Control method of frequency conversion air conditioner and photovoltaic air conditioner cooperative control system

Abstract

According to the control method of the variable-frequency air conditioner and the photovoltaic air conditioner cooperative control system, intelligent dispatching of loads of the variable-frequency air conditioner is achieved by coordinating operation of a photovoltaic system, an energy storage battery, the power grid electricity price and the variable-frequency air conditioner; besides, the target set temperature is determined through the first optimization model with the long duration and the second optimization model with the short duration, the variable-frequency air conditioner is controlled according to the target control parameters corresponding to the target set temperature, photovoltaic power generation, power grid electricity price and the like are fully utilized to plan an air conditioner operation strategy, the electricity utilization cost is reduced, and the energy consumption is reduced. And refined and flexible load regulation of the variable-frequency air conditioner is also realized, so that deep cooperative intelligent control of the load of the variable-frequency air conditioner and the household new energy system is realized on the basis of comprehensively considering economy, comfort and system constraints.

Description

Technical Field

[0001] This invention relates to the field of smart home energy management technology, and in particular to a control method for inverter air conditioners and a photovoltaic air conditioning collaborative control system. Background Technology

[0002] With the widespread adoption of distributed photovoltaic (PV) and energy storage systems in homes, the efficient use of clean electricity and reduction of electricity costs have become crucial issues. Air conditioners, as a major household appliance, have significant load adjustment potential. However, existing methods for coordinating air conditioner load with renewable energy sources suffer from the following shortcomings: ① Simple control strategies: Most solutions employ a simple start-stop control mechanism, turning on the air conditioner when PV power is generated and turning it off when there is no sunlight, without considering electricity price signals, battery storage status, and indoor thermal inertia, resulting in insufficient economic optimization; ② Lack of predictive optimization: Existing solutions only perform reactive control based on the current state, failing to utilize PV power generation forecasts and time-of-use pricing information for forward-looking scheduling, making cross-time-period energy transfer difficult; ③ Significant sacrifice of comfort: To respond to grid demands or save on electricity costs, existing solutions often involve directly shutting down the air conditioner or drastically adjusting the set temperature, severely impacting user comfort; ④ Limited control dimensions: Existing solutions typically only control the air conditioner's start-stop or set temperature, failing to fully utilize the multi-variable coordinating adjustment capabilities of inverter air conditioners, such as compressor frequency and fan speed. Therefore, achieving deep coordination between air conditioner load and household renewable energy systems is an urgent problem to be solved. Summary of the Invention

[0003] In view of this, the purpose of the present invention is to provide a control method for a variable frequency air conditioner and a photovoltaic air conditioner coordinated control system to alleviate at least some of the above-mentioned technical problems.

[0004] In a first aspect, embodiments of the present invention provide a control method for a variable frequency air conditioner. The method includes: acquiring detection parameters at the current moment and prediction parameters for a future first time period; wherein the detection parameters include the current power generation of a photovoltaic system, the current state of charge of an energy storage battery, the current indoor temperature, the current outdoor temperature, and the current power consumption of the variable frequency air conditioner; the prediction parameters include the predicted power generation value of the photovoltaic system and the predicted electricity price value of the power grid; determining, based on the detection parameters, prediction parameters, and a first optimization model, a base plan curve for the set temperature and a reference charge-discharge plan curve for the variable frequency air conditioner and the energy storage battery respectively for the future first time period; wherein the first optimization model is the optimization model corresponding to the future first time period; determining the target set temperature at the current moment based on the base plan curve for the set temperature, the prediction parameters, and a second optimization model; wherein the second optimization model is the optimization model corresponding to a future second time period, and the future second time period is shorter than the future first time period; calculating the current temperature difference based on the target set temperature and the current indoor temperature; determining the target control parameters corresponding to the current moment based on the current temperature difference and a preset threshold; and controlling the variable frequency air conditioner to operate according to the target control parameters.

[0005] Preferably, determining the set temperature baseline plan curve and the reference charge-discharge plan curve corresponding to the variable frequency air conditioner and the energy storage battery respectively in the future first time period based on the detection parameters, prediction parameters, and the first optimization model includes: dividing the future first time period into multiple first time periods and obtaining first basic parameters; wherein, the first basic parameters include: the grid-connected electricity price, the power purchased from the grid, and the power sold to the grid for each first time period; and calculating the set temperature baseline plan curve and the reference charge-discharge plan curve based on the detection parameters, prediction parameters, the first basic parameters, and the first optimization model.

