WO2021232734A1 - Adaptive optimization control method, system, and apparatus for heat pump and electric heat storage device - Google Patents

Adaptive optimization control method, system, and apparatus for heat pump and electric heat storage device Download PDF

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WO2021232734A1
WO2021232734A1 PCT/CN2020/132226 CN2020132226W WO2021232734A1 WO 2021232734 A1 WO2021232734 A1 WO 2021232734A1 CN 2020132226 W CN2020132226 W CN 2020132226W WO 2021232734 A1 WO2021232734 A1 WO 2021232734A1
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heat
heat storage
heat pump
storage equipment
storage device
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PCT/CN2020/132226
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French (fr)
Chinese (zh)
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阎俏
田崇翼
张桂青
彭伟
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山东建筑大学
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Publication of WO2021232734A1 publication Critical patent/WO2021232734A1/en

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    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24D—DOMESTIC- OR SPACE-HEATING SYSTEMS, e.g. CENTRAL HEATING SYSTEMS; DOMESTIC HOT-WATER SUPPLY SYSTEMS; ELEMENTS OR COMPONENTS THEREFOR
    • F24D15/00—Other domestic- or space-heating systems
    • F24D15/02—Other domestic- or space-heating systems consisting of self-contained heating units, e.g. storage heaters
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24D—DOMESTIC- OR SPACE-HEATING SYSTEMS, e.g. CENTRAL HEATING SYSTEMS; DOMESTIC HOT-WATER SUPPLY SYSTEMS; ELEMENTS OR COMPONENTS THEREFOR
    • F24D13/00—Electric heating systems
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24D—DOMESTIC- OR SPACE-HEATING SYSTEMS, e.g. CENTRAL HEATING SYSTEMS; DOMESTIC HOT-WATER SUPPLY SYSTEMS; ELEMENTS OR COMPONENTS THEREFOR
    • F24D15/00—Other domestic- or space-heating systems
    • F24D15/04—Other domestic- or space-heating systems using heat pumps
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24D—DOMESTIC- OR SPACE-HEATING SYSTEMS, e.g. CENTRAL HEATING SYSTEMS; DOMESTIC HOT-WATER SUPPLY SYSTEMS; ELEMENTS OR COMPONENTS THEREFOR
    • F24D19/00—Details
    • F24D19/10—Arrangement or mounting of control or safety devices
    • F24D19/1096—Arrangement or mounting of control or safety devices for electric heating systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods

Definitions

  • the present invention relates to the related technical field of automatic control of heating equipment, and in particular to a method, system and device for adaptive optimization control of heat pumps and electric heat storage equipment.
  • air-source heat pumps have been widely used in buildings, residential quarters, and industrial parks in my country in recent years.
  • the working mode of solid electric heat storage equipment is divided into heat storage mode and heat release mode. When the electricity is low at night, the heat is stored in the solid material integrated device through electric heating to obtain a higher sensible heat storage temperature. The heat stored in the solid is transferred out, and then the feng-shui heat exchange is performed to complete the terminal heating.
  • Public buildings mainly refer to schools, shopping malls, office buildings, etc. that need to be heated by time periods. Usually, there is a large demand for heating during the day and no heating at night. The average heating time per day is less than 14 hours.
  • the air source heat pump is usually made to bear the main heat load during the day, and the heat storage equipment stores heat at night and releases heat during the day when the electricity price peaks.
  • my country’s electricity prices are fixed-period time-of-use electricity prices (peak and valley electricity prices). Therefore, the traditional equipment operation mode is relatively fixed. Stop the heat pump to reduce operating costs.
  • the traditional equipment control system has a fixed or single operation mode, and has poor control flexibility and cannot be based on grid prices.
  • the change adaptively controls the start and stop of the heat storage equipment, so that the energy generated by the heat storage cannot be fully utilized, resulting in high operating costs and poor customer experience, which can no longer meet the requirements of use.
  • the purpose of the present invention is to provide a heat pump and electric heat storage equipment adaptive optimization control method, system and device.
  • the method is composed of two parts: an optimized prefabricated part and an online adjustment part.
  • the first part is to optimize the prefabricated part.
  • the simplex method of linear programming is used to find the output and working time of the heat pump and the electric heat storage equipment, and find the best investment of the electric heat storage equipment Time, use this as a benchmark to pre-make the working mode of the equipment on the next day.
  • the second part is the online adjustment part. At the beginning of each time period of the second day, the working mode is fine-tuned according to real-time data such as building heat load and heat storage to ensure the practicability and accuracy of system operation.
  • an embodiment of the present invention provides an adaptive optimization control method for a heat pump and a heat storage device, which includes the following steps:
  • the objective function constructed with the goal of minimizing the electricity cost of the next day is:
  • P HP.i as the i-th period average electric power air source heat pump system consumption; i-th period T HP.i air source heat pump is working; P TS.i for the i-th The average electric power consumed by the solid electric heat storage device during the heat storage period; t TS.Xi is the ith time period when the solid electric heat storage device works in the heat storage mode; P P&F.i is the storage time during the ith time period The average electric power consumed by the fans and water pumps when the thermal equipment releases heat; t TS.Fi is the i-th time period when the solid electric heat storage equipment works in heat release mode; E i is the day-a-day electricity price for the i-th time period.
  • the constraints for solving the objective function include the working constraints of the heat pump and the working constraints of the heat storage equipment;
  • the working constraints of the heat pump include: the electric-heat conversion model of the air-source heat pump; the average electric power of the heat pump's heating consumption is less than that of the heat pump Rated power; within the set working time, the converted energy is corrected by a correction factor according to weather conditions.
  • the electric heat conversion model of the heat storage device is not greater than the heat storage rating of the heat storage device.
  • the linear programming method is used to solve the output situation of the heat pump and heat storage equipment in each time period, and the simplex method in the linear programming method is adopted.
  • the prefabricated working modes include prefabricated working mode I: heat pump heating, heat storage equipment does not work; prefabricated working mode II: solid heat storage equipment heat storage, heat pump does not work; prefabricated working mode III: solid heat storage equipment If heat is released, the heat pump does not work; prefabricated working mode IV: the heat storage device releases heat first, and then the heat pump generates heat.
  • the online correction method when working in the prefabricated working mode I, is: obtain the actual heat storage capacity of the heat storage device in real time. When the rated heat storage capacity is reached, the heat storage stops and the heat pump does not work until the next time period is executed. Prefabricated work mode corresponding to the next time period;
  • the online correction method is as follows:
  • the online correction method in this mode is: obtain real-time heat supply in real time, and judge whether the actual heat storage equipment meets the heating demand. , The heat storage device supplies heat separately; otherwise, the heat pump is turned on to supply heat at the same time;
  • the online correction strategy in this mode is to obtain real-time heat supply in real time, and judge whether the actual heat storage capacity of the heat storage device meets the heating demand.
  • Prefabricated working mode III otherwise, when the actual heat storage capacity of the solid heat storage device is less than the minimum limit, the heat storage device is turned off to stop heat supply, and the heat pump is turned on for heat supply.
  • an embodiment of the present invention also provides an adaptive optimization control device for a heat pump and a heat storage device, including:
  • Prediction unit It is configured to obtain the day-ahead electricity price and weather forecast data of the grid on the next day, and predict the building heat load value in each period of the next day based on the acquired data;
  • Solving unit It is configured to construct an objective function with the goal of minimizing electricity cost, and use linear programming to solve the output of heat pumps and heat storage equipment in each time period according to the data obtained by prediction;
  • Working mode configuration unit It is configured to determine the prefabricated working mode of the heat pump and heat storage equipment working together in each time period according to the output of the heat pump and heat storage equipment in each time period obtained by the solution, and send it to the heat pump and Heat storage equipment;
  • Online correction unit It is configured to obtain real-time parameter data of the air source heat pump and electric heat storage equipment heating system, and adjust the working status of the air source heat pump and heat storage equipment online according to the real-time parameter data.
  • embodiments of the present invention also provide an adaptive optimization control system for heat pumps and heat storage equipment, including air source heat pumps, solid-state electric heat storage equipment, communication gateways, and control devices, as well as connected to air-source heat pumps and solid-state electricity.
  • the sensor and the actuator of the heat storage device, and the control device are respectively connected with the sensor and the actuator through the communication gateway, and the control device executes the steps of the above-mentioned method for adaptive optimization and control of the heat pump and the heat storage device.
  • embodiments of the present invention also provide a computer-readable storage medium for storing computer instructions, which when executed by a processor, complete the above-mentioned adaptive optimization control method for heat pumps and heat storage devices A step of.
  • the present invention is based on predicting the heat load required by the building on the next day, comprehensively considering factors such as day-a-day electricity prices, and aiming at minimizing the electricity cost of the day, solving the working modes of heat pumps and electric heat storage equipment, and adjusting on-line according to real-time data.
  • This method can adaptively track changes in building heat load and grid prices, dynamically adjust the working mode of equipment, find the best investment time and heat release of heat storage equipment, save users’ electricity costs, and maximize benefits.
  • the grid load is leveled, which has the effect of cutting peaks and filling valleys. This method has important significance and reference value for the popularization and use of heat pumps and electric heat storage equipment and the optimal dispatch of power grids.
  • FIG. 1 is a flowchart of the advance prefabrication part in the optimization control method of Embodiment 1 of the present invention
  • Figure 3 is a flow chart of the simplex method of embodiment 1 of the present invention.
  • FIG. 4 is a schematic diagram of the structure of an adaptive optimization control system according to Embodiment 1 of the present invention.
  • FIG. 5 is an example diagram of load curves and working mode effects before and after optimization in Embodiment 1 of the present invention.
  • the present invention proposes an adaptive optimization control method for a heat pump and a heat storage device.
  • an adaptive optimization control method for heat pumps and thermal storage equipment is proposed, which includes the following steps, where S1 to S3 are optimized prefabricated parts, and S4 is online adjustment part:
  • S1 obtains the day-ahead electricity price and weather forecast data of the power grid on the second day, and predicts the building heat load value in each period of the second day based on the historical data;
  • S2 constructs an objective function with the goal of minimizing electricity cost, and uses the linear programming method to solve the output of heat pumps and heat storage equipment in each time period based on the predicted data;
  • S3 Determines the prefabricated working mode of the heat pump and heat storage equipment for each time period based on the output of the heat pump and heat storage equipment obtained by the solution for each time period, and sends it to the heat pump and heat storage equipment;
  • S4 obtains the parameter data of the heat supply system of the heat pump and the heat storage device in real time, and adjusts the working status of the heat pump and the heat storage device on-line according to the real-time parameter data.
  • This embodiment adopts a pre-set working mode combined with an adaptive optimization control method of online adjustment, which is used to perform adaptive optimization control on the work of air source heat pumps and solid heat storage equipment, combined with the current electricity price to minimize the cost of electricity, and solve Obtain the prefabricated working mode that can be executed in each time period of the next day.
  • the prefabricated working mode is dynamically changed.
  • the heat storage equipment can be fully invested in the high electricity price period and avoid resources Waste, so that users can obtain better economic benefits; at the same time, the power load is leveled, and the effect of peak shaving and valley filling is achieved.
  • the real-time data is used to make online corrections to the working mode of each time period, and fine-tune the working status of the air source heat pump and heat storage equipment to further improve the adaptiveness of the system and ensure the stability and safety of the system.
  • step S1 the trained LSTM model can be used to predict the building heat load value in each period of the next day.
  • FIG. 4 it is a typical structure of an adaptive optimization control system for heat pumps and electric heat storage equipment.
  • the system includes heat pumps, solid electric heat storage equipment and water pumps, and is used to control heat pumps and solid electric heat storage equipment for optimization work. ⁇ Control device.
  • the adaptive optimization control system also includes sensors, actuators, and communication gateways that interact with the control device.
  • the sensors mainly collect the operating parameters of field equipment and related data such as power consumption, and Upload the data to the communication gateway.
