CN114251753A - Ice storage air conditioner cold load demand prediction distribution method and system - Google Patents

Ice storage air conditioner cold load demand prediction distribution method and system Download PDF

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CN114251753A
CN114251753A CN202111648774.1A CN202111648774A CN114251753A CN 114251753 A CN114251753 A CN 114251753A CN 202111648774 A CN202111648774 A CN 202111648774A CN 114251753 A CN114251753 A CN 114251753A
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cold
cold load
cooling
ice
energy consumption
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邓伟
梁少晨
张雨
牛源
庄芸萧
周湘田美
侯博
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Xian University of Architecture and Technology
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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
    • F24F5/00—Air-conditioning systems or apparatus not covered by F24F1/00 or F24F3/00, e.g. using solar heat or combined with household units such as an oven or water heater
    • F24F5/0007—Air-conditioning systems or apparatus not covered by F24F1/00 or F24F3/00, e.g. using solar heat or combined with household units such as an oven or water heater cooling apparatus specially adapted for use in air-conditioning
    • F24F5/0017—Air-conditioning systems or apparatus not covered by F24F1/00 or F24F3/00, e.g. using solar heat or combined with household units such as an oven or water heater cooling apparatus specially adapted for use in air-conditioning using cold storage bodies, e.g. ice
    • 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
    • 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
    • F24F11/61—Control or safety arrangements characterised by user interfaces or communication using timers
    • 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
    • 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
    • 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
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
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    • Y02E60/14—Thermal energy storage

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Abstract

The invention discloses a method and a system for predicting and distributing cold load demands of ice storage air conditioners, wherein a target building moment cold load prediction graph is established, and an ant colony is divided into a plurality of sub ant colonies; carrying out cold load demand predicted value search by utilizing each sub-ant colony, and sequencing the cold load demand predicted value search results of each sub-ant colony according to utility function values; obtaining ants corresponding to the cold load demand predicted value search result which tends to be centered in the cold load predicted value search results according to the sorting results, updating pheromones of the ants to neighbor sub-ant groups, comparing the cold load demand predicted values of the sub-ant groups at the current time of the day to be regulated and controlled, and outputting the optimal cold load demand predicted value of the current time of the day to be regulated and controlled; and (4) fitting by using a curve, and if the current moment is the last moment of the day to be regulated, obtaining the total cold load demand predicted value of the target building on the day to be regulated, so as to realize cold load demand prediction distribution. The invention improves the operation efficiency of the cold machine and obtains higher benefit.

Description

Ice storage air conditioner cold load demand prediction distribution method and system
Technical Field
The invention belongs to the technical field of air conditioner refrigeration, and particularly relates to a method and a system for predicting and distributing cold load demands of an ice storage air conditioner.
Background
The energy problem is increasingly serious, the energy conservation and emission reduction work becomes the focus of all countries, and the air conditioner is one of the most common energy consumption devices in the building and accounts for about 60 percent of the total power and energy consumption. In summer, the demand of electric quantity of public buildings is large, and the peak load of a power grid is increased again by the electricity consumption of an air conditioning system, so that the power grid faults such as insufficient peak power supply, excessive valley, power limitation and power failure become more common. The ice storage air conditioner uses ethylene glycol aqueous solution as a secondary refrigerant, exchanges heat with water in an ice storage device for storage of cold, the water stores cold in the form of sensible heat or phase change latent heat, and uses a refrigerating unit and the ice storage device for combined cold supply during the electric power valley and low electricity price period and during the power grid load peak period or electricity price peak period, so as to realize 'peak load shifting' and energy-saving operation work of the air conditioner.
The ice storage public regulation system is a large-scale system distributed in time and space, and is used for balancing the pressure of a power grid and reducing the operating cost of an air conditioner. The existing centralized control structure has the problems that the control network is complex to build, the configuration is difficult, the optimization algorithm is difficult to realize and the like in the application of the heating ventilation air-conditioning system, so that an improper control strategy in the actual engineering is difficult to realize, and the phenomena of low system efficiency, energy waste and the like are caused. In addition, as the system scale increases, problems such as link congestion and operation delay in data transmission frequently occur.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a method and a system for forecasting and distributing the cold load demand of an ice storage air conditioner aiming at the defects in the prior art, so as to achieve the aims of minimum operation energy consumption, minimum operation cost and minimum energy consumption loss of the ice storage air conditioner.
The invention adopts the following technical scheme:
a method for forecasting and distributing cold load demand of an ice storage air conditioner comprises the following steps:
s1, establishing a target building time cold load prediction graph, setting ant colony parameters, and dividing the ant colony into a plurality of sub-ant colonies;
s2, searching the cold load demand predicted value by using each sub-ant colony divided in the step S1 to obtain a plurality of cold load demand predicted value search results of the current time of the day to be regulated; sorting the cold load demand predicted value search results of each sub-ant colony according to utility function values;
s3, obtaining ants corresponding to the cold load demand predicted value search result which tends to be centered in the cold load predicted value search results according to the sorting results of the step S2, updating pheromones of the ants to the adjacent sub-ant groups, and enabling the sub-ant groups to carry out co-evolution; stopping ant search after the iteration is set for times, comparing the cold load demand predicted values of the current time of the day to be regulated and controlled searched by each sub ant colony, performing variance analysis, and outputting the optimal cold load demand predicted value of the current time of the day to be regulated and controlled;
s4, fitting the optimal cold load demand predicted value obtained in the step S3 by using a curve to complete the planning of the cold load demand predicted value at the corresponding moment of the target building;
and S5, if the current moment is the last moment of the day to be regulated, stopping circulation to obtain the total cold load demand predicted value of the target building on the day to be regulated, and realizing cold load demand prediction distribution.
Specifically, in step S1, the step of establishing the target building time cold load prediction map specifically includes:
s101, establishing an energy consumption model of primary side equipment provided by cold energy of an ice storage air conditioning system, wherein the energy consumption model comprises a cold machine, a cooling tower, a cooling pump and a solution pump;
s102, establishing an operation energy consumption objective function, an operation cost objective function and an energy consumption loss objective function according to the energy consumption obtained in the step S101;
s103, establishing a constraint condition of a total cold load demand predicted value of the target building, and establishing a cold load prediction graph at the moment of the target building according to the target function and the constraint condition.
Further, in step S101, the cold machine energy consumption model is:
Figure BDA0003444399660000021
Figure BDA0003444399660000022
wherein COP (k) is the energy efficiency ratio of the kth platform cooler; PLR (k) is k cold part load ratios; a is1，a2，a3，...，a10Ten model coefficients of the refrigerator are obtained; t isCHWSSupplying water temperature to the chilled water; t isCWRThe temperature of the cooling water return water is set; wcThe total energy consumption in the operation period of the cooler is obtained; t is sampling time in the operation period; pc(t) is the running power of the refrigerator at the moment t; k represents the number of coolers; qn(k) The rated power of the kth cold machine.