[0006] Preferably, determining the target set temperature at the current moment based on the set temperature baseline planning curve, prediction parameters, and the second optimization model includes: dividing the future second duration into multiple second time periods and determining the set temperature baseline value of the set temperature baseline planning curve for each second time period; obtaining second baseline parameters; wherein the second baseline parameters include the electricity price prediction value and the power purchased from the grid for each second time period; calculating the set temperature sequence at the current moment based on the set temperature baseline value, second baseline parameters, prediction parameters, and the second optimization model for each second time period; and taking the first value in the set temperature sequence as the target set temperature at the current moment.

[0007] Preferably, both the first optimization model and the second optimization model are provided with constraints; wherein, the constraints include: system power balance constraints, battery energy storage constraints, indoor temperature dynamic change constraints, and indoor temperature comfort constraints.

[0008] Preferably, the preset threshold includes a first threshold, and the target control parameter includes a first control parameter; determining the target control parameter corresponding to the current moment based on the current temperature difference and the preset threshold includes: if the current temperature difference is not less than the first threshold, determining the target control parameter as the first control parameter.

[0009] Preferably, the preset threshold includes a second threshold, and the target control parameter includes a second control parameter; determining the target control parameter corresponding to the current moment based on the current temperature difference and the preset threshold includes: if the current temperature difference is less than the first threshold and not less than the second threshold, determining the target control parameter as the second control parameter.

[0010] Preferably, the target control parameter includes a third control parameter; determining the target control parameter corresponding to the current time based on the current temperature difference and a preset threshold further includes: if the current temperature difference is less than a second threshold, determining the target control parameter as the third control parameter.

[0011] Preferably, the target control parameters include target frequency, target speed, and target angle, and the variable frequency air conditioner includes a compressor, an indoor fan, and an air guide plate; controlling the variable frequency air conditioner to operate according to the target control parameters includes: controlling the compressor to operate at the target frequency, controlling the indoor fan to operate at the target speed, and controlling the air guide plate to operate at the target angle.

[0012] Secondly, embodiments of the present invention also provide a photovoltaic air conditioning collaborative control system, including: a variable frequency air conditioner, an energy storage battery, a photovoltaic system, and a controller; wherein the controller is used to control the variable frequency air conditioner using the method described in the first aspect.

[0013] Thirdly, embodiments of the present invention also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the method described in the first aspect.

[0014] The embodiments of the present invention bring the following beneficial effects: This invention provides a control method for a variable frequency air conditioner and a photovoltaic air conditioning collaborative control system. The method acquires the detection parameters at the current moment and the predicted parameters for a first future time period. Based on the detection parameters, predicted parameters, and a first optimization model, it determines the set temperature baseline plan curve and the reference charge / discharge plan curve for the variable frequency air conditioner and the energy storage battery, respectively, for the first future time period. Based on the set temperature baseline plan curve, predicted parameters, and a second optimization model, it determines the target set temperature at the current moment. It calculates the current temperature difference based on the target set temperature and the current indoor temperature, and determines the target control parameters corresponding to the current moment based on the current temperature difference and a preset threshold. Finally, it controls the variable frequency air conditioner to operate according to the target control parameters. The aforementioned control method achieves intelligent scheduling of the variable frequency air conditioner load by coordinating the operation of the photovoltaic system, energy storage battery, grid electricity price, and variable frequency air conditioner. In addition, the target set temperature is determined through a first optimization model with a longer duration and a second optimization model with a shorter duration, and the variable frequency air conditioner is controlled according to the target control parameters corresponding to the target set temperature. This not only makes full use of photovoltaic power generation and grid electricity price to plan air conditioning operation strategies and reduce electricity costs, but also realizes refined and flexible load adjustment of the variable frequency air conditioner. Thus, based on a comprehensive consideration of economy, comfort, and system constraints, it achieves deep collaborative intelligent control between the variable frequency air conditioner load and the household new energy system.

[0015] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0016] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0017] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 A flowchart of a control method for a variable frequency air conditioner provided in an embodiment of the present invention; Figure 2 A flowchart of another control method for a variable frequency air conditioner provided in an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] To facilitate understanding of this embodiment, the embodiments of the present invention will be described in detail below.

[0021] This invention provides a control method for a variable frequency air conditioner, such as... Figure 1 As shown, the method includes the following steps: Step S102: Obtain the detection parameters at the current moment and the prediction parameters for the first time period in the future.