  • the gateway is used to realize the two-way data transmission between the sensor and the control device; the control device obtains the data and completes the calculation of the optimized control method, and can implement an adaptive optimization control method for heat pumps and heat storage equipment Steps, and send the results to the field actuator through the gateway, and the actuator converts the working status of the air source heat pump and the electric heat storage device to achieve the purpose of controlling the field device.
  • the parameter data of the heat pump and electric heat storage equipment heating system can include the heat pump and electric heat storage equipment operating parameters, circulating water pipeline operating parameters, and water pump operating parameters.
  • the circulating water pipeline operating parameters include circulating water heat in the pipeline. Load, flow, pressure and temperature, etc.
  • the day-ahead electricity price of the grid on the second day can be obtained through the data interface connected to the power system; through the API interface provided by "China Meteorological Data Network", the next day’s weather forecast data can be obtained; and through the heat pump product manual, as shown in Table 1.
  • the performance correction coefficient of the heat pump is related to the factory data of the model, and the performance correction coefficient f i-pre of the heat pump is obtained according to the weather data and the preset water temperature.
  • Long and short-term memory network model (Long Short-Term Memory, LSTM) is a special type of Recurrent Neural Networks (RNN).
  • the RNN network model includes an input layer, a hidden layer, and an output layer. Each layer is composed of several neurons.
  • the LSTM model adds a forget gate to the hidden layer to solve the problem of "gradient disappearance” or “gradient explosion” in the training process.
  • the steps for predicting building heat load based on LSTM model are as follows:
  • the same day in history refers to a day on the same date in previous years, which can be a lunar date.
  • the historical data can also be the data after the heating starts in the current year, and the historical data before the forecast date is directly selected.
  • the training data has 14*24 time steps, and each time step has the date type (working day/non-working day) where the time is located, and the heat load Data, weather parameters as feature values, that is, the set sum of [number of samples, time step, characteristics] is obtained.
  • the first 80% of the set samples are used as the training set of the LSTM prediction model, and the last 20% is used as the training set of the LSTM prediction model.
  • reasonably select the sequence hours of the impact factors that is, the number of neurons in the input layer and the number of neurons in the hidden layer.
  • step 3) the trained LTSM model to predict the building heat load Qi in each period of the next day.
  • step S2 the objective function is constructed with the goal of minimizing electricity cost, which can be:
  • P HP.i the average electric power consumed by the air source heat pump for heating during the i-th time period, in kW
  • t HP.i the i-th time period during which the air-source heat pump is working, in hours
  • P TS.i the average electric power consumed by the solid electric heat storage device during the ith time period, in kW
  • t TS.Xi the ith time period during which the solid electric heat storage device works in the heat storage mode
  • the unit P P&F.i the average electric power consumed by fans and water pumps when the heat storage device releases heat in the i-th time period, in kW
  • t TS.Fi the first time the solid electric heat storage device works in heat release mode i time period, the unit is hour
  • E i the real-time electricity price of the i-th time period
  • the unit is yuan/kWh.
  • the constraint conditions for solving the objective function may include: the working constraint of the air source heat pump and the working constraint of the heat storage device.
  • the working constraints of the air source heat pump can be set according to the operating conditions of the heat pump, which may include: the electric heat conversion model of the air source heat pump; the average electric power consumed by the heat pump for heating is less than the rated power of the heat pump; During the time period, the converted energy is corrected by a correction factor according to weather conditions.
  • Q HP.i the demand heating load of the air source heat pump in the i-th time period, in kW;
  • COP N the rated value of the energy efficiency conversion factor of the air source heat pump
  • the working constraints of the thermal storage device may include: an electric-to-heat conversion model of the thermal storage device; and the thermal storage amount of the thermal storage device is not greater than the thermal storage rating Q N of the thermal storage device.
  • the specific constraints are shown in formulas (4)-(6), as follows:
  • the efficiency ⁇ of the solid electric heat storage device is the ratio of the heat release to the heat storage, and the upper limit of the total heat storage is the rated value H N of the heat storage of the device.
  • Q TS.i is the heat load of the heat storage equipment in the i-th time period, in kW;
  • [ ⁇ TS-start , ⁇ TS-stop ] is the time period during which the heat storage device works in the heat storage mode.
  • the building heat load is composed of the heat load of the heat pump and the heat load of the heat storage device, as shown in the following formula:
  • Q i Predicted value of building heat load in the i-th time period, in kW.
  • the heat storage time of the heat storage equipment does not coincide with the working time of the heat pump; the heat storage equipment completes a heat storage-release work cycle in one day; The heat storage and heat release work of thermal equipment cannot be performed at the same time.
  • the cooperative work of heat pump and heat storage equipment can include four cooperative working modes. The working modes and working hours are shown in Table 2 below:
  • ⁇ is the step size, which is a constant value.
  • P HP.i the i-th period of the air source heat pump system of the average electric power consumption, in units of kW
  • t HP.i i-th period of the air source heat pump
  • P TS.i in The average electric power consumed by the solid electric heat storage device during the ith time period
  • t TS.Xi the ith time period during which the solid electric heat storage device works in the heat storage mode
  • P P&F.i the ith time The average electric power consumed by the fans and water pumps when the heat storage equipment releases heat in the segment
  • t TS.Fi the i-th time period when the solid-state electric heat storage equipment works in heat release mode
  • E i the real-time electricity price of the i-th time period, The unit is yuan/kWh.
  • H N The rated value of the heat storage capacity of the heat storage device.
  • Q HP.i demand air source heat pump heating load of the i-th period
  • COP N an air source heat pump the energy efficiency of the conversion factor rating
  • f i-pre air-source heat pump performance correction coefficient.
  • step 3) Repeat step 3) to perform optimization iterations until the test numbers are all non-positive, and the optimal solution is obtained.
  • ⁇ j is the test number
  • a ij represents the coefficients of the variables P HP ⁇ i and P TS ⁇ i
  • b i represents the constant on the right side of the constraint equation.
  • the linear programming problem is the study of the extreme value problem of the linear objective function under the linear constraint.
  • the simplex method is used to solve the linear programming problem with clear logic and simple calculation.
  • step S4 the prefabricated working mode of the equipment is judged according to the output of the heat pump and the heat storage equipment in each time period solved, and the judgment conditions are shown in Table 3.
  • the operating status of the equipment in each time period is determined.
  • the air source heat pump and the heat storage equipment are controlled to work in a predetermined working mode, as shown in Figure 5 for example.
  • the cost of electricity in China is the smallest.
  • Step S4 is an online adjustment process, obtaining real-time parameter data of the air source heat pump and the heat storage equipment heating system, and fine-tune the working status of the air source heat pump and the heat storage equipment on-line according to the real-time data.
  • the aforementioned steps S1 to S3 are the optimized prefabricated parts of the optimized control method, which are optimized results obtained based on predicted data, and may deviate from actual operating conditions. Therefore, this step is added, which is more practical and engineering significance.
  • the parameter data of the heating system obtained in real time includes the actual heat storage capacity of the heat storage device and the real-time heat load data of the building.
  • the online adjustment method may be: at the beginning of the current period, first determine the prefabricated working mode executed in the current period, and execute the online adjustment strategy in the corresponding working mode according to the different working modes.
  • the online adjustment strategy in this mode includes the following steps:
  • the solid heat storage device When the ratio of the real -time heat load Q real to the rated heat load Q N of all heat pumps is less than the second set ratio set, the solid heat storage device is turned off to stop heat release; wherein the first set ratio is greater than the second set ratio.
  • the real-time heat load Q real of the building can be collected from the heat meter installed on the circulating water pipeline.
  • the first set ratio is within the range where the heat pump is close to the upper limit of output, and the first set ratio can be set to about 95%.
  • heating equipment needs to be added, and solid storage is turned on at this time.
  • the heat device supplements the exotherm.
  • the second setting ratio can be set to about 80%, and the heat pump can meet the heating demand by its own output.
  • the online adjustment strategy in this mode can be: according to the real-time heat load Q real size, judge this time Whether the actual heat storage capacity of the heat storage device meets the demand, if it is satisfied, the heat storage device supplies heat separately; otherwise, the heat pump is turned on and the heat is supplied at the same time.
  • PID algorithm can be used to control the heating energy of the heat storage device: according to the difference between the actual value of the outlet water temperature and the set value, PID adjustment is used to control the speed of the fan set inside the heat storage device, and then achieve the control of solids.
  • the heat storage device outputs heat by outputting hot air, and transfers the heat of the hot air to water through a heat exchanger.
  • the judgment condition for judging whether the actual heat storage capacity of the heat storage device meets the heating demand may be as follows:
  • Q real the real-time heat load read by the heat meter, in kW
  • K rel reliability coefficient
  • ⁇ the remaining time in this period, in h
  • H store the actual heat storage capacity of the solid heat storage device, The unit is kWh.
  • the online adjustment strategy in this mode can be: first determine whether the condition of formula (7) is satisfied, if it is satisfied, the heat storage device will supply heat alone, and switch It is the prefabricated working mode III; otherwise, when the actual heat storage Q store of the solid heat storage device is less than the minimum limit Q min , the heat storage device is turned off to stop heating, and the heat pump is turned on.
  • a simulation experiment is carried out.
  • the power load changes of the heating system after and before the optimization using the method of this embodiment are compared.
  • the optimized system can track the grid price
  • the height of the heat storage equipment makes the heat storage equipment release heat at the peak of the grid price, and combined with the heat demand of the building, the equipment can fully release the heat, improve the utilization rate of the equipment, and reduce the user's operating cost.
  • the grid load is leveled. The effect of peak clipping and valley filling.
  • This embodiment provides an adaptive optimization control device for heat pump and heat storage equipment, including:
  • Prediction unit It is configured to obtain the day-ahead electricity price of the grid on the next day, and predict the building heat load value in each period of the next day based on historical data;
  • Optimization control unit It is configured to construct an objective function with the goal of minimizing electricity cost, and use linear programming to solve the output of heat pumps and heat storage equipment in each time period based on the data obtained by prediction;
  • Working mode configuration unit It is configured to determine the prefabricated working mode of the heat pump and heat storage equipment working together in each time period according to the output of the heat pump and heat storage equipment in each time period obtained by the solution, and send it to the heat pump and Heat storage equipment;
  • Online correction unit It is configured to obtain real-time parameter data of the air source heat pump and electric heat storage equipment heating system, and adjust the working status of the air source heat pump and heat storage equipment online according to the real-time parameter data.
  • This embodiment provides a computer-readable storage medium for storing computer instructions.
  • the steps described in the method of Embodiment 1 are completed.
  • the processor may be a central processing unit CPU, the processor may also be other general-purpose processors, digital signal processors DSP, application-specific integrated circuits ASIC, ready-made programmable gate array FPGA or other programmable Logic devices, discrete gates or transistor logic devices, discrete hardware components, etc.
  • the general-purpose processor may be a microprocessor or the processor may also be any conventional processor or the like.
  • each step of the above method can be completed by an integrated logic circuit of hardware in the processor or instructions in the form of software.
  • the steps of the method disclosed in combination with the present disclosure may be directly embodied as being executed and completed by a hardware processor, or executed and completed by a combination of hardware and software modules in the processor.
  • the software module may be located in a mature storage medium in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, or electrically erasable programmable memory, registers.
  • the storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware. To avoid repetition, it will not be described in detail here.
  • the disclosed system, device, and method may be implemented in other ways.
  • the device embodiments described above are only illustrative.
  • the division of the units is only a division of logical functions.
  • there may be other divisions for example, multiple units or components can be combined. Or it can be integrated into another system, or some features can be ignored or not implemented.
  • the displayed or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some interfaces, devices or units, and may be in electrical, mechanical or other forms.
  • the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.
  • the technical solution of the present disclosure essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including Several instructions are used to make a computer device (which may be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in the various embodiments of the present disclosure.
  • the aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disks or optical disks and other media that can store program codes. .

