CN111767682A - Heat pump energy storage system design control co-optimization method based on dynamic coupling model - Google Patents
Heat pump energy storage system design control co-optimization method based on dynamic coupling model Download PDFInfo
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Abstract
The invention provides a heat pump energy storage system design control common optimization method based on a dynamic coupling model, which comprises the following steps: optimizing the internal structure of the cold and heat storage water tank based on the CFD numerical model to obtain the optimized internal structure of the energy storage water tank; establishing a cold and heat storage water tank dynamic model according to the optimized internal structure of the energy storage water tank, and then coupling and associating the cold and heat storage water tank dynamic model with a heat pump system dynamic model to obtain a heat pump energy storage system dynamic coupling model; determining a two-order cycle optimization method, an optimization index and an optimization objective function to obtain an optimization result, wherein the two-order cycle optimization method comprises the following steps: randomly selecting system structure parameters, and performing operation optimization to obtain optimized operation parameters; fixing the optimized operation parameters, and performing system structure optimization to obtain optimized structure parameters; repeating the two steps until the difference between the two continuous optimized structural parameters is less than the tolerance error to obtain the optimal system structural parameter; and inputting the optimal structure parameters to carry out multi-objective optimization of the whole energy storage process to obtain all optimization results.
Description
Technical Field
The invention belongs to the technical field of heat pump energy storage, and particularly relates to a heat pump energy storage system design control co-optimization method based on a dynamic coupling model.
Background
The comprehensive energy system is a trend of development of a future energy system, and the permeability and stability of renewable energy in the comprehensive energy system can be improved by the heat pump energy storage. Jet transcritical CO2The heat pump energy storage system has higher energy conversion efficiency, but the system efficiency is influenced by the combined action of various factors such as the internal geometric dimension of the ejector, the performance of the compressor, the fluid flow rate and the temperature of the energy storage water tank and the like, and the system is instantaneously changed and has complex rule. Chinese patent CN201811482644.3 proposes a method for controlling an electronic expansion valve of a transcritical carbon dioxide heat pump system, but only the electronic expansion valve in the system operation process is optimized, and the system is not optimized as a whole. Chinese patent CN107704705A provides an optimization method of an air source heat pump water heater condenser pipe based on a kriging model, an AutoCAD is used for establishing an air source heat pump physical model, but only structural parameters of a condenser are optimized, and the system is not integrally optimized. Therefore, it is necessary to design a method for optimizing the heat pump energy storage system in the whole energy storage process.
Disclosure of Invention
The present invention is made to solve the above problems, and an object of the present invention is to provide a method for jointly optimizing design and control of a heat pump energy storage system based on a dynamic coupling model.
The invention provides a heat pump energy storage system design control co-optimization method based on a dynamic coupling model, which has the characteristics that the method comprises the following steps: step 1, optimizing the internal structure of a cold and heat storage water tank based on a CFD numerical model to obtain the optimized internal structure of the energy storage water tank; step 2, establishing a cold storage and heat storage water tank dynamic model according to the optimized internal structure of the energy storage water tank, and then coupling and associating the cold storage and heat storage water tank dynamic model with a heat pump system dynamic model to obtain a heat pump energy storage system dynamic coupling model; and 3, determining a two-order cycle optimization method, an optimization index and an optimization objective function to obtain an optimization result, wherein the two-order cycle optimization method specifically comprises the following substeps: step 3-1, setting structural parameters of a heat pump energy storage system and initial temperature of a water tank, setting a control optimization objective function and constraint conditions based on a system dynamic model, and performing operation optimization by adopting a genetic algorithm GA to obtain optimized control parameters; step 3-2, fixing the optimized operation parameters, setting a system structure optimization objective function and constraint conditions based on a system dynamic model, and performing system structure optimization based on a genetic algorithm GA to obtain optimized structure parameters; 3-3, repeating the step 3-1 and the step 3-2 until the difference of the system structure parameters of the two continuous optimization is smaller than an allowable error to obtain an optimal structure parameter; and 3-4, inputting the optimal structure parameters into the dynamic optimization model of the energy storage process system, carrying out real-time control optimization of the whole energy storage process based on a genetic algorithm GA to obtain the optimal control variable value changing along with the energy storage time, and finishing the optimization process when the average temperature of the hot water tank and/or the cold water tank reaches the set temperature.