[0022] Specifically, the controller collects the detection parameters of the photovoltaic-air conditioning collaborative control system in real time or periodically. For ease of explanation, we will use the current time t as an example. The detection parameters at the current time include the current power generation of the photovoltaic system. P pv ( t Current state of charge of the energy storage battery SOC ( t Current indoor temperature T in ( t Current outdoor temperature T out ( t ) and the current power consumption of the inverter air conditioner P ac ( t In addition, in some scenarios, the controller also obtains the time-of-use electricity price from the power grid in real time, such as the current electricity price being... C grid ( t It should be noted that the preferred duration for the first timeframe is the next 24 hours, but this can be adjusted based on actual circumstances.

[0023] In addition, after acquiring the detection parameters at the current moment, the controller also acquires the prediction parameters for the first time period in the future; among these, the prediction parameters include the predicted power generation value of the photovoltaic system. P pv-pred ( t ) and the electricity price forecast of the power grid C grid-pred ( tSpecifically, every day at midnight (e.g., 00:00), the controller first automatically acquires local weather forecast data for the first time period (e.g., the next 24 hours). This weather forecast data includes, but is not limited to, irradiance and ambient temperature. Then, based on the photovoltaic power generation prediction model, it generates a predicted power generation value for the photovoltaic system for the first time period (e.g., the next 24 hours) with a time resolution of 15 minutes. P pv-pred ( t Here, the photovoltaic power generation prediction model is used to predict the output power of the photovoltaic system (i.e., the predicted power generation value) based on weather forecast data (such as irradiance, temperature, etc.) for the next 24 hours. Additionally, the controller also obtains the accurate time-of-use electricity price curve (i.e., the predicted electricity price value) for the first time period (e.g., the next 24 hours) from the grid company's data interface. C grid-pred ( t ).

[0024] Step S104: Based on the detection parameters, prediction parameters, and the first optimization model, determine the set temperature baseline plan curve and the baseline charge-discharge plan curve corresponding to the variable frequency air conditioner and the energy storage battery respectively in the first time period in the future.

[0025] The first optimization model is the optimization model corresponding to the first future time period. If the first future time period is the next 24 hours, the first optimization model can also be called the day-ahead scheduling optimization model (i.e., the optimization model covering the next 24 hours), which is used for day-ahead optimization scheduling calculations. Therefore, by implementing day-ahead optimization scheduling through the first optimization model, the photovoltaic power generation and low-priced electricity during off-peak hours are fully utilized, and air conditioning operation strategies are planned in advance, reducing electricity costs from the source.

[0026] Step S106: Determine the target set temperature at the current moment based on the set temperature baseline planning curve, prediction parameters, and the second optimization model.

[0027] The second optimization model is the optimization model corresponding to the second future duration, and the second future duration is shorter than the first future duration; for example, the first future duration is 24 hours in the future, and the second future duration is 2 hours in the future. Thus, the optimization of the shorter prediction time domain is achieved through the second optimization model, which can quickly respond to fluctuations in actual operation such as photovoltaic power generation and temperature, correct the day-ahead plan, and improve the robustness and economic feasibility of the photovoltaic air conditioning collaborative control system.

[0028] Step S108: Calculate the current temperature difference based on the target set temperature and the current indoor temperature, and determine the target control parameters corresponding to the current moment based on the current temperature difference and the preset threshold.

[0029] Step S110: Control the inverter air conditioner to operate according to the target control parameters.

[0030] The control method for variable frequency air conditioners provided in this invention achieves intelligent scheduling of the variable frequency air conditioner load by coordinating the operation of the photovoltaic system, energy storage battery, grid electricity price, and variable frequency air conditioner. Furthermore, by determining the target set temperature through a first optimization model with a longer duration and a second optimization model with a shorter duration, and controlling the variable frequency air conditioner according to the target control parameters corresponding to the target set temperature, this method not only fully utilizes photovoltaic power generation and grid electricity price to plan air conditioning operation strategies and reduce electricity costs, but also achieves refined and flexible load adjustment of the variable frequency air conditioner. Thus, based on a comprehensive consideration of economy, comfort, and system constraints, it realizes deep collaborative intelligent control between the variable frequency air conditioner load and the household renewable energy system.

[0031] In one embodiment, determining the set temperature baseline plan curve and the reference charge-discharge plan curve corresponding to the variable frequency air conditioner and the energy storage battery respectively in the future first time period based on the detection parameters, prediction parameters, and the first optimization model includes: dividing the future first time period into multiple first time periods and obtaining first basic parameters; wherein, the first basic parameters include: the grid-connected electricity price, the power purchased from the grid, and the power sold to the grid for each first time period; and calculating the set temperature baseline plan curve and the reference charge-discharge plan curve based on the detection parameters, prediction parameters, the first basic parameters, and the first optimization model.

