CN114322209A - Data processing method and device of ice storage air conditioning system and electronic equipment - Google Patents
Data processing method and device of ice storage air conditioning system and electronic equipment Download PDFInfo
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Abstract
The invention provides a data processing method and device of an ice storage air conditioning system and electronic equipment. The method is applied to a controller of an ice storage air conditioning system and comprises the following steps: acquiring an optimization control target and a target parameter corresponding to the optimization control target; determining the building cold load of the ice storage air conditioning system; inputting the target parameters, the optimization control target and the building cold load into a pre-established ice storage system model, and outputting the operation parameters of each device of the ice storage air conditioning system; and controlling each device of the ice storage air conditioning system to execute cooling operation based on the operation parameters. In the method, the running parameters of each device of the ice storage air-conditioning system are output through the ice storage system model, so that the performance of the ice storage air-conditioning system can be improved, the peak load shifting effect of the ice storage air-conditioning system is improved, and the electric power cost of the ice storage air-conditioning system is reduced.
Description
Technical Field
The invention relates to the technical field of air conditioning systems, in particular to a data processing method and device of an ice storage air conditioning system and electronic equipment.
Background
Along with the implementation of the double-carbon strategy and the aggravation of the power supply and demand balance problem of the power grid, the application and popularization of the ice storage air conditioning system are continuously increased. The ice storage air conditioning system can utilize the ice storage device (ice groove) to prepare and store cold energy at night when the electricity price is low and release the cold energy at daytime when the electricity price is high, so as to achieve the purposes of shifting the electricity peak to fill the valley and reducing the capacity of the water chiller assembling machine.
The existing ice storage engineering project mainly adopts a control method based on fixed logic, and has poor adaptability to working conditions and climate change. Regarding the performance optimization of the ice cold storage system, the physical modeling mode is still used as a main mode, and the professional threshold is high and the engineering popularization is difficult. Compared with a traditional central air-conditioning cold source system, the ice cold storage system is more complex in system configuration, the optimization control has higher complexity, the coupling degree between most of the optimization control strategies and field devices is high at present, and once the optimization strategies are in problem, the system can be abnormal or even incapable of running.
Disclosure of Invention
In view of the above, the present invention provides a data processing method and apparatus for an ice storage air conditioning system, and an electronic device, so as to improve the performance of the ice storage air conditioning system, improve the peak load shifting effect of the ice storage air conditioning system, and reduce the power cost of the ice storage air conditioning system.
In a first aspect, an embodiment of the present invention provides a data processing method for an ice storage air conditioning system, which is applied to a controller of the ice storage air conditioning system, and the method includes: acquiring an optimization control target and a target parameter corresponding to the optimization control target; determining the building cold load of the ice storage air conditioning system; inputting the target parameters, the optimization control target and the building cold load into a pre-established ice storage system model, and outputting the operation parameters of each device of the ice storage air conditioning system; and controlling each device of the ice storage air conditioning system to execute cooling operation based on the operation parameters.
In a preferred embodiment of the present application, the step of inputting the target parameter, the optimization control target, and the building cooling load into the pre-established ice storage system model and outputting the operation parameters of each device of the ice storage air conditioning system includes: inputting the target parameters, the optimization control target and the building cold load into a pre-established ice storage system model, and outputting the proportion of each device of the ice storage air conditioning system bearing the building cold load; and determining the operation parameters of each device of the ice storage air conditioning system based on the corresponding proportion.
In a preferred embodiment of the present application, the optimization control target is an electric charge; the step of outputting the proportion of the cold load of the building borne by each device of the ice storage air conditioning system comprises the following steps: each device of the output ice storage air conditioning system bears the proportion of the cold load of the building under the condition that the electricity charge of the ice storage air conditioning system is lowest; the chemical control target is energy consumption; the step of outputting the proportion of the cold load of the building borne by each device of the ice storage air conditioning system comprises the following steps: each device of the output ice storage air conditioning system bears the proportion of the cold load of the building under the condition that the energy consumption of the ice storage air conditioning system is the lowest.
In a preferred embodiment of the present application, the ice storage system model includes an ice storage system sub-model corresponding to each device of the ice storage air conditioning system; inputting the target parameters, the optimization control target and the building cold load into a pre-established ice storage system model, and outputting the operation parameters of each device of the ice storage air conditioning system, wherein the steps comprise: and inputting the target parameters, the optimization control target and the building cold load into the ice storage system submodel corresponding to each device, and respectively outputting the operation parameters of each device of the ice storage air-conditioning system.
In a preferred embodiment of the present application, after the step of controlling each device of the ice storage air conditioning system to perform a cooling operation based on the operation parameters, the method further includes: acquiring the load rate of each host of the ice storage air conditioning system; if the load factor of the target host is larger than a preset first threshold value, increasing the number of running hosts of the ice storage air-conditioning system; and if the load factor of the target host is smaller than a preset second threshold value, reducing the number of the running hosts of the ice storage air-conditioning system.