In the method for jointly optimizing the design and control of the heat pump energy storage system based on the dynamic coupling model, the method can also have the following characteristics: in step 1, the optimized internal structure of the cold and hot water storage tank comprises the size and the position of an internal baffle.
In the method for jointly optimizing the design and control of the heat pump energy storage system based on the dynamic coupling model, the method can also have the following characteristics: and 2, establishing a dynamic model of the cold and heat storage water tank by adopting node layering or flow layering.
In the method for jointly optimizing the design and control of the heat pump energy storage system based on the dynamic coupling model, the method can also have the following characteristics: in step 3, the optimization index is the system dynamic performance efficiency.
In the method for jointly optimizing the design and control of the heat pump energy storage system based on the dynamic coupling model, the method can also have the following characteristics: before the two-stage cycle optimization, multiple times of simulation are needed to predict the operation time when the heat storage temperature reaches the set temperature or the cold storage temperature is lower than the set temperature.
In the method for jointly optimizing the design and control of the heat pump energy storage system based on the dynamic coupling model, the method can also have the following characteristics: in step 3-1, the operation optimization parameters include compressor frequency, cold and hot water pump flow and ejector throat area, and are all random values within a constraint condition range, in step 3-1, the optimization objective function is a maximization system instantaneous COP value, namely a fitness function value, and the formula is as follows:
in the formula (1), the reaction mixture is,
and
respectively the flow rate of cold and hot water pumps, f the frequency of the compressor, AthIs the ejector throat area.
In the method for jointly optimizing the design and control of the heat pump energy storage system based on the dynamic coupling model, the method can also have the following characteristics: wherein in the step 3-2, the parameters of the structure optimization comprise geometrical parameters including a heat exchanger and a water storage tank,
in the step 3-2, only the system structure parameters are optimized, and the target function is still the maximized system instantaneous COP (coefficient of performance), namely a fitness function value, and the formula is as follows:
MAXCOP＝fsystem(Lplate,Dplate,phiplate,N2,Din,evap,Dout,evap,Levap,Dhot,Dcold,Hhot,Hcold) (2)
in the formula (2), LplateFor plate heat exchangers, i.e. gas cooler length, DplateFor plate heat exchangers, i.e. gas cooler width, phiplateFor plate heat exchangers, i.e. gas coolers, with a corrugated inclination angle, N2Number of plates of gas cooler, Din，evapFor double-pipe heat exchangers, i.e. the internal diameter of the evaporator, Dout，evapFor double-pipe heat exchangers, i.e. the external diameter of the evaporator, LevapFor double-pipe heat exchangers, i.e. evaporator length, DhotAnd DcoldRespectively the diameter of the heat storage and cold storage water tank, HhotAnd HcoldThe heights of the heat storage water tank and the cold storage water tank are respectively.
In the method for jointly optimizing the design and control of the heat pump energy storage system based on the dynamic coupling model, the method can also have the following characteristics: in the step 3-3, when the optimization times are more than or equal to 2, the structural parameters of two continuous optimization times are compared, and in the step 3-3, the allowable error is 5%.
In the method for jointly optimizing the design and control of the heat pump energy storage system based on the dynamic coupling model, the method can also have the following characteristics: in the step 3-4, the parameters of operation optimization comprise compressor frequency, cold and hot water pump flow and ejector throat area, which are all random values within the range of constraint conditions, and in the step 3-4, the multi-objective function is optimized to maximize the instantaneous heat storage capacity of the system
Instantaneous cold storage capacity
And an instantaneous COP value, i.e. a fitness function value, the formula is as follows:
in the formula (3), the reaction mixture is,
and
respectively the flow rate of cold and hot water pumps, f the frequency of the compressor, AthIs the ejector throat area.
Action and Effect of the invention
According to the heat pump energy storage system design control common optimization method based on the dynamic coupling model, a two-stage cycle optimization method is provided, the coupling optimization of the structural parameters and the operating parameters of the system can be realized, the structural parameters and the control operating parameters are jointly optimized by adding the whole cycle, and the actual operating performance efficiency of the heat pump energy storage system is improved.