The energy consumption model of the cooling tower is as follows:
Figure BDA0003444399660000031
Figure BDA0003444399660000032
wherein the content of the first and second substances,ct(t) is the loading of the cooling tower at t; qcs(t) is the cold supply capacity of the refrigerator at t hours; wctFor the total operation period of the cooling towerEnergy consumption; wct(t) energy consumption of the cooling tower t; α represents a direct scaling factor;
the pump energy consumption model is as follows:
Figure BDA0003444399660000033
Figure BDA0003444399660000034
Figure BDA0003444399660000035
wherein, PCHWpump、PCWpumpAnd PEGSpumpPower consumption of the freezing pump, the cooling pump and the solution pump respectively; rhowAnd ρsThe density is chilled water and cooling water; m isCHW、mCWAnd mEGSThe flow rates are freezing water flow, cooling water flow and ethylene glycol solution flow; hCHW、HCWAnd HEGSIs a differential pressure, ηCHW、ηCWAnd ηEGSThe working efficiency of the freezing pump, the cooling pump and the solution pump is respectively improved;
the energy consumption of the cooling pump and the freezing pump is as follows:
Figure BDA0003444399660000036
Figure BDA0003444399660000037
the ethylene glycol solution pump runs under the ice storage working condition and the ice tank cooling working condition, and the energy consumption is as follows:
Figure BDA0003444399660000041
wherein m, n, j represent cold, respectivelyThe number of freezing pumps, cooling pumps and ethylene glycol solution pumps; t is t1,t2,t3Respectively the ice storage time, the cold machine working time and the ice tank cold supply time.
Further, in step S102, the energy consumption objective function f is operated1Comprises the following steps:
f1＝WT＝Wc+Wct+Wpump
wherein, WTIs the total energy consumption, W, in the operation cycle of the air conditioning systemcIs total energy consumption in the running period of the cooler, WctFor total energy consumption of cooling tower operating cycle, WpumpIs the total energy consumed during the pump operation cycle.
Further, in step S102, the operation cost objective function f2Comprises the following steps:
Figure BDA0003444399660000042
wherein, Wc(t) energy consumption of refrigerator t, Wct(t) is the energy consumption of the cooling tower t, Wpump(t) power consumption at pump time t, and e (t) electricity price per sampling step.
Further, in step S102, the energy consumption loss objective function f3Comprises the following steps:
Figure BDA0003444399660000043
wherein, Wc(t) energy consumption of refrigerator t, Wct(t) is the energy consumption of the cooling tower t, Wpump(t) is the energy consumption of the pump t, delta is the cold energy conversion rate of the cold machine ice storage in the ice storage stage, and t1And t3Respectively the ice storage time and the cold supply time of the ice tank.
Further, in step S103, the constraint conditions of the predicted value of the total cooling load demand of the target building include that the refrigerating capacity of the refrigerator in each time interval should be less than the rated refrigerating capacity of the refrigerator, the total ice storage capacity in the ice storage stage is less than the capacity of the ice tank, the cooling capacity of the ice tank at the current time is less than the remaining cooling capacity of the ice tank at the current time, and the sum of the maximum cooling capacity of the ice tank at the current time and the cooling capacities provided by the refrigerator and the ice tank reaches the precision range meeting the cooling load demand of the building.
Furthermore, the refrigerating capacity Q (k) of each time period of the refrigerator is less than the rated refrigerating capacity Q of the refrigeratorn(k) The method specifically comprises the following steps:
Q(k)＝Qn(k)·PLR(k)≤Qn(k)
the specific steps that the total ice storage amount is less than the capacity of the ice tank in the ice storage stage are as follows:
Qice.st·0.95≤Qtank≤Qice.st
the cold supply capacity of the ice tank at the current moment is less than the residual cold capacity of the ice tank at the current moment and less than the maximum cold supply capacity of the ice storage tank at the current moment, and the method specifically comprises the following steps:
Figure BDA0003444399660000051
the sum of the cooling capacities provided by the cold machine and the ice tank meets the precision range of the cold load requirement of the building, and specifically comprises the following steps:
|Qc(t)+Qtank(t)-Qdemand(t)|≤ε·Qdemand(t)
wherein Q isice.stAs a total ice storage quantity, QtankSupplying cold energy to the ice groove; qtank(t) the cooling capacity of the ice tank is t; h is1，h2Fitting according to actual engineering data; qdemand(t) the building end cold load demand at t; epsilon is the range of accuracy to meet the cold load demand, Qc(t) Cold supply to the refrigerator, Qn(k) For rated capacity, plr (k) is the k chiller part load rate.
Specifically, in step S5, if the current time is not the last time of the day to be controlled, the next time of the day to be controlled is made equal to the current time, and the step S2 is executed to obtain the predicted value of the cold load demand at the next time of each ant colony.
Another technical solution of the present invention is a demand forecast distribution system for a cooling load of an ice storage air conditioner, comprising:
the division module is used for establishing a target building time cold load prediction graph, setting ant colony parameters and dividing the ant colony into a plurality of sub ant colonies;
the sorting module is used for searching the cold load demand predicted value by utilizing each sub ant colony divided by the dividing module to obtain a plurality of cold load demand predicted value searching results at the current time of the day to be regulated; sorting the cold load demand predicted value search results of each sub-ant colony according to utility function values;
the analysis module is used for acquiring ants corresponding to the cold load demand predicted value search result which tends to be centered in the cold load predicted value search results according to the sorting results of the sorting module, updating pheromones of the ants to the adjacent sub-ant groups and enabling the sub-ant groups to carry out co-evolution; stopping ant search after the iteration is set for times, comparing the cold load demand predicted values of the current time of the day to be regulated and controlled searched by each sub ant colony, performing variance analysis, and outputting the optimal cold load demand predicted value of the current time of the day to be regulated and controlled;
the fitting module is used for fitting the optimal cold load demand predicted value obtained by the analysis module by using a curve to complete the planning of the cold load demand predicted value at the corresponding moment of the target building;
and the distribution module stops circulation if the current moment is the last moment of the day to be regulated, so that the total cold load demand predicted value of the target building on the day to be regulated is obtained, and the cold load demand prediction distribution is realized.
