CN117366799A - Control method of building cold source system - Google Patents
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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
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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
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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
- F24F2140/00—Control inputs relating to system states
- F24F2140/50—Load
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Abstract
The invention discloses a control method of a building cold source system, which comprises the following steps: based on the load measurement value of the current unit combination tau-delta tau moment, predicting a load prediction value of tau moment: forming a plurality of selectable unit combinations based on the load predicted value and the load value of the current unit combination; optimizing and calculating the operation parameters of each unit combination to obtain the operation optimization parameters of each unit combination; and calculating the comprehensive operation power of each unit combination based on the load rate of each unit combination, and selecting one unit combination as an optimization result according to the comprehensive operation power. The invention aims at comprehensive energy efficiency of the water chiller, comprehensively optimizes the operation strategy of the cold source system consisting of start-stop of the chiller, load rate of each chiller, water supply temperature of chilled water of each chiller, frequency of chilled water pump corresponding to each chiller and fan control frequency set value of each cooling tower, ensures that the cold source system operates in a more efficient zone and improves the operation energy efficiency of the system on the premise of ensuring the cooling effect.
Description
Technical Field
The invention relates to the technical field of control of building cold source systems, in particular to a control method of a building cold source system.
Background
Buildings play an important role in global energy use and carbon dioxide emissions, accounting for one third of world energy use and one fourth of carbon dioxide emissions. Since HVAC (heating, ventilation and air conditioning) systems account for 38% of building energy consumption, the energy saving potential of building energy consumption is enormous. The building cold source system is designed to meet the air conditioning requirement of a building and consists of a water chilling unit, a transmission and distribution pipeline, heat exchange equipment and a cold source control system. The cold supply equipment of the building cold source system comprises an air-cooled water chilling unit, a ground source heat pump, a direct expansion water chilling unit and the like, the transmission and distribution pipeline comprises a water supply pipe, a water return pipe, a branch pipeline and the like, and the heat exchange equipment comprises a radiation plate type heat exchanger, a fan coil and the like. The cold source control system is responsible for controlling the operation of the building cold source system, including cold load calculation, temperature control, water flow control and the like.
The development process of the cold source group control algorithm along with the development of building energy management and intelligent technology is divided into the following stages: the initial stage: the cold source group control algorithm initially appears in the eighties of the last century, and mainly aims to solve the problems of energy waste and comfort level of the traditional cold source system. The stage is mainly based on experience and rule-formulated cold source group control algorithm, and the control strategy is mainly a fixed control scheme formulated according to season, time, building type and other factors. Mid-stage: with the progress of computer technology and the application of intelligent control technology, the cold source group control algorithm gradually develops to intelligence and self-adaption. The control strategy of the cold source group control algorithm mainly based on the technologies of model predictive control, artificial neural network control and the like can be dynamically adjusted according to the real-time cold load demand, and the efficient utilization of energy and the improvement of comfort level are realized. Modern stage: with the wide application of big data, cloud computing, internet of things and other technologies, the cold source group control algorithm further develops to intelligence and networking. The cold source group control algorithm based on the cloud computing platform and the Internet of things technology can realize remote monitoring and control of the cold source system, and improves the running efficiency and stability of the cold source system.
At present, the cold source group control algorithm still has some defects, which are expressed in the following aspects:
1. load prediction is inaccurate: the core of the cold source group control algorithm is to dynamically adjust according to the real-time cold load demand, however, errors exist in the prediction of the cold load, so that the control effect of the cold source system is not satisfactory. 2. Dependency on control strategy: the control effect of the cold source group control algorithm is greatly dependent on the formulation of a control strategy, and if the control strategy is unreasonable or not suitable for a specific building, the control effect is poor. 3. The system complexity is high: the cold source group control algorithm needs to build a model of a cold source system, collect a large amount of real-time data, and perform complex calculation and control, so that the complexity of the system is high and the system is not easy to realize.
Accordingly, further research and improvement are needed to improve the technical application effect and stability.
Disclosure of Invention
The invention aims to provide a control method of a building cold source system aiming at the problems in the prior art.
A control method of a building cold source system comprises the following steps:
based on the load measurement value of the current unit combination tau-delta tau moment, predicting a load prediction value of tau moment:
forming a plurality of selectable unit combinations based on the relation between the load predicted value and the load value of the current unit combination;
optimizing and calculating the operation parameters of each unit combination to obtain the operation optimization parameters of each unit combination;
and calculating the comprehensive operation power of each unit combination based on the load rate of the operation optimization parameters, selecting one unit combination as an optimization result according to the comprehensive operation power, and outputting an operation strategy formed by the corresponding operation optimization parameters.