Therefore, the design control common optimization method of the heat pump energy storage system based on the dynamic coupling model is used for realizing the common optimization of the structure and the control of the heat pump energy storage system, improving the performance efficiency of the dual-mode heat pump energy storage system in actual operation, and solving the problems of how to improve the overall performance efficiency of the system in the whole energy storage process, reduce the operation cost and the like by combining with the actual situation.
Drawings
FIG. 1 is a flow chart of a method for designing and controlling a heat pump energy storage system to optimize jointly based on a dynamic coupling model according to the present invention;
FIG. 2 shows an injection type transcritical CO of the present invention2The structure of the heat pump energy storage system is schematically shown.
Detailed Description
In order to make the technical means and functions of the present invention easy to understand, the present invention is specifically described below with reference to the embodiments and the accompanying drawings.
Fig. 1 is a flow chart of a method for jointly optimizing design control of a heat pump energy storage system based on a dynamic coupling model.
As shown in fig. 1, a method for jointly optimizing design and control of a heat pump energy storage system based on a dynamic coupling model, wherein the dynamic coupling model is a nonlinear dynamic coupling model, specifically includes the following steps:
In the invention, the optimized internal structure of the cold and heat storage water tank comprises the size and the position of the internal baffle.
And 2, establishing a cold and heat storage water tank dynamic model according to the optimized internal structure of the energy storage water tank, and then coupling and associating the cold and heat storage water tank dynamic model with a heat pump system dynamic model to obtain a heat pump energy storage system dynamic coupling model.
In this embodiment, a dynamic model of the cold and heat storage water tank is established by node stratification or flow stratification.
FIG. 2 shows an exemplary embodiment of the present invention of an injected transcritical CO2The structure of the heat pump energy storage system is schematically shown.
In the invention, the cold and heat storage water tank is a jet type transcritical CO2The heat pump energy storage system, as shown in fig. 2, includes: the heat pump energy storage system dynamic coupling model is formed by coupling sub models of all components.
Further, the hot water storage tank 201 is connected to a gas cooler 202, the gas cooler 202 is connected to a compressor 203 and a regenerator 204, the compressor 203 is also connected to the regenerator 204, the regenerator 204 is also connected to an ejector 205 and a gas-liquid separator 206, the ejector 205 and the gas-liquid separator 206 are both connected to an evaporator 207, and the evaporator 207 is also connected to a cold water storage tank 208.
Step 3, determining a two-stage cycle optimization method and optimization indexes, or establishing an optimization objective function based on different requirements of users, so as to obtain an optimization result, wherein the two-stage cycle optimization method specifically comprises the following substeps:
and 3-1, setting structural parameters of the heat pump energy storage system and the initial temperature of the water tank, setting a control optimization objective function and constraint conditions based on a system dynamic model, and performing operation optimization by adopting a genetic algorithm GA to obtain optimized control parameters.
In the invention, the operation optimization parameters comprise compressor frequency, cold and hot water pump flow and ejector throat area, which are random values, but the values are required to be in a two-stage constraint range, otherwise, operation errors caused by mismatching of structure and operation parameters are easily caused, and meanwhile, the optimization objective function in the stage is the maximization of the system instantaneous COP value or instantaneous heat/cold storage capacity, namely fitness function value, and the formula is as follows:
in the formula (1), the reaction mixture is,
and
respectively the flow rate of cold and hot water pumps, f the frequency of the compressor, AthIs the ejector throat area.
And 3-2, fixing the optimized operation parameters, setting a system structure optimization objective function and constraint conditions based on the system dynamic model, and performing system structure optimization based on the genetic algorithm GA to obtain optimized structure parameters.