Compared with the prior art, the invention has at least the following beneficial effects:
the invention discloses a method for predicting and distributing cold load demands of ice storage air conditioners, which is based on an ice storage air conditioner system mathematical model and based on three targets of minimum operation energy consumption, minimum operation cost and minimum energy consumption loss under the condition of ensuring the comfort degree of a target building end user, controls the load distribution of a water chilling unit and an ice tank of the ice storage air conditioner system according to an optimization result by using a parallel sorting ant colony algorithm optimized based on cold load demands. The ice storage air conditioning system can not only ensure the indoor environment quality, but also meet the running requirements of energy conservation and economy by multi-objective optimization control based on constraint conditions; based on the parallel sorting ant colony algorithm, in the genetic algorithm, in order to improve the searching speed of the algorithm, a sorting mode is used for selection, the higher the individual fitness is, the more the sorting is, the higher the probability of being selected next time is. The concept in the genetic algorithm is expanded to the parallel sorting ant colony algorithm, namely after all ants complete one iteration, w-1 ants with contribution degrees sorted in the ant colony and ants forming the optimal solution up to now are selected, and pheromones of paths constructed by the w ants are updated.
Further, an ant colony algorithm is used for collecting a cold load predicted value of the current time of the day to be regulated, pheromones of ants are updated to neighbor ant colonies, and a plurality of ant colonies are enabled to evolve synergistically; stopping ant search after the iteration is set for times, comparing the cold load demand predicted values of the current time of the day to be regulated and controlled searched by each sub ant colony, performing variance analysis, and outputting the optimal cold load demand predicted value of the current time of the day to be regulated and controlled; if the current moment is not the last moment of the day to be regulated, the next moment of the day to be regulated is made equal to the current moment to be executed in a circulating mode, and a cold load demand predicted value of the next moment is obtained; and if the current moment is the last moment of the day to be regulated and controlled, stopping circulation, obtaining the cold load demand predicted values at all the moments, and obtaining the total cold load demand predicted value of the day to be regulated and controlled of the target building by using a spanning tree addition method. The parallel ant colony algorithm is efficient, the ant colony can be divided into all sub ant colonies, and cold load predicted values at different moments are searched simultaneously.
Further, the condition that the cold quantity distribution result meets the minimum energy consumption, cost and energy consumption loss of the target building means that the three target functions of the operation energy consumption target function, the operation cost target function and the energy consumption loss target function are minimum.
Further, the operation energy consumption is the total energy consumption of the operation period of the air conditioning system, and is the sum of the total energy consumption in the operation period of the cooling machine, the total energy consumption in the operation period of the cooling tower and the total energy consumption in the operation period of the pump.
Further, the operation cost is 24 hours, the operation energy consumption at different moments and the electricity price of each sampling step at the moment.
Further, the energy consumption loss is the sum of the energy consumption consumed by the cooling tower and the cold machine when the cooling tower and the cold machine are converted into cold energy in the ice storage stage and the energy consumption generated by the pump in the ice storage stage and the cold supply stage of the ice tank.
Further, the cold distribution result also needs to satisfy: the refrigerating capacity of the refrigerator in each time period is less than or equal to the rated refrigerating capacity of the refrigerator; the cold supply amount of the ice tank in the operation period is less than or equal to the total ice storage amount of the ice tank and more than or equal to 95 percent of the total ice storage amount; the cold supply capacity of the ice tank at the current moment is less than or equal to the residual cold capacity of the ice tank at the current moment and the maximum cold supply capacity of the ice storage tank at the current moment.
Further, when the ant colony algorithm seeks a cold load predicted value at a certain moment, if the current moment is not the last moment of the day to be regulated, the next moment of the day to be regulated is equal to the current moment, and the step S2 is executed to obtain a cold load demand predicted value at the next moment; and if the current moment is the last moment of the day to be regulated and controlled, stopping circulation, obtaining the cold load demand predicted values at all the moments, and obtaining the total cold load demand predicted value of the day to be regulated and controlled of the target building by using a spanning tree addition method.
In conclusion, the invention optimizes the system based on the analysis of variance of the parallel ant colony algorithm based on the three targets of minimum system operation energy consumption, minimum operation cost and minimum energy consumption loss based on the mathematical model of the ice storage air-conditioning system under the requirement of ensuring the comfort degree of the end user of the target building, and controls the load distribution of the water chilling unit and the ice tank of the ice storage air-conditioning system according to the optimization result. And the ice storage air conditioning system can not only ensure the indoor environment quality, but also meet the requirements of energy saving and economic operation based on the multi-objective optimization control of constraint conditions.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
FIG. 1 is a diagram of a distributed control architecture of an ice storage air conditioning system;
FIG. 2 is a flowchart of a process for performing an optimal solution search for each sub-ant colony;
FIG. 3 is a schematic diagram of the chiller/ice bank load rate;
FIG. 4 is a cold mass distribution diagram;
FIG. 5 is a schematic diagram of an iterative process of a parallel ranking ant colony algorithm, wherein (a) is operation energy consumption, (b) is operation cost, and (c) is energy consumption loss;
FIG. 6 is a schematic diagram of optimal solution set distribution for parallel ordered ant colonies.
Detailed Description
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, not all, embodiments of the present 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.
In the description of the present invention, it should be understood that the terms "comprises" and/or "comprising" indicate the presence of the stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
Various structural schematics according to the disclosed embodiments of the invention are shown in the drawings. The figures are not drawn to scale, wherein certain details are exaggerated and possibly omitted for clarity of presentation. The shapes of various regions, layers and their relative sizes and positional relationships shown in the drawings are merely exemplary, and deviations may occur in practice due to manufacturing tolerances or technical limitations, and a person skilled in the art may additionally design regions/layers having different shapes, sizes, relative positions, according to actual needs.
Parallel sequencing ant colony algorithm: the ant colony can always find the shortest path to a food source in different environments, which is an intelligent behavior that the ant colony can embody, because ants can release a substance called 'pheromone' on a path that the ants pass through, perception cells of ant individuals in the ant colony have receptors which are combined with the pheromone, so that the behavior of the ant individuals is influenced, the path with higher pheromone concentration is prone to be selected, each passed ant can leave the pheromone, the whole process is a positive feedback process, and as the number of generations selected by the ant colony is more, all ants can be finally selected on one path in a concentrated manner, and the path is the optimal solution path from an ant nest to the food source. The italian scholaro et al inspired from this ant foraging behavior, proposed an ant colony algorithm to solve various combinatorial optimization problems.
(1) Transfer probability of ants
The way each ant in the ant colony chooses the next marching position is roulette, ant k chooses the next marching direction according to the node choice probability when constructing the path, and when ant k is at node i, the probability of choosing node j (city not visited) is:
Figure BDA0003444399660000091
if the node has already been visited,
Figure BDA0003444399660000092
wherein tau isijIs the pheromone on edge (i, j), μijIs heuristic information that the edge (i, j) has, for a general path search problem, muijTaking the reciprocal of the path length, α and β are algorithm parameters.
