Valley electricity and peak use control method and temperature control strategy and power management system applying valley electricity and peak use control method

Abstract

The invention relates to an off-peak electricity peak use control method and a temperature control strategy and a power management system applying the off-peak electricity peak use control method, and belongs to the technical field of energy-saving control. Respectively establishing objective functions of the total cost of the storage battery module, the volume of the storage battery, the recovery period and the loss in the electricity storage process of the storage battery; s2, solving the plurality of objective functions through an NSGA-II algorithm to obtain a Pareto frontier solution; and S3, calculating the weighted sum of each objective function by using a minimize function, obtaining an optimal solution with the minimum weighted sum in the solutions of the Pareto frontier, configuring the storage battery according to the optimal solution, and switching the commercial power or the storage battery to supply power to the compressor according to the electricity price of the peak valley electricity period. According to the valley electricity peak use control method, the problems that the capacity configuration of the storage battery is not clear and the recovery period is not clear are solved, the power consumption cost is minimized, and the pressure of the peak period of the power grid is relieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy-saving control, and in particular relates to a valley power peak control method and a temperature control strategy and a power management system using the same. Background Art

[0002] With the continuous increase in electricity demand, especially during peak hours, the pressure on the power system is increasing. The peak-to-valley difference in electricity prices has gradually emerged, providing a new opportunity for energy conservation and cost reduction for various types of electrical equipment, especially refrigeration equipment. According to statistics, in April 2024, the peak-to-valley electricity price difference in 16 provinces and cities exceeded 0.7 yuan / kWh, and in 19 provinces and cities it exceeded 0.6 yuan / kWh; among them, Guangdong Province had the highest peak-to-valley price difference, about 1.3607 yuan / kWh. The traditional mode of electricity use is usually to obtain electricity directly from the power grid without considering the fluctuation of electricity prices. This method is not only expensive during peak hours, but may also increase the burden on the power grid.

[0003] In the prior art, Chinese utility model patent CN216056351U provides a battery energy storage regulation system for valley power and peak power, including a switching power supply, a battery pack, a data acquisition unit, a charge and discharge control management module, a thermal runaway management module, a balancing control management module and a central microprocessor system. When the power consumption is low and flat, the charge and discharge control management module controls the switching power supply to store power in the battery; when the power consumption is at a peak, the charge and discharge control management module controls the switching power supply to discharge the battery. Although the patent takes into account the difference in electricity costs caused by peak and valley power, it does not explain the capacity of the battery, resulting in unclear initial investment costs and recovery cycles for the battery, and thus failing to achieve the optimal economic benefits.

[0004] Therefore, how to provide a highly economical method for utilizing valley power and peak power is a technical problem that urgently needs to be solved. Summary of the invention

[0005] In view of the shortcomings in the prior art, the present invention provides a valley power peak control method and a temperature control strategy and power management system using the same. By taking the battery storage energy as the independent variable, objective functions of the total cost of the battery module, the battery volume, the recovery period, and the loss during the battery storage process are established respectively. The battery capacity can be accurately configured, avoiding the blind investment in traditional methods and significantly reducing the initial investment cost.

[0006] In a first aspect, the present invention provides a valley power peak control method, comprising the following steps:

[0007] S1, taking the battery storage energy as the independent variable, establish the objective functions of the total cost of the battery module, the battery volume, the recycling cycle, and the loss during the battery storage process;

[0008] S2, solve multiple objective functions through NSGA-Ⅱ algorithm to obtain the solution of Pareto frontier;

[0009] S3, using the minimize function to calculate the weighted sum of each objective function, obtain the optimal solution with the smallest weighted sum among the solutions of the Pareto front, configure the battery according to the optimal solution, and switch to AC power or battery to power the compressor according to the electricity price during peak and valley hours.

[0010] This technical solution makes full use of the difference in electricity prices, minimizes electricity costs, alleviates the pressure on the power grid during peak hours, improves energy utilization efficiency and system operation stability, and significantly improves the overall economic benefits and operation stability of the system. It is an efficient and economical valley peak control method and a dynamic power supply method.