In the invention, the optimization of the stage is similar to the optimization of the previous stage, and only the system structure parameters are optimized, wherein the parameters comprise the geometrical parameters of a heat exchanger and a water storage tank,
in the step 3-2, only the system structure parameters are optimized, and the target function is still the maximized system instantaneous COP (coefficient of performance), namely a fitness function value, and the formula is as follows:
MAXCOP＝fsystem(Lplate,Dplate,phiplate,N2,Din,evap,Dout,evap,Levap,Dhot,Dcold,Hhot,Hcold) (2)
in the formula (2), LplateFor plate heat exchangers, i.e. gas cooler length, DplateFor plate heat exchangers, i.e. gas cooler width, phiplateFor plate heat exchangers, i.e. gas coolers, with a corrugated inclination angle, N2Number of plates of gas cooler, Din，evapFor double-pipe heat exchangers, i.e. the internal diameter of the evaporator, Dout，evapFor double-pipe heat exchangers, i.e. the external diameter of the evaporator, LevapFor double-pipe heat exchangers, i.e. evaporator length, DhotAnd DcoldRespectively the diameter of the heat storage and cold storage water tank, HhotAnd HcoldThe heights of the heat storage water tank and the cold storage water tank are respectively.
In the invention, the optimized variables are respectively constrained according to actual equipment parameters and the maximum allowable limit of operation, and the variables in an unlimited range are properly constrained according to the arrangement space and the economy in order to meet the operation requirements of the genetic algorithm.
And 3-3, repeating the step 3-1 and the step 3-2 until the difference of the system structure parameters of two continuous optimization is less than an allowable error, such as 5%, so as to obtain the optimal structure parameters.
In the invention, the results of two-round optimization are needed to be compared after two rounds of optimization are respectively carried out in the first and second stages, the results are output when the average difference value of the optimization variables of the two rounds is smaller than an allowable error, the comparison operation is carried out only when the number of optimization rounds is larger than or equal to 2, and the first-round optimization part is directly returned to start the second round of optimization after the first round of optimization is finished.
In the invention, the cyclic operation of the step 3-1 and the step 3-2 ensures that the structure and the operation parameters of the system equipment are jointly optimal.
And 3-4, inputting the optimal structure parameters into an energy storage process dynamic system optimization model, carrying out real-time control optimization on the whole energy storage process based on a genetic algorithm GA to obtain an optimal control variable value changing along with energy storage time, and finishing the optimization process when the average temperature of the hot water tank and/or the cold water tank reaches a set temperature.
In the invention, the parameters of operation optimization comprise compressor frequency, cold and hot water pump flow and ejector throat area, and are all random values within the range of constraint conditions.
In the invention, the energy storage process optimizes the objective function to sequentially maximize the instantaneous heat storage capacity of the system
Instantaneous cold storage capacity
And instantaneous COP value, the formula is as follows:
in the formula (3), the reaction mixture is,
and
respectively the flow rate of cold and hot water pumps, f the frequency of the compressor, AthIs the ejector throat area.
In the present invention, the optimization index is instantaneous performance efficiency, not energy consumption or cost.
In the invention, because the optimization time is longer, multiple times of simulation are needed before the two-stage cycle optimization is carried out to predict the running time when the heat storage temperature reaches 60 ℃ or the cold storage temperature is lower than 5 ℃, so as to prevent the waste of computing resources caused by selecting overlong simulation time during the optimization.
Example (b):
firstly, according to the heating and refrigerating capacity of the heat pump, the volume and the internal structure of the heat and cold storage water tank are designed, numerical simulation is carried out, and the internal geometric structure of the heat and cold storage water tank is optimized based on a CFD model, wherein the internal geometric structure comprises the size and the position of a baffle.
And then, establishing a dynamic model of the cold and heat storage water tank according to the optimized internal structure of the energy storage water tank, and then coupling and associating the dynamic model with the dynamic model of the heat pump system to obtain a dynamic coupling model of the heat pump energy storage system.