[0032] Specifically, the first optimization model includes a first optimization objective function, which is a function that minimizes the total household electricity cost for a future first time period. For example, if the future first time period, such as the next 24 hours, is divided into multiple first time periods, and each first time period is 15 minutes, then the next 24 hours can be divided into 96 first time periods of 15 minutes each. Each first time period also defines two main decision variables: the set temperature T of the inverter air conditioner. set (Continuous variable) and the charging and discharging power of the energy storage battery P bat Here, charging and discharging power P bat It is a continuous variable, with positive values ​​indicating that the energy storage battery is discharging and negative values ​​indicating that the energy storage battery is charging.

[0033] The expression for the first optimization objective function is now as follows: (1) in, C grid-pred ( k ) indicates the first k The electricity price forecast for the power grid in the first time period. P grid-buy ( k ) indicates the first kThe first time period's power purchased from the grid, in kW; C feed-in ( k ) indicates the first k The on-grid electricity price for the first time period is expressed in yuan / kWh; P grid-sell ( k ) indicates the first k The power sold to the grid in the first time period, in kW; k The value of is 1, 2, 3, ..., 96. Therefore, the first optimization objective function means that in any first time period, the total electricity cost is equal to the cost of purchasing electricity from the grid minus the revenue from selling electricity to the grid, so as to minimize the total electricity cost in any first time period.

[0034] It should be noted that the above-mentioned on-grid electricity price C feed-in ( k This refers to the price at which electricity is sold to the grid. This price is usually publicly available policy or contractual information, such as a fixed subsidy price stipulated by the state, and is written in local electricity policy documents. Therefore, it can be preset using historical publicly available data from the grid. Furthermore, regarding the purchased power capacity... P grid-buy ( k )and P grid-sell ( k The electricity sold is used as a decision variable in the first objective function, and combined with subsequent constraints, the optimization solution yields a set of optimized values ​​for the decision variables (electricity purchased and electricity sold), as well as the corresponding set temperature. T set The optimized values ​​(i.e., the target set temperature) and the optimal charge and discharge power of the energy storage battery.

[0035] In addition, the first optimization objective function also includes constraints; these constraints include: system power balance constraints, battery energy storage constraints, indoor temperature dynamic change constraints, and indoor temperature comfort constraints. For ease of understanding, each constraint is explained in detail here: (1) System power balance constraint condition; that is, in any first time period k, the total power generation on the photovoltaic air conditioning collaborative control system must be equal to the total power consumption on the consumer side. Specifically: (2) in, P pv-pred ( k ) indicates the first k The predicted power generation of the photovoltaic system within the first time period. P bat ( k ) indicates the firstk The battery charging and discharging power of the energy storage battery during the first time period, in kW; P load-base ( k ) indicates the first k The household basic load value excluding inverter air conditioners during the first time period, in kW; P ac ( k ) indicates the first k The predicted power consumption value of the inverter air conditioner in the first time period.

[0036] (2) Battery energy storage constraints; where, in any given... k The state of charge of the energy storage battery during the first time period. SOC ( k It needs to be kept within a preset range, which is preferably 20% to 95%.

[0037] In addition, the state of charge is also analyzed using the following dynamic model. SOC ( k The state of charge is updated every 15 minutes. The expression for the dynamic model is as follows: (3) in, SOC ( k +1) indicates the first k +1 State of charge of the energy storage battery during the first time period. SOC ( k ) indicates the first k The state of charge of the energy storage battery during the first time period. This indicates the charging efficiency of the energy storage battery. This represents the discharge efficiency of the energy storage battery, where Δt represents the interval between two adjacent first time periods, such as 15 minutes. E bat This indicates the total capacity of the energy storage battery.

[0038] It should be noted that for the above formula (3). P bat ( k The absolute value of ) does not exceed the maximum power of the energy storage battery; and if P bat ( k A negative value indicates the charging power of the energy storage battery; if P bat ( k A positive value indicates the battery discharge power of the energy storage battery.

[0039] Therefore, the battery energy storage constraints are achieved through the above formula (3) and the preset range, which realize the state of charge constraint, charge and discharge power constraint and capacity constraint of the energy storage battery.