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Abstract

An adaptive optimization control method for a heat pump and an electric heat storage device, comprising the following steps: predicting a thermal load value of a building within each time period of a next day; constructing a target function by using a minimum electric cost as a target, and according to predicted data, using a linear programming method to calculate the contributing conditions of the heat pump and the heat storage device within each time period; determining the preset working modes of the heat pump and the heat storage device within each time period for collaborative working, and sending the preset working modes to the heat pump and the heat storage device; and obtaining, in a real time mode, the parameter data of a system to which an air source heat pump and the electric heat storage device supplies heat, and according to the real-time parameter data, online correcting and adjusting the working states of the air source heat pump and the heat storage device. The method can achieve the adaptive automatic control of the system, so that scheduling is timelier and more accurate, thereby being capable of greatly reducing the running cost of the system, and maximally utilizing electric energy; moreover, the electric load can be leveled, thereby achieving a peak load shifting effect and avoiding wasting a resource. Further disclosed a corresponding system and an apparatus.

Description

一种热泵与电蓄热设备自适应优化控制方法、系统及装置Self-adaptive optimization control method, system and device for heat pump and electric heat storage equipment 技术领域Technical field
本发明涉及供热设备自动控制相关技术领域，具体的说，是涉及一种热泵与电蓄热设备自适应优化控制方法、系统及装置。The present invention relates to the related technical field of automatic control of heating equipment, and in particular to a method, system and device for adaptive optimization control of heat pumps and electric heat storage equipment.
背景技术Background technique
这里的陈述仅提供与本发明相关的背景技术，而不必然地构成现有技术。The statements here only provide background art related to the present invention, and do not necessarily constitute prior art.
空气源热泵作为一种高效节能和清洁供暖方式，近年来在我国楼宇、居民小区、工业园区等场合获得了广泛应用。有研究表明，在我国华北地区和黄河流域影响空气源热泵的主要问题表现在低温适用性问题，即当室外温度降低时，热泵机组出力明显减小。固体电蓄热设备的工作模式分为蓄热模式、放热模式，在夜间低谷电时，通过电加热将热量储存在固体材料集成装置内，以获得较高的显热蓄热温度，白天热量将固体中储存的热量传递出来，再进行风水换热，完成末端供暖。As a high-efficiency, energy-saving and clean heating method, air-source heat pumps have been widely used in buildings, residential quarters, and industrial parks in my country in recent years. Studies have shown that the main problem affecting air source heat pumps in North China and the Yellow River Basin is the low temperature applicability, that is, when the outdoor temperature decreases, the output of the heat pump unit is significantly reduced. The working mode of solid electric heat storage equipment is divided into heat storage mode and heat release mode. When the electricity is low at night, the heat is stored in the solid material integrated device through electric heating to obtain a higher sensible heat storage temperature. The heat stored in the solid is transferred out, and then the feng-shui heat exchange is performed to complete the terminal heating.
对于供热量需求量较大的场所如公共建筑内，结合采用空气源热泵和固体电蓄热设备可以提高能源的利用率，减少常规能源的消耗。公共建筑主要指需要分时段采暖的学校、商场、写字楼等，通常白天的供热需求较大，夜晚不需供暖，平均每天供暖时长小于14h。将空气源热泵和固体电蓄热设备作为供热系统热源时，通常使空气源热泵承担白天主要的热负荷，蓄热设备在夜晚蓄热，在白天电价高峰时放热。目前，我国电价实行固定时段的分时电价(峰谷电价)，因此，传统的设备运行模式比较固定，如：每天11-12点为尖峰电价时间，此时就开启蓄热设备放热，关停热泵，以降低运行成本。For places with large heat demand, such as public buildings, the combined use of air source heat pumps and solid electric heat storage equipment can improve energy utilization and reduce conventional energy consumption. Public buildings mainly refer to schools, shopping malls, office buildings, etc. that need to be heated by time periods. Usually, there is a large demand for heating during the day and no heating at night. The average heating time per day is less than 14 hours. When air source heat pumps and solid electric heat storage equipment are used as the heat source of the heating system, the air source heat pump is usually made to bear the main heat load during the day, and the heat storage equipment stores heat at night and releases heat during the day when the electricity price peaks. At present, my country’s electricity prices are fixed-period time-of-use electricity prices (peak and valley electricity prices). Therefore, the traditional equipment operation mode is relatively fixed. Stop the heat pump to reduce operating costs.
发明人发现，随着我国电力零售市场的放开，动态变化的实时电价模式会取代原来的峰谷电价，传统的设备控制系统运行模式固定或者单一，控制灵活性较差，不能根据电网价格的变动自适应地控制蓄热设备的启停，从而不能充分利用蓄热产生的能量，导致运行成本较高，客户体验较差，已无法满足使用要求。The inventor found that with the liberalization of my country’s electricity retail market, the dynamically changing real-time electricity price model will replace the original peak and valley electricity price. The traditional equipment control system has a fixed or single operation mode, and has poor control flexibility and cannot be based on grid prices. The change adaptively controls the start and stop of the heat storage equipment, so that the energy generated by the heat storage cannot be fully utilized, resulting in high operating costs and poor customer experience, which can no longer meet the requirements of use.
发明内容Summary of the invention
针对现有技术存在的不足，本发明的目的是提供一种热泵与电蓄热设备自适应优化控制方法、系统及装置，该方法分为两部分组成：优化预制部分和在线调 整部分。第一部分为优化预制部分，以一天用电成本最小为目标函数，采用线性规划问题的单纯形法求出热泵与电蓄热设备的出力情况和工作时长，找到电蓄热设备的投入的最佳时间，以此为基准来预制第二天设备的工作模式。第二部分为在线调整部分，在第二天每个时间段开始时根据建筑热负荷和蓄热量等实时数据来微调工作模式，以保证系统运行的实用性和准确性。In view of the shortcomings of the prior art, the purpose of the present invention is to provide a heat pump and electric heat storage equipment adaptive optimization control method, system and device. The method is composed of two parts: an optimized prefabricated part and an online adjustment part. The first part is to optimize the prefabricated part. With the minimum cost of electricity in a day as the objective function, the simplex method of linear programming is used to find the output and working time of the heat pump and the electric heat storage equipment, and find the best investment of the electric heat storage equipment Time, use this as a benchmark to pre-make the working mode of the equipment on the next day. The second part is the online adjustment part. At the beginning of each time period of the second day, the working mode is fine-tuned according to real-time data such as building heat load and heat storage to ensure the practicability and accuracy of system operation.
为了实现上述目的，本发明是通过如下的技术方案来实现：In order to achieve the above objectives, the present invention is achieved through the following technical solutions:
第一方面，本发明的实施例提供了一种热泵与蓄热设备自适应优化控制方法，包括如下步骤：In the first aspect, an embodiment of the present invention provides an adaptive optimization control method for a heat pump and a heat storage device, which includes the following steps:
获取第二天电网的日前电价和天气预报数据，并根据历史数据预测第二天各时段的建筑热负荷值；Obtain the day-ahead electricity price and weather forecast data of the grid on the next day, and predict the building heat load value of each period of the next day based on historical data;
以用电成本最小为目标构建目标函数，根据预测获得的热负荷值，采用线性规划方法求解每个时间段热泵和蓄热设备的出力情况；Construct an objective function with the goal of minimizing electricity cost, and use linear programming to solve the output of heat pumps and heat storage equipment in each time period based on the predicted heat load value;
根据求解得到的每个时间段热泵和蓄热设备的出力情况，确定每个时间段的热泵和蓄热设备协同工作的预制工作模式，并发送至热泵和蓄热设备；According to the output of the heat pump and heat storage equipment for each time period obtained by the solution, determine the prefabricated working mode of the heat pump and heat storage equipment for each time period and send it to the heat pump and heat storage equipment;
实时获取热泵和电蓄热设备供热系统的参数数据，根据实时的参数数据在线调整空气源热泵和蓄热设备的工作状态。Real-time acquisition of parameter data of heat pumps and electric heat storage equipment heating systems, and online adjustment of the working status of air source heat pumps and heat storage equipment based on real-time parameter data.
作为进一步的技术方案，以第二天的用电成本最小为目标构建的目标函数为：As a further technical solution, the objective function constructed with the goal of minimizing the electricity cost of the next day is:
Figure PCTCN2020132226-appb-000001
Figure PCTCN2020132226-appb-000001
式中：P HP.i为在第i个时间段内空气源热泵制热消耗的平均电功率；t HP.i为空气源热泵工作的第i个时间段；P TS.i为在第i个时间段内固体电蓄热设备蓄热消耗的平均电功率；t TS.X.i为固体电蓄热设备以蓄热模式工作的第i个时间段；P P&F.i为在第i个时间段内蓄热设备放热时风机和水泵消耗的平均电功率；t TS.F.i为固体电蓄热设备以放热模式工作的第i个时间段；E i为第i个时间段的日前电价。 Where: P HP.i as the i-th period average electric power air source heat pump system consumption; i-th period T HP.i air source heat pump is working; P TS.i for the i-th The average electric power consumed by the solid electric heat storage device during the heat storage period; t TS.Xi is the ith time period when the solid electric heat storage device works in the heat storage mode; P P&F.i is the storage time during the ith time period The average electric power consumed by the fans and water pumps when the thermal equipment releases heat; t TS.Fi is the i-th time period when the solid electric heat storage equipment works in heat release mode; E i is the day-a-day electricity price for the i-th time period.
作为进一步的技术方案，求解目标函数的约束条件包括热泵的工作约束和蓄热设备的工作约束；热泵的工作约束包括：空气源热泵的电热转换模型；热泵的制热消耗的平均电功率小于热泵的额定功率；在设定的工作时长内，转换的能量 按天气情况通过修正系数修正。As a further technical solution, the constraints for solving the objective function include the working constraints of the heat pump and the working constraints of the heat storage equipment; the working constraints of the heat pump include: the electric-heat conversion model of the air-source heat pump; the average electric power of the heat pump's heating consumption is less than that of the heat pump Rated power; within the set working time, the converted energy is corrected by a correction factor according to weather conditions.
作为进一步的技术方案，蓄热设备的电热转换模型；蓄热设备的蓄热量不大于蓄热设备的蓄热额定值。As a further technical solution, the electric heat conversion model of the heat storage device; the heat storage capacity of the heat storage device is not greater than the heat storage rating of the heat storage device.
作为进一步的技术方案，根据历史数据，基于LSTM模型预测第二天各时段的建筑热负荷值；As a further technical solution, according to historical data, based on the LSTM model to predict the building heat load value in each period of the next day;
或者or
采用线性规划方法求解每个时间段热泵和蓄热设备的出力情况的方法，采用线性规划方法中的单纯形法。The linear programming method is used to solve the output situation of the heat pump and heat storage equipment in each time period, and the simplex method in the linear programming method is adopted.
作为进一步的技术方案，预制工作模式包括预制工作模式Ⅰ：热泵制热，蓄热设备不工作；预制工作模式Ⅱ：固体蓄热设备蓄热，热泵不工作；预制工作模式Ⅲ：固体蓄热设备放热，热泵不工作；预制工作模式Ⅳ：蓄热设备先放热，而后热泵制热。As a further technical solution, the prefabricated working modes include prefabricated working mode I: heat pump heating, heat storage equipment does not work; prefabricated working mode II: solid heat storage equipment heat storage, heat pump does not work; prefabricated working mode III: solid heat storage equipment If heat is released, the heat pump does not work; prefabricated working mode IV: the heat storage device releases heat first, and then the heat pump generates heat.