(2) Pheromone update rule
Ants need to perform pheromone updating once after completing path construction once. First, all pheromones on a path volatilize a portion and then release the corresponding pheromone on the path that has passed. The pheromone volatilization rule is as follows:
τij←(1-ρ)τij
rho is an pheromone volatilization factor, rho is more than 0 and less than 1, the ants release the pheromone after the pheromone is volatilized, and the updating formula of the pheromone is as follows:
Figure BDA0003444399660000101
wherein the content of the first and second substances,
Figure BDA0003444399660000102
is the amount of pheromone released by the kth ant on the path it traveled. When the edge (i, j) is on the path constructed by ant k,
Figure BDA0003444399660000103
and when the edge (i, j) is not on the path constructed by ant k,
Figure BDA0003444399660000104
referring to fig. 1, the distributed control structure of the ice storage air conditioning system is composed of a refrigerating unit, an ice storage device, a connecting pipeline, an adjusting controller and the like, and is mainly divided into a cold source structure, a chilled water structure, a cooling water structure and a control structure.
The cold source structure of the ice storage air conditioner consists of a double-working-condition refrigerating unit and an ice storage device. The dual-working-condition refrigerating unit is used for producing ice at night, the glycol water solution is pressurized and conveyed into the coil pipe of the ice storage device by the solution pump after being cooled by the refrigerating unit, heat is exchanged with water in the ice tank, and the glycol water solution after heat exchange and temperature rise flows back to the refrigerating unit for cooling again. The refrigerating unit with double working conditions uses the working conditions of the air conditioner for cooling, refrigerating water is refrigerated by the heat exchange between refrigerating unit secondary refrigerant and the refrigerating water, the refrigerating water is delivered to the heat exchanger by the refrigerating water pump, and the refrigerating water returns to the refrigerating unit through the refrigerating pump for cooling after the heat exchange and the temperature rise. The ice storage device adopts a heat preservation and insulation material layer to isolate heat and cold exchange with the outside and keep the temperature of an internal energy storage medium. When the ice tank melts ice and supplies cold, the backwater of the chilled water flows back to the ice tank through the freezing pump, and exchanges heat with ice with low temperature in the ice tank to cool and supply cold. The ice tank is different from a refrigerator, the constant outlet water temperature cannot be controlled, the heat capacity of the ice tank is mainly determined by the structure and the material of the ice tank, the heat exchange area, the temperature and the flow of the solution at the inlet of the ice storage tank, and the ice melting rate can be controlled by adjusting the inlet temperature of the ice tank and the flow.
The cooling water structure comprises a cooling pump, a cooling tower and a cooling water pipeline. The double-working-condition unit works to provide low-temperature secondary refrigerant cooling chilled water, the cooling water absorbs the heat released by the cooling water, and the temperature of the cooling water is increased. The cooling water is pressurized by a cooling water pump and is sent to a cooling tower for cooling and flowing into a water collecting disc of the cooling tower, and the heat exchange quantity of the refrigerating unit is taken away in a reciprocating circulation manner.
The control structure is the basis of economical and energy-saving operation of the ice storage air conditioner. Besides the start-stop control of the electromechanical equipment of the system, the control structure also controls and adjusts the working state of the ice making working condition of the refrigerating unit, the cold quantity distribution between the refrigerating unit and the ice storage device under the cold supply working condition and the working state of the refrigerating unit under the cold supply working condition by combining the peak-valley electricity price and the next day cold load requirement, thereby realizing economic and energy-saving benefits. The control system is composed of a communication system, a sensor, a control, an actuating mechanism and the like.
Referring to fig. 2, the method for demand forecasting and distributing of the cooling load of the ice storage air conditioner of the present invention includes the following steps:
s1, establishing a target building time cold load prediction graph, setting ant colony parameters, and dividing the ant colony into a plurality of sub-ant colonies;
establishing the objective function according to the energy consumption specifically comprises the following steps:
s101, establishing an energy consumption model of primary side equipment provided by cold energy of an ice storage air conditioning system, wherein the energy consumption model comprises a cold machine, a cooling tower, a cooling pump and a solution pump;
a cold machine energy consumption model:
Figure BDA0003444399660000111
Figure BDA0003444399660000112
wherein COP (k) is the energy efficiency ratio of the kth platform cooler; PLR (k) is k cold part load ratios; a is1，a2，a3，...，a10Ten model coefficients of the refrigerator are obtained; t isCHWSSupplying water to the chilled water at a temperature of DEG C; t isCWRThe return water temperature of cooling water is DEG C; wcThe total energy consumption in the operation period of the refrigerator is kkk; t is sampling time in the operation period, sampling is carried out once per hour, and k is obtained; pc(t) is the running power of the refrigerator at the moment t, kk; k represents the number of coolers; qn(k) The rated power of the kth cold machine is kk.
Cooling tower energy consumption model:
Figure BDA0003444399660000113
Figure BDA0003444399660000114
wherein the content of the first and second substances,ct(t) is the loading of the cooling tower at t, kk; qcs(t) is the cold supply capacity of the refrigerator at t, kk; wctThe total energy consumption of the cooling tower in the operation period is kkk; wct(t) is the energy consumption of the cooling tower t, kkk; alpha represents a direct proportion coefficient and is obtained by fitting according to actual engineering data; other parameters have the same meanings as above.
Pump energy consumption model:
Figure BDA0003444399660000121
Figure BDA0003444399660000122
Figure BDA0003444399660000123
wherein, PCHWpump、PCWpumpAnd PEGSpumpPower consumption, kk, of the refrigeration pump, the cooling pump and the solution pump, respectively; w is the density of the chilled water and the cooling water in kg/m3；mCHW、mCWAnd mEGSIs the flow rate of the freezing water, the cooling water and the flow rate of the ethylene glycol solution, m3/k；HCHW、HCWAnd HEGSRepresents a pressure difference, kPa; CHW, CW, and EGS represent the operating efficiencies of these three types of pumps, with the pressure differential and pump operating efficiency given by equations (13) - (19).
HCHW＝b0mCHW 2+b1wmCHW+b2w2 (13)
ηCHW＝c0(mCHW/w)2+c1(mCHW/w)+c2 (14)
HCW＝d0mCW 2+d1wmCW+d2w2 (15)
ηCW＝e0(mCW/w)2+e1(mCW/w)+e2 (16)
HEGS＝f0mEGS 2+f1wmEGS+f2w2 (17)
ηEGS＝g0(mEGS/w)2+g1(mEGS/w)+g2 (18)
Figure BDA0003444399660000124
The ratio of the pump rotational speed w is as in equation (14), n0The rated rotation speed of the pump, r/min; n is the actual rotating speed under the working condition, r/min; b0，b1，b2，...，g0，g1，g2Parameters are obtained according to actual engineering fitting; other parameters have the same meanings as above. The working time of the cooling pump and the refrigerating pump is consistent with that of the refrigerating machine, namely the ice storage working condition and the refrigerating machine cooling working condition, and the cooling pump and the refrigerating machineThe energy consumption of the freeze pump is shown in the formulas (20) and (21). The ethylene glycol solution pump operates under the ice storage working condition and the ice tank cooling working condition, and the energy consumption is as shown in the formula (22).