[0040] (3) Constraints on dynamic changes in indoor temperature; wherein, the room thermodynamic model adopts the first-order equivalent thermal parameter (RC) model to describe the thermal dynamic characteristics of the room, and the specific expression is as follows: (4) in, T in ( k +1) indicates the first k +1 indoor temperature forecast value for the first time period, T in ( k ) indicates the first k The predicted indoor temperature value for the first time period. T out ( k ) indicates the first k The predicted outdoor temperature for the first time period. R This represents the room's equivalent thermal resistance, expressed in °C / kW. C This indicates the equivalent heat capacity of the room, expressed in kWh / ℃. COP This indicates the energy efficiency ratio of the inverter air conditioner under the current operating conditions. P ac ( k ) indicates the first k The predicted power consumption value of the inverter air conditioner in the first time period, that is, the inverter air conditioner in the first time period. k The average power consumption prediction value for the first time period.

[0041] In practical applications, P ac ( k The calculation formula for ) is as follows: (5) in, T set ( k ) indicates the first k The set temperature for the first time period. and These represent the characteristic parameters of a variable frequency air conditioner. In practical applications, power consumption data under different operating conditions is determined based on the product specifications or performance curves of the variable frequency air conditioner, and the characteristic parameters are obtained by fitting the power consumption data. and .

[0042] Therefore, the change in the predicted indoor temperature between two adjacent first time periods should not exceed a preset difference, which is preferably 1℃, i.e., meets the requirement. .

[0043] (4) Indoor temperature comfort constraints; In order to ensure the user's air conditioning comfort, the predicted indoor temperature value in any k-th first time period is... T in ( k It must also meet the following condition: 24℃≤ T in ( k ≤28℃, where 24℃ and 28℃ can be adjusted according to the actual situation.

[0044] In summary, the first optimization model, consisting of the first objective function and constraints, can be understood as a mixed-integer linear programming (MILP) problem. The controller solves this problem using the detection parameters, prediction parameters, and the first basic parameters, and by calling built-in commercial or open-source solvers (such as Gurobi or CPLEX). This allows the calculation of the setpoint base plan curve for the first future time period. T set-base ( k ) and the baseline charge / discharge schedule curve P bat-plant ( k ).

[0045] In one embodiment, determining the target set temperature at the current moment based on the set temperature baseline planning curve, prediction parameters, and the second optimization model includes: dividing the future second duration into multiple second time periods and determining the set temperature baseline value of the set temperature baseline planning curve for each second time period; obtaining second baseline parameters; wherein the second baseline parameters include the electricity price prediction value and the power purchased from the grid for each second time period; calculating the set temperature sequence at the current moment based on the set temperature baseline value, the second baseline parameters, prediction parameters, and the second optimization model for each second time period; and taking the first value in the set temperature sequence as the target set temperature at the current moment.

[0046] Specifically, the second optimization model includes a second optimization objective function, which, starting from the current time, employs Model Predictive Control (MPC) to conduct a new round of optimization within a shortened prediction time domain (i.e., the second future duration). Within this second future duration, the predicted power generation of the photovoltaic system is updated based on a short-term weather forecast model. This second future duration is divided into multiple second time periods, which are then distinguished using [a specific model / method]. t iPreferably, the second time period is the same as the first time period. For example, if the future second duration is 2 hours, and the second time period is also 15 minutes, then the number of second time periods is 8. i =0,1,2,…,7. It should be noted that the short-term forecast model here is consistent with the photovoltaic power generation forecast model mentioned above. The difference is that the short-term forecast model uses the updated weather forecast data at the current time for forecast output, while the photovoltaic power generation forecast model uses the weather forecast data for the first time period in the future (such as the next 24 hours) for forecast output.

[0047] In addition, the second optimization objective function aims to minimize the difference between the set temperature of the inverter air conditioner and the set temperature baseline curve. T set-base ( k The objective function aims to minimize the tracking deviation and real-time electricity purchase cost. The specific expression for the second optimization objective function is as follows: (6) in, Indicates the tracking weight coefficient. T set-base ( t i ) indicates the first t i The set base temperature value for the second time period. C grid ( t i ) indicates the first t i The electricity price forecast for the power grid during the second time period. P grid-buy ( t i ) indicates the first t i The second period is the power purchased from the grid.

[0048] Similarly, the second optimization model also has constraints; these constraints include: system power balance constraints, battery energy storage constraints, indoor temperature dynamic change constraints, and indoor temperature comfort constraints. These constraints are the same as those of the first optimization model, except that the time range is shortened to the second future duration.