[0011] In some embodiments, the objective function of the battery device cost is as shown in formula (1):

[0012]

[0013] In formula (1), C total is the total cost of the battery module, C baid is the cost of the battery auxiliary device, C bat is the cost of positive electrode materials used per kilowatt-hour of electricity in the battery module, q bat is the battery storage energy, cop is the coefficient of performance of the compressor;

[0014] The objective function of battery volume is shown in formula (2):

[0015]

[0016] In formula (2), V min is the minimum volume of the battery, a1 is the battery volume weight, v bat is the battery volume per kilowatt-hour of electricity, m bat is the battery mass corresponding to each kilowatt-hour of electricity, and a2 is the battery mass weight;

[0017] The objective function of the recycling cycle is shown in formula (3):

[0018]

[0019] In formula (3), T min is the minimum recovery period, Δp1 is the peak-valley electricity price difference, t peak is the peak time length, q heat is the environmental heat dissipation coefficient, Δp2 is the flat-valley electricity price difference;

[0020] The objective function of battery loss is shown in formula (4):

[0021] L min =kη bat q bat (4);

[0022] In formula (4), L min is the minimum loss of the battery during the storage process, k is the loss ratio per kilowatt-hour of electricity, η bat It is the battery storage efficiency.

[0023] In some embodiments, the range of the battery storage energy is as shown in formula (5):

[0024] t peak q heat ≤q bat ≤(t peak +t line )q heat (5).

[0025] In a second aspect, the present invention provides a temperature control strategy using a valley power peak control method, which is used for refrigeration equipment and is applied to the valley power peak control method as described above, comprising the following steps:

[0026] Data collection and storage: collect the internal temperature of the refrigeration equipment, the ambient temperature, the number of times the door of the refrigeration equipment is opened and closed, and the working time of the compressor of the refrigeration equipment, and store the data;

[0027] Data preprocessing: remove abnormal data and fill in missing values, normalize the data so that the data range is controlled within [0,1];

[0028] Establishing a temperature prediction SVR model: Train the temperature prediction SVR model based on the stored data to predict the internal temperature of the refrigeration equipment at a future time point in real time;

[0029] Adjust the temperature control strategy of refrigeration equipment: According to the prediction results of the temperature prediction SVR model and the electricity price during peak and valley periods, adjust the operation strategy of refrigeration equipment.

[0030] This technical solution combines the prediction results of the temperature prediction SVR model with the electricity prices during peak and valley periods to dynamically adjust the operating strategy of the refrigeration equipment. It can ensure optimal electricity use during different electricity price periods and reduce electricity costs. It can also adjust the working status of the equipment in time according to temperature changes and improve energy utilization efficiency.

[0031] In some embodiments, in the step of adjusting the temperature control strategy of the refrigeration equipment, the predicted result of the internal temperature of the refrigeration equipment at a future time point is recorded as R, and the maximum threshold value of the predicted result is set as R. max ;Record the battery power as SOC, set the minimum battery power threshold SOC lowand the maximum threshold SOC high ;

[0032] When R ≥ R max , and in the period of low electricity price, the compressor is powered by the mains and the compressor is controlled to start immediately; if SOC<SOC high , while charging the battery;

[0033] When R ≥ R max , and in the high electricity price period, the battery is used to power the compressor first, and the compressor is controlled to start immediately; if SOC < SOC low , transfer the mains electricity to power the compressor;

[0034] When R < R max When the power price is low, the compressor is temporarily turned off to save energy; if SOC<SOC high , while charging the battery;

[0035] When R < R max When the power supply is low and the electricity price is high, it is preferred to use batteries to power the compressor or temporarily shut down the compressor.

[0036] In some of the embodiments, in the step of establishing a temperature prediction SVR model, a radial basis function is used as a kernel function, a data set is constructed using historical data, the hyperparameters of the model are optimized and adjusted during the training process, the optimal hyperparameter combination is found, and the temperature prediction SVR model is established with the optimal hyperparameter combination.

[0037] In some of the embodiments, the data set uses the internal temperature of the refrigeration equipment, the ambient temperature, the number of times the door of the refrigeration equipment is opened and closed, and the compressor working time of the refrigeration equipment collected in the past N minutes as feature input data, and the predicted result of the internal temperature of the refrigeration equipment in the future M minutes as the target value.

[0038] In some of these embodiments, the optimal hyperparameter combination is obtained by grid search or cross validation.