The method for predicting the load predicted value at the moment tau based on the load measured value at the moment tau-delta tau of the current unit combination comprises the following steps:
load measurement E based on current unit combination tau-delta tau moment d,τ-Δτ Obtaining a deterministic load prediction value at the time tauRandom load measurement X with time τ - Δτ d,τ-Δτ ；
Executing a weighted recursive algorithm target function FFRLS, and correcting a stochastic load autoregressive coefficientThen executing a random load autoregressive model to obtain a random load predicted value +.>
The deterministic load predictorAnd a random load predictor->The sum is the load predictive value +.>
The deterministic load predictorObtained by a deterministic model based on a one-time moving average algorithm, the expression is as follows:
in the method, in the process of the invention,is a deterministic prediction value of time tau-delta tau, E d,τ-Δτ Is the load measurement value at the moment tau-delta tau, lambda is the exponential smoothing coefficient;
unit combination τ moment load measurement E d,τ The cooling capacity and the indoor temperature monitoring value of the real-time water chiller system are calculated, and the cooling capacity and the indoor temperature monitoring value are expressed as follows:
wherein ρ is water C p,water Is the volume weight of water, is defined by the density rho of chilled water water Specific heat capacity C with water p,water Multiplication is carried out to obtain;is the flow of chilled water at time tau-1; t (T) chws And T chwr The water supply temperature and the water return temperature of cold water at the moment tau-1 are respectively; v (V) B The cooling volume of the building is the product of the cooling area and the floor height of each layer of the building; />And h n,des Respectively the detection value and the design value of the indoor air enthalpy value tau at the moment t τ The building cooling adjusting time is; said random load predictor +.>Random load measurement X, described as the first n moments τ-jΔτ Error value +.>The error value is the random load measurement value X of the first n moments τ-jΔτ Random load predictive value +.>The difference, the expression is as follows:
the weighted recursive algorithm objective function FFRLS is:
alpha is a forgetting factor, if the intermediate transfer matrix P is not positive, the latest load data is taken to retrain a load prediction model formed by the deterministic model and the stochastic load autoregressive model; otherwise, obtaining the load measurement E at tau moment d,τ After the training data is formed, a deterministic model is executed to obtain random load measurement values X at the first n times which are the same as the number of the training data τ-jΔτ Then initializing a random load autoregressive coefficient by a least square methodCalculating a random load predictor +.>
The plurality of machine set combinations are formed based on the current machine set combination, and the corresponding machine set combination is obtained by adding, reducing or replacing one currently running water chilling unit and maintaining the adding and subtracting machine strategy of the currently running water chilling unit on the current machine set combination. The number of the machine set combinations obtained by the adding, reducing and replacing operations is not unique, and should be consistent with the number of cold water machine sets which are not started at present, the formed machine set can be respectively a first machine set combination formed by adding machines on the current machine set, a second machine set combination formed by subtracting machines on the current machine set, a third machine set combination formed by replacing one machine set in the current machine set and keeping the operation control strategy of the current machine set unchanged, and the operation control strategy of the current machine set is kept as a fourth machine set combination.
Wherein each of the selectable plurality of crew combinations satisfies the following condition:
and the load predicted value is smaller than the product of the total load and the maximum load rate of the unit combination, and meanwhile, if the number of the units in the unit combination is greater than 1, the load predicted value is greater than the product of the rated load and the minimum load rate of the minimum unit in the unit combination.