Secondly, setting an objective function as a performance efficiency instantaneous value based on a system dynamic model, setting a first-stage system control optimization constraint condition as shown in table 1, and setting a second-stage system structure optimization constraint condition as shown in table 2; the min value and the max value are the upper and lower optimized limits, the optimized range is determined according to the economy, the space and the physical characteristics if the variables without clear range limitation exist, the start value is the first generation optimized value which can be randomly selected and is between the min value and the max value;
and finally, calling an optimization model library, and inputting a structure and an operation parameter start value as a first round of optimization parameters. Performing operation control parameter optimization on the fixed structure parameter value until the optimization iteration number reaches a set value, such as 1000, extracting operation control parameters corresponding to the optimal COP, then fixing the operation parameter value to start system structure parameter optimization until the optimization iteration number reaches the set value, such as 1000, at the moment, completing the first round of optimization, and obtaining the value of a decision variable from the second generation by adopting a genetic algorithm GA; and extracting the system structural parameters obtained by the first round of optimization to serve as the optimization fixed values of the operation control parameters in the second round of optimization until the optimization algebra reaches a set value, extracting the second round of operation control parameters corresponding to the optimal COP, and then fixing the operation parameter values to start structural parameter optimization until the optimization algebra reaches the set value, wherein the second round of optimization is completed. Comparing the structure obtained by the two-round optimization with the operation control parameters, repeating the steps until the variation of all the structure parameters obtained by the two-round optimization is lower than the error allowance 5%, exiting the optimization, and storing the structure and the operation control parameters corresponding to the optimal COP; and inputting the obtained structure and control parameters into a simulation model, performing real-time control optimization on the energy storage process, and finishing energy storage after the cold and hot water tank reaches the set temperature to obtain the overall COP (coefficient of performance) optimization value of the energy storage process system.
TABLE 1 System operational control constraints
TABLE 2 System structural optimization constraints
The dynamic circulation optimization strategy of the heat pump structure parameters and the two-order operation parameters based on the genetic algorithm is used for carrying out simulation optimization on the physical model of the jet type trans-critical carbon dioxide heat pump energy storage system based on the Modelica language, so that the total COP of the whole energy storage process system is increased to more than 8.7.
Effects and effects of the embodiments
According to the heat pump energy storage system design control common optimization method based on the dynamic coupling model, a two-stage cycle optimization method is provided, the coupling optimization of the structural parameters and the operating parameters of the system can be realized, the structural parameters and the control operating parameters are jointly optimized by adding the whole cycle, and the actual operating performance efficiency of the heat pump energy storage system is improved.
Therefore, the design control common optimization method of the heat pump energy storage system based on the dynamic coupling model is used for realizing the common optimization of the structure and the control of the heat pump energy storage system, improving the performance efficiency of the dual-mode heat pump energy storage system in actual operation, and solving the problems of how to improve the overall performance efficiency of the system in the whole energy storage process, reduce the operation cost and the like by combining with the actual situation.
The above embodiments are preferred examples of the present invention, and are not intended to limit the scope of the present invention.
Claims (9)
1. A heat pump energy storage system design control co-optimization method based on a dynamic coupling model is characterized by comprising the following steps:
step 1, optimizing the internal structure of a cold and heat storage water tank based on a CFD numerical model to obtain the optimized internal structure of the energy storage water tank;
step 2, establishing a cold and heat storage water tank dynamic model according to the optimized internal structure of the energy storage water tank, and then coupling and associating the cold and heat storage water tank dynamic model with a heat pump system dynamic model to obtain a heat pump energy storage system dynamic coupling model;
step 3, determining a two-stage cycle optimization method, an optimization index and an optimization objective function to obtain an optimization result,
the two-stage cycle optimization method specifically comprises the following substeps:
step 3-1, setting structural parameters of a heat pump energy storage system and initial temperature of a water tank, setting a control optimization objective function and constraint conditions based on a system dynamic model, and performing operation optimization by adopting a genetic algorithm GA to obtain optimized control parameters;
step 3-2, fixing the optimized operation parameters, setting a system structure optimization objective function and constraint conditions based on the system dynamic model, and performing system structure optimization based on the genetic algorithm GA to obtain optimized structure parameters;
3-3, repeating the step 3-1 and the step 3-2 until the difference of the system structure parameters of the two continuous optimization is smaller than an allowable error to obtain an optimal structure parameter;
and 3-4, inputting the optimal structure parameters into a dynamic optimization model of the energy storage process system, carrying out real-time control optimization of the whole energy storage process based on the genetic algorithm GA to obtain optimal control variable values changing along with energy storage time, and finishing the optimization process when the average temperature of the hot water tank and/or the cold water tank reaches a set temperature.