Figure BDA0003444399660000125
Figure BDA0003444399660000131
Figure BDA0003444399660000132
Wherein m, n and j respectively represent the number of a freezing pump, a cooling pump and a ethylene glycol solution pump; t is t1,t2,t3Respectively the ice storage time, the cold machine working time and the ice tank cold supply time k.
S102, establishing an objective function
The ice storage air conditioning system aims at solving the problems of energy utilization rate, power grid balancing, cost saving and the like. The running cost can be effectively reduced by increasing the ice storage amount of the off-peak electricity price at night, but the energy consumption loss is inevitably caused by multi-layer state conversion and heat dissipation in the ice storage process. The reduction of the ice storage amount is faced with the increase of the operation cost. Therefore, in order to improve the energy utilization efficiency and reduce the energy consumption loss and the operating cost of the system to the maximum extent, the invention takes the minimum operating energy consumption, the minimum operating cost and the minimum energy consumption loss as the objective function and optimizes the cooling strategy of each time duration in the operating period of the ice storage air conditioning system.
The three objective functions of the operation energy consumption objective function, the operation cost objective function and the energy consumption loss objective function are minimum
1) Running energy consumption objective function f1The following were used:
f1＝WT＝Wc+Wct+Wpump (23)
wherein, WTIs the total energy consumption, W, in the operation cycle of the air conditioning systemcFor the operation period of the refrigeratorTotal energy consumption, WctFor total energy consumption of cooling tower operating cycle, WpumpIs the total energy consumed during the pump operation cycle.
2) Cost of operation objective function
The operation cost of the ice storage air conditioning system in the operation period is the sum of the products of the total energy consumption at each moment of the operation period and the electricity price at the corresponding moment:
Figure BDA0003444399660000133
wherein Cost is the total operation Cost of the ice storage air conditioning system in the operation period; e (t) electricity price per sampling step; other parameters have the same meanings as above.
3) Energy consumption loss objective function
In the ice storage stage of the ice storage system, the cold quantity is provided by the cold machine for storing ice, the thermal resistance is increased along with the thickening of an ice layer in the ice storage process, the heat exchange is weakened, the ice storage capacity of the cold machine is reduced, and part of energy consumption loss is generated. Neglecting the influence of heat loss formed by heat exchange between the ice tank and air, the cold energy conversion rate of the cold machine ice storage in the ice storage stage is considered to be delta. Compared with the traditional air conditioner, the ice cold accumulation system increases the transmission and transfer of cold quantity, and the energy consumption generated by the solution pump in the ice accumulation stage and the ice tank cold supply stage is also used as part of energy consumption loss.
Figure BDA0003444399660000141
Wherein delta is the cold quantity conversion rate of the cold machine ice storage in the ice storage stage, t1And t3Respectively the ice storage time and the cold supply time of the ice tank.
S103, the constraint condition established by the total cold load demand predicted value of the target building comprises the following steps:
1) the refrigerating capacity of the refrigerator in each time period is less than the rated refrigerating capacity of the refrigerator;
Q(k)＝Qn(k)·PLR(k)≤Qn(k) (26)
2) the total ice storage amount in the ice storage stage is smaller than the capacity of the ice tank, and in order to prevent the phenomenon of ice in ten thousand years, the cooling capacity of the ice tank in the operation period is smaller than the total ice storage amount of the ice tank and larger than 95 percent of the total ice storage amount;
Qice.st·0.95≤Qtank≤Qice.st (27)
3) the cold supply capacity of the ice tank at the current moment is smaller than the residual cold capacity of the ice tank at the current moment and smaller than the maximum cold supply capacity of the ice storage tank at the current moment;
Figure BDA0003444399660000142
4) in order to meet the requirement of indoor comfort level of a building and guarantee the saving of electric energy, the sum of the cooling capacity provided by the cold machine and the ice tank reaches the precision range meeting the requirement of cold load of the building.
|Qc(t)+Qtank(t)-Qdemand(t)|≤ε·Qdemand(t) (29)
Wherein Q istankSupplying cold energy, kk, to the ice tank; qtank(t) the cooling capacity of the ice tank at t, kk; h is1，h2Fitting according to actual engineering data; qdemand(t) the building end cold load demand at t, kk; epsilon is the range of accuracy to meet the cold load demand, QcAnd (t) supplying cold energy to the cooler.
Referring to fig. 5 and 6, an analysis experiment is performed according to a large number of parameters of the ice storage air conditioner in certain department of western security. Determining that the sub-ant colony scale is set to be 50, the maximum iteration times are 200, the path pheromone updating period is 20, and after each iteration of the parallel sorting ant colony algorithm is completed, sorting according to the contribution values of ants in the current sub-ant colony to ensure the diversity of the path pheromone.
A large number of parameters of the ice storage air conditioner in a certain western Ann market are tested by adopting a parallel sequencing ant colony algorithm, the hourly partial load rate and the ice bath cooling proportion of the cold machine are solved, and an optimal solution distribution diagram is shown in figure 6.
Fig. 5 shows an evolution process of parallel sorting three targets (operation energy consumption, operation cost, energy consumption loss) of the ant colony algorithm, in an initial iteration stage of the algorithm, the energy consumption loss shows a trend of changing in a reverse direction compared with other two target functions, and after a certain number of iterations, the three targets tend to be in a stable state.
S2, searching the cold load demand predicted value by using each sub-ant colony divided in the step S1 to obtain a plurality of cold load demand predicted value search results of the current time of the day to be regulated; sorting the cold load demand predicted value search results of each sub-ant colony according to utility function values;
s3, obtaining superior ants according to the sorting result of S2, (namely, the ants corresponding to the searching result of a plurality of cold load predicted values in the utility function value sorting tend to a certain relatively centered cold load demand predicted value), updating pheromones of the superior ants to the adjacent sub-ant groups, and enabling the sub-ant groups to carry out co-evolution; stopping ant search after the iteration is set for times, comparing the cold load demand predicted values of the current time of the day to be regulated and controlled searched by each sub ant colony, performing variance analysis, and outputting the optimal cold load demand predicted value of the current time of the day to be regulated and controlled;
s4, fitting the optimal cold load demand predicted values of different moments of the day to be regulated and controlled output in the step S3 by using a curve, and completing the planning of the cold load demand predicted values of the target building at the moment;
s5, if the current time is not the last time of the day to be regulated, enabling the next time of the day to be regulated to be equal to the current time, returning to execute the step S2, and obtaining a cold load demand prediction value of each ant colony at the next time; and if the current moment is the last moment of the day to be regulated, stopping circulation, and obtaining the cold load demand predicted value of the ant colony at all moments so as to obtain the total cold load demand predicted value of the day to be regulated of the target building.