Wherein the operation optimization parameters comprise the load rate PLR of each unit in the unit combination ch,i The cold water supply temperature set value T of each unit chws The frequency set value f of the water pump corresponding to each unit set,i The frequency set value f of the fan of the cooling tower corresponding to each unit twr,i 。
Wherein, the cold water supply temperature of each unit combination is set at a value T chws,i The optimization is calculated by:
the cold water supply temperature set value T of each unit combination chws With indoor wet bulb temperature T n,wb Difference T of n,wb -T chws The linear relation with the total load factor PLR is satisfied, namely:
wherein T is n,wb,des Indicating the indoor wet bulb temperature set value T chws,des T represents the set value of the cold water supply temperature of the unit chws,min Representing the cold water supply temperature of the unitMinimum degree, T chws,max Represents the maximum value of the cold water supply temperature of the unit, P ch Des is the rated power of the water chilling unit, P chwp,des Is the rated power of the chilled water pump S chws The sensitivity coefficient of the running power of the water chilling unit to the temperature of cooling water is obtained for any unit i by the following formula in the face of the working condition of a single unit:
wherein P is ch,i The running power of the water chilling unit is represented by T chws,i The water supply temperature T of the water chilling unit i is represented cwr,set Indicating the return water temperature set value of cooling water, PLR ch,set Representing the running power set value of the water chilling unit, P rated,i The rated power of the water chilling unit i is represented by a coefficient a i ,b i A performance characteristic equation from the chiller i;
if a plurality of units are combined to operate, S chws Weighting calculation is carried out by taking rated power of each water chilling unit as weight;
wherein P is ch,des,i The rated power of the water chilling unit i under the design working condition is represented;
the water chilling unit performance characteristic equation sets the unit power P ch Described as cold water supply temperature T chws Backwater temperature T of cooling water cwr The function of the total load rate PLR of the water chilling unit, and the running power of a water chilling unit are expressed as follows:
solving the set value T of the cold water supply temperature of each unit according to the linear relation chws,i 。
Wherein the frequency of the water pump is set to be a value f set,i The optimization is calculated by the following equation:
in the method, in the process of the invention,setting value of chilled water supply flow rate for tau-1,/->The chilled water pump frequency at time τ -1; />And respectively representing the rated load of each water chilling unit under the current optimized unit combination and the rated load of each water chilling unit under the unit combination at the last moment. In the working condition of the machine, there is no +.>Finally, the water pump frequency set value f is obtained by the following method set,i Chilled water supply flow set point:
wherein f set,min 、f set,max Respectively represent the minimum and maximum values of the frequency set value of the water pump，ΔT set F represents the temperature difference set value of the chilled water supply and return water max Indicating the maximum frequency of the water pump.
Wherein, the load rate PLR of each unit combination ch,i The method is obtained by processing and optimizing in the following way:
for the unit combination, the unit load and cold water supply temperature optimization function is described as follows:
in which Q rated,i Represents rated load of the water chilling unit i, T chws,min Representing the lower limit value of the water supply temperature set value of the chiller, obtaining an equation constraint matrix equation by using the chiller load and the chilled water supply temperature optimization function, and solving the equation constraint matrix equation to obtain an optimization result X, wherein the optimization result X comprises the load rate PLR of each chiller ch,i And cold water supply temperature T chws,i I is the number of the water chilling unit: the equation constraint matrix equation is as follows:
monitoring the calculation result of X, if the inequality constraint is not satisfied, constructing an augmentation matrix to constrain the inequality into an equality constraint, and then solvingUntil the calculation result meets all inequality constraints;
wherein the cooling tower fan controls the frequency f twr,i Load factor PLR based on each unit combination ch,i And (3) calculating and optimizing to obtain:
wherein f twr,max Represents the maximum power of a fan of the cooling tower, a twr,des Is the difference between the wet bulb temperature of the air and the water supply temperature of the cooling water under the design working condition; r is (r) twr,des Is the temperature difference of the cooling water supply and return water under the design working condition; p (P) ch,des Is the rated power of the water chilling unit; p (P) twr,des Is the rated power of a fan of the cooling tower; s is S cwr The sensitivity coefficient of the running power of the water chilling unit to the temperature of the cooling water is shown as follows if the sensitivity coefficient of the running power of the water chilling unit to the temperature of the cooling water is shown as follows for the running condition of the single unit i:
wherein a is i ,b i The coefficient comes from the performance characteristic equation of the water chilling unit, T cwr,i Is the return water temperature of the cooling water of the water chilling unit i; t (T) chws,i The chilled water supply temperature of the water chilling unit i;
if a plurality of units are operated in a combined way, the sensitivity coefficient S of the operation power of the plurality of water chilling units to the temperature of cooling water cwr,des Weighting calculation is carried out by taking rated power of each water chilling unit as weight;
the method for calculating the comprehensive operation power of each unit combination based on the load rate of each unit combination comprises the steps of:
load factor PLR based on each unit combination ch,i Obtaining the unit comprehensive operation power P of each unit combination;
comparing the lowest unit comprehensive operation power P min Unit integrated operating power combined with an operating unitIf->Selecting the unit combination with the lowest power as an optimization result; otherwise, selecting the running unit combination as an optimization result.
The invention realizes the group control of the water chilling unit of the primary pump (variable frequency pump) system, and rapidly responds to the change trend of the building load according to the real-time change of the building heat demand so as to ensure the heat consumption of the tail end heat exchange equipment and the heat comfort of the indoor environment; the performance characteristics of each water chilling unit can be identified efficiently, and the characteristic distortion of the water chilling unit in the running process can be tracked, so that the water chilling unit is more fit with the performance of actual equipment; the comprehensive energy efficiency of the water chilling unit is used as an objective function, and the operation strategy of the cold source system consisting of the start-stop of the unit, the load rate of each unit, the water supply temperature of each unit, the corresponding water pump frequency set value of each unit and the control frequency set value of each cooling tower fan is comprehensively optimized, so that the cold source system can be operated in a higher-efficiency interval on the premise of guaranteeing the cooling effect, and the operation energy efficiency of the system is improved.