[0039] In a third aspect, the present invention provides a power management system, comprising:

[0040] The battery module is configured by using the control method for valley power and peak power as described above, and the power supply line for the compressor powered by the battery module is connected in parallel with the mains power supply line;

[0041] A switching switch is provided with a mains power grid input terminal and a battery input terminal, and is used for switching between battery power supply and mains power supply; an output terminal of the switching switch is connected to the refrigeration equipment;

[0042] The data processing module is connected to the battery module for calculating the electricity cost saved and the recovery period of the battery module.

[0043] This technical solution optimizes the configuration and power supply strategy of the battery by combining the valley power and peak power control method, which not only improves the power utilization efficiency and economic benefits, but also enhances the reliability and stability of the system.

[0044] In a fourth aspect, the present invention provides a power management system, comprising:

[0045] A battery module, wherein a power supply circuit for the compressor powered by the battery module is connected in parallel with the mains power supply circuit;

[0046] A switching switch is provided with a mains power grid input terminal and a battery input terminal, and is used to adopt the temperature control strategy of applying the valley power peak control method as described above to switch between battery power supply and mains power supply; the output terminal of the switching switch is connected to the refrigeration equipment;

[0047] Temperature collectors are installed inside and outside the refrigeration equipment to collect the internal temperature of the refrigeration equipment and the ambient temperature respectively;

[0048] A proximity switch connected to the door of the refrigeration equipment to measure the number of times the door of the refrigeration equipment is opened;

[0049] A timer connected to the compressor and used to measure the working time of the compressor;

[0050] The data processing module is connected to the battery module for calculating the electricity cost and recovery period saved by the battery module; the data processing module is also connected to the temperature collector, proximity switch and timer for generating a temperature prediction SVR model.

[0051] This technical solution realizes intelligent power supply and precise temperature control by combining the temperature control strategy of valley power and peak power control methods, greatly reduces electricity costs, and improves the economic benefits and operational stability of the system.

[0052] Based on the above scheme, the valley power peak control method in the embodiment of the present invention, by taking the battery energy storage as the independent variable, respectively establishes the objective functions of the total cost of the battery module, the battery volume, the recovery period, and the loss during the battery storage process, so as to accurately configure the battery capacity, avoid the blind investment in the traditional method, and significantly reduce the initial investment cost; the NSGA-II algorithm is used to solve multiple objective functions to obtain the solution of the Pareto frontier. The Pareto frontier solution provides a variety of optimization schemes, so that the system can find the optimal balance point between multiple objectives such as cost, volume, recovery period and energy loss, thereby improving the overall optimization effect and the economy of the system; the minimize function is used to calculate the weighted sum of each objective function, and the optimal solution with the smallest weighted sum is selected from the Pareto frontier solution, thereby ensuring the comprehensive optimization of the system configuration in multiple aspects, and further improving the economic benefits and utilization efficiency of the battery; according to the high and low electricity prices during the peak and valley power periods, the compressor is automatically switched to AC power or battery. This technical solution not only solves the problems of unclear battery capacity configuration, unclear initial investment cost and payback period in the prior art, but also achieves energy saving and cost reduction through optimization algorithm and dynamic power supply management, makes full use of the difference in electricity prices, minimizes electricity costs, and alleviates the pressure on the power grid during peak hours, improves energy utilization efficiency and system operation stability, and significantly improves the overall economic benefits and operation stability of the system. It is an efficient and economical valley peak control method and this dynamic power supply method. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0054] Figure 1 Flow chart of the valley power peak control method in an embodiment of the present invention;

[0055] Figure 2 It is a flow chart of the temperature control strategy of applying the valley power peak control method in the embodiment of the present invention;

[0056] Figure 3 4 is a structural block diagram of a power management system in an embodiment of the present invention.

[0057] In the figure:

[0058] 1. Battery module; 2. Temperature collector; 3. Proximity switch; 4. Timer; 5. Data processing module; 6. Refrigeration equipment. DETAILED DESCRIPTION

[0059] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0060] In the description of the present invention, it should be understood that the terms "center", "lateral", "longitudinal", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside" and "outside" etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0061] The terms "first", "second", and "third" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the technical features indicated. Thus, a feature defined as "first", "second", and "third" may explicitly or implicitly include one or more of the features.