In another embodiment of the present invention, a system for predicting and distributing a cold load demand of an ice storage air conditioner is provided, where the system can be used to implement the method for predicting and distributing a cold load demand of an ice storage air conditioner.
The division module is used for establishing a target building time cold load prediction graph, setting ant colony parameters and dividing the ant colony into a plurality of sub ant colonies;
the sorting module is used for searching the cold load demand predicted value by utilizing each sub ant colony divided by the dividing module to obtain a plurality of cold load demand predicted value searching results at the current time of the day to be regulated; sorting the cold load demand predicted value search results of each sub-ant colony according to utility function values;
the analysis module is used for acquiring ants corresponding to the cold load demand predicted value search result which tends to be centered in the cold load predicted value search results according to the sorting results of the sorting module, updating pheromones of the ants to the adjacent sub-ant groups and enabling the sub-ant groups to carry out co-evolution; stopping ant search after the iteration is set for times, comparing the cold load demand predicted values of the current time of the day to be regulated and controlled searched by each sub ant colony, performing variance analysis, and outputting the optimal cold load demand predicted value of the current time of the day to be regulated and controlled;
the fitting module is used for fitting the optimal cold load demand predicted value obtained by the analysis module by using a curve to complete the planning of the cold load demand predicted value at the corresponding moment of the target building;
and the distribution module stops circulation if the current moment is the last moment of the day to be regulated, obtains the cold load demand predicted value of all the moments of the ant colony, obtains the total cold load demand predicted value of the day to be regulated of the target building, and realizes cold load demand prediction distribution.
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.
An ice storage air conditioner load distribution optimization method is characterized in that according to a parameter model of main equipment of an ice storage air conditioner system, based on dynamic electricity price and equipment operation constraint conditions, ice storage and ice melting scheduling of demand response is considered, and power consumption models of a refrigerator, a cooling tower and pump electromechanical equipment of the ice storage air conditioner system are established with daily operation energy consumption, operation cost and energy consumption loss as optimization targets. And provides a parallel sequencing ant colony algorithm for solving the hourly load rate of the cold machine and the hourly cooling proportion of the ice tank. The algorithm comprises k +1 nodes which are connected, each node sequentially executes an optimization and adjustment task based on a parallel sorting ant colony algorithm to control a corresponding refrigerator or ice tank, a feasible solution set is obtained after the optimization and adjustment task is executed for each node, and any one group in the feasible solution set is selected to set the cooling capacity of the ice tank and the refrigerating capacity of the k refrigerators in a target building.
For each node, firstly, carrying out primary construction of a cold load distribution proportion on all ants in each sub-ant colony in the node, and then volatilizing pheromone; secondly, sorting and selecting the cold load distribution proportion of the front-ranked ants and the ants generating the optimal solution so far according to the contribution value of the ants of the current sub-ant colony, releasing the pheromone, then receiving the information of the neighbor sub-ant colony, and allowing the ants with higher contribution degree of the neighbor sub-ant colony to release the pheromone again from the current sub-ant colony; and then, after the maximum iteration times are reached, outputting the optimal cold load distribution proportion, and selecting the global optimal cold load distribution proportion to output according to the search result of each sub-ant colony.
The parallel sorting ant colony algorithm comprises the following substeps:
1. all ants in each sub ant colony are subjected to primary path construction respectively;
2. performing pheromone volatilization, and updating the pheromone of each ant colony path;
specifically, according to the contribution value sequence of ants in the current sub-ant colony, selecting the front ants and the paths where the ants generating the optimal solution so far are located, and releasing path pheromones; then receiving path information of the neighbor sub-ant colony, and allowing ants with higher contribution degree of the neighbor sub-ant colony to release path pheromone in the self-ant colony;
and after the iteration is carried out for the set times, outputting the optimal path to obtain the search result of each path.
In step 2, after each ant generates a path, before updating the path pheromone, volatilizing the pheromone existing on the path before, wherein the volatilization formula is as follows:
τij←(1-ρ)τij
wherein rho is an information volatilization factor and tauijIs the path pheromone on edge (i, j).
In the step 2, in the process of constructing the cold load proportion of the ant target building, when the ants sink into the dead angle state, a path constructed without considering the ants is adopted.
Note that: the dead angle state refers to the path that an individual ant deviates from the path constructed by most ants.
The path refers to the proportion of the cold load distribution of the target building established by the ants.
In the embodiment, a certain western-security market is taken as a model verification experiment environment, the ice storage air-conditioning system of the market uses 3 double-working-condition centrifugal chiller units, the rated power is 4430kk, and ten model coefficients of the chiller are given in table 1; the total ice storage capacity of the U-shaped internal ice melting coil pipe is 73000kk, and the U-shaped internal ice melting coil pipe is designed according to 30% of the typical design daily cooling load requirement; the freezing pump, the cooling pump and the solution pump are respectively 3 devices with the same specification and model, the rated power is 160kk, and the rated rotating speed is 1450 r/min. The system comprises a direct proportional coefficient alpha of the power consumption of the cooling tower and the load capacity of the cooling tower, model coefficients b0, b1 and b2 of a pump, a temperature g0, g1 and g2 of the pump, an ice storage rate delta of the refrigerating capacity of the refrigerator under an ice storage working condition, ice melting and cold supply parameters k1 and k2, an accuracy range epsilon that cold supply of a market meets the requirement of a cooling load, and the system is obtained by fitting according to historical data of an energy consumption acquisition system of the market, wherein the detailed model parameters are shown in table 1. At present, the ice storage system in the market adopts a component storage mode and a fixed-proportion control strategy, and each sampling step size ice tank and the cold machine bear the cold load requirement together in proportion. Table 2 shows the time-of-use electricity rates of the city of iean.