In a preferred embodiment of the present application, the method further includes: determining a predicted cold quantity value of each host of the ice storage air conditioning system based on the proportion; after the step of controlling the respective devices of the ice storage air conditioning system to perform a cooling operation based on the operation parameters, the method further includes: acquiring actual cold quantity values of all hosts of the ice storage air conditioning system; and adjusting the operation parameters of each host of the ice storage air conditioning system based on the actual cold quantity value and the predicted cold quantity value.
In a preferred embodiment of the present application, the method further includes: acquiring historical operation data of each device of the ice storage air conditioning system; wherein the historical operating data at least comprises: historical climate data and historical operating parameter data; and training an ice storage system model based on historical operating data and preset constraint conditions of the ice storage air conditioning system.
In a second aspect, an embodiment of the present invention further provides a data processing device for an ice storage air conditioning system, which is applied to a controller of the ice storage air conditioning system, and the device includes: the model condition determining module is used for acquiring an optimized control target and a target parameter corresponding to the optimized control target; the building cold load determining module is used for determining the building cold load of the ice storage air conditioning system; the operation parameter output module is used for inputting the target parameters, the optimization control target and the building cold load into a pre-established ice storage system model and outputting the operation parameters of each device of the ice storage air conditioning system; and the cold supply operation execution module is used for controlling each device of the ice storage air conditioning system to execute cold supply operation based on the operation parameters.
In a third aspect, an embodiment of the present invention further provides an ice storage air conditioning system, including: a user interaction layer, an optimized scheduling layer and a local control layer; the user interaction layer is used for acquiring an optimized control target and a target parameter corresponding to the optimized control target; the optimized dispatching layer is used for determining the building cold load of the ice storage air conditioning system; inputting the target parameters, the optimization control target and the building cold load into a pre-established ice storage system model, and outputting the operation parameters of each device of the ice storage air conditioning system; and the local control layer is used for controlling each device of the ice storage air conditioning system to execute cooling operation based on the operation parameters.
In a preferred embodiment of the present application, the ice storage air conditioning system is applied to a physical server or a cloud server.
In a fourth aspect, an embodiment of the present invention further provides an electronic device, including a processor and a memory, where the memory stores computer-executable instructions capable of being executed by the processor, and the processor executes the computer-executable instructions to implement the data processing method of the ice storage air conditioning system.
In a fifth aspect, embodiments of the present invention also provide a computer-readable storage medium storing computer-executable instructions, which, when invoked and executed by a processor, cause the processor to implement the data processing method of the ice thermal storage air conditioning system.
The embodiment of the invention has the following beneficial effects:
the embodiment of the invention provides a data processing method and device for an ice storage air-conditioning system and electronic equipment, which can input the acquired target parameters, an optimization control target and a building cold load into a pre-established ice storage system model, output the operation parameters of each equipment of the ice storage air-conditioning system and control each equipment of the ice storage air-conditioning system to execute cold supply operation based on the operation parameters. In the method, the running parameters of each device of the ice storage air-conditioning system are output through the ice storage system model, so that the performance of the ice storage air-conditioning system can be improved, the peak load shifting effect of the ice storage air-conditioning system is improved, and the electric power cost of the ice storage air-conditioning system is reduced.
Additional features and advantages of the disclosure will be set forth in the description which follows, or in part may be learned by the practice of the above-described techniques of the disclosure, or may be learned by practice of the disclosure.
In order to make the aforementioned objects, features and advantages of the present disclosure more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of a data processing method of an ice storage air conditioning system according to an embodiment of the present invention;
fig. 2 is a flowchart of a data processing method of another ice storage air conditioning system according to an embodiment of the present invention;
fig. 3 is a schematic view of an ice storage air conditioning system according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a data-driven modeling and optimization control flow of an ice storage air conditioning system according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a data-driven modeling process incorporating physical knowledge constraints according to an embodiment of the present invention;
fig. 6 is a schematic view of an ice storage air conditioning system according to an embodiment of the present invention;
fig. 7 is a schematic diagram of an ice thermal storage system optimization controller according to an embodiment of the present invention;
fig. 8 is a schematic diagram of a deployment manner of an ice thermal storage system optimal controller according to an embodiment of the present invention;
fig. 9 is a schematic structural diagram of a data processing device of an ice storage air conditioning system according to an embodiment of the present invention;
fig. 10 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some, but 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.
