CN117433113A - Building refrigerating system operation method, system and equipment - Google Patents
Building refrigerating system operation method, system and equipment Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000005057 refrigeration Methods 0.000 claims abstract description 67
- 230000005611 electricity Effects 0.000 claims abstract description 18
- 238000005457 optimization Methods 0.000 claims abstract description 14
- 230000005012 migration Effects 0.000 claims abstract description 9
- 238000013508 migration Methods 0.000 claims abstract description 9
- 230000000694 effects Effects 0.000 claims abstract description 7
- 230000008569 process Effects 0.000 claims abstract description 7
- 230000008859 change Effects 0.000 claims abstract description 4
- 238000012546 transfer Methods 0.000 claims abstract description 4
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 29
- 238000001816 cooling Methods 0.000 claims description 22
- 230000006870 function Effects 0.000 claims description 18
- 239000011159 matrix material Substances 0.000 claims description 12
- 238000012545 processing Methods 0.000 claims description 12
- 238000006243 chemical reaction Methods 0.000 claims description 8
- 238000005192 partition Methods 0.000 claims description 6
- 230000000452 restraining effect Effects 0.000 claims description 6
- 238000005070 sampling Methods 0.000 claims description 6
- 238000013211 curve analysis Methods 0.000 claims description 3
- 238000013486 operation strategy Methods 0.000 abstract 1
- 239000000498 cooling water Substances 0.000 description 14
- 238000011217 control strategy Methods 0.000 description 7
- 238000005265 energy consumption Methods 0.000 description 4
- 238000004378 air conditioning Methods 0.000 description 2
- 238000007710 freezing Methods 0.000 description 2
- 230000008014 freezing Effects 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010438 heat treatment Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000009423 ventilation Methods 0.000 description 1
Classifications
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
- F24F11/46—Improving electric energy efficiency or saving
- F24F11/47—Responding to energy costs
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
- F24F11/63—Electronic processing
- F24F11/64—Electronic processing using pre-stored data
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- Physics & Mathematics (AREA)
- Fuzzy Systems (AREA)
- Mathematical Physics (AREA)
- Air Conditioning Control Device (AREA)
Abstract
The invention relates to the technical field of refrigeration system control, in particular to a method, a system and equipment for operating a building refrigeration system, which comprises the following steps: establishing a dynamic model of the refrigeration system; obtaining a dynamic prediction model of the refrigerating system through discretization; taking the refrigeration quantity change range constraint into account to establish an optimization objective function taking the energy cost and peak demand cost tracking effect as a core under the time-of-use electricity price structure; solving an optimization objective function to obtain optimal refrigerating capacity, transferring the load in the peak time to other time, and performing tracking control on the operation of the refrigerating system; and in the process of tracking control, acquiring the load condition of each period after migration and the optimal operation interval of the variable frequency host of the refrigeration system under the load condition, and operating the refrigeration system under the maximum energy efficiency ratio. The invention transfers the load of peak time, and each time is based on the maximum energy efficiency ratio operation strategy, which can effectively reduce the total refrigeration cost of a single day and improve the cold comfort and economy of the refrigeration system during operation.
Description
Technical Field
The invention relates to the technical field of refrigeration system control, in particular to a method, a system and equipment for operating a building refrigeration system.
Background
Buildings represent a significant proportion of the world's energy consumption, with about 40% -50% being attributed to heating, ventilation and air conditioning (HVAC) systems. It is therefore of great importance to find an appropriate control strategy in hvac equipment to reduce the energy consumption of the building sector.
At present, the traditional control method mainly only considers the real-time temperature control problem of an air conditioning system and ignores the energy consumption of the system; particularly, the current time-of-use price policy is implemented to divide a single day of electricity price into a plurality of time periods, such as the time-of-use price published by Sichuan, and peak-to-valley time periods are divided as follows: peak time: 11:00-12:00, 14:00-21:00; and (3) flat section: 7:00-11:00, 12:00-14:00, 21:00-23:00; low valley period: the electricity price difference between the electricity price and the current price in different time periods can reach 60 percent, and the ratio of the electricity price to the current price is 1.6:1:0.4.
Therefore, the load in the peak period is transferred to the flat period or the low valley by considering the time-sharing electricity price, the total refrigeration cost in a single day can be effectively reduced, and the method has larger application potential and good economy.