The invention dynamically adjusts the operation mode and parameters of the cold source system based on the real-time change of the cold supply demand of the building, realizes the effective utilization of energy, reduces the energy consumption and the emission of the carbon dioxide isothermal chamber gas, and achieves the aims of energy conservation and emission reduction; the indoor temperature control precision of the building can be improved, the temperature fluctuation is reduced, the thermal comfort is improved, and the quality of living and working environments is improved by optimizing the operation of the cold source system; by optimizing the operation of the cold source system, the stability and the reliability of the system can be improved, the failure rate of the system can be reduced, and the maintenance and replacement cost can be reduced, thereby reducing the maintenance cost of the building.
The invention is suitable for the operation scene of one machine, one pump and one tower unit system connection, requires real-time monitoring of water temperature, frequency, cold machine power and indoor and outdoor temperature and humidity state data, and is suitable for the water chilling unit equipment capable of realizing self-adjustment of the unit by resetting the cold water supply temperature.
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FIG. 1 is a flow chart of a control method of a cold source system of a building according to an embodiment of the 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 in the embodiments of the present invention will be clearly and completely described in the following in conjunction with 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.
According to the control method of the building cold source system, disclosed by the embodiment of the invention, the load is regulated in real time by utilizing the real-time supply and return water temperature difference and the indoor temperature and humidity state monitoring, the load prediction is realized by carrying out time series analysis on the historical load measured value, the historical load change trend is learned, meanwhile, the running load of the water chilling unit is predicted when the indoor temperature and humidity state change is responded quickly, and the heat requirement of terminal equipment is ensured.
The water chilling unit is optimized, the water chilling unit water supply temperature, the frequency of a chilled water pump set, the water chilling unit water supply temperature set value and the water chilling unit load distribution of various unit combinations are optimized in a coupling mode, the optimal water chilling unit water supply temperature set value and the optimal water chilling pump frequency set value are given, the various unit combinations are selected in a specific mode, an optimal operation strategy is determined, a start-stop control instruction of water chilling unit equipment is output, and the water chilling unit water supply temperature set value and the water pump frequency set value are output. The water chilling unit optimization comprises the following steps: a) Under specific total load, selecting various cold machine adding and subtracting strategies including the current operation strategy, combining specific cold machines, determining cold water supply temperature according to unit power characteristics and indoor temperature and humidity states, and ensuring that the water supply temperature meets indoor cold supply requirements; b) And (3) performing coupling optimization on the running frequency of the cold water pump under the specific chiller combination, the cold water supply temperature set value of the chiller and the load distribution of each chiller to obtain an optimal strategy of the chiller combination. And comparing a plurality of operation strategies to ensure the stable operation of the unit and determine the optimal operation strategy aiming at reducing the operation energy consumption of the system cooling machine.
The frequency of the cooling tower is optimized, the optimal real-time running frequency of the cooling tower fan is determined based on an optimal cooling machine optimizing strategy and unit load distribution, a set value of the frequency of the cooling tower fan is output, the energy consumption of the cooling water side is reduced on the premise of guaranteeing the cooling water cooling effect, and the running frequency of the cooling tower fan is optimized.
Referring to fig. 1, the control method of the cold source system of the building according to the embodiment of the invention comprises the following steps:
s1, predicting a load predicted value at the moment tau based on a load measured value at the moment tau-delta tau of the current unit combination:
s2, forming a plurality of selectable unit combinations based on the relation between the load predicted value and the calculated load value of the current unit combination;
s3, optimizing and calculating the operation parameters of each unit combination to obtain the operation optimization parameters of each unit combination;
s4, calculating the comprehensive operation power of each unit combination based on the load rate in the operation optimization parameters, selecting one unit combination as an optimization result according to the comprehensive operation power, and outputting an operation strategy according to the corresponding operation optimization parameters.
In step S1, based on the load measurement value of the current unit combination tau-delta tau moment, the load prediction value of the predicted tau moment can be realized by a load prediction model, the load prediction model consists of a deterministic model and an autoregressive model, and the deterministic model gives the deterministic load prediction of the tau momentValue ofThe autoregressive model gives the random load prediction value +.>Load prediction value at time τ +.>From deterministic load prediction value +.>Random load predictorThe composition, expression is as follows:
wherein the deterministic load prediction value among the load prediction valuesObtained by a one-time moving average algorithm, as follows:
wherein the method comprises the steps ofIs the deterministic load predictive value of tau-delta tau moment, E d,τ-Δτ Is the load measurement at time τ - Δτ, λ is the exponential smoothing coefficient, λ=0.1.