TABLE 1 model parameters
Figure BDA0003444399660000181
TABLE 2 time-of-use electricity price table of Xian city
Figure BDA0003444399660000182
Figure BDA0003444399660000191
And (3) adopting a grey correlation degree analysis method, and carrying out statistical analysis on the strength, the size and the sequence of the influence of each factor on the cold load result according to sample data of factors influencing the cold load data at the current moment, such as the outdoor air temperature at the current moment, the solar radiation intensity at the current moment, the outdoor wind speed at the current moment, the relative humidity at the current moment, the outdoor air temperature at the previous moment, the cold load at the previous moment and the like. The grey correlation degree of each influence factor and the air conditioner cooling load at the time T is shown in a table 3;
TABLE 3 Grey correlation between various influence factors and air conditioner cooling load at T moment
Figure BDA0003444399660000192
Setting ice storage time from night 00:00 to morning 08:00 for 8 hours in total, and cold supply time from 08:00 to 24:00 for 16 hours in total. And taking the partial load rate PLRt (k) of the kth cold machine t and the ratio of the cold supply of the ice tank to the current cold load demand at t, tan.c (t), as decision variables, and forming an N-dimensional decision variable by using 24 row vector groups of the matrix A and 16 row vector groups beta of 9-24 rows of the matrix B. According to the COP of the refrigerator and the PLR change rule, when the PLR is 0.3 or below, the refrigerating capacity of the refrigerator is small and the energy consumption is high, so the upper and lower boundary values of the row vector group alpha are set to be 0.3 and 1, and the value range of the ice tank cooling proportion row vector group is set to be [0,1 ].
xi＝[α1,α2,...,α24,β9,...,β24] (30)
Feasible solution set decision variable xiAnd calculating the running energy consumption, the running cost and the energy consumption loss of the ice storage system in the running period. The ten-item coefficient of the cold machine, the rated power of the pump, the rated rotating speed of the pump and other equipment parameters are provided by an equipment supplier, the running parameters of the chilled water supply temperature, the cooling water return temperature, the density, the flow rate, the actual rotating speed of the pump and the like are obtained from actual engineering data, and the ice storage time length, the cold supply time length of the cold machine and the cold supply time length of the ice tank are obtained by calculating decision variables.
Figure BDA0003444399660000201
Figure BDA0003444399660000202
Figure BDA0003444399660000203
According to relevant parameters of the ice storage air-conditioning system in the market, hourly cooling load prediction data of 12 days in 7 months in 2017 are selected for carrying out night ice storage optimization, and the load distribution optimization of the next-day refrigerator and the ice tank is carried out. And after the iteration of the algorithm is terminated, selecting a group of solutions with the minimum crowding distance degree from the optimal solution set, wherein fig. 3 is an optimal solution set distribution diagram of the optimization algorithm, and fig. 3 is the proportion of the partial load rate of each cold machine and the cooling capacity of the ice groove in the cooling time to the cooling load demand at the current moment in the operation period. The total ice storage amount is calculated according to an ice storage air conditioning system mathematical model and is 68986.6kw, which is 36% of the cold load demand of the next day of the building, and the total cold supply of the ice tank is 67710.35kw, which reaches 98.15% of the total ice storage amount, and meets the design requirement. The hourly part load rate of the cold machine fluctuates at the 0.85 position of the COP highest point, and the operation efficiency is high. Fig. 4 shows the distribution of the ice storage amount in the night valley period of each sampling step length and the cold amounts of the refrigerator and the ice tank in the cold supply stage, wherein the errors of the total cold supply amount and the required amount of the ice tank and the refrigerating unit in the cold supply stage are in an ideal state.
In summary, the method and the system for forecasting and distributing the cooling load demand of the ice storage air conditioner provided by the invention take the energy consumption, the operating cost and the energy consumption loss of the ice storage air conditioner system as optimization targets, and solve the hourly load rate of the cold machine and the hourly cooling proportion of the ice tank. By the method, the operation efficiency of the cold machine is improved by an optimized result, the contradiction between the operation energy consumption and the operation cost of the system is balanced by the load distribution of the cold machine and the ice tank, and higher benefit is obtained.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above-mentioned contents are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modification made on the basis of the technical idea of the present invention falls within the protection scope of the claims of the present invention.

Claims (10)

1. A method for predicting and distributing the cold load demand of an ice storage air conditioner is characterized by comprising the following steps:
s1, establishing a target building time cold load prediction graph, setting ant colony parameters, and dividing the ant colony into a plurality of sub-ant colonies;
s2, searching the cold load demand predicted value by using each sub-ant colony divided in the step S1 to obtain a plurality of cold load demand predicted value search results of the current time of the day to be regulated; sorting the cold load demand predicted value search results of each sub-ant colony according to utility function values;
s3, obtaining ants corresponding to the cold load demand predicted value search result which tends to be centered in the cold load predicted value search results according to the sorting results of the step S2, updating pheromones of the ants to the adjacent sub-ant groups, and enabling the sub-ant groups to carry out co-evolution; stopping ant search after the iteration is set for times, comparing the cold load demand predicted values of the current time of the day to be regulated and controlled searched by each sub ant colony, performing variance analysis, and outputting the optimal cold load demand predicted value of the current time of the day to be regulated and controlled;
s4, fitting the optimal cold load demand predicted value obtained in the step S3 by using a curve to complete the planning of the cold load demand predicted value at the corresponding moment of the target building;
and S5, if the current moment is the last moment of the day to be regulated, stopping circulation to obtain the total cold load demand predicted value of the target building on the day to be regulated, and realizing cold load demand prediction distribution.
2. The method for demand forecast allocation of cooling load of ice storage air conditioners as claimed in claim 1, wherein in step S1, the establishment of the target building time cooling load forecast map specifically comprises:
s101, establishing an energy consumption model of primary side equipment provided by cold energy of an ice storage air conditioning system, wherein the energy consumption model comprises a cold machine, a cooling tower, a cooling pump and a solution pump;
s102, establishing an operation energy consumption objective function, an operation cost objective function and an energy consumption loss objective function according to the energy consumption obtained in the step S101;
s103, establishing a constraint condition of a total cold load demand predicted value of the target building, and establishing a cold load prediction graph at the moment of the target building according to the target function and the constraint condition.