Disclosure of Invention
The invention aims to provide a method, a system and equipment for operating a building refrigeration system, which can effectively reduce the total refrigeration cost of a single day by tracking and controlling the operation of the refrigeration system and transferring the load in a peak period to a flat section or a valley.
The embodiment of the invention is realized by the following technical scheme: a method of operating a building refrigeration system comprising the steps of:
step one, establishing a dynamic model of a refrigerating system;
step two, discretizing the dynamic model of the refrigerating system to obtain a dynamic prediction model of the refrigerating system;
step three, restraining the variation range of the refrigerating capacity of the refrigerating system in the dynamic model, and establishing an optimized objective function taking the energy cost and the peak demand cost tracking effect as a core under a time-of-use electricity price structure;
step four, utilizing a dynamic prediction model to solve an optimization objective function on line in real time to obtain optimal refrigerating capacity, transferring the load in the peak time to other time periods, and performing tracking control on the operation of the refrigerating system;
and in the process of tracking control, acquiring the load condition of each period after migration and the optimal operation interval of the variable frequency host of the refrigeration system under the load condition, and operating the refrigeration system under the maximum energy efficiency ratio.
According to a preferred embodiment, the expression of the optimization objective function is as follows:
in the above formula, t represents a sampling time step, N represents a total time step number of a single day, E c (T) represents the energy cost in the time step T, P (T) represents the average power consumption in the time step T, T S Represents the sampling time interval, D c (t d ) Representing the peak power consumption occurrence time step t d Peak demand cost at that time.
According to a preferred embodiment, step four comprises: and converting the minimum-maximum problem of the optimized objective function into a standard linear programming problem, and solving the standard linear programming problem through Matlab.
According to a preferred embodiment, the post-conversion standard linear programming problem is expressed as follows:
E c (k+1)、E c (k+2),…,E c (k+N p ) Representing time steps k+1, k+2, k+N, respectively p Is used for the energy cost of the water heater,P(i) Representing the actual power consumption before the current step k, P (k+1), P (k+2), P (k+N) p ) Representing time steps k+1, k+2, k+N, respectively p Average power consumption of N p Representing the prediction time domain,and +.>Respectively represent the prediction vision N p Prediction matrix of power, actual refrigeration and refrigeration set point in the interior, i represents area, i=1, 2, …, Φ, Φ represents total number of areas, T zi (k+1)、T zi (k+2)、T zi (k+N p ) Representing time steps k+1, k+2, k+N, respectively p In the actual refrigerating capacity T spi (k)、T spi (k+1)、T spi (k+N p -1) represent time steps k, k+1, k+n, respectively p -1, lb represents the lower limit of the refrigeration capacity, ub represents the upper limit of the refrigeration capacity setpoint, and T represents the inversion.
According to a preferred embodiment, the constraint expression of the refrigerating capacity variation range is as follows:
where lb (k) represents the lower limit of the cooling capacity in time step k, ub (k) represents the upper limit of the cooling capacity set point in time step k,representing the minimum value of the actual cooling capacity in time step k +.>Representing the refrigeration set point in time step kMinimum value (min.)>Represents the maximum value of the actual cooling capacity in time step k,/>Representing the maximum value of the refrigeration set point in time step k.
According to a preferred embodiment, the expression of the dynamic prediction model is as follows:
A P (q)Pk＝B P (q)u(k)+e(k)
in the above formula, u (k) represents a prediction matrix of a refrigerating capacity set point, N O The order of the model is represented and, representing the parameters that the model needs to identify, respectively.
According to a preferred embodiment, the dynamic prediction model takes the actual refrigerating capacity as output, and the expression is as follows:
A T (q)y T (k)＝B T (q)u(k)+e(k)
y T (k)＝[T z1 (k),T z2 (k),…,T zΦ (k)] T
in the above, A T (q)、B T (q) represents a polynomial transfer matrix to be determined, T z1 (k)、T z2 (k)、T zΦ (k) A prediction matrix representing the actual cooling capacity.
According to a preferred embodiment, in step four, operating the refrigeration system at a maximum energy efficiency ratio comprises:
acquiring a performance curve of a water chilling unit of a refrigeration system, and carrying out energy efficiency partition on the full working range of the water chilling unit in the period through performance curve analysis;
and determining the optimal switching critical point of the running number of the variable-frequency main engine of the refrigerating system based on the energy efficiency partition, increasing the unit when the load condition of the period meets the optimal loading switching point, and reducing the unit when the load condition of the period meets the optimal load shedding switching point.