Wherein the load measurement E at time τ d,τ The cooling capacity and the indoor temperature monitoring value of the real-time water chiller system are calculated, namely:
the first part on the right of the equation is the cooling capacity of a water chilling unit system, ρ water C p,water Is the volume weight of water (kj/(m) 3 ·K))；Is the flow rate (m) of the chilled water at time τ -1 3 /h)；T chws And T chwr The temperatures (DEG C) of the chilled water supply and return water at the time τ -1 are respectively. The second part is to evaluate the indoor cooling capacity by comparing the indoor air enthalpy value with the design working condition, wherein V B Is the cooling volume of the building, namely the product (m) 3 )；/>And h n,des The detection value and the design value of the indoor air enthalpy value tau at the moment can be calculated through the corresponding indoor temperature/humidity value T, RH; t is t τ The cooling adjustment time of the building is generally 3600s;
wherein, the randomness load predicted value at tau moment in the load predicted valuesObtained by means of an autoregressive model, the stochastic load predictor +.>Random load measurement X, described as time τ -jΔτ τ-jΔτ Error value +.>Is a linear combination of->As the random load predictive value of tau-j delta tau moment, the autoregressive coefficientCalculation using weighted recursive least squares FFRLS:
the objective function FFRLS of the weighted recursive algorithm is set to:
correction of stochastic load autoregressive coefficients phi using weighted recursive least squares FFRLS recursion i The forgetting factor α may be set to 0.995. In addition, due to FFRLS algorithm, if the intermediate transfer matrix P is not positive, coefficients can be causedSmaller and smaller, positive characterization of the monitoring matrix P is required. If the transmission matrix P is in an abnormal condition, the latest load data are taken to retrain the load prediction model.
When the load prediction model pre-trains the process, a sufficient random load prediction value E is obtained at tau moment as training data d,τ Later (generally, 3-7 days of operation data of a building cold source system are adopted, the period is 15-30 min), and a random load measurement value X at the moment tau-j delta tau, which is the same as the quantity of training data, is obtained by executing a deterministic model τ-jΔτ As a random load measurement sample. Then, initializing a random load autoregressive coefficient by a least square method
After the pre-training of the load prediction model is completed, the load prediction model is based on the updated load measurement value E at the time tau-delta tau of the water chilling unit d,τ-Δτ Firstly, executing a deterministic model to obtain a deterministic load predicted value at tau momentRandom load measurement X with time τ - Δτ d,τ-Δτ Then, the weighted recursive least square FFRLS is executed, and the stochastic load autoregressive coefficient is corrected>Finally, executing a random load autoregressive model to obtain a random load predicted value ++at the moment tau>Finally, the load predictive value +.>
In the embodiment of the present invention, in step S2, the forming of a plurality of selectable chiller units based on the load prediction value and the calculated load of the current chiller unit includes:
based on the operation combination of the current water chilling unit, various operation combinations of the water chilling units are proposed according to the following four processes and two requirements.
a) And (3) reducing: if the historical load is gradually reduced and the number of running units is more than 1, the machine is allowed to be reduced;
b) And (3) maintaining: a control strategy for maintaining the operation of the original unit (namely the current operation unit);
c) And (3) replacing: under the original unit operation control strategy, replacing one unit currently operated in the unit combination;
d) Adding: if the historical load is gradually increasing and a set which is not operated exists, adding the machine is allowed;
in addition, the proposed operation combination of the water chiller needs to meet the following requirements:
a) Load predictive value<Total load of unit combination maximum load rate PLR of water chilling unit max ；
b) If the number of the medium-sized units is>1, then the load predicted value is required>Rated load of minimum unit in unit combination x minimum load rate PLR of water chilling unit min 。
In the embodiment of the invention, the units and the water chilling unit are synonymous.
In the embodiment of the invention, the process of optimizing the running strategy of the water chilling unit is as follows:
3.1. initializing/updating a chiller performance characteristic equation
In the invention, the unit power P ch,i Described as cold water supply temperature T chws,i Backwater temperature T of cooling water cwr,i Unit load factor PLR ch,i Is a function of (1), namely:
FFRLS recursive correction coefficient a using weighted recursive least squares i ,b i The forgetting factor α may be set to 0.995. In the formula, the subscript i is used for referring to parameters of the ith water chilling unit. ,
3.2. group control strategy optimization for water chiller
And optimizing the operation strategy of each group of unit operation combinations based on the plurality of groups of different unit operation combinations.