At present, the ice cold storage engineering project mainly adopts a control method based on fixed logic, and can be mainly summarized into three types: 1) the ice groove is prior, namely the ice groove is operated preferentially at any time in the daytime, and the main machine is supplemented when the capacity of the ice groove is insufficient; 2) the main machine (water chilling unit) has priority, namely the main machine runs preferentially at any time in the daytime, and when the capacity of the main machine is insufficient, the ice tank is replenished; 3) the electricity price is prior, namely the operation mode is judged according to the electricity price in the daytime, for example, the ice tank is operated in the peak time period, and the host machine is operated in the flat time period. Although the peak load shifting effect and the reduction of the electric power cost can be achieved to a certain extent by using the cold storage of the ice storage tank, a large optimized lifting space still exists, and the important reason is that the methods are poor in adaptivity to working conditions and climate, for example, under the influence of load proportion and weather, the efficiency of the refrigeration unit operating at different time intervals is different, and the fixed logic control method does not take the difference into consideration, so that an optimized control algorithm for the ice storage system needs to be developed to further improve the performance of the system.
At present, performance optimization of an ice storage system is mainly achieved in a physical modeling mode, however, the physical modeling has a high professional threshold, modeling personnel are required to deeply master the physical mechanism of equipment, and the problems of low working efficiency and difficulty in popularization exist in practical engineering application.
Compared with a traditional central air-conditioning cold source system, the system configuration of the ice storage system is more complex (an ice tank and an attached pipeline assembly are added), the quantity and the dimensionality of parameters which need to be considered in the optimization control are large, the optimization control of the ice storage system has large complexity, great challenges are brought to the automatic operation of the system optimization control strategy, the coupling degree between most of the optimization control strategies and field equipment is high at present, and once the optimization strategy is in a problem, the system can be abnormally operated or even cannot be operated.
Based on the above, the embodiment of the invention provides a data processing method, a data processing device and an electronic device for an ice storage air conditioning system, and particularly relates to a data-driven hierarchical optimization control framework and a data-driven hierarchical optimization control system device for an ice storage central air conditioning system, and provides a modeling method combining data drive with physical knowledge constraint aiming at the defects of high professional threshold, difficult engineering popularization and the like of a physical modeling method of an ice storage system; aiming at the problem that a control method of an ice storage air conditioning system based on fixed logic has poor adaptability to working conditions and climate change, an optimization control method based on model prediction is provided, the performance of the system is further improved, the peak load shifting effect is improved, and the electric power cost is reduced; aiming at the complexity and close coupling of the optimal control of the ice storage system, a layered control framework is provided, the system control is divided into an optimal scheduling layer and a field local control layer, wherein the optimal scheduling layer outputs an operation set value of field system equipment, and the field local control layer tracks the set value by controlling the operation of the field equipment, so that decoupling is realized, and the robustness and reliability of the optimal control of the system in practical engineering application are improved.
For the convenience of understanding the embodiment, a detailed description will be given to a data processing method of an ice storage air conditioning system disclosed in the embodiment of the present invention.
The first embodiment is as follows:
the embodiment of the invention provides a data processing method of an ice storage air conditioning system, which is applied to a controller of the ice storage air conditioning system. The ice cold storage technology is a complete set of technology which utilizes the off-peak time of a power grid at night, utilizes low-price electricity to make ice and store cold energy, dissolves water at the peak time of electricity utilization in the daytime, supplies cold with a refrigerating unit together, and releases the stored ice cold energy to meet the peak load requirement of an air conditioner at the peak time of the air conditioner in the daytime.
Based on the above description, referring to the flowchart of the data processing method of the ice storage air conditioning system shown in fig. 1, the data processing method of the ice storage air conditioning system includes the following steps:
step S102, obtaining an optimization control target and a target parameter corresponding to the optimization control target.
The optimization control target in the embodiment of the invention can be the electricity charge and the energy consumption, and can also be determined according to the actual requirements of users, wherein the users can be clients, after-sales debugging personnel, research and development personnel and the like.
If the optimization control objective includes electricity rates, the objective parameters may include peak-to-valley electricity rates, which are also referred to as "time-of-use electricity rates," which is an electricity rate system that calculates electricity rates for peak electricity usage and valley electricity usage, respectively. Peak power utilization generally means that power utilization units are concentrated, and the charging standard is higher when the power supply is in short supply, such as in the daytime; the low ebb electricity consumption generally refers to the electricity consumption when the electricity consumption unit is less and the power supply is more sufficient, for example, the charging standard is lower at night.
The optimization control target is an object to be emphasized in the data processing, for example: if the optimized control target is the electric charge, the emphasis is placed on the lowest electric charge; if the optimization control target is energy consumption, the emphasis is on the lowest energy consumption at this time; if the optimized control target is the electricity charge and the energy consumption, the attention is paid to the lowest comprehensive coefficient of the electricity charge and the energy consumption.
And step S104, determining the building cold load of the ice storage air conditioning system.