[0049] Therefore, by solving the second optimization model, the current time step can be obtained. k Set temperature sequence The first value in the set temperature sequence is taken as the target set temperature at the current moment, i.e., the target set temperature. Similarly, we can also obtain the current time. k Power purchased from the power grid The optimal charging and discharging power of the energy storage battery at the current moment is calculated using the system power balance constraints. P bat-opt and according to P bat-opt Manage energy storage batteries to achieve optimal charge and discharge power. P bat-opt The system power balance constraint is satisfied.

[0050] Furthermore, by using the second optimization model for real-time rolling optimization, it can quickly respond to fluctuations in actual operation such as photovoltaic power generation and temperature, correct the day-ahead plan, and improve the robustness and economic feasibility of the photovoltaic air conditioning collaborative control system.

[0051] In one embodiment, the preset threshold includes a first threshold, and the target control parameter includes a first control parameter; determining the target control parameter corresponding to the current time based on the current temperature difference and the preset threshold includes: if the current temperature difference is not less than the first threshold, determining the target control parameter as the first control parameter.

[0052] Specifically, the above determines the target set temperature at the current moment. T set-opt Then, set the temperature according to the target. T set-opt and current indoor temperature T in ( t The current temperature difference Δ is calculated. T The current temperature difference Δ T for T set-opt and T in ( t The absolute value of the difference between Δ and Δ. T The target control parameters corresponding to the current moment are determined by a preset threshold. The preset threshold includes a first threshold, preferably 2°C, when Δ... T When the temperature is ≥2℃, the target control parameter is determined as the first control parameter.

[0053] Furthermore, the preset threshold includes a second threshold, which is preferably 1℃. If the current temperature difference is less than the first threshold and not less than the second threshold, i.e., when 1℃ ≤ Δ T When the temperature difference is <2℃, the target control parameter is determined as the second control parameter. Also, if the current temperature difference is less than the second threshold, i.e., when Δ... T When the temperature is <1℃, the target control parameter is determined as the third control parameter.

[0054] Therefore, based on the current temperature difference and the preset threshold, the corresponding target control parameters are determined so that the inverter air conditioner can be controlled according to the target control parameters, thereby improving the control accuracy of the inverter air conditioner.

[0055] In one embodiment, the target control parameters include a target frequency, a target speed, and a target angle, and the variable frequency air conditioner includes a compressor, an indoor fan, and an air guide plate; controlling the variable frequency air conditioner to operate according to the target control parameters includes: controlling the compressor to operate at the target frequency, controlling the indoor fan to operate at the target speed, and controlling the air guide plate to operate at the target angle.

[0056] Specifically, when the target control parameter is the first control parameter, the target frequency is the first frequency, the target speed is the first speed, and the target angle is the first angle; where the first frequency is high frequency and the value range is 70Hz~90Hz; the first speed is high speed and the first angle is a fixed angle, and it meets the requirements of centralized air supply, thereby controlling the inverter air conditioner through the first control parameter to achieve rapid cooling.

[0057] Similarly, when the target control parameter is the second control parameter, the target frequency is the second frequency, the target speed is the second speed, and the target angle is the second angle; where the second frequency is the intermediate frequency, with a value range of 40Hz~60Hz; the second speed is the medium speed; and the second angle satisfies the automatic up and down swing function, thereby controlling the inverter air conditioner through the second control parameter to achieve appropriate cooling or heating.

[0058] Furthermore, when the target control parameter is the third control parameter, the target frequency is the third frequency, the target speed is the third speed, and the target angle is the third angle; where the third frequency is a low frequency with a value range of 20Hz~35Hz; the third speed is a low setting or a silent setting; and the third angle satisfies horizontal air supply to avoid direct blowing. Thus, the inverter air conditioner is controlled through the third control parameter to achieve precise temperature maintenance.

[0059] Therefore, by setting the target temperature, the corresponding target frequency, target speed, and target angle are determined to achieve coordinated control of the compressor, indoor fan, and air guide plate of the inverter air conditioner. This enables precise and flexible load adjustment of the inverter air conditioner, ensuring low noise and comfortable air delivery while saving energy, thus improving the user's air conditioning comfort.

[0060] In summary, the variable frequency air conditioner control method provided in this invention achieves intelligent load scheduling of the variable frequency air conditioner by coordinating photovoltaic system power generation, energy storage battery, grid electricity price, and variable frequency air conditioner operation in real time. While ensuring indoor thermal comfort, it achieves "photovoltaic cold storage + peak avoidance," significantly reducing household electricity costs and increasing the self-consumption rate of photovoltaic energy. Furthermore, this method forms a complete "prediction-optimization-real-time control-precise execution" closed-loop solution, providing a practical technical solution for the deep integration of home energy management and intelligent air conditioning control, achieving both economic and comfort effects without user intervention.