作为进一步的技术方案，当按照预制工作模式Ⅰ工作，在线修正方法为：实时获取蓄热设备的实际蓄热量，当到达额定蓄热量时，蓄热停止，热泵不工作，直到下一时间段执行下一时间段对应的预制工作模式；As a further technical solution, when working in the prefabricated working mode I, the online correction method is: obtain the actual heat storage capacity of the heat storage device in real time. When the rated heat storage capacity is reached, the heat storage stops and the heat pump does not work until the next time period is executed. Prefabricated work mode corresponding to the next time period;
或者or
当按照预制工作模式Ⅱ工作，热泵制热，蓄热设备不工作，在线修正方法为：When working in the prefabricated working mode II, the heat pump is heating, and the heat storage device does not work, the online correction method is as follows:
获取热泵的实际出水温度，计算出水温度实际值与设定值的差值，采用PID调节控制空气源热泵的出水温度维持在设定值；Obtain the actual outlet water temperature of the heat pump, calculate the difference between the actual value of the water temperature and the set value, and use PID adjustment to control the outlet water temperature of the air source heat pump to maintain the set value;
实时获取实时供热量，当建筑的实时热负荷与所有热泵的额定热负荷的比值大于设定的第一设定比值时，开启固体蓄热设备补充放热；Acquire real-time heat supply in real time. When the ratio of the real-time heat load of the building to the rated heat load of all heat pumps is greater than the first set ratio set, turn on the solid heat storage device to supplement heat release;
当实时热负荷与所有热泵的额定热负荷的比值小于设定的第二设定比值时，关闭固体蓄热设备停止放热；其中第一设定比值大于第二设定比值；When the ratio of the real-time thermal load to the rated thermal load of all heat pumps is less than the second set ratio, turn off the solid heat storage device to stop heat release; wherein the first set ratio is greater than the second set ratio;
或者or
采用预制工作模式Ⅲ时，固体蓄热设备放热，热泵不工作，该模式下的在线修正方法为：实时获取实时供热量，判断蓄热设备实际的蓄热量是否满足供热需求，若满足，则蓄热设备单独供热；否则开启热泵同时供热；When the prefabricated working mode III is adopted, the solid heat storage equipment releases heat and the heat pump does not work. The online correction method in this mode is: obtain real-time heat supply in real time, and judge whether the actual heat storage equipment meets the heating demand. , The heat storage device supplies heat separately; otherwise, the heat pump is turned on to supply heat at the same time;
或者or
采用预制工作模式Ⅳ时，该模式下的在线修正策略为：实时获取实时供热量，判断蓄热设备实际的蓄热量是否满足供热需求，若满足，则蓄热设备单独供热，转换为预制工作模式Ⅲ；否则，当固体蓄热设备实际蓄热量小于最小限值时，关闭蓄热设备停止供热，开启热泵供热。When using prefabricated working mode IV, the online correction strategy in this mode is to obtain real-time heat supply in real time, and judge whether the actual heat storage capacity of the heat storage device meets the heating demand. Prefabricated working mode III; otherwise, when the actual heat storage capacity of the solid heat storage device is less than the minimum limit, the heat storage device is turned off to stop heat supply, and the heat pump is turned on for heat supply.
第二方面，本发明实施例还提供了一种热泵与蓄热设备自适应优化控制装置，包括：In the second aspect, an embodiment of the present invention also provides an adaptive optimization control device for a heat pump and a heat storage device, including:
预测单元：被配置为用于获取第二天电网的日前电价和天气预报数据，根据获取的数据预测第二天各时段的建筑热负荷值；Prediction unit: It is configured to obtain the day-ahead electricity price and weather forecast data of the grid on the next day, and predict the building heat load value in each period of the next day based on the acquired data;
求解单元：被配置为用于以用电成本最小为目标构建目标函数，根据预测获得的数据，采用线性规划方法求解每个时间段热泵和蓄热设备的出力情况；Solving unit: It is configured to construct an objective function with the goal of minimizing electricity cost, and use linear programming to solve the output of heat pumps and heat storage equipment in each time period according to the data obtained by prediction;
工作模式配置单元：被配置为用于根据求解得到的每个时间段热泵和蓄热设备的出力情况，确定每个时间段的热泵和蓄热设备协同工作的预制工作模式，并发送至热泵和蓄热设备；Working mode configuration unit: It is configured to determine the prefabricated working mode of the heat pump and heat storage equipment working together in each time period according to the output of the heat pump and heat storage equipment in each time period obtained by the solution, and send it to the heat pump and Heat storage equipment;
在线修正单元：被配置为用于实时获取空气源热泵和电蓄热设备供热的系统的参数数据，根据实时的参数数据在线修正调整空气源热泵和蓄热设备的工作状态。Online correction unit: It is configured to obtain real-time parameter data of the air source heat pump and electric heat storage equipment heating system, and adjust the working status of the air source heat pump and heat storage equipment online according to the real-time parameter data.
第三方面，本发明实施例还提供了一种热泵与蓄热设备自适应优化控制系统，包括空气源热泵、固体电蓄热设备、通信网关以及控制装置，以及连接在空气源热泵、固体电蓄热设备的传感器和执行器，控制装置分别通过通信网关与传感器、执行器连接，控制装置执行上述的一种热泵与蓄热设备自适应优化控制方法的步骤。In the third aspect, embodiments of the present invention also provide an adaptive optimization control system for heat pumps and heat storage equipment, including air source heat pumps, solid-state electric heat storage equipment, communication gateways, and control devices, as well as connected to air-source heat pumps and solid-state electricity. The sensor and the actuator of the heat storage device, and the control device are respectively connected with the sensor and the actuator through the communication gateway, and the control device executes the steps of the above-mentioned method for adaptive optimization and control of the heat pump and the heat storage device.
第三方面，本发明实施例还提供了一种计算机可读存储介质，用于存储计算机指令，所述计算机指令被处理器执行时，完成上述的一种热泵与蓄热设备自适应优化控制方法的步骤。In a third aspect, embodiments of the present invention also provide a computer-readable storage medium for storing computer instructions, which when executed by a processor, complete the above-mentioned adaptive optimization control method for heat pumps and heat storage devices A step of.
上述本发明的实施例的有益效果如下：The beneficial effects of the above-mentioned embodiments of the present invention are as follows:
本发明以预测第二天建筑所需的热负荷为基础，综合考虑日前电价等因素，以当天用电成本最小为目标，求解热泵和电蓄热设备的工作模式，并且根据实时数据在线调整。这种方法可以自适应跟踪建筑热负荷及电网价格的变化，动态调整设备的工作模式，找到蓄热设备最佳的投入时间和放热热量，节省用户的用电 成本，实现效益的最大化，并且使电网负荷平准化，起到削峰填谷的效果，该方法对于热泵和电蓄热设备的推广使用及电网的优化调度都有着重要意义与参考价值。The present invention is based on predicting the heat load required by the building on the next day, comprehensively considering factors such as day-a-day electricity prices, and aiming at minimizing the electricity cost of the day, solving the working modes of heat pumps and electric heat storage equipment, and adjusting on-line according to real-time data. This method can adaptively track changes in building heat load and grid prices, dynamically adjust the working mode of equipment, find the best investment time and heat release of heat storage equipment, save users’ electricity costs, and maximize benefits. In addition, the grid load is leveled, which has the effect of cutting peaks and filling valleys. This method has important significance and reference value for the popularization and use of heat pumps and electric heat storage equipment and the optimal dispatch of power grids.
附图说明Description of the drawings
构成本发明的一部分的说明书附图用来提供对本发明的进一步理解，本发明的示意性实施例及其说明用于解释本发明，并不构成对本发明的不当限定。The accompanying drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The exemplary embodiments and descriptions of the present invention are used to explain the present invention, and do not constitute an improper limitation of the present invention.
图1为本发明实施例1的优化控制方法中提前预制部分的流程图；FIG. 1 is a flowchart of the advance prefabrication part in the optimization control method of Embodiment 1 of the present invention;
图2为本发明实施例1的优化控制方法中在线调整部分的流程图；2 is a flowchart of the online adjustment part in the optimization control method of Embodiment 1 of the present invention;
图3为本发明实施例1的单纯形法的流程图；Figure 3 is a flow chart of the simplex method of embodiment 1 of the present invention;
图4为本发明实施例1的自适应优化控制系统结构示意图；4 is a schematic diagram of the structure of an adaptive optimization control system according to Embodiment 1 of the present invention;
图5为本发明实施例1的优化前后的负荷曲线和工作模式效果例图。FIG. 5 is an example diagram of load curves and working mode effects before and after optimization in Embodiment 1 of the present invention.
具体实施方式Detailed ways
应该指出，以下详细说明都是例示性的，旨在对本发明提供进一步的说明。除非另有指明，本发明使用的所有技术和科学术语具有与本发明所属技术领域的普通技术人员通常理解的相同含义。It should be noted that the following detailed descriptions are all illustrative and are intended to provide further descriptions of the present invention. Unless otherwise specified, all technical and scientific terms used in the present invention have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.
需要注意的是，这里所使用的术语仅是为了描述具体实施方式，而非意图限制根据本发明的示例性实施方式。如在这里所使用的，除非本发明另外明确指出，否则单数形式也意图包括复数形式，此外，还应当理解的是，当在本说明书中使用术语“包含”和/或“包括”时，其指明存在特征、步骤、操作、器件、组件和/或它们的组合；It should be noted that the terms used here are only for describing specific embodiments, and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the present invention clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should also be understood that when the terms "comprising" and/or "including" are used in this specification, they Indicate the existence of features, steps, operations, devices, components, and/or combinations thereof;
为了方便叙述，本发明中如果出现“上”、“下”、“左”“右”字样，仅表示与附图本身的上、下、左、右方向一致，并不对结构起限定作用,仅仅是为了便于描述本发明和简化描述，而不是指示或暗示所指的设备或元件必须具有特定的方位，以特定的方位构造和操作，因此不能理解为对本发明的限制。For the convenience of description, if the words "up", "down", "left" and "right" appear in the present invention, they only indicate that they are consistent with the up, down, left, and right directions of the drawings themselves, and do not limit the structure, but only It is for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present invention.
术语解释部分:本发明中如出现术语“安装”、“相连”、“连接”、“固定”等，应做广义理解，例如，可以是固定连接，也可以是可拆卸连接，或为一体；可以是机械连接，也可以是电连接，可以是直接连接，也可以是通过中间媒介间接相连，可以是两个元件内部连接，或者两个元件的相互作用关系，对于本领域的普通技术人员而言，可以根据具体情况理解上述术语在本发明的具体含 义。Term explanation part: In the present invention, if the terms "installed", "connected", "connected", "fixed", etc. appear, they should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or a whole; It can be a mechanical connection, an electrical connection, a direct connection, or an indirect connection through an intermediary, an internal connection between two components, or an interaction relationship between two components. In other words, the specific meaning of the above terms in the present invention can be understood according to the specific situation.
实施例1Example 1
正如背景技术所介绍的，现有技术中存在不足，为了解决如上的技术问题，本发明提出了一种热泵与蓄热设备自适应优化控制方法。As described in the background art, there are deficiencies in the prior art. In order to solve the above technical problems, the present invention proposes an adaptive optimization control method for a heat pump and a heat storage device.
本发明的一种典型的实施方式中，如图1-3所示，提出一种热泵与蓄热设备自适应优化控制方法，包括如下步骤，其中S1～S3为优化预制部分，S4为在线调整部分：In a typical implementation of the present invention, as shown in Figures 1-3, an adaptive optimization control method for heat pumps and thermal storage equipment is proposed, which includes the following steps, where S1 to S3 are optimized prefabricated parts, and S4 is online adjustment part:
S1获取第二天电网的日前电价和天气预报数据，并根据历史数据预测第二天各时段的建筑热负荷值；S1 obtains the day-ahead electricity price and weather forecast data of the power grid on the second day, and predicts the building heat load value in each period of the second day based on the historical data;
S2以用电成本最小为目标构建目标函数，根据预测获得的数据，采用线性规划方法求解每个时间段热泵和蓄热设备的出力情况；S2 constructs an objective function with the goal of minimizing electricity cost, and uses the linear programming method to solve the output of heat pumps and heat storage equipment in each time period based on the predicted data;
S3根据求解得到的每个时间段热泵和蓄热设备的出力情况，确定每个时间段的热泵和蓄热设备协同工作的预制工作模式，并发送至热泵和蓄热设备；S3 Determines the prefabricated working mode of the heat pump and heat storage equipment for each time period based on the output of the heat pump and heat storage equipment obtained by the solution for each time period, and sends it to the heat pump and heat storage equipment;
S4实时获取热泵和蓄热设备供热的系统的参数数据，根据实时的参数数据在线修正调整热泵和蓄热设备的工作状态。S4 obtains the parameter data of the heat supply system of the heat pump and the heat storage device in real time, and adjusts the working status of the heat pump and the heat storage device on-line according to the real-time parameter data.