2. The heat pump energy storage system design control co-optimization method based on the dynamic coupling model according to claim 1, characterized in that:
in the step 1, the optimized internal structure of the cold and hot water storage tank comprises the size and the position of an internal baffle.
3. The heat pump energy storage system design control co-optimization method based on the dynamic coupling model according to claim 1, characterized in that:
and 2, establishing a dynamic model of the cold and hot water storage tank by adopting node layering or flow layering.
4. The heat pump energy storage system design control co-optimization method based on the dynamic coupling model according to claim 1, characterized in that:
in step 3, the optimization index is the system dynamic performance efficiency.
5. The heat pump energy storage system design control co-optimization method based on the dynamic coupling model according to claim 1, characterized in that:
before the two-stage cycle optimization, multiple times of simulation are needed to predict the operation time when the heat storage temperature reaches the set temperature or the cold storage temperature is lower than the set temperature.
6. The heat pump energy storage system design control co-optimization method based on the dynamic coupling model according to claim 1, characterized in that:
wherein, in the step 3-1, the operation optimization parameters comprise compressor frequency, cold and hot water pump flow rate and ejector throat area, which are all random values within the range of constraint conditions,
in the step 3-1, the optimization objective function is a maximum system instantaneous COP value, i.e. a fitness function value, and the formula is as follows:
in the formula (1), the reaction mixture is,
and
respectively the flow rate of cold and hot water pumps, f the frequency of the compressor, AthIs the ejector throat area.
7. The heat pump energy storage system design control co-optimization method based on the dynamic coupling model according to claim 1, characterized in that:
wherein in the step 3-2, the parameters of the structure optimization comprise geometrical parameters including a heat exchanger and a water storage tank,
in the step 3-2, only the system structure parameters are optimized, and the objective function is still the maximized system instantaneous COP, i.e. the fitness function value, and the formula is as follows:
MAX COP＝fsystem(Lplate,Dplate,phiplate,N2,Din,evap,Dout,evap,Levap,Dhot,Dcold,Hhot,Hcold) (2)
in the formula (2), LplateFor plate heat exchangers, i.e. gas cooler length, DplateFor plate heat exchangers, i.e. gas cooler width, phiplateFor plate heat exchangers, i.e. gas coolers, with a corrugated inclination angle, N2Number of plates of gas cooler, Din，evapFor double-pipe heat exchangers, i.e. the internal diameter of the evaporator, Dout，evapFor double-pipe heat exchangers, i.e. the external diameter of the evaporator, LevapFor double-pipe heat exchangers, i.e. evaporator length, DhotAnd DcoldRespectively the diameter of the heat storage and cold storage water tank, HhotAnd HcoldThe heights of the heat storage water tank and the cold storage water tank are respectively.
8. The heat pump energy storage system design control co-optimization method based on the dynamic coupling model according to claim 1, characterized in that:
wherein, in the step 3-3, when the optimization times is more than or equal to 2, the comparison between the structural parameters of the two continuous optimization times is carried out,
in the step 3-3, the allowable error is 5%.
9. The heat pump energy storage system design control co-optimization method based on the dynamic coupling model according to claim 1, characterized in that:
wherein, in the step 3-4, the parameters of the operation optimization comprise the frequency of the compressor, the flow rate of the cold and hot water pump and the throat area of the ejector, and are all random values within the range of the constraint condition,
in the step 3-4, the energy storage process optimizes the objective function to sequentially maximize the instantaneous heat storage capacity of the system
Instantaneous cold storage capacity
And instantaneous COP value, the formula is as follows:
in the formula (3), the reaction mixture is,
and
respectively the flow rate of cold and hot water pumps, f the frequency of the compressor, AthIs the ejector throat area.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113553675A (en) * | 2021-07-29 | 2021-10-26 | 上海电力大学 | Ejector optimization method and device |
| CN113688466A (en) * | 2021-08-26 | 2021-11-23 | 苏州同元软控信息技术有限公司 | Vehicle energy consumption simulation method, device and equipment |
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| CN115143818B (en) * | 2022-08-31 | 2023-01-06 | 新乡市特美特热控技术股份有限公司 | Design method of titanium and aluminum alloy heat exchanger |
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