3. The method for demand predictive allocation of cooling load of an ice storage air conditioner as claimed in claim 2, wherein in step S101, the model of cold machine energy consumption is:
COP(k)＝a1+a2·PLR(k)+a3·TCHWS+a4·TCWR+a5·PLR(k)2+a6·TCHWS 2+a7·TCWR 2+a8·PLR(k)·TCHWS+a9·PLR(k)·TCWR+a10·TCHWS·TCWR
Figure FDA0003444399650000021
wherein COP (k) is the energy efficiency ratio of the kth platform cooler; PLR (k) is k cold part load ratios; a is1，a2，a3，...，a10Ten model coefficients of the refrigerator are obtained; t isCHWSSupplying water temperature to the chilled water; t isCWRThe temperature of the cooling water return water is set; wcThe total energy consumption in the operation period of the cooler is obtained; t is sampling time in the operation period; pc(t) is the running power of the refrigerator at the moment t; k represents the number of coolers; qn(k) Rated power of the kth cooling machine;
the energy consumption model of the cooling tower is as follows:
Figure FDA0003444399650000022
Figure FDA0003444399650000023
wherein the content of the first and second substances,ct(t) is the loading of the cooling tower at t; qcs(t) is the cold supply capacity of the refrigerator at t hours; wctThe total energy consumption is the operation period of the cooling tower; wct(t) energy consumption of the cooling tower t; α represents a direct scaling factor;
the pump energy consumption model is as follows:
Figure FDA0003444399650000024
Figure FDA0003444399650000025
Figure FDA0003444399650000026
wherein the content of the first and second substances,
Figure FDA0003444399650000027
and
Figure FDA0003444399650000028
power consumption of the freezing pump, the cooling pump and the solution pump respectively; rhowAnd ρsThe density is chilled water and cooling water; m isCHW、mCWAnd mEGSThe flow rates are freezing water flow, cooling water flow and ethylene glycol solution flow; hCHW、HCWAnd HEGSIs a differential pressure, ηCHW、ηCWAnd ηEGSThe working efficiency of the freezing pump, the cooling pump and the solution pump is respectively improved;
the energy consumption of the cooling pump and the freezing pump is as follows:
Figure FDA0003444399650000029
Figure FDA0003444399650000031
the ethylene glycol solution pump runs under the ice storage working condition and the ice tank cooling working condition, and the energy consumption is as follows:
Figure FDA0003444399650000032
wherein m, n and j respectively represent the number of a freezing pump, a cooling pump and a ethylene glycol solution pump; t is t1,t2,t3Respectively the ice storage time, the cold machine working time and the ice tank cold supply time.
4. The method for demand predictive allocation of cooling load of ice storage air conditioners as claimed in claim 2 wherein in step S102, the objective function f of energy consumption is run1Comprises the following steps:
f1＝WT＝Wc+Wct+Wpump
wherein, WTIs the total energy consumption, W, in the operation cycle of the air conditioning systemcIs total energy consumption in the running period of the cooler, WctFor total energy consumption of cooling tower operating cycle, WpumpIs the total energy consumed during the pump operation cycle.
5. An ice bank according to claim 2The method for predicting and distributing the cold load demand of the cold air conditioner is characterized in that in the step S102, an operation cost objective function f2Comprises the following steps:
Figure FDA0003444399650000033
wherein, Wc(t) energy consumption of refrigerator t, Wct(t) is the energy consumption of the cooling tower t, Wpump(t) power consumption at pump time t, and e (t) electricity price per sampling step.
6. The method for demand predictive allocation of cooling load of ice storage air conditioners as claimed in claim 2 wherein in step S102, the objective function f of energy consumption loss is3Comprises the following steps:
Figure FDA0003444399650000034
wherein, Wc(t) energy consumption of refrigerator t, Wct(t) is the energy consumption of the cooling tower t, Wpump(t) is the energy consumption of the pump t, delta is the cold energy conversion rate of the cold machine ice storage in the ice storage stage, and t1And t3Respectively the ice storage time and the cold supply time of the ice tank.
7. The method for demand forecast allocation of ice storage air conditioners according to claim 2, wherein in step S103, the constraint conditions of the total demand forecast value of the target building include that the refrigerating capacity of the refrigerator at each time interval should be less than the rated refrigerating capacity of the refrigerator, the total ice storage capacity at the ice storage stage is less than the capacity of the ice tank, the cooling capacity of the ice tank at the current time is less than the remaining cooling capacity of the ice tank at the current time, and is less than the maximum cooling capacity of the ice tank at the current time and the sum of the cooling capacities provided by the refrigerator and the ice tank reaches the precision range satisfying the demand of the cooling load of the building.
8. The method as claimed in claim 7, wherein the time periods of the chiller are differentThe refrigerating capacity Q (k) is less than the rated refrigerating capacity Q of the refrigeratorn(k) The method specifically comprises the following steps:
Q(k)＝Qn(k)·PLR(k)≤Qn(k)
the specific steps that the total ice storage amount is less than the capacity of the ice tank in the ice storage stage are as follows:
Qice.st·0.95≤Qtank≤Qice.st
the cold supply capacity of the ice tank at the current moment is less than the residual cold capacity of the ice tank at the current moment and less than the maximum cold supply capacity of the ice storage tank at the current moment, and the method specifically comprises the following steps:
Figure FDA0003444399650000041
the sum of the cooling capacities provided by the cold machine and the ice tank meets the precision range of the cold load requirement of the building, and specifically comprises the following steps:
|Qc(t)+Qtank(t)-Qdemand(t)|≤ε·Qdemand(t)
wherein Q isice.stAs a total ice storage quantity, QtankSupplying cold energy to the ice groove; qtank(t) the cooling capacity of the ice tank is t; h is1，h2Fitting according to actual engineering data; qdemand(t) the building end cold load demand at t; epsilon is the range of accuracy to meet the cold load demand, Qc(t) Cold supply to the refrigerator, Qn(k) For rated capacity, plr (k) is the k chiller part load rate.
9. The method for demand forecast allocation of ice storage air conditioners according to claim 1, wherein in step S5, if the current time is not the last time of the day to be controlled, the next time of the day to be controlled is made equal to the current time, and the method returns to step S2 to obtain the forecast value of demand of the cooling load at the next time of each ant colony.
10. An ice storage air conditioner cooling load demand forecasting and distributing system is characterized by comprising:
the division module is used for establishing a target building time cold load prediction graph, setting ant colony parameters and dividing the ant colony into a plurality of sub ant colonies;
the sorting module is used for searching the cold load demand predicted value by utilizing each sub ant colony divided by the dividing module to obtain a plurality of cold load demand predicted value searching results at the current time of the day to be regulated; sorting the cold load demand predicted value search results of each sub-ant colony according to utility function values;
the analysis module is used for acquiring ants corresponding to the cold load demand predicted value search result which tends to be centered in the cold load predicted value search results according to the sorting results of the sorting module, updating pheromones of the ants to the adjacent sub-ant groups and enabling the sub-ant groups to carry out co-evolution; stopping ant search after the iteration is set for times, comparing the cold load demand predicted values of the current time of the day to be regulated and controlled searched by each sub ant colony, performing variance analysis, and outputting the optimal cold load demand predicted value of the current time of the day to be regulated and controlled;
the fitting module is used for fitting the optimal cold load demand predicted value obtained by the analysis module by using a curve to complete the planning of the cold load demand predicted value at the corresponding moment of the target building;
and the distribution module stops circulation if the current moment is the last moment of the day to be regulated, so that the total cold load demand predicted value of the target building on the day to be regulated is obtained, and the cold load demand prediction distribution is realized.
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