本实施例采用预先设定工作模式结合在线调整的自适应优化控制方法，用于对空气源热泵与固体蓄热设备的工作进行自适应优化控制，结合日前电价以用电成本最小为目标，求解获得次日每个时间段可以执行的预制工作模式，预制工作模式是动态变化的，按照预制工作模式控制热泵和蓄热设备的工作，可以使蓄热设备在电价较高时段充分投入，避免资源浪费，使用户获得较好的经济效益；同时使电力负荷平准化，达到了削峰填谷的效果。并且，通过实时获取的数据对每个时段的工作模式进行在线修正，微调空气源热泵和蓄热设备的工作状态，进一步提高系统的自适应度，保证系统工作的稳定性和安全性。This embodiment adopts a pre-set working mode combined with an adaptive optimization control method of online adjustment, which is used to perform adaptive optimization control on the work of air source heat pumps and solid heat storage equipment, combined with the current electricity price to minimize the cost of electricity, and solve Obtain the prefabricated working mode that can be executed in each time period of the next day. The prefabricated working mode is dynamically changed. According to the prefabricated working mode to control the work of the heat pump and the heat storage equipment, the heat storage equipment can be fully invested in the high electricity price period and avoid resources Waste, so that users can obtain better economic benefits; at the same time, the power load is leveled, and the effect of peak shaving and valley filling is achieved. In addition, the real-time data is used to make online corrections to the working mode of each time period, and fine-tune the working status of the air source heat pump and heat storage equipment to further improve the adaptiveness of the system and ensure the stability and safety of the system.
在一些实施例中，步骤S1中，可以利用训练好的LSTM模型预测第二天各时段的建筑热负荷值。In some embodiments, in step S1, the trained LSTM model can be used to predict the building heat load value in each period of the next day.
如图4所示，为热泵和电蓄热设备的自适应优化控制系统的典型结构，系统包括热泵、固体电蓄热设备和水泵等，以及用于控制热泵与固体电蓄热设备进行优化工作的控制装置。As shown in Figure 4, it is a typical structure of an adaptive optimization control system for heat pumps and electric heat storage equipment. The system includes heat pumps, solid electric heat storage equipment and water pumps, and is used to control heat pumps and solid electric heat storage equipment for optimization work.的控制装置。 Control device.
可以理解的，为实现控制装置的控制目的，自适应优化控制系统还包括与 控制装置进行交互的传感器、执行器和通信网关，传感器主要采集现场设备的运行参数以及用电量等相关数据，并将数据上传至通信网关，网关用于实现传感器和控制装置之间的双向数据传输；控制装置获得数据并完成优化控制方法的计算，可以执行实现一种热泵与蓄热设备自适应优化控制方法的步骤，并将结果通过网关下发给现场执行器，执行器转换空气源热泵和电蓄热设备的工作状态，达到控制现场设备的目的。It is understandable that in order to achieve the control purpose of the control device, the adaptive optimization control system also includes sensors, actuators, and communication gateways that interact with the control device. The sensors mainly collect the operating parameters of field equipment and related data such as power consumption, and Upload the data to the communication gateway. The gateway is used to realize the two-way data transmission between the sensor and the control device; the control device obtains the data and completes the calculation of the optimized control method, and can implement an adaptive optimization control method for heat pumps and heat storage equipment Steps, and send the results to the field actuator through the gateway, and the actuator converts the working status of the air source heat pump and the electric heat storage device to achieve the purpose of controlling the field device.
可以理解的，热泵和电蓄热设备供热的系统的参数数据可以包括热泵和电蓄热设备运行参数、循环水管路运行参数以及水泵运行参数，循环水管路运行参数包括管道内的循环水热负荷、流量、压力和温度等。It is understandable that the parameter data of the heat pump and electric heat storage equipment heating system can include the heat pump and electric heat storage equipment operating parameters, circulating water pipeline operating parameters, and water pump operating parameters. The circulating water pipeline operating parameters include circulating water heat in the pipeline. Load, flow, pressure and temperature, etc.
第二天电网的日前电价可以通过连接电力系统的数据接口获得；通过“中国气象数据网”提供的API接口，可以获得第二天的天气预报数据；并可通过热泵的产品手册，如表1所示，热泵的性能修正系数与型号有关的出厂数据，根据天气数据和预设的出水温度得到热泵的性能修正系数f i-pre。 The day-ahead electricity price of the grid on the second day can be obtained through the data interface connected to the power system; through the API interface provided by "China Meteorological Data Network", the next day’s weather forecast data can be obtained; and through the heat pump product manual, as shown in Table 1. As shown, the performance correction coefficient of the heat pump is related to the factory data of the model, and the performance correction coefficient f i-pre of the heat pump is obtained according to the weather data and the preset water temperature.
表1某型号空气源热泵的性能修正系数表Table 1 Performance correction coefficient table of a certain type of air source heat pump
Figure PCTCN2020132226-appb-000002
Figure PCTCN2020132226-appb-000002
长短期记忆网络模型(Long Short-Term Memory,LSTM)是循环神经网络(RecurrentNeural Networks,RNN)的一种特殊类型。RNN网络模型包括输入层、隐藏层、输出层，每一层又都由若干个神经元组成。LSTM模型在隐藏层增加了遗忘门，用于解决训练过程中的“梯度消失”或“梯度爆炸”的问题。基于LSTM模型预测建筑热负荷的步骤如下：Long and short-term memory network model (Long Short-Term Memory, LSTM) is a special type of Recurrent Neural Networks (RNN). The RNN network model includes an input layer, a hidden layer, and an output layer. Each layer is composed of several neurons. The LSTM model adds a forget gate to the hidden layer to solve the problem of "gradient disappearance" or "gradient explosion" in the training process. The steps for predicting building heat load based on LSTM model are as follows:
1)选取历史热负荷和气象数据构成时间序列样本；1) Select historical heat load and meteorological data to form time series samples;
2)对样本集进行归一化处理，去除量纲；2) Normalize the sample set to remove dimensions;
3)根据预测日，选取历史同一天之前的历史数据，历史同一天是指往年同日期的一天，可以为农历日期。历史数据也可以是当前年度供暖开始后的数据， 直接选取预测日之前的历史数据。3) According to the forecast date, select historical data before the same day in history. The same day in history refers to a day on the same date in previous years, which can be a lunar date. The historical data can also be the data after the heating starts in the current year, and the historical data before the forecast date is directly selected.
选取该日期设定时长如14天的时间序列进行学习，则训练数据具有14*24个时间步长，每个时间步长有此时间所在的日期类型(工作日/非工作日)，热负荷数据，天气参数作为特征值，即得到[样本数、时间步长、特性]的集和，将集合样本的前百分之八十数据作为LSTM预测模型的训练集，后百分之二十作为测试集，合理选取影响因子的序列时数，即输入层神经元个数及隐藏层神经元个数。Select a time series with a duration of 14 days for learning on this date, then the training data has 14*24 time steps, and each time step has the date type (working day/non-working day) where the time is located, and the heat load Data, weather parameters as feature values, that is, the set sum of [number of samples, time step, characteristics] is obtained. The first 80% of the set samples are used as the training set of the LSTM prediction model, and the last 20% is used as the training set of the LSTM prediction model. In the test set, reasonably select the sequence hours of the impact factors, that is, the number of neurons in the input layer and the number of neurons in the hidden layer.
4)用步骤3)训练好的LTSM模型预测第二天的各时段的建筑热负荷Qi。4) Using step 3) the trained LTSM model to predict the building heat load Qi in each period of the next day.
步骤S2中，以用电成本最小为目标构建目标函数，可以为：In step S2, the objective function is constructed with the goal of minimizing electricity cost, which can be:
Figure PCTCN2020132226-appb-000003
Figure PCTCN2020132226-appb-000003
式中：P HP.i：在第i个时间段内空气源热泵制热消耗的平均电功率，单位为kW；t HP.i：空气源热泵工作的第i个时间段，单位为小时；P TS.i：在第i个时间段内固体电蓄热设备蓄热消耗的平均电功率，单位为kW；t TS.X.i：固体电蓄热设备以蓄热模式工作的第i个时间段，单位为小时；P P&F.i：在第i个时间段内蓄热设备放热时风机和水泵消耗的平均电功率，单位为kW；t TS.F.i：固体电蓄热设备以放热模式工作的第i个时间段，单位为小时；E i：第i个时间段的实时电价，单位为元/kWh。 Where: P HP.i : the average electric power consumed by the air source heat pump for heating during the i-th time period, in kW; t HP.i : the i-th time period during which the air-source heat pump is working, in hours; P TS.i : the average electric power consumed by the solid electric heat storage device during the ith time period, in kW; t TS.Xi : the ith time period during which the solid electric heat storage device works in the heat storage mode, the unit P P&F.i : the average electric power consumed by fans and water pumps when the heat storage device releases heat in the i-th time period, in kW; t TS.Fi : the first time the solid electric heat storage device works in heat release mode i time period, the unit is hour; E i : the real-time electricity price of the i-th time period, the unit is yuan/kWh.
可选的，时间段根据具体的控制精度进行划分，由于需要考虑蓄热设备蓄热放热的工作周期，因此可以按一天24小时为一个计算周期，这24h从前一天蓄热开始的时间开始计算，如前一天的23点至第二天的23点，将其分成n个时间长度为τ的等距时间段，如τ＝0.25h、0.5h、1h时，n＝96、48、24。公式(1)中i＝1.2.3……n，表示划分的各个时间段。Optionally, the time period is divided according to the specific control accuracy. Since the working cycle of the heat storage device's heat storage and heat release needs to be considered, 24 hours a day can be a calculation cycle, which is calculated from the time when the heat storage started on the previous day , Such as from 23:00 on the previous day to 23:00 on the next day, divide it into n equidistant time periods of τ, such as τ=0.25h, 0.5h, 1h, n=96, 48, 24. In formula (1), i=1.2.3...n, which represents the divided time periods.
其中，求解目标函数的约束条件可以包括：空气源热泵的工作约束和蓄热设备的工作约束。Among them, the constraint conditions for solving the objective function may include: the working constraint of the air source heat pump and the working constraint of the heat storage device.
可选的，空气源热泵的工作约束可以根据热泵的运行条件进行设定，可以包括：空气源热泵的电热转换模型；热泵的制热消耗的平均电功率小于热泵的额定功率；在设定的工作时长内，转换的能量按天气情况通过修正系数修正。Optionally, the working constraints of the air source heat pump can be set according to the operating conditions of the heat pump, which may include: the electric heat conversion model of the air source heat pump; the average electric power consumed by the heat pump for heating is less than the rated power of the heat pump; During the time period, the converted energy is corrected by a correction factor according to weather conditions.
空气源热泵消耗的电功率按空气源热泵的电热转换模型来计算，且不超过 设备的额定功率P N，第i时段的能效转换系数COP N＝COP N·f i-pre。并且，定义热泵允许工作的时间段为[τ HP-start,τ HP-stop]。 The electric power consumed by the air source heat pump is calculated according to the electric heat conversion model of the air source heat pump, and does not exceed the rated power P N of the equipment, and the energy efficiency conversion coefficient COP N = COP N · f i-pre in the i- th period. Moreover, the time period during which the heat pump is allowed to work is defined as [τ HP-start ,τ HP-stop ].
具体的，空气源热泵的工作约束的表达式为：Specifically, the expression of the working constraint of the air source heat pump is:
Figure PCTCN2020132226-appb-000004
Figure PCTCN2020132226-appb-000004
0≤P HP·i≤P N     (3) 0≤P HP·i ≤P N (3)
式中：Q HP.i：第i个时间段空气源热泵的需求制热负荷，单位为kW； Where: Q HP.i : the demand heating load of the air source heat pump in the i-th time period, in kW;
COP N：空气源热泵的能效转换系数额定值； COP N : the rated value of the energy efficiency conversion factor of the air source heat pump;
f i-pre：空气源热泵的性能修正系数。 f i-pre : The performance correction factor of the air source heat pump.