The invention also provides a building refrigeration control system, which is applied to the method, and comprises the following steps:
the first processing module is used for establishing a dynamic model of the refrigerating system;
the second processing module is used for discretizing the dynamic model of the refrigerating system to obtain a dynamic prediction model of the refrigerating system;
the third processing module is used for restraining the refrigerating capacity change range of the refrigerating system in the dynamic model and establishing an optimization objective function taking the energy cost and peak demand cost tracking effect as a core under the time-of-use electricity price structure;
the fourth processing module is used for solving the optimization objective function on line in real time by utilizing the dynamic prediction model to obtain the optimal refrigerating capacity, transferring the load in the peak time to other time periods and carrying out tracking control on the operation of the refrigerating system;
and in the process of tracking control, acquiring the load condition of each period after migration and the optimal operation interval of the variable frequency host of the refrigeration system under the load condition, and operating the refrigeration system under the maximum energy efficiency ratio.
The present invention also provides an electronic device including:
a memory storing execution instructions; and
a processor executing the memory-stored execution instructions, causing the processor to perform the method as described above.
The technical scheme of the operation method of the building refrigeration system provided by the embodiment of the invention has at least the following advantages and beneficial effects: the invention comprehensively considers that the difference of different electricity consumption costs at different electricity consumption moments under the time-sharing electricity price is large, and by tracking and controlling the operation of the refrigerating system, the load in the peak period is transferred to the flat section or the valley, so that the total refrigerating cost in a single day can be effectively reduced, and the cold comfort and the economy of the refrigerating system in operation are improved.
Drawings
Fig. 1 is a schematic flow chart of a method for operating a refrigeration system for a building according to embodiment 1 of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. 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.
Example 1
Referring to fig. 1, fig. 1 is a schematic flow chart of a method for operating a refrigeration system for a building according to an embodiment of the invention. Specifically, the method comprises the following steps:
step one, establishing a dynamic model of a refrigerating system; the standard mechanism model of the refrigeration system is established, and redundant description is omitted here.
Step two, discretizing the dynamic model of the refrigerating system to obtain a dynamic prediction model of the refrigerating system; in one implementation of this embodiment, the expression of the dynamic prediction model is as follows:
A P (q)Pk＝B P (q)u(k)+e(k)
in the above formula, u (k) represents a prediction matrix of a refrigerating capacity set point, N O The order of the model is represented and, representing the parameters that the model needs to identify, respectively.
The dynamic prediction model takes actual refrigerating capacity as output, and the expression is as follows:
A T (q)y T (k)＝B T (q)u(k)+e(k)
y T (k)＝[T z1 (k),T z2 (k),…,T zΦ (k)] T
in the above, A T (q)、B T (q) represents a polynomial transfer matrix to be determined, T z1 (k)、T z2 (k)、T zΦ (k) A prediction matrix representing the actual cooling capacity.
Step three, restraining the variation range of the refrigerating capacity of the refrigerating system in the dynamic model, and establishing an optimized objective function taking the energy cost and the peak demand cost tracking effect as a core under a time-of-use electricity price structure; in one implementation of this embodiment, the expression of the optimization objective function is as follows:
in the above formula, t represents a sampling time step, N represents a total time step number of a single day, E c (T) represents the energy cost in the time step T, P (T) represents the average power consumption in the time step T, T S Represents the sampling time interval, D c (t d ) Representing the peak power consumption occurrence time step t d Peak demand cost at that time.
Further, the constraint expression of the refrigerating amount variation range is as follows:
in the above, lb (k)Representing the lower limit of the cooling capacity in time step k, ub (k) representing the upper limit of the cooling capacity setpoint in time step k,representing the minimum value of the actual cooling capacity in time step k +.>Representing the minimum value of the refrigerating capacity setpoint in time step k +.>Represents the maximum value of the actual cooling capacity in time step k,/>Representing the maximum value of the refrigeration set point in time step k.
And fourthly, solving the optimization objective function on line in real time by utilizing the dynamic prediction model to obtain the optimal refrigerating capacity, and transferring the load in the peak time to other time periods to perform tracking control on the operation of the refrigerating system.
