the respectively connected one-hop neighbor intelligent computing nodes CPN, corresponding distributed computation is completed aiming at the adjusting task, and the dynamic hydraulic balance of the chilled water pipe network is realized by the minimum total system power; establishing an optimization objective function, obtaining the minimum supply and return water pressure difference and total flow of the system meeting the flow demand of each air conditioning box at the tail end of the system through information interaction calculation by an intelligent calculation node CPN in the air conditioning box, and calculating the opening of a water valve of a surface cooler of the air conditioning box; an intelligent computing node CPN in the water pump equipment combines the impedance of a connecting pipe section in a parallel water pump according to the supply and return water pressure difference and the total flow demand of the system, utilizes an alternating direction multiplier method with a regular term to select the flow of the water pump as an optimization variable, takes the minimum total energy consumption of the parallel water pump system as an optimization target, takes the supply and return water pressure difference and the total flow demand as constraint conditions, calculates the optimal operation strategy of the parallel water pump system, and realizes the optimization of the dynamic hydraulic balance of the freezing water pipe network and energy conservation and consumption reduction according to the optimal operation strategy.
2. The group intelligent optimization method for the dynamic hydraulic balance of the chilled water pipe network of the central air conditioner according to claim 1, wherein the minimum water supply and return pressure difference and the total flow of the system, which meet the flow demand of each air conditioning box at the tail end of the system, are obtained by performing information interaction calculation on an intelligent calculation node CPN in the air conditioning box, and specifically comprise the following steps:
s101, receiving a hydraulic balance adjustment starting signal flag _ AHU (equal to 1) by an intelligent computing node CPN of an air conditioning box;
s102, assuming the opening openning of a water valve of a surface cooler of an air conditioning boxiCalculating the pressure drop of a branch where the air conditioning box is located as 1;
s103, judging whether the intelligent computing nodes CPN of the air-conditioning box meet the intelligent computing nodes CPN of which the number of the intelligent computing nodes CPN of the neighbor air-conditioning box is 1 and which do not have a neighbor bypass valve, and turning to the step S104 if the intelligent computing nodes CPN meet the conditions, or turning to the step S105 if the intelligent computing nodes CPN do not meet the conditions;
s104, setting flag to be 1, and calculating a forward iteration pressure difference variable H1And flow variable Q1Flag, H1、Q1Sending to the intelligent computing node CPN of the neighbor, and going to step S111;
s105, the intelligent computing node CPN of the air-conditioning box judges whether flag and H transmitted by the adjacent intelligent computing node CPN are received1、Q1If the variable and flag are 1, the step is switched to step S106, otherwise, the step continues to wait;
s106, the intelligent computing node CPN of the air-conditioning box judges the pressure drop H _ AHU _ self of the branch where the intelligent computing node CPN is locatediWhether or not it is greater than the neighbor's transmitted forward iteration pressure difference variable H1If it is greater than, let H1＝H_AHU_selfiOtherwise H1＝H1；
S107, updating the forward iteration flow variable Q1And differential pressure variable H1；
S108, judging whether the number of the intelligent computing nodes CPN of the neighbor air-conditioning box is 1 or not and whether the intelligent computing nodes CPN meet the requirement of the neighbor air-conditioning box or not, if so, turning to the step S109, and if not, setting flag and H to be equal to the number of the intelligent computing nodes CPN of the neighbor air-conditioning box, otherwise, turning to the step S1091、Q1The variable is sent to the intelligent computing node CPN of the neighbor, and the step S111 is carried out;
s109, making the minimum water supply and return pressure difference Hsr＝H1Total flow demand Qall＝Q1The intelligent computing node CPN of the water feeding pump is sent through the intelligent computing node CPN of the bypass valve to start a water pump optimization algorithm;
s110, calculating a reverse iteration pressure difference variable H2And flow variable Q2And setting flag to be 2, transmitting the flag to a neighbor intelligent computing node CPN, and according to Ki(openingi) The pressure drop of the control valve i is determined and the openning is determinediAnd outputting, and making flag _ AHU equal to 0;
s111, judging whether H transmitted by CPN of neighbor intelligent computing node is received2、Q2If the variable flag is 2, the step S112 is carried out if the condition is met, otherwise, the step S continues to wait;
s112, calculating a reverse iteration pressure difference variable H2And flow variable Q2Is prepared from H2、Q2Sending the flag variable to the intelligent computing node CPN of the neighbor according to Ki(openingi) The pressure drop of the control valve i is determined and the openning is determinediAnd output, and let flag_AHU＝0。
3. The group intelligent optimization method for the dynamic hydraulic balance of the chilled water pipe network of the central air conditioner according to claim 1 or 2, wherein the minimum supply and return water pressure difference of the system and the valve opening of each parallel branch are solved according to the flow demand of each air conditioning box connected in parallel at the tail end as follows:
wherein i is 1,2, …, n, Q _ AHUset iThe required flow of the ith air conditioning box at the tail end of the pipe network, HsrSupply and return water pressure difference for system, Ki(openingi) Opening the pressure drop of the regulation valve of the surface cooler of the ith air-conditioning boxiThe opening degree of an adjusting valve of a surface cooler of the ith air-conditioning box is n, the number of the air-conditioning boxes connected in parallel at the tail end of the system is HbiThe pressure drop of the surface cooler regulating valve of the air conditioning box is subtracted from the sum of the pressure drops of the water supply and return pipelines of the air conditioning box i.