3.2.1. Determining the cold water supply temperature:
cold water supply temperature T of water chiller chws,i With indoor wet bulb temperature T n,wb Difference T of n,wb -T chws The total load rate PLR of the unit meets the linear relation, namely:
wherein P is ch,des Is rated power of a water chilling unit, P twr,des Is a cooling tower fanRated power, S chws Is the sensitivity coefficient of the running power of the water chilling unit to the temperature of the cooling water;
the sensitivity coefficient of the operation power of the single unit to the temperature of the cooling water can be obtained by the following formula, and the coefficient a i ,b i From the chiller performance characteristic equation.
If a plurality of units are operated in a combined way, the sensitivity coefficient S of the operating power of the plurality of units to the temperature of cooling water chws Weighting calculation is carried out by taking rated power of the unit as weight:
3.2.2 optimizing Water Pump delivery parameters (including Water Pump frequency set point f set,i Cold water flow set point)；
For a unit combination, the adjustment ratio of the water pump frequency is defined as:
r f,i is the frequency set value f of the water pump set,i Water pump frequency at time tau-1>Is a ratio of (2);
frequency set value f of water pump set,i With the water pump frequency at tau-1 momentRatio r of (2) f,i Positive correlation with the front-to-back load ratio of the unit combination and the total rated load of the unit combination, the ratio r f,i Expressed as follows;
from the ratio r f,i Calculating the frequency set value f of the water pump set,i Set value of cold water flowIf no water pump frequency at time τ -1 is present during the addition operation>Cold water flow set point->Giving a hypothesis value according to a preset method;
the water pump frequency f is performed by the following expression set,i Cold water flow set value at time tauIs optimized by:
/>
the water pump frequency set value f can be obtained through the above processing set,i Cold water flow set point
3.2.3 optimizing the load distribution of the chiller (optimizing the load factor PLR of the set combination) ch,i With the temperature set value T of the cold water supply chws,i )：
For a certain unit combination, the load and cold water supply temperature optimization function of the unit combination is described as follows:
obtaining an equation constraint matrix equation by the functions, and solving the equation constraint equation to obtain an optimization result X, wherein the 3i and 3i+1 items (i is the number of the water chilling unit) in the X are the load factors PLR of each unit ch,i And cold water supply temperature T chws,i ：
Monitoring the calculation result of X, if the calculation result does not meet the inequality constraint, constructing an augmentation matrix to enable the inequality constraint to be an equality constraint, and then solvingUntil the calculation results satisfy all inequality constraints. The augmentation matrix is as follows:
the inequality constraint to equality constraint is as follows:
the method can realize the optimization of the load rate PLR of the unit combination ch,i With the temperature set value T of the cold water supply chws,i 。
3.2.4 optimizing Cooling tower Fan control parameters (optimizing Cooling tower Fan control frequency setpoint f twr,i )；
Load factor PLR based on unit combination ch,i The cooling tower fan control frequency f is obtained by the following calculation twr,i ：
Wherein a is twr,des Is the difference between the wet bulb temperature of the air and the water supply temperature of the cooling water under the design working condition; r is (r) twr,des Is the temperature difference of the cooling water supply and return water under the design working condition; p (P) ch,des Is the rated power of the water chilling unit; p (P) twr,des Is the rated power of a fan of the cooling tower; s is S cwr Is the sensitivity coefficient of the running power of the water chilling unit to the temperature of the cooling water; the method comprises the steps of carrying out a first treatment on the surface of the
If the working condition of a single unit is faced, the coefficient a can be obtained for any water chilling unit i by the following formula i ,b i From the chiller performance characteristic equation.
If a plurality of units are operated in a combined way, the sensitivity coefficient S of the operation power of the plurality of water chilling units to the temperature of cooling water cwr,des The weight calculation should be performed by taking the rated power of the unit as the weight.
Through the above processing, the fan control frequency set value f of the cooling tower can be optimized twr,i 。
In the embodiment of the invention, the load rate PLR of each unit combination is obtained based on the above ch,i The comprehensive operation power of the water chilling unit of each unit combination is obtained through the following formula.
Comparing the lowest unit comprehensive operation power P in the unit combination min Integrated operating power combined with an operating unitIf->Outputting an operation strategy by taking the combination of the units with the lowest power as a result; otherwise, outputting the operation strategy by taking the combination of the running units as a result. The output operation strategy comprises a chiller unit load rate PLR ch,i Setting value T of cold water supply temperature chws,i Frequency setting f of water pump set,i Fan control frequency set point f for cooling tower twr,i . The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention. />
Claims (10)
1. The control method of the building cold source system is characterized by comprising the following steps:
based on the load measurement value of the current unit combination tau-delta tau moment, predicting a load prediction value of tau moment:
forming a plurality of selectable unit combinations based on the relation between the load predicted value and the load value of the current unit combination;
optimizing and calculating the operation parameters of each unit combination to obtain the operation optimization parameters of each unit combination;
and calculating the comprehensive operation power of each unit combination based on the load rate of the operation optimization parameters, selecting one unit combination as an optimization result according to the comprehensive operation power, and outputting an operation strategy formed by the corresponding operation optimization parameters.