Specifically, the present embodiment converts the min-max problem of the optimization objective function into a standard linear programming problem, and solves by Matlab. The post-conversion standard linear programming problem is expressed as follows:
E c (k+1)、E c (k+2),…,E c (k+N p ) Representing time steps k+1, k+2, k+N, respectively p Is used for the energy cost of the water heater,P(i) Representing the actual power consumption before the current step k, P (k+1), P (k+2), P (k+N) p ) Representing time steps k+1, k+2, k+N, respectively p Average power consumption of N p Representing pre-emphasisThe time domain is measured and the time domain is measured,and +.>Respectively represent the prediction vision N p Prediction matrix of power, actual refrigeration and refrigeration set point in the interior, i represents area, i=1, 2, …, Φ, Φ represents total number of areas, T zi (k+1)、T zi (k+2)、T zi (k+N p ) Representing time steps k+1, k+2, k+N, respectively p In the actual refrigerating capacity T spi (k)、T spi (k+1)、T spi (k+N p -1) represent time steps k, k+1, k+n, respectively p -1, lb represents the lower limit of the refrigeration capacity, ub represents the upper limit of the refrigeration capacity setpoint, and T represents the inversion.
Considering that the frequency conversion host has an optimal operation interval under partial load, after load migration, the frequency conversion host in each period should be correspondingly adjusted according to the load condition after migration. Therefore, in the process of tracking control, the embodiment further obtains the load condition of each period after migration and the optimal operation interval of the variable frequency host of the refrigeration system under the load condition, and operates the refrigeration system under the maximum energy efficiency ratio. Specifically, a performance curve of a cooling water unit of the refrigeration system is obtained, and the energy efficiency partition is carried out on the full working range of the cooling water unit in the period through performance curve analysis; and determining the optimal switching critical point of the running number of the variable-frequency main engine of the refrigerating system based on the energy efficiency partition, increasing the unit when the load condition of the period meets the optimal loading switching point, and reducing the unit when the load condition of the period meets the optimal load shedding switching point. It should be noted that, the COP can be maximized when the load rate of the general frequency conversion host is about 60% to 70%, so that the embodiment regulates and controls the number of frequency conversion host operation according to the load condition, and maintains the load of the frequency conversion host between 60% and 70%. The following is illustrative:
(1) When the load condition in the period meets the optimal load switching point: at the moment, the average load of the frequency converter unit is higher than 70%, for example 90%, the freezing backwater temperature of the water chilling unit is reduced by less than 0.5 ℃/min, or the freezing water supply temperature is more than 7 ℃, and the frequency converter unit is added when any condition is continuously met for a set time, for example 10 minutes;
(2) When the load condition in the period meets the optimal load shedding switching point: at this time, the average load of the frequency converter unit is lower than 60%, for example 45%, or the chilled water supply temperature is less than 7 ℃, and if any of the conditions is continuously met for a set time, for example 10 minutes, one frequency converter unit is reduced.
In addition, the method for operating the building refrigeration system provided in the embodiment further includes: the variable-frequency chilled water pump and the variable-frequency cooling water pump both adopt a constant-temperature difference control strategy, wherein the variable-frequency chilled water pump has optimal high-efficiency flow intervals under different frequencies, and the variable-frequency chilled water pump is used for automatically correcting the variable-frequency chilled water pump number load and load setting values according to the characteristic curve of the variable-frequency chilled water pump, so that the energy consumption of the pump set in the period is reduced to the maximum extent, and the method comprises the following steps of: (1) When the temperature difference of the water supply and return water is larger than a set value and the water supply temperature is larger than the set value, the frequency of the variable-frequency chilled water pump is increased; (2) When the temperature difference of the water supply and return is smaller than a set value and the water supply temperature is smaller than the set value, the frequency of the variable-frequency chilled water pump is reduced; (3) And setting a lower frequency limit, ensuring that the water flow of the variable-frequency chilled water pump is not lower than the minimum flow, and ensuring the safe operation of the evaporator.
The constant temperature difference control strategy of the variable-frequency cooling water pump is specifically as follows: (1) When the compressors of the variable-frequency cooling water pump are all standby, the variable-frequency cooling water pump is automatically turned off, so that the energy-saving purpose is achieved; (2) Before the compressor of the variable-frequency cooling water pump is about to be started, the variable-frequency cooling water pump is automatically started, so that the stable operation of the unit is ensured; (3) When the temperature difference of the water supply and return is larger than a set value, the frequency of the variable-frequency cooling water pump is increased; (4) When the temperature difference of the water supply and return is smaller than a set value, the frequency of the variable-frequency cooling water pump is reduced; (5) And setting a lower frequency limit, ensuring that the water flow of the variable-frequency cooling water pump is not lower than the minimum flow, and ensuring the safe operation of the condenser.