The cold load is the cold load of an air-conditioning room, namely the refrigerating capacity required by the building where the ice storage air-conditioning system is arranged, in order to maintain the hot and humid environment and the required indoor temperature of the building, the heat which is required to be taken away from the room by the air-conditioning system is called the cold load of the air-conditioning room.
And S106, inputting the target parameters, the optimization control target and the building cold load into a pre-established ice storage system model, and outputting the operation parameters of each device of the ice storage air conditioning system.
In the embodiment of the invention, the ice storage system model is stored in advance and can be obtained by training according to the historical operation data of each device of the ice storage air conditioning system. In the embodiment of the invention, the target parameters, the optimization control target and the building cold load can be input into the pre-established ice storage system model, and the operation parameters of each device of the ice storage air conditioning system can be output. Wherein, the operation parameters may include: whether to operate, operating power per time period, etc.
And S108, controlling each device of the ice storage air conditioning system to execute cold supply operation based on the operation parameters.
After the operation parameters of each device of the ice storage air-conditioning system are determined, each device of the ice storage air-conditioning system can be respectively controlled to execute cooling operation according to the operation parameters so as to meet the acquired requirement of the building cooling load.
The embodiment of the invention provides a data processing method of an ice storage air-conditioning system, which can input the acquired target parameters, the optimization control target and the building cold load into a pre-established ice storage system model, output the operation parameters of each device of the ice storage air-conditioning system and control each device of the ice storage air-conditioning system to execute cold supply operation based on the operation parameters. In the method, the running parameters of each device of the ice storage air-conditioning system are output through the ice storage system model, so that the performance of the ice storage air-conditioning system can be improved, the peak load shifting effect of the ice storage air-conditioning system is improved, and the electric power cost of the ice storage air-conditioning system is reduced.
Example two:
the present embodiment provides another data processing method for an ice storage air conditioning system, which is implemented on the basis of the foregoing embodiments, and as shown in a flowchart of another data processing method for an ice storage air conditioning system shown in fig. 2, the data processing method for an ice storage air conditioning system in the present embodiment includes the following steps:
step S202, obtaining an optimization control target and a target parameter corresponding to the optimization control target.
Referring to fig. 3, the schematic diagram of an ice storage air conditioning system is shown, the cold source of the ice storage air conditioning system mainly comprises an ice tank and a dual-working-condition host, the dual-working-condition host is connected with the ice tank in series and provides the cold quantity required by the building load side together, and the load side, the ice tank and the dual-working-condition host are respectively provided with corresponding cold quantity measuring instruments which can measure the cold quantity released or absorbed by the load side, the ice tank and the dual-working-condition host. The cold release amount of the ice tank can be dynamically adjusted by adjusting a regulating valve on the ice tank branch and a regulating valve on the bypass branch.
Referring to a schematic diagram of a data-driven modeling and optimization control flow of the ice storage air conditioning system shown in fig. 4, a user can input a corresponding peak-to-valley electricity price and an optimization control target, wherein the optimization control target can be the lowest electricity charge, the lowest energy consumption and the like, and the steps can be completed in a user-defined layer.
And step S204, determining the building cold load of the ice storage air conditioning system.
And S206, inputting the target parameters, the optimization control target and the building cold load into a pre-established ice storage system model, and outputting the operation parameters of each device of the ice storage air conditioning system.
The ice storage system model can be trained through the following steps: acquiring historical operation data of each device of the ice storage air conditioning system; wherein the historical operating data at least comprises: historical climate data and historical operating parameter data; and training an ice storage system model based on historical operating data and preset constraint conditions of the ice storage air conditioning system.
As shown in fig. 4, machine learning models of main components (an ice tank, a pump, a host, and the like) of the ice storage system are respectively established in a data-driven manner based on system operation data and are integrated into a system model, and certain system physical knowledge constraints are combined in order to improve the reliability of modeling during data-driven modeling. Referring to a schematic diagram of a data-driven modeling process combined with physical knowledge constraint shown in fig. 5, a physical knowledge (i.e., constraint conditions of the ice storage air conditioning system) constraint model may be used.
For example, in training a machine learning model of an ice bank, constraints that consider the ice bank energy balance model are fused. The model can be deployed and used after being trained and passing through the model evaluation criterion, in addition, the model can be automatically updated by combining with the newly generated data of the system, and the steps can be completed in an optimization scheduling layer.
For the application process of the ice storage system model, specifically, the ice storage system model can output the operation parameters of each device of the ice storage air conditioning system through the following steps: inputting the target parameters, the optimization control target and the building cold load into a pre-established ice storage system model, and outputting the proportion of each device of the ice storage air conditioning system bearing the building cold load; and determining the operation parameters of each device of the ice storage air conditioning system based on the corresponding proportion.