4. The group intelligent optimization method for the dynamic hydraulic balance of the chilled water pipe network of the central air conditioner according to claim 1, wherein an alternating direction multiplier method with a regular term is used, the flow of a water pump is used as an optimization variable, the minimum total energy consumption of a parallel water pump system is used as an optimization target, the pressure difference of supply and return water and the total flow demand are used as constraint conditions, and the optimal operation strategy of the parallel water pump system is specifically calculated as follows:
s201, after receiving a parallel water pump optimization algorithm starting signal flag _ pump ═ 1, an intelligent computing node CPN of a water pump makes an initial iteration flow Q _ pump of the water pumpi1, the initial value lambda is 100;
s202, judging whether the number of the CPNs of the neighbor water pump intelligent computing nodes is 1 and the CPNs of the neighbor water pump intelligent computing nodes are neighbors without bypass valves, if so, turning to the step S203, otherwise, turning to the step S204;
s203, reverse iteration flow variable Q3＝Q_pumpi kAnd flag is 1, the information is sent to the intelligent computing node CPN of the neighbor,and go to step S208;
s204, judging whether Q transmitted by the CPN of the neighbor intelligent computing node is received or not3If yes, the step is shifted to step S205, otherwise, the process continues to wait;
s205, judging whether the number of the intelligent computing nodes CPN of the neighbor water pump is 1 or not and whether the intelligent computing nodes CPN of the neighbor bypass valve exist or not, if so, turning to S206, otherwise, enabling a reverse iteration flow variable Q3＝Q3+Q_pumpi kIs mixing Q with3The flag variable is sent to an intelligent computing node CPN of the neighbor water pump, and the step S208 is carried out;
s206, judging Q3+Q_pump1 k-QallWhether the control precision requirement is met or the iteration frequency reaches the maximum value is judged, if yes, the algorithm iteration is finished, flag _ pump is set to be 0, and the intelligent computing nodes CPN of all the water pumps output water pump flow Q _ pumpi kSpeed ratio wiOtherwise, go to step S207;
s207, solving lambdak+1Let the forward iteration pressure difference variable H3＝Hsr+(S_pump_uLi+S_pump_uRi)×Qall 2And calculating Q _ pump by combining a standard distribution estimation algorithm1 k+1And λ'k+1Let the forward iterate the flow variable Q4＝Qall-Q_pump1 k+1And H is equal to 23、Q4、λk+1β, sending the flag variable to the neighbor CPN;
s208, judging whether H transmitted by CPN of neighbor intelligent computing node is received3、Q4、λ′k+1β, changing the flag variable, wherein the flag is 2, if yes, going to step S209, otherwise, continuing to wait;
s209, enabling a forward iteration pressure difference variable H3＝H3+(S_pump_uLi+S_pump_uRi)×Q4 2And calculating Q _ pump by combining with a distribution estimation algorithmi k+1Then let the forward iteration flow variable Q4＝Q4–Q_pumpi k+1；
S210, judging whether the number of the intelligent computing nodes CPN of the neighbor water pump is 1 and the intelligent computing nodes CPN without the neighbor bypass valve are met, if the number of the intelligent computing nodes CPN meets the judgment condition, turning to S203, and if not, turning to H3、Q4、λ′k+1β, and sending the flag variable to the neighboring intelligent computing node CPN, and going to step S204.
5. The group intelligent optimization method for the dynamic hydraulic balance of the chilled water pipe network of the central air conditioner according to claim 1 or 4, wherein the flow of the water pump is used as an optimization variable, the minimum total energy consumption of a parallel water pump system is used as an optimization target, the pressure difference of supply and return water and the total flow demand are used as constraint conditions, and an optimization model is established as follows:
min(Wtotal)
wherein i is 1,2, …, m, WtotalTotal power of the parallel water pump system, HiIs the head of the ith water pump, H _ siThe pump lift required to be provided when the impedance of a branch where the pump is located is not considered for the ith water pump; qallFor the total flow demand of the system, m is the number of water pumps connected in parallel, Q _ pumpiFor the branch flow of the water pump i, S _ pump _ self Li、S_pump_selfRiRespectively, the impedance of the branch in which the water pump i is located, HsrSupplying and returning water pressure difference for the system, wherein Sc is equivalent impedance record of the parallel connection cold machine and the auxiliary pipeline thereof, S _ pump _ u LjAnd S _ pump _ uRjRespectively, the impedance of the upper two side branches connected with the branch in which the water pump i is located, Q _ pumpkThe flow of the branch where the water pump k is located.
6. The group intelligent optimization method for the dynamic hydraulic balance of the chilled water pipe network of the central air conditioner according to claim 5, wherein the distributed solution by adopting the alternating direction multiplier method with the regular term specifically comprises the following steps:
wherein β is a penalty factor, mu is an algorithm parameter, and mu is more than m + 1.
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