In addition, the method for operating the building refrigeration system provided in the embodiment further includes: a cooling tower control strategy for calculating a minimum temperature of a cooling tower output by monitoring an outdoor temperature and a performance curve of the cooling tower, and determining a cooling tower turn-on number and frequency by using the minimum temperature as a target temperature (TwcSB), comprising: (1) When the return water temperature of the cooling water is more than TfcSB+0.5deg.C, the cooling fan is started and put into operation, and the control sequence of the cooling fan is as follows: when the base point temperature TfcD is more than the set value plus 0.5 ℃, increasing the fan frequency until the maximum frequency value is reached; when the base point temperature TfcD is less than the set value +0.5deg.C, reducing the fan frequency until the minimum frequency value; (2) When the TfcSB is smaller than the return water temperature of the cooling water is smaller than TfcSB plus 0.5 ℃, if the fan is in a closed state, the fan is kept in the closed state; if the fan is in an operating state, the fan gradually decelerates until the fan is closed.
In summary, the invention comprehensively considers that the difference of different electricity consumption costs at different electricity consumption moments under the time-sharing electricity price is large, and by tracking and controlling the operation of the refrigerating system, the load at the peak time is transferred to the flat section or the valley, and the invention is further based on the starting and stopping regulation strategy of the variable-frequency main machine, the constant temperature difference control strategy of the variable-frequency chilled water pump, the constant temperature difference control strategy of the variable-frequency cooling water pump and the control strategy of the cooling tower, so that the total refrigerating cost of a single day can be effectively reduced, and the cold comfort and economy of the refrigerating system during operation can be improved.
Example 2
The present invention also provides a building refrigeration control system applied to the method of embodiment 1, comprising: the first processing module is used for establishing a dynamic model of the refrigerating system; the second processing module is used for discretizing the dynamic model of the refrigerating system to obtain a dynamic prediction model of the refrigerating system; the third processing module is used for restraining the refrigerating capacity change range of the refrigerating system in the dynamic model and establishing an optimization objective function taking the energy cost and peak demand cost tracking effect as a core under the time-of-use electricity price structure; the fourth processing module is used for solving the optimization objective function on line in real time by utilizing the dynamic prediction model to obtain the optimal refrigerating capacity, transferring the load in the peak time to other time periods and carrying out tracking control on the operation of the refrigerating system; and in the process of tracking control, acquiring the load condition of each period after migration and the optimal operation interval of the variable frequency host of the refrigeration system under the load condition, and operating the refrigeration system under the maximum energy efficiency ratio.
Example 3
The present invention also provides an electronic device including: a memory storing execution instructions; and a processor executing the execution instructions stored in the memory, causing the processor to perform the method as described in embodiment 1.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method of operating a building refrigeration system comprising the steps of:
step one, establishing a dynamic model of a refrigerating system;
step two, discretizing the dynamic model of the refrigerating system to obtain a dynamic prediction model of the refrigerating system;
step three, restraining the variation range of the refrigerating capacity of the refrigerating system in the dynamic model, and establishing an optimized objective function taking the energy cost and the peak demand cost tracking effect as a core under a time-of-use electricity price structure;
step four, utilizing a dynamic prediction model to solve an optimization objective function on line in real time to obtain optimal refrigerating capacity, transferring the load in the peak time to other time periods, and performing tracking control on the operation of the refrigerating system;
and in the process of tracking control, acquiring the load condition of each period after migration and the optimal operation interval of the variable frequency host of the refrigeration system under the load condition, and operating the refrigeration system under the maximum energy efficiency ratio.
2. The method of operating a building refrigeration system according to claim 1, wherein said optimizing an objective function is expressed as follows:
in the above formula, t represents a sampling time step, N represents a total time step number of a single day, E c (T) represents the energy cost in the time step T, P (T) represents the average power consumption in the time step T, T S Represents the sampling time interval, D c (t d ) Representing the peak power consumption occurrence time step t d Peak demand cost at that time.
3. The method of operating a building refrigeration system according to claim 2, wherein step four comprises: and converting the minimum-maximum problem of the optimized objective function into a standard linear programming problem, and solving the standard linear programming problem through Matlab.