[0062] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a direct connection, or an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0063] The terms "system", "unit" and "module" used in this document are a method for distinguishing different components, elements, parts, portions or assemblies at different levels, and these terms may be replaced by other expressions that can achieve the same purpose.

[0064] First, as Figure 1As shown, in one embodiment of the valley power peak control method of the present invention and the temperature control strategy and power management system using the same, the valley power peak control method includes the following steps: S1, taking the battery storage energy as the independent variable, respectively establishing the objective functions of the total cost of the battery module 1, the battery volume, the recovery period, and the loss during the battery storage process; S2, solving multiple objective functions through the NSGA-Ⅱ algorithm to obtain the solution of the Pareto front; S3, using the minimize function to calculate the weighted sum of each objective function, obtaining the optimal solution with the smallest weighted sum in the solution of the Pareto front, configuring the battery according to the optimal solution, and switching the AC power or the battery to power the compressor according to the electricity price during the peak and valley power periods.

[0065] In the above-mentioned schematic embodiment, the valley power peak control method, by taking the battery energy storage as the independent variable, respectively establishes the objective functions of the total cost of the battery module 1, the battery volume, the recovery period, and the loss during the battery storage process, so as to accurately configure the battery capacity, avoid the blind investment in the traditional method, and significantly reduce the initial investment cost; the NSGA-II algorithm is used to solve multiple objective functions to obtain the solution of the Pareto frontier. The Pareto frontier solution provides a variety of optimization schemes, so that the system can find the optimal balance point between multiple objectives such as cost, volume, recovery period and energy loss, thereby improving the overall optimization effect and the economy of the system; the minimize function is used to calculate the weighted sum of each objective function, and the optimal solution with the smallest weighted sum is selected from the Pareto frontier solution, thereby ensuring the comprehensive optimization of the system configuration in multiple objectives, and further improving the economic benefits and utilization efficiency of the battery; according to the high and low electricity prices during the peak and valley power periods, the compressor is automatically switched to AC power or battery. This technical solution not only solves the problems of unclear battery capacity configuration, unclear initial investment cost and payback period in the prior art, but also achieves energy saving and cost reduction through optimization algorithm and dynamic power supply management, makes full use of the difference in electricity prices, minimizes electricity costs, and alleviates the pressure on the power grid during peak hours, improves energy utilization efficiency and system operation stability, and significantly improves the overall economic benefits and operation stability of the system. It is an efficient and economical valley peak control method and this dynamic power supply method.

[0066] In some embodiments, the objective function of the battery device cost is as shown in formula (1):

[0067]

[0068] In formula (1), C total is the total cost of battery module 1, C baid is the cost of the battery auxiliary device, C bat is the cost of positive electrode material used per kWh of electricity in battery module 1, q batis the battery storage energy, cop is the coefficient of performance of the compressor;

[0069] The objective function of battery volume is shown in formula (2):

[0070]

[0071] In formula (2), V min is the minimum volume of the battery, a1 is the battery volume weight, v bat is the battery volume per kilowatt-hour of electricity, m bat is the battery mass corresponding to each kilowatt-hour of electricity, and a2 is the battery mass weight;

[0072] The objective function of the recycling cycle is shown in formula (3):

[0073]

[0074] In formula (3), T min is the minimum recovery period, Δp1 is the peak-valley electricity price difference, t peak is the peak time length, q heat is the environmental heat dissipation coefficient, Δp2 is the flat-valley electricity price difference;

[0075] The objective function of battery loss is shown in formula (4):

[0076] L min =kη bat q bat (4);

[0077] In formula (4), L min is the minimum loss of the battery during the storage process, k is the loss ratio per kilowatt-hour of electricity, η bat It is the battery storage efficiency.

[0078] As an illustrative embodiment, the battery auxiliary device includes an inverter, a housing, a switch, a charger and a relay. The user can adjust the composition of the battery auxiliary device according to the needs, and different compositions of the battery auxiliary device correspond to different battery auxiliary device costs C baid .

[0079] As an illustrative embodiment, when the battery uses lithium iron phosphate as the positive electrode material, k is 0.0028, η bat It is 92%.

[0080] In some embodiments, the range of the battery storage energy is as shown in formula (5):

[0081] t peak q heat ≤q bat ≤(t peak+t line )q heat (5).