可选的，蓄热设备的工作约束可以包括：蓄热设备的电热转换模型；蓄热设备的蓄热量不大于蓄热设备的蓄热额定值Q N。具体的约束条件如公式(4)-(6)所示，如下： Optionally, the working constraints of the thermal storage device may include: an electric-to-heat conversion model of the thermal storage device; and the thermal storage amount of the thermal storage device is not greater than the thermal storage rating Q N of the thermal storage device. The specific constraints are shown in formulas (4)-(6), as follows:
考虑透过保温层的热量损失，固体电蓄热设备的效率η为放热量与蓄热量之比，蓄热量总和的上限为设备蓄热量的额定值H N。 Considering the heat loss through the thermal insulation layer, the efficiency η of the solid electric heat storage device is the ratio of the heat release to the heat storage, and the upper limit of the total heat storage is the rated value H N of the heat storage of the device.
Figure PCTCN2020132226-appb-000005
Figure PCTCN2020132226-appb-000005
Figure PCTCN2020132226-appb-000006
Figure PCTCN2020132226-appb-000006
式中：
Figure PCTCN2020132226-appb-000007
为设备的蓄热量； Where:
Figure PCTCN2020132226-appb-000007
Store heat for the equipment;
Figure PCTCN2020132226-appb-000008
为设备的放热量；
Figure PCTCN2020132226-appb-000008
For the heat release of the equipment;
Q TS.i为第i个时间段蓄热设备放热的热负荷,单位为kW； Q TS.i is the heat load of the heat storage equipment in the i-th time period, in kW;
[τ TS-start,τ TS-stop]为蓄热设备以蓄热模式工作的时间段。 [τ TS-start ,τ TS-stop ] is the time period during which the heat storage device works in the heat storage mode.
在第i个时间段建筑热负荷由热泵热负荷和蓄热设备放热的热负荷组成，如下式：In the i-th time period, the building heat load is composed of the heat load of the heat pump and the heat load of the heat storage device, as shown in the following formula:
Q TS·i+Q HP·i＝Q i     (6) Q TS·i +Q HP·i =Q i (6)
式中：Q i：第i个时间段建筑热负荷预测值，单位为kW。 Where: Q i : Predicted value of building heat load in the i-th time period, in kW.
根据空气源热泵和固体蓄热设备协同工作的实际工作情况，包括：蓄热设备的蓄热时间与热泵工作的时间不重合；蓄热设备在一天内完成蓄热-放热一个工作周期；蓄热设备的蓄热和放热工作不能同时进行。可以确定热泵和蓄热设备协同工作可以包括四种协同工作模式，其工作模式和工作时间如下表2所示：According to the actual working conditions of the air source heat pump and solid heat storage equipment working together, including: the heat storage time of the heat storage equipment does not coincide with the working time of the heat pump; the heat storage equipment completes a heat storage-release work cycle in one day; The heat storage and heat release work of thermal equipment cannot be performed at the same time. It can be determined that the cooperative work of heat pump and heat storage equipment can include four cooperative working modes. The working modes and working hours are shown in Table 2 below:
表2热泵和固体蓄热协同工作模式表Table 2 Cooperative working mode of heat pump and solid heat storage
工作模式Operating mode t HP·i t HP·i t TS·X·i t TS·X·i t TS·F·i t TS·F·i 工作模式描述Working mode description
ⅠⅠ 00 τ τ 00 固体蓄热设备蓄热，热泵不工作The solid heat storage device stores heat, and the heat pump does not work
Ⅱ Ⅱ ττ 00 00 热泵制热，蓄热设备不工作Heat pump heating, heat storage equipment does not work
Ⅲ Ⅲ 00 00 ττ 固体蓄热设备放热，热泵不工作The solid heat storage equipment releases heat and the heat pump does not work
Ⅳ Ⅳ ττ 00 ττ 蓄热设备先放热，而后热泵制热The heat storage device releases heat first, and then the heat pump generates heat
表中，τ为步长，为恒定数值。In the table, τ is the step size, which is a constant value.
以P HP·i、P TS·i为待求变量，式(2)～(6)为约束条件求解满足目标函数式(1)及表2所示的工作模式的最优解，可以采用线性规划方法。求解方法可采用单纯形法，如图3所示，具体步骤为： Taking P HP·i and P TS·i as the variables to be sought, and formulas (2)～(6) as constraints to solve the optimal solution that satisfies the objective function formula (1) and the working mode shown in Table 2, linearity can be used Planning method. The solution method can use the simplex method, as shown in Figure 3. The specific steps are:
1)引入松弛变量x i，y，将目标函数及其约束条件的问题描述转化成线性规划的标准形，如下； 1) Introduce slack variables x i , y, and transform the problem description of the objective function and its constraints into the standard form of linear programming, as follows;
Figure PCTCN2020132226-appb-000009
Figure PCTCN2020132226-appb-000009
Figure PCTCN2020132226-appb-000010
Figure PCTCN2020132226-appb-000010
P HP·i+x i＝P N P HP·i +x i ＝P N
Figure PCTCN2020132226-appb-000011
Figure PCTCN2020132226-appb-000011
Q TS·i+Q HP·i＝Q i Q TS·i +Q HP·i =Q i
P TS·i≥0，i＝1，…，n P TS·i ≥0, i=1,...,n
P HP·i≥0，i＝1，…，n P HP·i ≥0, i=1,...,n
其中，P HP.i：在第i个时间段内空气源热泵制热消耗的平均电功率，单位为kW；t HP.i：空气源热泵工作的第i个时间段；P TS.i：在第i个时间段内固体电蓄热设备蓄热消耗的平均电功率；t TS.X.i：固体电蓄热设备以蓄热模式工作的第i个时间段；P P&F.i：在第i个时间段内蓄热设备放热时风机和水泵消耗的平均电功率；t TS.F.i：固体电蓄热设备以放热模式工作的第i个时间段；E i：第i个时间段的实时电价，单位为元/kWh。H N蓄热设备蓄热量的额定值。Q HP.i：第i个时间段空气源热泵的需求制热负荷；COP N：空气源热泵的能效转换系数额定值；f i-pre：空气源热泵的性能修正系数。 Wherein, P HP.i: the i-th period of the air source heat pump system of the average electric power consumption, in units of kW; t HP.i: i-th period of the air source heat pump; P TS.i: in The average electric power consumed by the solid electric heat storage device during the ith time period; t TS.Xi : the ith time period during which the solid electric heat storage device works in the heat storage mode; P P&F.i : the ith time The average electric power consumed by the fans and water pumps when the heat storage equipment releases heat in the segment; t TS.Fi: the i-th time period when the solid-state electric heat storage equipment works in heat release mode; E i : the real-time electricity price of the i-th time period, The unit is yuan/kWh. H N The rated value of the heat storage capacity of the heat storage device. Q HP.i: demand air source heat pump heating load of the i-th period; COP N: an air source heat pump the energy efficiency of the conversion factor rating; f i-pre: air-source heat pump performance correction coefficient.
2)以松弛变量为一组基变量，构建一个基可行解即为初始值，生成单纯形表；2) Taking slack variables as a set of basic variables, constructing a basic feasible solution is the initial value, and generating a simplex table;
3)在单纯形表里找到检验数σ j最小列和系数θ最小行对应的变量作为换入变量，然后利用初等行变换，将换入变量对应的列中的其他元素变为0； 3) Find the variable corresponding to the smallest column of test number σ j and the smallest row of coefficient θ in the simplex table as the input variable, and then use elementary row transformation to change the other elements in the column corresponding to the input variable to 0;
4)重复步骤3)进行优化迭代，直到检验数全非正，获得最优解。4) Repeat step 3) to perform optimization iterations until the test numbers are all non-positive, and the optimal solution is obtained.
图3中，σ j为检验数，a ij代表变量P HP·i、P TS·i的系数，b i代表约束等式右侧常数。 In Figure 3, σ j is the test number, a ij represents the coefficients of the variables P HP·i and P TS·i , and b i represents the constant on the right side of the constraint equation.
线性规划问题是研究线性约束条件下线性目标函数的极值问题，用单纯形方法来解决线性规划问题逻辑清晰，计算简便。The linear programming problem is the study of the extreme value problem of the linear objective function under the linear constraint. The simplex method is used to solve the linear programming problem with clear logic and simple calculation.
步骤S4中，根据求解的每个时间段热泵和蓄热设备的出力情况，来判断设备的预制工作模式，判断条件如表3所示。In step S4, the prefabricated working mode of the equipment is judged according to the output of the heat pump and the heat storage equipment in each time period solved, and the judgment conditions are shown in Table 3.
表3设备工作模式的判断条件Table 3 Judgment conditions of equipment working mode
判断条件Analyzing conditions 工作模式ModeWorking mode Mode
P HP.i＝0且P TS.i>0且t TS.X.i>0 P HP.i = 0 and P TS.i> 0 and t TS.Xi> 0 ⅠⅠ
P HP.i>0且P TS.i＝0且t TS.F.i＝0 P HP.i> 0 and P TS.i = 0 and t TS.Fi = 0 ⅡⅡ
P HP.i＝0且P TS.i＝0且t TS.F.i>0 P HP.i = 0 and P TS.i = 0 and t TS.Fi> 0 ⅢⅢ
P HP.i>0且P TS.i＝0且t TS.F.i>0 P HP.i> 0 and P TS.i = 0 and t TS.Fi> 0 ⅣⅣ
上述步骤中确定了每个时间段设备的运行状态，在相应的时间段i内，控制空气源热泵和蓄热设备按照预定工作模式工作，如图5举例所示，按此模式工作可使一天中用电成本最小。In the above steps, the operating status of the equipment in each time period is determined. In the corresponding time period i, the air source heat pump and the heat storage equipment are controlled to work in a predetermined working mode, as shown in Figure 5 for example. The cost of electricity in China is the smallest.
步骤S4为在线调整过程，实时获取空气源热泵和蓄热设备供热系统的参数数据，根据实时数据在线微调空气源热泵和蓄热设备的工作状态。前述步骤S1～S3为优化控制方法的优化预制部分，是根据预测数据得到的优化结果，与实际运行情况可能存在偏差，因此增加了该步骤，更具实用性和工程意义。Step S4 is an online adjustment process, obtaining real-time parameter data of the air source heat pump and the heat storage equipment heating system, and fine-tune the working status of the air source heat pump and the heat storage equipment on-line according to the real-time data. The aforementioned steps S1 to S3 are the optimized prefabricated parts of the optimized control method, which are optimized results obtained based on predicted data, and may deviate from actual operating conditions. Therefore, this step is added, which is more practical and engineering significance.
可选的，实时获取的供热系统的参数数据包括蓄热设备的实际蓄热量和建筑实时热负荷数据。Optionally, the parameter data of the heating system obtained in real time includes the actual heat storage capacity of the heat storage device and the real-time heat load data of the building.
可选的，在线调整方法，可以为：在本时段开始时，先判断当前时段所执行的预制工作模式，根据工作模式不同，执行对应工作模式下的在线调整策略。Optionally, the online adjustment method may be: at the beginning of the current period, first determine the prefabricated working mode executed in the current period, and execute the online adjustment strategy in the corresponding working mode according to the different working modes.