If the optimized control target is the electricity charge, outputting the proportion of the cold load of the building of each device of the ice storage air-conditioning system under the condition that the electricity charge of the ice storage air-conditioning system is the lowest; if the optimization control target is energy consumption; each device of the output ice storage air conditioning system bears the proportion of the cold load of the building under the condition that the energy consumption of the ice storage air conditioning system is the lowest.
Moreover, the ice storage system model may include ice storage system submodels corresponding to each device of the ice storage air conditioning system, such as: and inputting the target parameters, the optimization control target and the building cold load into the ice storage system submodel corresponding to each device, and respectively outputting the operation parameters of each device of the ice storage air-conditioning system.
In the modeling process of the ice storage system model in the method, a machine learning model (namely, an ice storage system submodel) of each component of the system can be established first, and then the machine learning model is integrated into a system model (namely, the ice storage system model).
As shown in fig. 4, by combining the trained ice storage system model, the predicted building load, the peak-to-valley flat electricity price input by the user, and the optimization control target, model prediction control can be established, as shown in the following formulas (1) to (3), and the above steps can be completed in the optimization scheduling layer.
The formula (1) is an objective function for optimization solution, for example, the total electricity charge of the ice storage system in a refrigeration period (1 day) in one cycle is established according to the user setting in the step 1, the formulas (2) and (3) are constraint conditions, and the formula (3) indicates that the total cold released by the ice tank refrigeration in 1 day is less than or equal to the total cold stored in the ice tank.
In the above-mentioned formulas (1) to (3),
the proportion of the cold load of the building is borne by the dual-working-condition host at the ith moment,
the unit of the power of the ice storage system at the moment i can be kW, the power of the ice storage system comprises a dual-working-condition host, a refrigeration pump, a cooling tower and the like, power (-) is a system energy consumption model obtained in a data driving mode, and q isb，iCooling load of the building at the ith moment, kW, Tamb,iIs an outdoor meteorological parameter at moment i, priceiThe unit of the electricity price at the ith moment can be element/kWh, delta tau is time interval, h, tausta、τendIs an integer and respectively represents the starting time and the ending time of the refrigeration time interval of the ice storage air conditioning system, for example, the refrigeration time interval is 8:00-23:00, and the corresponding tausta、τend8 and 23 respectively, and Qice is the total cold storage capacity of the ice tank before the refrigeration period begins, and the unit can be kWh.
The idea of model predictive control can be briefly described as: at the th taustaTime, predicted cooling time period N ═ τend-τstaSystem control sequences that differ within an hour
And (3) solving an optimal control sequence through an optimization algorithm, namely solving the optimization problem described by the formulas (1) to (3), taking the finally obtained control sequence (namely the proportion of each device bearing the cold load of the building) which minimizes the electric charge as an optimization scheduling command, and determining the operation parameters of each device of the ice storage air-conditioning system.
And S208, controlling each device of the ice storage air conditioning system to execute cooling operation based on the operation parameters.
When each device for controlling the ice storage air conditioning system executes the cooling operation according to the operation parameters output by the ice storage system model, the ice storage air conditioning system can be adjusted, for example: acquiring the load rate of each host of the ice storage air conditioning system; if the load factor of the target host is larger than a preset first threshold value, increasing the number of running hosts of the ice storage air-conditioning system; and if the load factor of the target host is smaller than a preset second threshold value, reducing the number of the running hosts of the ice storage air-conditioning system.
As shown in fig. 4, the number of hosts may be controlled by adding or subtracting loads according to the operating parameters: at the ith moment, when the load rate of the host is greater than a certain first threshold (for example, 0.95), one host is added, and when the load rate of the host is lower than a certain second threshold (for example, 0.45), one host is reduced, and the threshold can be judged and valued according to the actual host combination configuration and the performance curve.
Step S210, acquiring actual cold quantity values of all hosts of the ice storage air conditioning system; and adjusting the operation parameters of each host of the ice storage air conditioning system based on the actual cold quantity value and the predicted cold quantity value.
Specifically, the method further includes: and determining the predicted cold quantity value of each host of the ice storage air-conditioning system based on the proportion. After the loading and unloading strategy of the host is implemented, the load of the host can be adjusted through PID (Proportional, Integral and Differential) algorithm control, specifically, PID operation is carried out on the deviation of the actual cold quantity value and the predicted cold quantity value of the cold quantity of the host and a control law is output to adjust the set value of the water supply temperature of the host, so that the feedback control of the cold load of the host is realized to match the optimal host load sequence.
The method provided by the embodiment of the invention can be combined with data-driven modeling of physical constraints, and based on historical operating data of the system, a machine learning model of the corresponding system or component is obtained through training and learning. The modeling method can be used for directly modeling according to the operation data of the system, and compared with the traditional physical modeling method, the modeling method has lower requirements on physics and field knowledge, can greatly improve the modeling efficiency, and is easier to popularize and apply in engineering practice.