[0082] Second, as Figure 2 As shown, the present invention also provides a temperature control strategy using a valley power peak control method, which is used for refrigeration equipment and is applied to the above-mentioned valley power peak control method, including data collection and storage steps, data preprocessing steps, establishing a temperature prediction SVR model step, and adjusting the temperature control strategy step of the refrigeration equipment 6; wherein the data collection and storage step includes: collecting the internal temperature of the refrigeration equipment 6, the ambient temperature, the number of times the door of the refrigeration equipment 6 is opened and closed, and the working time of the compressor of the refrigeration equipment 6, and storing the data; the data preprocessing step includes: removing abnormal data and filling missing values, and normalizing the data so that the data range is controlled within [0,1]; the temperature prediction SVR model step includes: training the temperature prediction SVR model according to the stored data to predict the internal temperature of the refrigeration equipment 6 at a future time point in real time; the temperature control strategy step of the refrigeration equipment 6 includes: adjusting the operation strategy of the refrigeration equipment 6 according to the prediction results of the temperature prediction SVR model and combining the high and low electricity prices during the peak and valley power periods.

[0083] In the above exemplary embodiment, the temperature control strategy of the valley power peak control method uses the data collection and storage steps to obtain multi-dimensional data such as the internal temperature of the refrigeration equipment 6, the ambient temperature, the number of door openings and closings, and the working time of the compressor, thereby ensuring the comprehensiveness and accuracy of the data required for temperature prediction; the data preprocessing step ensures the quality and continuity of the data by eliminating abnormal data and filling missing values, and normalizes the data so that its range is controlled within [0,1], thereby improving the efficiency and accuracy of model training and ensuring that the model can better adapt to actual conditions; by establishing a temperature prediction SVR model and training based on the collected data, the future internal temperature of the refrigeration equipment 6 can be predicted in real time, thereby making the temperature control more accurate and avoiding excessive temperature fluctuations that affect the equipment performance; combining the prediction results of the temperature prediction SVR model and the high and low electricity prices during the peak and valley power periods, the operation strategy of the refrigeration equipment 6 is dynamically adjusted, which can ensure the optimal use of electricity in different electricity price periods, reduce electricity costs, and timely adjust the working state of the equipment according to temperature changes to improve energy utilization efficiency.

[0084] In some embodiments, in the step of adjusting the temperature control strategy of the refrigeration device 6, the predicted result of the internal temperature of the refrigeration device 6 at a future time point is recorded as R, and the highest threshold value of the predicted result is set as R. max ;Record the battery power as SOC, set the minimum battery power threshold SOC low and the maximum threshold SOC high ;

[0085] When R ≥ R max, and in the period of low electricity price, the compressor is powered by the mains and the compressor is controlled to start immediately; if SOC<SOC high , while charging the battery;

[0086] When R ≥ R max , and in the high electricity price period, the battery is used to power the compressor first, and the compressor is controlled to start immediately; if SOC < SOC low , transfer the mains electricity to power the compressor;

[0087] When R < R max When the power price is low, the compressor is temporarily turned off to save energy; if SOC<SOC high , while charging the battery;

[0088] When R < R max When the power supply is low and the electricity price is high, it is preferred to use batteries to power the compressor or temporarily shut down the compressor.

[0089] In some embodiments, in the step of establishing a temperature prediction SVR model, a radial basis function is used as a kernel function, a data set is constructed using historical data, the hyperparameters of the model are optimized and adjusted during training, the best hyperparameter combination is found, and the temperature prediction SVR model is established with the best hyperparameter combination.

[0090] In some embodiments, the data set uses the internal temperature of the refrigeration equipment 6, the ambient temperature, the number of times the door of the refrigeration equipment 6 is opened and closed, and the working time of the compressor of the refrigeration equipment 6 collected in the past N minutes as feature input data, and the predicted result of the internal temperature of the refrigeration equipment 6 in the future M minutes as the target value.

[0091] In some embodiments, the data set is divided into a training set and a test set. The training set is used to obtain a temperature prediction SVR model. The temperature prediction SVR model is used to test the test set, and the prediction error of the model is evaluated by the mean square error (MSE).