[0061] Based on the above method embodiments, this invention provides another control method for a variable frequency air conditioner, such as... Figure 2 As shown, the method includes the following three stages: (A1) Data acquisition phase, used to collect detection parameters at the current moment and prediction parameters within the next 24 hours; among which, detection parameters include the current power generation of the photovoltaic system, the current state of charge of the energy storage battery, the current indoor temperature, the current outdoor temperature and the current power consumption of the inverter air conditioner; prediction parameters include the predicted power generation of the photovoltaic system and the predicted electricity price of the power grid.

[0062] (A2) Scheduling optimization phase: First, a 24-hour day-ahead optimization scheduling is performed based on the constraints and the first optimization objective function, and the baseline plan curve for the set temperature and the baseline charge-discharge plan curve are output; then, a 15-minute real-time rolling optimization is performed based on the constraints and the second optimization objective function, and the target set temperature is output. The constraints, the first optimization objective function, and the second optimization objective function can be referred to in the foregoing embodiments, and will not be described in detail here.

[0063] (A3) Control execution stage: Calculate the current temperature difference based on the target set temperature and the current indoor temperature, determine the target control parameters corresponding to the current moment based on the current temperature difference and the preset threshold, and control the inverter air conditioner based on the target control parameters.

[0064] Furthermore, during the operation of the inverter air conditioner, the real-time power consumption of the inverter air conditioner is fed back to the real-time rolling optimization. In addition, during the real-time rolling optimization process, the constraints are updated based on the newly acquired detection parameters, thereby improving the accuracy of the target set temperature and further enhancing the precision of the inverter air conditioner's refined and flexible load regulation.

[0065] Therefore, the control method for variable frequency air conditioners provided in this invention achieves refined coordinated scheduling of the load of the variable frequency air conditioner and the photovoltaic system through a two-layer optimization framework of "day-ahead planning + real-time correction". The day-ahead scheduling layer formulates an economically optimal plan from a global perspective, while the real-time optimization layer responds quickly to uncertainties, ensuring robust control. Simultaneously, by mapping the optimized target temperature setting to the coordinated control of multiple execution variables of the variable frequency air conditioner, precise temperature control is achieved while maintaining low-noise operation, improving the user experience. Furthermore, the photovoltaic system reduces the variable frequency air conditioner's dependence on the power grid and can quickly respond to fluctuations in grid electricity prices, enabling the variable frequency air conditioner to store cold during periods of low electricity prices. Finally, based on a comprehensive consideration of economy, comfort, and system constraints, deep coordinated intelligent control of the variable frequency air conditioner load and the household renewable energy system is achieved.

[0066] Furthermore, embodiments of the present invention also provide a photovoltaic air conditioning collaborative control system, including: a variable frequency air conditioner, an energy storage battery, a photovoltaic system, and a controller; wherein the controller is used to control the variable frequency air conditioner using the above-described method embodiments.

[0067] In addition, the photovoltaic air conditioning collaborative control system also includes, but is not limited to, photovoltaic grid-connected inverters, bidirectional converters for energy storage batteries, smart meters with communication functions, and indoor temperature sensors. Among these, the inverter air conditioner, photovoltaic grid-connected inverter, bidirectional converter for energy storage batteries, smart meters with communication functions, and indoor temperature sensors are all connected to the controller via a home LAN or dedicated bus, so that the controller can collect data and issue control commands to control the energy storage battery, inverter air conditioner, photovoltaic system, etc. It should be noted that specific photovoltaic air conditioning collaborative control systems can refer to existing technologies, and the embodiments of this invention will not be described in detail here.

[0068] The photovoltaic air conditioning collaborative control system provided in this embodiment of the invention has the same technical features as the variable frequency air conditioner control method provided in the above embodiment, so it can also solve the same technical problems and achieve the same technical effects.

[0069] This embodiment also provides a computer-readable storage medium storing a computer program. When the computer program is called and executed by a processor, the computer-readable storage medium causes the processor to implement the above-described control method for the variable frequency air conditioner.

[0070] The computer program product of the variable frequency air conditioner control method and photovoltaic air conditioner collaborative control system provided in the embodiments of the present invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods described in the preceding method embodiments. For specific implementation, please refer to the method embodiments, which will not be repeated here.