The method provided by the embodiment of the invention can also carry out model prediction control, and adopts the model prediction-based ice storage air-conditioning system optimization control method, firstly, a simplified model of the ice storage air-conditioning system is established according to the operation data of the system, and then, the model is used for predicting the system performance under different control strategies, so that an optimal control strategy is optimized to realize the real-time optimization control of the ice storage system, wherein the system performance (system control target) is defined by the user-defined criterion. Compared with the traditional control method based on fixed logic, the method can be used for optimizing the system operation strategy by combining the prediction information of the system model, can further improve the system performance, and has strong self-adaptability to the changes of working conditions and climate conditions.
Example three:
the embodiment provides an ice storage air conditioning system, including: a user interaction layer, an optimized scheduling layer and a local control layer; the user interaction layer is used for acquiring an optimized control target and a target parameter corresponding to the optimized control target; the optimized dispatching layer is used for determining the building cold load of the ice storage air conditioning system; inputting the target parameters, the optimization control target and the building cold load into a pre-established ice storage system model, and outputting the operation parameters of each device of the ice storage air conditioning system; and the local control layer is used for controlling each device of the ice storage air conditioning system to execute cooling operation based on the operation parameters.
Referring to fig. 6, which is a schematic diagram of an ice storage air conditioning system, fig. 6 shows a data-driven layered optimization control framework of the ice storage air conditioning system, where the top layer is a user interaction layer for user setting of a control target (e.g., lowest electricity charge, lowest energy consumption, etc.) and feedback presentation of system operation effect, the middle layer is an optimized scheduling layer, and mainly adopts model predictive control to output a scheduling policy, the bottom layer is a local control layer for controlling operation of a field device to implement a scheduling policy output by the scheduling layer, and in functional division, the scheduling layer is only responsible for optimization of system setting values and is not responsible for specific operation control and adjustment of the field device, and even if the optimization policy of the scheduling layer has a problem, normal operation of the field device is not affected, and operation of the field device is implemented by the local control layer, based on the layered control framework, the engineering practicability, robustness and reliability of the optimized control are stronger.
Specifically, the ice storage air conditioning system is applied to a physical server or a cloud server, and the data-driven hierarchical optimization control framework provided in this embodiment is not limited to be implemented by being deployed at a local end as a control program, for example, the hardware scheme of the ice storage system optimization controller provided in this embodiment may also be deployed on a cloud platform to implement remote control.
Referring to the schematic diagram of an optimal controller of an ice storage system shown in fig. 7, a data-driven hierarchical optimal control framework can be implemented by hardware in fig. 7, the controller mainly comprises two parts, namely a communication interface and a main control circuit, wherein the communication interface comprises a south interface and a north interface, the communication interface comprises a wired interface (network port, serial port and the like) and a wireless interface (wifi, Zigbee, bluetooth, 4G, 5G and the like), the main control circuit mainly comprises two parts, namely a central processing unit and a storage unit, the central processing unit can be a DSP, a MCU, an FPGA and the like, and the storage unit is mainly used for persistently storing control programs and related service data. The southbound direction of the hardware of the ice storage optimization controller can be connected and communicated with the weak current control cabinet of the ice storage system on the engineering site to send a control instruction to the system, and the northbound direction of the controller can be connected and communicated with a client (a mobile client or a PC server) through a network (Internet/Intranet) to realize local or remote monitoring interaction, as shown in the schematic diagram of the deployment mode of the ice storage system optimization controller shown in FIG. 8.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the ice storage air conditioning system described above may refer to the corresponding process in the embodiment of the data processing method of the ice storage air conditioning system, and will not be described herein again.
Example four:
corresponding to the above method embodiment, an embodiment of the present invention provides a data processing device of an ice storage air conditioning system, which is applied to a controller of the ice storage air conditioning system, and referring to a schematic structural diagram of the data processing device of the ice storage air conditioning system shown in fig. 9, the data processing device of the ice storage air conditioning system includes:
the model condition determining module 91 is configured to obtain an optimal control target and a target parameter corresponding to the optimal control target;
a building cold load determination module 92 for determining a building cold load of the ice storage air conditioning system;
the operation parameter output module 93 is used for inputting target parameters, an optimization control target and a building cold load into a pre-established ice storage system model and outputting operation parameters of each device of the ice storage air conditioning system;
and a cooling operation execution module 94 for controlling the respective devices of the ice storage air conditioning system to execute cooling operation based on the operation parameters.