2. The method for controlling a cold source system of a building according to claim 1, wherein the predicting the load predicted value at the time τ based on the load measured value at the time τ - Δτ of the current unit combination comprises:
load measurement E based on current unit combination tau-delta tau moment d,τ-Δτ Obtaining a deterministic load prediction value at the time tauRandom load measurement X with time τ - Δτ d,τ-Δτ ；
Executing a weighted recursive algorithm target function FFRLS, and correcting a stochastic load autoregressive coefficientThen executing a random load autoregressive model to obtain a random load predicted value +.>
The deterministic load predictorAnd a random load predictor->Load prediction with sum of τ timeValue->
The deterministic load predictorObtained by a deterministic model based on a one-time moving average algorithm, the expression is as follows:
in the method, in the process of the invention,is a deterministic prediction value of time tau-delta tau, E d,τ-Δτ Is the load measurement value at the moment tau-delta tau, lambda is the exponential smoothing coefficient;
wherein, the unit is combined with the load measurement value E at tau moment d,τ The real-time cooling capacity and the indoor temperature monitoring value of the unit combination are calculated, and the real-time cooling capacity and the indoor temperature monitoring value are expressed as follows:
wherein ρ is water C p,water Is the volume weight of water, is defined by the density rho of chilled water water Specific heat capacity C with water p,water Multiplication is carried out to obtain;is the flow of chilled water at time tau-1; t (T) chws And T chwr The water supply temperature and the backwater temperature of cold water at the moment tau-1 are respectively; v (V) B The cooling volume of the building is the product of the cooling area and the floor height of each layer of the building; />And h n,des Respectively the detection value and the design value of the indoor air enthalpy value tau at the moment t τ The building cooling adjusting time is;
the random load predictive valueRandom load measurement X, described as time τ -jΔτ τ-jΔτ Error value thereofIs a random load measurement X at time τ -jΔτ τ-jΔτ With corresponding X τ-jΔτ Time-of-day random load predictor +.>The difference, the expression is as follows:
the weighted recursive algorithm objective function FFRLS is:
alpha is a forgetting factor, if the intermediate transfer matrix P is not positive, the latest load data is taken to retrain a load prediction model formed by the deterministic model and the stochastic load autoregressive model; otherwise, obtaining the load measurement E at tau moment d,τ After the training data is formed, a deterministic model is executed to obtain random load measurement value X at the corresponding moment with the same quantity of the training data τ-jΔτ Then initializing a random load autoregressive coefficient by a least square methodCalculating a random load predictor +.>
3. The control method of a building cold source system according to claim 1, wherein the plurality of unit combinations are formed by adding, subtracting or replacing one currently operated water chiller and maintaining an add-subtract strategy of the currently operated water chiller based on the current unit combination, and are respectively a first unit combination formed by adding machines on the current unit, a second unit combination formed by subtracting machines on the current unit, a third unit combination formed by replacing one unit of the current unit combinations and maintaining a unit operation control strategy unchanged, and an operation control strategy of the current unit combination is maintained as a fourth unit combination.
4. The method of claim 1, wherein each of the selectable plurality of combinations of units satisfies the following condition:
and the load predicted value is smaller than the product of the total load and the maximum load rate of the unit combination, and meanwhile, if the number of the units in the unit combination is greater than 1, the load predicted value is greater than the product of the rated load and the minimum load rate of the minimum unit in the unit combination.