[0071] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system and apparatus described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0072] Furthermore, in the description of the embodiments of the present invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in the present invention based on the specific circumstances.

[0073] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0074] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0075] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A control method for a variable frequency air conditioner, characterized in that, The method includes: The system acquires the detection parameters at the current moment and the prediction parameters for the next first time period. The detection parameters include the current power generation of the photovoltaic system, the current state of charge of the energy storage battery, the current indoor temperature, the current outdoor temperature, and the current power consumption of the inverter air conditioner. The prediction parameters include the predicted power generation of the photovoltaic system and the predicted electricity price of the power grid. Based on the detection parameters, the prediction parameters, and the first optimization model, the set temperature baseline plan curve and the baseline charge-discharge plan curve corresponding to the variable frequency air conditioner and the energy storage battery respectively in the future first time period are determined; wherein, the first optimization model is the optimization model corresponding to the future first time period; Based on the set temperature baseline plan curve, the prediction parameters, and the second optimization model, the target set temperature at the current moment is determined; wherein, the second optimization model is an optimization model corresponding to a second future duration, and the second future duration is less than the first future duration; The current temperature difference is calculated based on the target set temperature and the current indoor temperature. The target control parameters corresponding to the current moment are determined based on the current temperature difference and the preset threshold. The inverter air conditioner is controlled to operate according to the target control parameters.

2. The method according to claim 1, characterized in that, The step of determining the base plan curve for the set temperature and the baseline charge-discharge plan curve for the variable frequency air conditioner and the energy storage battery respectively within the future first time period based on the detection parameters, the prediction parameters, and the first optimization model includes: The future first duration is divided into multiple first time periods, and first basic parameters are obtained; wherein, the first basic parameters include: the on-grid electricity price, the power purchased from the grid and the power sold to the grid for each first time period; Based on the detection parameters, the prediction parameters, the first basic parameters, and the first optimization model, the set temperature basic plan curve and the reference charge-discharge plan curve are calculated.

3. The method according to claim 2, characterized in that, The step of determining the target set temperature at the current moment based on the set temperature baseline plan curve, the prediction parameters, and the second optimization model includes: The future second duration is divided into multiple second time periods, and the set temperature base plan curve is determined for each second time period. Obtain the second basic parameters; wherein the second basic parameters include the electricity price forecast for each second time period and the power purchased from the grid; Based on the set temperature baseline value, the second baseline parameter, the prediction parameter, and the second optimization model for each second time period, the set temperature sequence for the current moment is calculated. The first value in the set temperature sequence is taken as the target set temperature at the current moment.

4. The method according to any one of claims 1-3, characterized in that, Both the first optimization model and the second optimization model are subject to constraints; wherein, the constraints include: system power balance constraints, battery energy storage constraints, indoor temperature dynamic change constraints, and indoor temperature comfort constraints.

5. The method according to claim 1, characterized in that, The preset threshold includes a first threshold, and the target control parameter includes a first control parameter; The step of determining the target control parameters corresponding to the current moment based on the current temperature difference and the preset threshold includes: If the current temperature difference is not less than the first threshold, the target control parameter is determined to be the first control parameter.

6. The method according to claim 5, characterized in that, The preset threshold includes a second threshold, and the target control parameter includes a second control parameter; The step of determining the target control parameters corresponding to the current moment based on the current temperature difference and the preset threshold includes: If the current temperature difference is less than the first threshold and not less than the second threshold, the target control parameter is determined to be the second control parameter.

7. The method according to claim 6, characterized in that, The target control parameters include a third control parameter; the step of determining the target control parameters corresponding to the current moment based on the current temperature difference and a preset threshold further includes: If the current temperature difference is less than the second threshold, the target control parameter is determined to be the third control parameter.

8. The method according to claim 1, characterized in that, The target control parameters include target frequency, target speed and target angle, and the variable frequency air conditioner includes a compressor, an indoor fan and an air guide plate; Controlling the inverter air conditioner to operate according to the target control parameters includes: The compressor is controlled to operate at the target frequency, the indoor fan is controlled to operate at the target speed, and the air guide plate is controlled to operate at the target angle.

9. A photovoltaic air conditioning collaborative control system, characterized in that, include: A variable frequency air conditioner, an energy storage battery, a photovoltaic system, and a controller; wherein the controller is used to control the variable frequency air conditioner using the method described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, performs the steps of the method described in any one of claims 1-8.

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