[0092] In the third aspect, the present invention also provides a power management system, including a battery module 1, a switching switch, and a data processing module 5; wherein the battery module 1 adopts the above-mentioned valley power peak control method for battery configuration, and the power supply line of the battery module 1 for the compressor is connected in parallel with the AC power supply line; the switching switch is provided with a AC power grid input terminal and a battery input terminal, for switching between battery power supply and AC power supply; the output terminal of the switching switch is connected to the refrigeration equipment 6; the data processing module 5 is communicated with the battery module 1, for calculating the electricity cost saved and the recovery period of the battery module 1.

[0093] In the above-mentioned exemplary embodiment, the power management system is connected in parallel with the battery module 1 and the mains power supply line. The system can charge during the low electricity price period and discharge during the peak electricity price period to supply power to the refrigeration equipment 6. This effectively utilizes the difference in electricity prices, reduces the pressure on the power grid during peak hours, and improves the overall power utilization efficiency; the switching switch can automatically switch between mains power supply and battery power supply, and make intelligent choices based on real-time electricity prices and power demand; the data processing module 5 is connected in communication with the battery module 1, and can calculate the electricity cost saving and recovery period of the battery module 1 in real time. In summary, the power management system optimizes the configuration and power supply strategy of the battery by combining the valley power peak control method, which not only improves the power utilization efficiency and economic benefits, but also enhances the reliability and stability of the system. It is an efficient and reliable power management solution.

[0094] In some embodiments, the battery module 1 includes a battery body and a battery auxiliary device, wherein the battery body includes a battery cell, and the battery auxiliary device includes an inverter, a housing, a switch, a charger, and a relay.

[0095] In some embodiments, the battery module 1 is further provided with a first electric meter, which is used to measure the power consumption of the battery, and the mains power supply line is provided with a second electric meter. The first electric meter and the second electric meter can help users to grasp the usage of peak power and valley power in real time.

[0096] In some embodiments, the data processing module 5 includes an industrial computer and a display. The industrial computer is used to calculate the saved electricity cost and the recovery period, and the display is used to intuitively display the calculation results to the user.

[0097] Fourthly, Figure 3 As shown, the present invention also provides a power management system, including a battery module 1, a switching switch, a temperature collector 2, a proximity switch 3, a timer 4 and a data processing module 5; wherein the power supply line of the battery module 1 for the compressor is connected in parallel with the mains power supply line; the switching switch ( Figure 3 (not shown) is provided with a mains grid input and a battery input, which are used to adopt the temperature control strategy of the above-mentioned valley-peak control method to switch between battery power supply and mains power supply; the output end of the switching switch is connected to the refrigeration equipment 6; the temperature collector 2 is installed inside and outside the refrigeration equipment 6, respectively, and is used to collect the internal temperature and ambient temperature of the refrigeration equipment 6; the proximity switch 3 is connected to the door of the refrigeration equipment 6, and is used to measure the number of times the door of the refrigeration equipment 6 is opened; the timer 4 is connected to the compressor, and is used to measure the working time of the compressor; the data processing module 5 is communicated with the battery module 1, and is used to calculate the electricity cost and recovery period saved by the battery module 1; the data processing module 5 is also communicated with the temperature collector 2, the proximity switch 3, and the timer 4, respectively, to generate a temperature prediction SVR model.

[0098] In the above exemplary embodiment, the power management system is connected in parallel with the battery module 1 and the mains power supply line. The system can charge during the low electricity price period and discharge during the peak electricity price period to supply power to the refrigeration equipment 6, effectively utilizing the difference in electricity prices, reducing electricity costs and improving electricity utilization efficiency; the switching switch can intelligently switch between mains power supply and battery power supply, and optimize the power supply mode according to the real-time electricity price and temperature control strategy; the temperature collector 2 is installed inside and outside the refrigeration equipment 6, respectively, to collect the internal temperature and ambient temperature data in real time, and can accurately understand the working state of the refrigeration equipment 6 by combining the number of door openings measured by the proximity switch 3 and the compressor working time recorded by the timer 4; the data processing module 5 uses these data to generate a temperature prediction SVR model, and dynamically adjusts the operation strategy of the refrigeration equipment 6 according to the predicted temperature changes and the high and low electricity prices, so as to achieve precise temperature control and energy saving and cost reduction. In summary, the power management system realizes intelligent power supply and precise temperature control by combining the temperature control strategy of the valley power peak control method, greatly reduces the electricity cost, improves the economic benefits and operation stability of the system, and is an efficient and intelligent power management solution.