5. The method for controlling a cold source system of a building according to claim 1, wherein the operation optimization parameter includes a load factor PLR of a combination of units ch,i Cold water supply temperature set point T of machine set combination chws,i Water pump frequency set value f of unit combination set,i Set value f of fan frequency of cooling tower combined by unit twr,i 。
6. The method for controlling a cold source system of a building according to claim 5, wherein the cold water supply temperature T of the unit assembly chws,i Is determined by the following means:
cold water supply temperature T for each unit combination chws,i With indoor wet bulb temperature T n,wb The difference and the load rate PLR of the unit combination satisfy the linear relation, namely:
wherein T is n,wb,des T represents the indoor wet bulb temperature set value under the design working condition chws,des T represents the set value of the cold water supply temperature under the design working condition chws,min Represents the minimum value of the cold water supply temperature T chws,max Represents the maximum value of the water supply temperature of cold water, P ch,des Is rated power of a water chilling unit, P chwp,des Is the rated power of the chilled water pump; s is S chws Is the sensitivity coefficient of the running power of the water chilling unit to the temperature of the cooling water;
the sensitivity coefficient of the single water chilling unit is obtained by the following formula:
wherein P is ch,i The running power of the water chilling unit is represented by T cwr,set Indicating the return water temperature set value of cooling water, PLR ch,set Representing the running power set value of the water chilling unit, P rated,i The rated power of the water chilling unit is represented by a coefficient a i ,b i From the chiller performance characteristic equation;
if a plurality of units are operated in a combined way, the operation power of the water chilling unit is sensitive to the temperature of cooling waterPerceptual coefficient S chws Weighting calculation is carried out by taking rated power of the unit as weight;
P ch,des,i the rated power of the water chilling unit under the design working condition is represented;
the running power P of the water chilling unit is calculated by the performance characteristic equation of the water chilling unit ch Described as cold water supply temperature T chws Backwater temperature T of cooling water cwr The function of the total load rate PLR of the water chilling unit, and the running power of a water chilling unit are expressed as follows:
obtaining a water supply temperature set value T through the linear relation chws,i 。
7. The method of claim 6, wherein the water pump frequency is set to a set value f set,i The optimization is calculated by the following equation:
in the method, in the process of the invention,setting the flow rate of chilled water supply at tau-1, f i τ-1 The chilled water pump frequency at time τ -1; q (Q) rated,i 、/>Respectively representing rated load of each water chilling unit of the unit combination and rated load of each water chilling unit under the unit combination at the last moment; during the working condition of the adding machine, f is not existed i τ-1 、/>The water pump frequency set point f is obtained by the following method set,i Chilled water supply flow set value +.f at time τ>f set,min 、f set,max Respectively represent the minimum value and the maximum value of the frequency set value of the water pump, f max Represents the maximum frequency of the water pump, delta T set The set value of the temperature difference of the chilled water supply and return water is shown:
8. the method of controlling a cold source system of a building according to claim 7, wherein the load factor PLR of the unit combination ch,i Setting value T of cold water supply temperature chws,i The method is optimized by the following steps:
unit load factor PLR ch,i Setting value T of cold water supply temperature chws,i The optimization function of (2) is:
in which Q rated,i Represents rated load of the water chilling unit i, T chws,min Representing the lower limit value of the water supply temperature set value of the water chilling unit; obtaining an equation constraint matrix equation from the optimization function, solving the equation constraint matrix equation to obtain an optimization result X, wherein the optimization result X comprises the load rate PLR of each unit ch,i And cold water supply temperature T chws,i I is the number of the water chilling unit: the equation constraint matrix equation is as follows:
AX＝B
monitoring the calculation result of X, if the inequality constraint is not satisfied, constructing an augmentation matrix to constrain the inequality into an equality constraint, and then solvingUntil the calculation result meets all inequality constraints;
9. the method for controlling a cooling system of a building according to claim 8, wherein the cooling tower fan controls a frequency f twr,i Load factor PLR based on each unit combination ch,i And (3) calculating and optimizing to obtain:
wherein f twr,max Represents the maximum power of a fan of the cooling tower, a twr,des Is the difference between the wet bulb temperature of the air and the water supply temperature of the cooling water under the design working condition; r is (r) twr,des Is the temperature difference of the cooling water supply and return water under the design working condition; p (P) ch,des Is the rated power of the water chilling unit; p (P) twr,des Is the rated power of a fan of the cooling tower; s is S cwr The sensitivity coefficient of the running power of the water chilling unit to the temperature of the cooling water is obtained by the following formula:
wherein a is i ,b i The coefficient comes from the performance characteristic equation of the water chilling unit, T cwr,i Is the return water temperature of cooling water of the water chilling unit i, T chws,i The chilled water supply temperature of the water chilling unit i;
if a plurality of units are operated in a combined way, the sensitivity coefficient S of the operation power of the plurality of water chilling units to the temperature of cooling water cwr,des Weighting calculation is carried out by taking rated power of the unit as weight;
10. the method for controlling a cold source system of a building according to claim 1, wherein calculating the integrated operation power of each unit combination based on the load factor of each unit combination, and selecting one unit combination as an optimization result according to the integrated operation power, comprises:
load factor PLR based on each unit combination ch,i Obtaining the unit comprehensive operation power P of each unit combination;
comparing the lowest unit comprehensive operation power P in the unit combination min Unit integrated operating power combined with an operating unitIf->Selecting the unit combination with the lowest power as an optimization result; otherwise, selecting the running unit combination as an optimization result.
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