作为进一步地改进，为提高系统控制的自适应性，不同的预制工作模式并采用不同的在线调整策略，具体如下：As a further improvement, in order to improve the adaptiveness of system control, different prefabricated working modes and different online adjustment strategies are adopted, as follows:
1)对于当前时间段，当设备处于预制工作模式Ⅰ时，热泵不工作，电蓄热设备开始蓄热，当到达额定蓄热量时，蓄热停止，直到下一时间段执行下一时间段对应的预制工作模式；1) For the current time period, when the equipment is in the prefabricated working mode I, the heat pump does not work, and the electric heat storage device starts to store heat. When the rated heat storage capacity is reached, the heat storage stops until the next time period is executed. Prefabricated working mode;
2)对于当前时间段，当设备处于预制工作模式Ⅱ时，空气源热泵制热，蓄热设备不工作；该模式下的在线调整策略，包括如下步骤：2) For the current time period, when the equipment is in prefabricated working mode II, the air source heat pump produces heat, and the heat storage equipment does not work; the online adjustment strategy in this mode includes the following steps:
S51、开启空气源热泵制热，关闭蓄热设备；S51. Turn on the air source heat pump for heating, and turn off the heat storage equipment;
S52、获取热泵的实际出水温度，计算出水温度实际值与设定值的差值，采用PID调节控制空气源热泵的出水温度维持在设定值；S52. Obtain the actual outlet water temperature of the heat pump, calculate the difference between the actual value of the water temperature and the set value, and use PID adjustment to control the outlet water temperature of the air source heat pump to maintain the set value;
S53、当建筑的实时热负荷Q real与所有热泵的额定热负荷Q N的比值大于设定的第一设定比值时，开启固体蓄热设备补充放热； S53. When the ratio of the real-time heat load Q real of the building to the rated heat load Q N of all heat pumps is greater than the first set ratio set, turn on the solid heat storage device to supplement heat release;
当实时热负荷Q real与所有热泵的额定热负荷Q N的比值小于设定的第二设定比值时，关闭固体蓄热设备停止放热；其中第一设定比值大于第二设定比值。 When the ratio of the real -time heat load Q real to the rated heat load Q N of all heat pumps is less than the second set ratio set, the solid heat storage device is turned off to stop heat release; wherein the first set ratio is greater than the second set ratio.
建筑的实时热负荷Q real可以从安装在循环水管路上的热量表采集。 The real-time heat load Q real of the building can be collected from the heat meter installed on the circulating water pipeline.
本实施例中，第一设定比值在热泵已接近出力上限的范围内，可以设定第一设定比值为95％左右，为了保证供热质量，需要增加供热设备，此时开启固体蓄 热设备补充放热。可以设定第二设定比值80％左右，热泵自身出力即可满足供热需求。In this embodiment, the first set ratio is within the range where the heat pump is close to the upper limit of output, and the first set ratio can be set to about 95%. In order to ensure the quality of heating, heating equipment needs to be added, and solid storage is turned on at this time. The heat device supplements the exotherm. The second setting ratio can be set to about 80%, and the heat pump can meet the heating demand by its own output.
3)对于当前时间段，当设备处于预制工作模式Ⅲ时，固体蓄热设备放热，热泵不工作，该模式下的在线调整策略，可以为：根据实时热负荷的Q real大小，判断此时蓄热设备实际的蓄热量是否满足需求，若满足，则蓄热设备单独供热；否则开启热泵同时供热。 3) For the current time period, when the equipment is in prefabricated working mode III, the solid heat storage equipment releases heat and the heat pump does not work. The online adjustment strategy in this mode can be: according to the real-time heat load Q real size, judge this time Whether the actual heat storage capacity of the heat storage device meets the demand, if it is satisfied, the heat storage device supplies heat separately; otherwise, the heat pump is turned on and the heat is supplied at the same time.
可选的，可以采用PID算法控制蓄热设备的供热能量：根据出水温度的实际值与设定值之间的差值采用PID调节控制蓄热设备内部设置的风机的转速，继而达到控制固体蓄热设备出水温度的目的。蓄热设备内部通过输出热风输出热量，通过换热器将热风的热量传输给水。Optionally, PID algorithm can be used to control the heating energy of the heat storage device: according to the difference between the actual value of the outlet water temperature and the set value, PID adjustment is used to control the speed of the fan set inside the heat storage device, and then achieve the control of solids. The purpose of the outlet water temperature of the thermal storage device. The heat storage device outputs heat by outputting hot air, and transfers the heat of the hot air to water through a heat exchanger.
可选的，判断蓄热设备实际的蓄热量是否满足供热需求的判断条件，可以如下：Optionally, the judgment condition for judging whether the actual heat storage capacity of the heat storage device meets the heating demand may be as follows:
K rel·Q real·τ≤H store     (7) K rel ·Q real ·τ≤H store (7)
式中：Q real：热量表读取到的实时热负荷，单位为kW；K rel：可靠系数；τ：本时段的剩余时间，单位为h；H store：固体蓄热设备的实际蓄热量，单位为kWh。 Where: Q real : the real-time heat load read by the heat meter, in kW; K rel : reliability coefficient; τ: the remaining time in this period, in h; H store : the actual heat storage capacity of the solid heat storage device, The unit is kWh.
如果上式满足，说明蓄热设备的剩余蓄热量能够满足此时段建筑所需热量，那么保持工作模式不变。如果上述判断条件不满足，则增加热泵补充，转换为工作模式调整为Ⅳ。If the above formula is satisfied, it indicates that the remaining heat storage capacity of the heat storage device can meet the heat required by the building during this period, and the working mode is kept unchanged. If the above judgment conditions are not met, add heat pump supplement, switch to working mode and adjust to IV.
4)对于当前时间段，当设备处于预制工作模式Ⅳ时，该模式下的在线调整策略，可以为：先判断式(7)的条件是否满足，若满足，则蓄热设备单独供热，转换为预制工作模式Ⅲ；否则，当固体蓄热设备实际蓄热量Q store小于最小限值Q min时，关闭蓄热设备停止供热，开启热泵供热。 4) For the current time period, when the equipment is in the prefabricated working mode IV, the online adjustment strategy in this mode can be: first determine whether the condition of formula (7) is satisfied, if it is satisfied, the heat storage device will supply heat alone, and switch It is the prefabricated working mode III; otherwise, when the actual heat storage Q store of the solid heat storage device is less than the minimum limit Q min , the heat storage device is turned off to stop heating, and the heat pump is turned on.
为说明本实施例控制方法的效果，进行了仿真实验，如图5所示，比较采用本实施例方法优化后和优化前的供热系统的用电负荷变化，优化后的系统可跟踪电网价格的高低，使蓄热设备在电网价格高峰时放热，并结合建筑热量的需求，使设备放热充分，提高设备利用率，降低了用户的运行成本，同时使得电网负荷平准化，起到削峰填谷的效果。In order to illustrate the effect of the control method of this embodiment, a simulation experiment is carried out. As shown in Figure 5, the power load changes of the heating system after and before the optimization using the method of this embodiment are compared. The optimized system can track the grid price The height of the heat storage equipment makes the heat storage equipment release heat at the peak of the grid price, and combined with the heat demand of the building, the equipment can fully release the heat, improve the utilization rate of the equipment, and reduce the user's operating cost. At the same time, the grid load is leveled. The effect of peak clipping and valley filling.
实施例2Example 2
本实施例提供一种热泵与蓄热设备自适应优化控制装置，包括：This embodiment provides an adaptive optimization control device for heat pump and heat storage equipment, including:
预测单元：被配置为用于获取第二天电网的日前电价，根据历史数据预测第二天各时段的建筑热负荷值；Prediction unit: It is configured to obtain the day-ahead electricity price of the grid on the next day, and predict the building heat load value in each period of the next day based on historical data;
优化控制单元：被配置为用于以用电成本最小为目标构建目标函数，根据预测获得的数据，采用线性规划方法求解每个时间段热泵和蓄热设备的出力情况；Optimization control unit: It is configured to construct an objective function with the goal of minimizing electricity cost, and use linear programming to solve the output of heat pumps and heat storage equipment in each time period based on the data obtained by prediction;
工作模式配置单元：被配置为用于根据求解得到的每个时间段热泵和蓄热设备的出力情况，确定每个时间段的热泵和蓄热设备协同工作的预制工作模式，并发送至热泵和蓄热设备；Working mode configuration unit: It is configured to determine the prefabricated working mode of the heat pump and heat storage equipment working together in each time period according to the output of the heat pump and heat storage equipment in each time period obtained by the solution, and send it to the heat pump and Heat storage equipment;
在线修正单元：被配置为用于实时获取空气源热泵和电蓄热设备供热的系统的参数数据，根据实时的参数数据在线修正调整空气源热泵和蓄热设备的工作状态。Online correction unit: It is configured to obtain real-time parameter data of the air source heat pump and electric heat storage equipment heating system, and adjust the working status of the air source heat pump and heat storage equipment online according to the real-time parameter data.
实施例3Example 3
本实施例提供一种计算机可读存储介质，用于存储计算机指令，所述计算机指令被处理器执行时，完成实施例1的方法所述的步骤。This embodiment provides a computer-readable storage medium for storing computer instructions. When the computer instructions are executed by a processor, the steps described in the method of Embodiment 1 are completed.
应理解，在本公开中，该处理器可以是中央处理单元CPU，该处理器还可以是其他通用处理器、数字信号处理器DSP、专用集成电路ASIC，现成可编程门阵列FPGA或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。It should be understood that in the present disclosure, the processor may be a central processing unit CPU, the processor may also be other general-purpose processors, digital signal processors DSP, application-specific integrated circuits ASIC, ready-made programmable gate array FPGA or other programmable Logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor or the like.
在实现过程中，上述方法的各步骤可以通过处理器中的硬件的集成逻辑电路或者软件形式的指令完成。结合本公开所公开的方法的步骤可以直接体现为硬件处理器执行完成，或者用处理器中的硬件及软件模块组合执行完成。软件模块可以位于随机存储器、闪存、只读存储器、可编程只读存储器或者电可擦写可编程存储器、寄存器等本领域成熟的存储介质中。该存储介质位于存储器，处理器读取存储器中的信息，结合其硬件完成上述方法的步骤。为避免重复，这里不再详细描述。本领域普通技术人员可以意识到，结合本文中所公开的实施例描述的各示例的单元即算法步骤，能够以电子硬件或者计算机软件和电子硬件的结合来实现。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本公开的范围。In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or instructions in the form of software. The steps of the method disclosed in combination with the present disclosure may be directly embodied as being executed and completed by a hardware processor, or executed and completed by a combination of hardware and software modules in the processor. The software module may be located in a mature storage medium in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, or electrically erasable programmable memory, registers. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware. To avoid repetition, it will not be described in detail here. A person of ordinary skill in the art may be aware that, in combination with the examples described in the embodiments disclosed in this document, the units, that is, the algorithm steps, can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether these functions are executed by hardware or software depends on the specific application and design constraint conditions of the technical solution. Professionals and technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered as going beyond the scope of the present disclosure.
所属领域的技术人员可以清楚地了解到，为描述的方便和简洁，上述描述的系统、装置和单元的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。Those skilled in the art can clearly understand that, for the convenience and conciseness of description, the specific working process of the system, device and unit described above can refer to the corresponding process in the foregoing method embodiment, which will not be repeated here.
在本公开所提供的几个实施例中，应该理解到，所揭露的系统、装置和方法，可以通过其他的方式实现。例如，以上所描述的装置实施例仅仅是示意性的，例如，所述单元的划分，仅仅为一种逻辑功能的划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另外一点，所显示或讨论的相互之间的耦合或者直接耦合或者通信连接可以是通过一些接口，装置或单元的间接耦合或通信连接，可以是电性、机械或其它的形式。In the several embodiments provided in the present disclosure, it should be understood that the disclosed system, device, and method may be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of the units is only a division of logical functions. In actual implementation, there may be other divisions, for example, multiple units or components can be combined. Or it can be integrated into another system, or some features can be ignored or not implemented. In addition, the displayed or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some interfaces, devices or units, and may be in electrical, mechanical or other forms.
所述功能如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读存储介质中。基于这样的理解，本公开的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机、服务器或者网络设备等)执行本公开各个实施例所述方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、磁碟或者光盘等各种可以存储程序代码的介质。If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present disclosure essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including Several instructions are used to make a computer device (which may be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in the various embodiments of the present disclosure. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disks or optical disks and other media that can store program codes. .