[0099] In some embodiments, the battery module 1 includes a battery body and a battery auxiliary device, wherein the battery body includes a battery cell, and the battery auxiliary device includes an inverter, a housing, a switch, a charger, and a relay.

[0100] In some embodiments, the battery module 1 is further provided with a first electric meter, which is used to measure the power consumption of the battery, and the mains power supply line is provided with a second electric meter. The first electric meter and the second electric meter can help users to grasp the usage of peak power and valley power in real time.

[0101] In some embodiments, the data processing module 5 includes an industrial computer and a display. The industrial computer is used to calculate the saved electricity cost and the recovery period. The industrial computer is also used to generate a temperature prediction SVR model. The display is used to intuitively display the calculation results to the user.

[0102] Through the description of multiple embodiments of the valley power peak control method of the present invention and the temperature control strategy and power management system using the same, it can be seen that the valley power peak control method of the present invention and the temperature control strategy and power management system embodiments using the same have at least one or more of the following advantages:

[0103] 1. The valley-peak power control method provided by the present invention makes full use of the difference in electricity prices, minimizes the electricity cost, relieves the pressure of the power grid during peak hours, improves the energy utilization efficiency and the operation stability of the system, and significantly improves the overall economic benefits and operation stability of the system;

[0104] 2. The temperature control strategy of the present invention using the valley power peak control method combines the prediction results of the temperature prediction SVR model and the electricity price during the peak and valley power periods to dynamically adjust the operation strategy of the refrigeration equipment, thereby ensuring the optimal use of electricity in different electricity price periods, reducing electricity costs, and timely adjusting the working state of the equipment according to temperature changes to improve energy utilization efficiency;

[0105] 3. The power management system provided by the present invention optimizes the configuration and power supply strategy of the battery by combining the valley power peak control method, which not only improves the power utilization efficiency and economic benefits, but also enhances the reliability and stability of the system;

[0106] 4. The power management system provided by the present invention realizes intelligent power supply and precise temperature control by combining the temperature control strategy of valley power peak control method, greatly reduces electricity cost, and improves the economic benefit and operation stability of the system.

[0107] Finally, it should be noted that: the various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0108] The above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or some technical features can be replaced by equivalents without departing from the spirit of the technical solution of the present invention, which should be included in the scope of the technical solution for protection of the present invention.

Claims

1. A control method for valley power peak, characterized in that: The following steps are involved: S1, taking the battery storage energy as the independent variable, establish the objective functions of the total cost of the battery module, the battery volume, the recycling cycle, and the loss during the battery storage process; S2, solve multiple objective functions through NSGA-Ⅱ algorithm to obtain the solution of Pareto frontier; S3, using the minimize function to calculate the weighted sum of each objective function, obtain the optimal solution with the smallest weighted sum among the solutions of the Pareto front, configure the battery according to the optimal solution, and switch to AC power or battery to power the compressor according to the electricity price during peak and valley hours.

2. The valley peak control method according to claim 1, characterized in that: The objective function of the battery device cost is shown in formula (1): In formula (1), C total is the total cost of the battery module, C baid is the cost of the battery auxiliary device, C bat is the cost of positive electrode materials used per kilowatt-hour of electricity in the battery module, q bat is the battery storage energy, cop is the coefficient of performance of the compressor; The objective function of battery volume is shown in formula (2): In formula (2), V min is the minimum volume of the battery, a1 is the battery volume weight, v bat is the battery volume per kilowatt-hour of electricity, m bat is the battery mass corresponding to each kilowatt-hour of electricity, and a2 is the battery mass weight; The objective function of the recycling cycle is shown in formula (3): In formula (3), T min is the minimum recovery period, Δp1 is the peak-valley electricity price difference, t peak is the peak time length, q heat is the environmental heat dissipation coefficient, Δp2 is the flat-valley electricity price difference; The objective function of battery loss is shown in formula (4): L min =kη bat q bat (4); In formula (4), L min is the minimum loss of the battery during the storage process, k is the loss ratio per kilowatt-hour of electricity, η bat It is the battery storage efficiency.