The embodiment of the invention provides a data processing device of an ice storage air-conditioning system, which can input the acquired target parameters, the optimization control target and the building cold load into a pre-established ice storage system model, output the operation parameters of each device of the ice storage air-conditioning system and control each device of the ice storage air-conditioning system to execute cold supply operation based on the operation parameters. In the method, the running parameters of each device of the ice storage air-conditioning system are output through the ice storage system model, so that the performance of the ice storage air-conditioning system can be improved, the peak load shifting effect of the ice storage air-conditioning system is improved, and the electric power cost of the ice storage air-conditioning system is reduced.
The operation parameter output module is used for inputting the target parameters, the optimization control target and the building cold load into a pre-established ice storage system model and outputting the proportion of each device of the ice storage air conditioning system bearing the building cold load; and determining the operation parameters of each device of the ice storage air conditioning system based on the corresponding proportion.
The optimized control target is electric charge; the operation parameter output module is used for outputting the proportion of the cold load of the building borne by each device of the ice storage air-conditioning system under the condition that the electricity charge of the ice storage air-conditioning system is lowest; the optimized control target is energy consumption; the operation parameter output module is used for outputting the proportion of the cold load of the building borne by each device of the ice storage air-conditioning system under the condition that the energy consumption of the ice storage air-conditioning system is the lowest.
The ice storage system model comprises ice storage system submodels corresponding to all equipment of the ice storage air conditioning system; the operation parameter output module is used for inputting the target parameter, the optimization control target and the building cold load into the ice storage system submodel corresponding to each device and respectively outputting the operation parameters of each device of the ice storage air conditioning system.
The above-mentioned device still includes: the system comprises a host operation number adjusting module, a host operation number adjusting module and a control module, wherein the host operation number adjusting module is used for acquiring the load rate of each host of the ice storage air conditioning system; if the load factor of the target host is larger than a preset first threshold value, increasing the number of running hosts of the ice storage air-conditioning system; and if the load factor of the target host is smaller than a preset second threshold value, reducing the number of the running hosts of the ice storage air-conditioning system.
The above-mentioned device still includes: the host machine operation parameter adjusting module is used for determining the predicted cold quantity value of each host machine of the ice storage air conditioning system based on the proportion; the host machine operation parameter adjusting module is also used for acquiring the actual cold quantity value of each host machine of the ice storage air conditioning system; and adjusting the operation parameters of each host of the ice storage air conditioning system based on the actual cold quantity value and the predicted cold quantity value.
The above-mentioned device still includes: the ice storage system model training module is used for acquiring historical operating data of each device of the ice storage air conditioning system; wherein the historical operating data at least comprises: historical climate data and historical operating parameter data; and training an ice storage system model based on historical operating data and preset constraint conditions of the ice storage air conditioning system.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the data processing device of the ice storage air conditioning system described above may refer to the corresponding process in the embodiment of the data processing method of the ice storage air conditioning system, and is not described herein again.
Example four:
the embodiment of the invention also provides electronic equipment, which is used for operating the data processing method of the ice storage air conditioning system; referring to fig. 10, the electronic device includes a memory 100 and a processor 101, where the memory 100 is used to store one or more computer instructions, and the one or more computer instructions are executed by the processor 101 to implement the data processing method of the ice thermal storage air conditioning system.
Further, the electronic device shown in fig. 10 further includes a bus 102 and a communication interface 103, and the processor 101, the communication interface 103, and the memory 100 are connected through the bus 102.
The Memory 100 may include a high-speed Random Access Memory (RAM) and may further include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The communication connection between the network element of the system and at least one other network element is realized through at least one communication interface 103 (which may be wired or wireless), and the internet, a wide area network, a local network, a metropolitan area network, and the like can be used. The bus 102 may be an ISA bus, PCI bus, EISA bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 10, but this does not indicate only one bus or one type of bus.
The processor 101 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 101. The Processor 101 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the device can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, or a discrete hardware component. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in the memory 100, and the processor 101 reads the information in the memory 100, and completes the steps of the method of the foregoing embodiment in combination with the hardware thereof.
The embodiment of the invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are called and executed by a processor, the computer-executable instructions cause the processor to implement the data processing method of the ice storage air conditioning system.
The data processing method and device for the ice storage air conditioning system and the computer program product for the electronic device provided by the embodiments of the present invention include a computer readable storage medium storing a program code, where instructions included in the program code may be used to execute the method in the foregoing method embodiments, and specific implementation may refer to the method embodiments, and will not be described herein again.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the system and/or the apparatus described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In addition, in the description of the embodiments of the present invention, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (12)
1. A data processing method of an ice storage air conditioning system is characterized by being applied to a controller of the ice storage air conditioning system, and the method comprises the following steps:
acquiring an optimization control target and a target parameter corresponding to the optimization control target;
determining a building cold load of the ice storage air conditioning system;
inputting the target parameters, the optimization control target and the building cold load into a pre-established ice storage system model, and outputting the operation parameters of each device of the ice storage air conditioning system;
and controlling each device of the ice storage air conditioning system to execute cooling operation based on the operation parameters.