以上所述仅为本发明的优选实施例而已，并不用于限制本发明，对于本领域的技术人员来说，本发明可以有各种更改和变化。凡在本发明的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。The above are only preferred embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various modifications and changes. 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. 一种热泵与蓄热设备自适应优化控制方法，其特征是，包括如下步骤：An adaptive optimization control method for heat pumps and heat storage equipment, which is characterized in that it includes the following steps:
    获取第二天电网的日前电价和天气预报数据，并根据历史数据预测第二天各时段的建筑热负荷值；Obtain the day-ahead electricity price and weather forecast data of the grid on the next day, and predict the building heat load value of each period of the next day based on historical data;
    以用电成本最小为目标构建目标函数，根据预测获得的热负荷值，采用线性规划方法求解每个时间段热泵和蓄热设备的出力情况；Construct an objective function with the goal of minimizing electricity cost, and use linear programming to solve the output of heat pumps and heat storage equipment in each time period based on the predicted heat load value;
    根据求解得到的每个时间段热泵和蓄热设备的出力情况，确定每个时间段的热泵和蓄热设备协同工作的预制工作模式，并发送至热泵和蓄热设备；According to the output of the heat pump and heat storage equipment for each time period obtained by the solution, determine the prefabricated working mode of the heat pump and heat storage equipment for each time period and send it to the heat pump and heat storage equipment;
    实时获取热泵和电蓄热设备供热的系统的参数数据，根据实时的参数数据在线修正调整空气源热泵和蓄热设备的工作状态。Real-time acquisition of the parameter data of the heat supply system of the heat pump and the electric heat storage equipment, and online correction and adjustment of the working status of the air source heat pump and the heat storage equipment according to the real-time parameter data.
  2. 如权利要求1所述的一种热泵与蓄热设备自适应优化控制方法，其特征是：以第二天的用电成本最小为目标构建的目标函数为：An adaptive optimization control method for heat pumps and thermal storage equipment according to claim 1, wherein the objective function constructed with the goal of minimizing the electricity cost of the next day is:
    Figure PCTCN2020132226-appb-100001
    Figure PCTCN2020132226-appb-100001
    式中：P HP.i为在第i个时间段内空气源热泵制热消耗的平均电功率；t HP.i为空气源热泵工作的第i个时间段；P TS.i为在第i个时间段内固体电蓄热设备蓄热消耗的平均电功率；t TS.X.i为固体电蓄热设备以蓄热模式工作的第i个时间段；P P&F.i为在第i个时间段内蓄热设备放热时风机和水泵消耗的平均电功率；t TS.F.i为固体电蓄热设备以放热模式工作的第i个时间段；E i为第i个时间段的日前电价。 Where: P HP.i as the i-th period average electric power air source heat pump system consumption; i-th period T HP.i air source heat pump is working; P TS.i for the i-th The average electric power consumed by the solid electric heat storage device during the heat storage period; t TS.Xi is the ith time period when the solid electric heat storage device works in the heat storage mode; P P&F.i is the storage time during the ith time period The average electric power consumed by the fans and water pumps when the thermal equipment releases heat; t TS.Fi is the i-th time period when the solid electric heat storage equipment works in heat release mode; E i is the day-a-day electricity price for the i-th time period.
  3. 如权利要求1所述的一种热泵与蓄热设备自适应优化控制方法，其特征是：求解目标函数的约束条件包括热泵的工作约束和蓄热设备的工作约束；热泵的工作约束包括：空气源热泵的电热转换模型；热泵的制热消耗的平均电功率小于热泵的额定功率；在设定的工作时长内，转换的能量按天气情况通过修正系数修正。The heat pump and heat storage equipment adaptive optimization control method according to claim 1, characterized in that: the constraint conditions for solving the objective function include the work constraints of the heat pump and the heat storage equipment; the work constraints of the heat pump include: air The electric-heat conversion model of the source heat pump; the average electric power consumed by the heat pump for heating is less than the rated power of the heat pump; within the set working time, the converted energy is corrected by the correction coefficient according to the weather conditions.
  4. 如权利要求3所述的一种热泵与蓄热设备自适应优化控制方法，其特征是：蓄热设备的电热转换模型；蓄热设备的蓄热量不大于蓄热设备的蓄热额定值。An adaptive optimization control method for a heat pump and a heat storage device according to claim 3, characterized in that: the electric heat conversion model of the heat storage device; and the heat storage amount of the heat storage device is not greater than the heat storage rating of the heat storage device.
  5. 如权利要求1所述的一种热泵与蓄热设备自适应优化控制方法，其特征是：根据历史数据，基于LSTM模型预测第二天各时段的建筑热负荷值；An adaptive optimization control method for heat pumps and heat storage equipment according to claim 1, characterized in that: according to historical data, based on the LSTM model to predict the building heat load value at each time period of the next day;
    或者or
    采用线性规划方法求解每个时间段热泵和蓄热设备的出力情况的方法，采用线性规划方法中的单纯形法。The linear programming method is used to solve the output situation of the heat pump and heat storage equipment in each time period, and the simplex method in the linear programming method is adopted.
  6. 如权利要求1所述的一种热泵与蓄热设备自适应优化控制方法，其特征是：预制工作模式包括预制工作模式Ⅰ：热泵制热，蓄热设备不工作；预制工作模式Ⅱ：固体蓄热设备蓄热，热泵不工作；预制工作模式Ⅲ：固体蓄热设备放热，热泵不工作；预制工作模式Ⅳ：蓄热设备先放热，而后热泵制热。As claimed in claim 1, a heat pump and heat storage equipment adaptive optimization control method, characterized in that: the prefabricated work mode includes prefabricated work mode I: heat pump heating, heat storage equipment does not work; prefabricated work mode II: solid storage The heat equipment stores heat, and the heat pump does not work; prefabricated work mode III: the solid heat storage equipment releases heat, and the heat pump does not work; prefabricated work mode IV: the heat storage equipment first releases heat, and then the heat pump generates heat.
  7. 如权利要求6所述的一种热泵与蓄热设备自适应优化控制方法，其特征是：当按照预制工作模式Ⅰ工作，在线修正方法为：实时获取蓄热设备的实际蓄热量，当到达额定蓄热量时，蓄热停止，热泵不工作，直到下一时间段执行下一时间段对应的预制工作模式；An adaptive optimization control method for heat pumps and heat storage equipment according to claim 6, characterized in that: when working according to the prefabricated working mode I, the online correction method is: obtain the actual heat storage capacity of the heat storage equipment in real time, and when it reaches the rated When storing heat, the heat storage stops and the heat pump does not work until the next time period executes the corresponding prefabricated working mode for the next time period;
    或者or
    当按照预制工作模式Ⅱ工作，热泵制热，蓄热设备不工作，在线修正方法为：When working in the prefabricated working mode II, the heat pump is heating, and the heat storage device does not work, the online correction method is as follows:
    获取热泵的实际出水温度，计算出水温度实际值与设定值的差值，采用PID调节控制空气源热泵的出水温度维持在设定值；Obtain the actual outlet water temperature of the heat pump, calculate the difference between the actual value of the water temperature and the set value, and use PID adjustment to control the outlet water temperature of the air source heat pump to maintain the set value;
    实时获取实时供热量，当建筑的实时热负荷与所有热泵的额定热负荷的比值大于设定的第一设定比值时，开启固体蓄热设备补充放热；Acquire real-time heat supply in real time. When the ratio of the real-time heat load of the building to the rated heat load of all heat pumps is greater than the first set ratio set, turn on the solid heat storage device to supplement heat release;
    当实时热负荷与所有热泵的额定热负荷的比值小于设定的第二设定比值时，关闭固体蓄热设备停止放热；其中第一设定比值大于第二设定比值；When the ratio of the real-time thermal load to the rated thermal load of all heat pumps is less than the second set ratio, turn off the solid heat storage device to stop heat release; wherein the first set ratio is greater than the second set ratio;
    或者or
    采用预制工作模式Ⅲ时，固体蓄热设备放热，热泵不工作，该模式下的在线修正方法为：实时获取实时供热量，判断蓄热设备实际的蓄热量是否满足供热需求，若满足，则蓄热设备单独供热；否则开启热泵同时供热；When the prefabricated working mode III is adopted, the solid heat storage equipment releases heat and the heat pump does not work. The online correction method in this mode is: obtain real-time heat supply in real time, and judge whether the actual heat storage equipment meets the heating demand. , The heat storage device supplies heat separately; otherwise, the heat pump is turned on to supply heat at the same time;
    或者or
    采用预制工作模式Ⅳ时，该模式下的在线修正策略为：实时获取实时供热量，判断蓄热设备实际的蓄热量是否满足供热需求，若满足，则蓄热设备单独供热，转换为预制工作模式Ⅲ；否则，当固体蓄热设备实际蓄热量小于最小限值时，关闭蓄热设备停止供热，开启热泵供热。When using prefabricated working mode IV, the online correction strategy in this mode is to obtain real-time heat supply in real time, and judge whether the actual heat storage capacity of the heat storage device meets the heating demand. Prefabricated working mode III; otherwise, when the actual heat storage capacity of the solid heat storage device is less than the minimum limit, the heat storage device is turned off to stop heat supply, and the heat pump is turned on for heat supply.
  8. 一种热泵与蓄热设备自适应优化控制装置，其特征是，包括：An adaptive optimization control device for heat pump and heat storage equipment, which is characterized in that it includes:
    预测单元：被配置为用于获取第二天电网的日前电价和天气预报数据，根据 获取的数据预测第二天各时段的建筑热负荷值；Prediction unit: It is configured to obtain the day-ahead electricity price and weather forecast data of the grid on the next day, and predict the building heat load value in each period of the next day based on the obtained data;
    求解单元：被配置为用于以用电成本最小为目标构建目标函数，根据预测获得的数据，采用线性规划方法求解每个时间段热泵和蓄热设备的出力情况；Solving unit: It is configured to construct an objective function with the goal of minimizing electricity cost, and use linear programming to solve the output of heat pumps and heat storage equipment in each time period according to the data obtained by prediction;
    工作模式配置单元：被配置为用于根据求解得到的每个时间段热泵和蓄热设备的出力情况，确定每个时间段的热泵和蓄热设备协同工作的预制工作模式，并发送至热泵和蓄热设备；Working mode configuration unit: It is configured to determine the prefabricated working mode of the heat pump and heat storage equipment working together in each time period according to the output of the heat pump and heat storage equipment in each time period obtained by the solution, and send it to the heat pump and Heat storage equipment;
    在线修正单元：被配置为用于实时获取空气源热泵和电蓄热设备供热的系统的参数数据，根据实时的参数数据在线修正调整空气源热泵和蓄热设备的工作状态。Online correction unit: It is configured to obtain real-time parameter data of the air source heat pump and electric heat storage equipment heating system, and adjust the working status of the air source heat pump and heat storage equipment online according to the real-time parameter data.
  9. 一种热泵与蓄热设备自适应优化控制系统，包括空气源热泵和固体电蓄热设备，其特征是：还包括通信网关以及控制装置，以及连接在空气源热泵、固体电蓄热设备的传感器和执行器，控制装置分别通过通信网关与传感器、执行器连接，控制装置执行权利要求1-7任一项所述的一种热泵与蓄热设备自适应优化控制方法。An adaptive optimization control system for heat pumps and heat storage equipment, including air source heat pumps and solid electric heat storage equipment, characterized in that it also includes a communication gateway, a control device, and sensors connected to the air source heat pump and solid electric heat storage equipment And the actuator, the control device is respectively connected with the sensor and the actuator through the communication gateway, and the control device executes the heat pump and heat storage equipment adaptive optimization control method according to any one of claims 1-7.
  10. 一种计算机可读存储介质，其特征是，用于存储计算机指令，所述计算机指令被处理器执行时，完成权利要求1-7任一项方法所述的步骤。A computer-readable storage medium, characterized in that it is used to store computer instructions, and when the computer instructions are executed by a processor, the steps described in any one of the methods of claims 1-7 are completed.
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