3. The valley peak control method according to claim 2, characterized in that: The range of battery storage energy is shown in formula (5): t peak q heat ≤q bat ≤(t peak +t line )q heat (5)。 4. The temperature control strategy using the valley power peak control method is used for refrigeration equipment, characterized in that: The valley power peak control method applied to any one of claims 1 to 3 comprises the following steps: Data collection and storage: collect the internal temperature of the refrigeration equipment, the ambient temperature, the number of times the door of the refrigeration equipment is opened and closed, and the working time of the compressor of the refrigeration equipment, and store the data; Data preprocessing: remove abnormal data and fill in missing values, normalize the data so that the data range is controlled within [0,1]; Establishing a temperature prediction SVR model: Train the temperature prediction SVR model based on the stored data to predict the internal temperature of the refrigeration equipment at a future time point in real time; Adjust the temperature control strategy of refrigeration equipment: According to the prediction results of the temperature prediction SVR model and the electricity price during peak and valley periods, adjust the operation strategy of refrigeration equipment.

5. The temperature control strategy using the valley power peak control method according to claim 4 is characterized in that: In the step of adjusting the temperature control strategy of the refrigeration equipment, the predicted result of the internal temperature of the refrigeration equipment at a future time point is recorded as R, and the maximum threshold value of the predicted result is set as R max ;Record the battery power as SOC, set the minimum battery power threshold SOC low and the maximum threshold SOC high ; When R ≥ R max , and in the period of low electricity price, the compressor is powered by the mains and the compressor is controlled to start immediately; if SOC<SOC high , while charging the battery; When R ≥ R max , and in the high electricity price period, the battery is used to power the compressor first, and the compressor is controlled to start immediately; if SOC < SOC low , transfer the mains electricity to power the compressor; When R < R max When the electricity price is low, the compressor is temporarily turned off to save energy; if SOC<SOC high , while charging the battery; When R < R max When the power supply is low and the electricity price is high, it is preferred to use batteries to power the compressor or temporarily shut down the compressor.

6. The temperature control strategy using the valley power peak control method according to claim 4, characterized in that: In the step of establishing the temperature prediction SVR model, the radial basis function is used as the kernel function, and the data set is constructed with historical data. During the training process, the hyperparameters of the model are optimized and adjusted to find the best hyperparameter combination, and the temperature prediction SVR model is established with the best hyperparameter combination.

7. The temperature control strategy using the valley power peak control method according to claim 6, characterized in that: The dataset uses the internal temperature of the refrigeration equipment, ambient temperature, number of door openings and closings of the refrigeration equipment, and compressor operating hours of the refrigeration equipment collected in the past N minutes as feature input data, and the predicted result of the internal temperature of the refrigeration equipment in the future M minutes as the target value.

8. The temperature control strategy using the valley power peak control method according to claim 6, characterized in that: The best hyperparameter combination is obtained through grid search or cross-validation.

9. A power management system, characterized in that: include: A battery module, configured by using the valley power peak control method as described in any one of claims 1 to 3, wherein the power supply line for the compressor powered by the battery module is connected in parallel with the mains power supply line; A switching switch is provided with a mains power grid input terminal and a battery input terminal, and is used to switch between battery power supply and mains power supply; an output terminal of the switching switch is connected to the refrigeration equipment; The data processing module is connected to the battery module for calculating the electricity cost saved and the recovery period of the battery module.

10. A power management system, characterized in that: include: A battery module, wherein a power supply circuit for the compressor powered by the battery module is connected in parallel with the mains power supply circuit; A switching switch, which is provided with a mains power grid input terminal and a battery input terminal, and is used to adopt the temperature control strategy of applying the valley power peak control method as described in any one of claims 4 to 8 to switch between battery power supply and mains power supply; the output terminal of the switching switch is connected to the refrigeration equipment; Temperature collectors are installed inside and outside the refrigeration equipment to collect the internal temperature of the refrigeration equipment and the ambient temperature respectively; A proximity switch connected to the door of the refrigeration equipment to measure the number of times the door of the refrigeration equipment is opened; A timer connected to the compressor and used to measure the working time of the compressor; The data processing module is connected to the battery module for calculating the electricity cost and recovery period saved by the battery module; the data processing module is also connected to the temperature collector, proximity switch and timer for generating a temperature prediction SVR model.

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