2. The method of claim 1, wherein the step of inputting the target parameters, the optimization control target, and the building cooling load into a pre-established ice storage system model and outputting the operation parameters of each device of the ice storage air conditioning system comprises:
inputting the target parameters, the optimization control target and the building cold load into a pre-established ice storage system model, and outputting the proportion of each device of the ice storage air conditioning system bearing the building cold load;
and determining the operation parameters of each device of the ice storage air conditioning system based on the corresponding proportion.
3. The method of claim 2, wherein the optimal control objective is electricity charge; the step of outputting the proportion of each device of the ice storage air conditioning system bearing the cold load of the building comprises the following steps: outputting the proportion of the cold load of the building by each device of the ice storage air-conditioning system under the condition that the electricity charge of the ice storage air-conditioning system is lowest;
the optimization control target is energy consumption; the step of outputting the proportion of each device of the ice storage air conditioning system bearing the cold load of the building comprises the following steps: and each device outputting the ice storage air conditioning system bears the proportion of the cold load of the building under the condition that the energy consumption of the ice storage air conditioning system is the lowest.
4. The method of claim 1, wherein the ice storage system model comprises ice storage system submodels corresponding to respective devices of the ice storage air conditioning system; inputting the target parameters, the optimization control target and the building cold load into a pre-established ice storage system model, and outputting the operation parameters of each device of the ice storage air conditioning system, wherein the steps comprise:
and inputting the target parameters, the optimization control target and the building cold load into the ice storage system submodel corresponding to each device, and respectively outputting the operation parameters of each device of the ice storage air-conditioning system.
5. The method as claimed in claim 1, wherein after the step of controlling the respective devices of the ice storage air conditioning system to perform a cooling operation based on the operating parameters, the method further comprises:
acquiring the load rate of each host of the ice storage air conditioning system;
if the load factor of the target host is larger than a preset first threshold value, increasing the number of running hosts of the ice storage air-conditioning system;
and if the load factor of the target host is smaller than a preset second threshold value, reducing the number of the running hosts of the ice storage air-conditioning system.
6. The method of claim 2, further comprising: determining a predicted cold volume value of each host of the ice storage air conditioning system based on the ratio;
after the step of controlling the respective devices of the ice storage air conditioning system to perform a cooling operation based on the operating parameters, the method further comprises: acquiring actual cold quantity values of all hosts of the ice storage air-conditioning system; and adjusting the operation parameters of each host of the ice storage air-conditioning system based on the actual cold quantity value and the predicted cold quantity value.
7. The method of claim 1, further comprising:
acquiring historical operation data of each device of the ice storage air conditioning system; wherein the historical operating data includes at least: historical climate data and historical operating parameter data;
and training the ice storage system model based on the historical operating data and the preset constraint conditions of the ice storage air conditioning system.
8. A data processing device of an ice storage air conditioning system is characterized by being applied to a controller of the ice storage air conditioning system, and the device comprises:
the model condition determining module is used for acquiring an optimization control target and a target parameter corresponding to the optimization control target;
the building cold load determining module is used for determining the building cold load of the ice storage air conditioning system;
the operation parameter output module is used for inputting the target parameter, the optimization control target and the building cold load into a pre-established ice storage system model and outputting the operation parameters of each device of the ice storage air-conditioning system;
and the cold supply operation execution module is used for controlling each device of the ice storage air conditioning system to execute cold supply operation based on the operation parameters.
9. An ice storage air conditioning system, comprising: a user interaction layer, an optimized scheduling layer and a local control layer;
the user interaction layer is used for acquiring an optimization control target and a target parameter corresponding to the optimization control target;
the optimized dispatching layer is used for determining the building cold load of the ice storage air conditioning system; inputting the target parameters, the optimization control target and the building cold load into a pre-established ice storage system model, and outputting the operation parameters of each device of the ice storage air conditioning system;
the local control layer is used for controlling each device of the ice storage air conditioning system to execute cooling operation based on the operation parameters.
10. An ice storage air conditioning system according to claim 9, wherein the ice storage air conditioning system is applied in a physical server or a cloud server.
11. An electronic device comprising a processor and a memory, the memory storing computer executable instructions executable by the processor, the processor executing the computer executable instructions to implement the data processing method of the ice thermal storage air conditioning system of any one of claims 1 to 7.
12. A computer-readable storage medium, characterized in that the computer-readable storage medium stores computer-executable instructions which, when invoked and executed by a processor, cause the processor to implement the data processing method of the ice thermal storage air conditioning system of any one of claims 1 to 7.
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