4. A method of operating a building refrigeration system according to claim 3 wherein the post-conversion standard linear programming problem is expressed as follows:
E c (k+1)、E c (k+2),…,E c (k+N p ) Representing time steps k+1, k+2, k+N, respectively p In (2), P (i) represents the actual power consumption before the current step k, P (k+1), P (k+2), P (k+n) p ) Representing time steps k+1, k+2, k+N, respectively p Average power consumption of N p Representing the prediction time domain,and +.>Respectively represent the prediction vision N p Prediction matrix of power, actual refrigeration and refrigeration set point in the interior, i represents area, i=1, 2, …, Φ, Φ represents total number of areas, T zi (k+1)、T zi (k+2)、T zi (k+N p ) Representing time steps k+1, k+2, k+N, respectively p In the actual refrigerating capacity T spi (k)、T spi (k+1)、T spi (k+N p -1) represent time steps k, k+1, k+n, respectively p -1, lb represents the lower limit of the refrigeration capacity, ub represents the upper limit of the refrigeration capacity setpoint, and T represents the inversion.
5. The method of operating a building refrigeration system according to claim 4, wherein said constraint expression for said range of refrigeration variation is as follows:
where lb (k) represents the lower limit of the cooling capacity in time step k, ub (k) represents the upper limit of the cooling capacity set point in time step k,representing the minimum value of the actual cooling capacity in time step k +.>Representing the minimum value of the refrigerating capacity setpoint in time step k +.>Represents the maximum value of the actual cooling capacity in time step k,/>Representing the maximum value of the refrigeration set point in time step k.
6. The method of operating a building refrigeration system according to claim 5, wherein said dynamic predictive model is expressed as follows:
A P (q)Pk＝B P (q)u(k)+e(k)
in the above formula, u (k) represents a prediction matrix of a refrigerating capacity set point, N O The order of the model is represented and, representing the parameters that the model needs to identify, respectively.
7. The method of operating a building refrigeration system according to claim 6, wherein said dynamic predictive model takes as output the actual refrigeration capacity expressed as follows:
A T (q)y T (k)＝B T (q)u(k)+e(k)
y T (k)＝[T z1 (k)，T z2 (k)，…，T zΦ (k)] T
in the above, A T (q)、B T (q) represents a polynomial transfer matrix to be determined, T z1 (k)、T z2 (k)、T zΦ (k) A prediction matrix representing the actual cooling capacity.
8. The method of operating a refrigeration system for a building as set forth in any one of claims 1 to 7 wherein in step four, operating the refrigeration system at a maximum energy efficiency ratio comprises:
acquiring a performance curve of a water chilling unit of a refrigeration system, and carrying out energy efficiency partition on the full working range of the water chilling unit in the period through performance curve analysis;
and determining the optimal switching critical point of the running number of the variable-frequency main engine of the refrigerating system based on the energy efficiency partition, increasing the unit when the load condition of the period meets the optimal loading switching point, and reducing the unit when the load condition of the period meets the optimal load shedding switching point.
9. A building refrigeration control system for application to the method of any one of claims 1 to 8, comprising:
the first processing module is used for establishing a dynamic model of the refrigerating system;
the second processing module is used for discretizing the dynamic model of the refrigerating system to obtain a dynamic prediction model of the refrigerating system;
the third processing module is used for restraining the refrigerating capacity change range of the refrigerating system in the dynamic model and establishing an optimization objective function taking the energy cost and peak demand cost tracking effect as a core under the time-of-use electricity price structure;
the fourth processing module is used for solving the optimization objective function on line in real time by utilizing the dynamic prediction model to obtain the optimal refrigerating capacity, transferring the load in the peak time to other time periods and carrying out tracking control on the operation of the refrigerating system;
and in the process of tracking control, acquiring the load condition of each period after migration and the optimal operation interval of the variable frequency host of the refrigeration system under the load condition, and operating the refrigeration system under the maximum energy efficiency ratio.
10. An electronic device, comprising:
a memory storing execution instructions; and
a processor executing the memory-stored execution instructions, causing the processor to perform the method of any one of claims 1 to 8.
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311491665.2A CN117433113A (en) | 2023-11-09 | 2023-11-09 | Building refrigerating system operation method, system and equipment |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311491665.2A CN117433113A (en) | 2023-11-09 | 2023-11-09 | Building refrigerating system operation method, system and equipment |
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| CN117433113A true CN117433113A (en) | 2024-01-23 |
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