An energy management method, device and equipment of an energy storage system based on photovoltaic sound-absorbing louvers and a storage medium

Abstract

This application discloses an energy management method, device, equipment, and storage medium for an energy storage system based on photovoltaic anechoic louvers, relating to the field of smart energy storage technology. The method includes: estimating the current thermal state of the heat-loaded equipment based on multi-source sensor data; making predictions based on the current thermal state and historical data of the heat-loaded equipment to obtain a temperature prediction sequence and a corresponding ventilation volume prediction sequence; and constructing an objective function based on photovoltaic power generation, the ventilation volume prediction sequence, the temperature prediction sequence, and the current actual parameter values ​​for rolling optimization to obtain energy storage charging and discharging power commands. Because the current thermal state of the heat-loaded equipment is determined through state estimation, the system is always based on the actual state of the equipment, avoiding prediction deviations caused by parameter inaccuracies. By predicting future temperature and ventilation demands, the system shifts from passive response to active scheduling, improving the accuracy of energy management in the photovoltaic anechoic louver-based energy storage system.

Description

Technical Field

[0001] This application relates to the field of smart energy storage technology, and in particular to an energy management method, device, equipment and storage medium for an energy storage system based on photovoltaic silencing louvers. Background Technology

[0002] In applications such as industrial cooling towers and building ventilation openings, it is often necessary to simultaneously meet multiple requirements, including ventilation and heat dissipation, photovoltaic power generation, and energy storage. Existing solutions mainly achieve these requirements by combining photovoltaic sound-absorbing louvers with energy storage devices.

[0003] Existing solutions typically treat ventilation demand as an unadjustable rigid constraint, failing to consider actively altering the temporal distribution of ventilation demand by adjusting the power of heat load equipment. This results in low utilization of photovoltaic power generation, ineffective energy storage participation in regulation, and low overall system operating efficiency. Summary of the Invention

[0004] The main purpose of this application is to provide an energy management method, device, equipment, storage medium and program product for an energy storage system based on photovoltaic sound-absorbing louvers, which aims to solve the technical problem that the existing solutions do not consider actively changing the time distribution of ventilation demand by adjusting the power of heat load equipment when performing energy management, resulting in low utilization of photovoltaic power generation.

[0005] To achieve the above objectives, this application proposes an energy management method for an energy storage system based on photovoltaic anechoic louvers, the energy management method for the energy storage system based on photovoltaic anechoic louvers comprising:

[0006] Acquire multi-source sensor data and perform equipment state estimation based on the multi-source sensor data to obtain the current thermal state of the heat load equipment;

[0007] Based on the current thermal state and historical data of the heat load equipment, a temperature prediction sequence for the heat load equipment is obtained, and a ventilation volume prediction sequence corresponding to the temperature prediction sequence is determined.

[0008] The photovoltaic power generation is obtained, and an objective function is constructed based on the photovoltaic power generation, the ventilation volume prediction sequence, the temperature prediction sequence, and the current actual parameter values ​​for rolling optimization to obtain the energy storage charging and discharging power command.

[0009] Energy storage charging and discharging management is performed according to the energy storage charging and discharging power command.

[0010] In one embodiment, the step of constructing an objective function based on the photovoltaic power generation, the ventilation volume prediction sequence, the temperature prediction sequence, and the current actual parameter values, and performing rolling optimization to obtain the energy storage charging and discharging power command, includes:

[0011] A target function is constructed based on the photovoltaic power generation, the ventilation volume prediction sequence, the temperature prediction sequence, and the current actual parameter values.

[0012] The thermal dynamic constraints, the power adjustment range constraints of the heat load equipment, the energy storage constraints, the power balance constraints, and the louver angle range constraints are used as the conditional constraints of the objective function. The objective function is then subjected to rolling optimization to obtain the decision quantity, which includes at least the energy storage charging and discharging power command.

[0013] In one embodiment, the objective function is:

[0014] ;

[0015] in, Indicates the first The required ventilation volume for each step Indicates the first The angle of the photovoltaic noise-absorbing louvers at each time step is: The corresponding ventilation volume at that time The weight corresponding to the ventilation volume; Indicates the first The adjustment amount of the equipment power of the time-step heat load equipment relative to the reference power. The weight corresponding to the device power; Indicates the first The charging and discharging power corresponding to each time step energy storage system The weights corresponding to the charging and discharging power.

[0016] In one embodiment, the step of predicting the temperature prediction sequence of the heat load device based on the current thermal state and historical data of the heat load device includes:

[0017] Obtain the day-ahead reference value for the equipment power;

[0018] Based on the thermal dynamic equation, the daytime baseline value and the current thermal state are recursively extrapolated to obtain the temperature prediction sequence of the heat load equipment.

[0019] In one embodiment, the step of determining the ventilation volume prediction sequence corresponding to the temperature prediction sequence includes:

[0020] Obtain the heat dissipation characteristic curve of the heat load equipment;

[0021] The corresponding ventilation volume prediction sequence is determined based on the heat dissipation characteristic curve and the temperature prediction sequence.

[0022] In one embodiment, the step of estimating the device state based on the multi-source sensor data to obtain the current thermal state of the heat load device includes:

[0023] Obtain the energy efficiency ratio of the equipment under heat load;

[0024] A thermal dynamic model is constructed based on the device energy efficiency ratio and the multi-source sensor data.

[0025] The thermal inertia and thermal capacity of the thermal dynamic model are determined based on the dynamic model.

[0026] The current thermal state of the heat load equipment is determined based on the thermal inertia and the heat capacity.

[0027] Furthermore, to achieve the above objectives, this application also proposes an energy management device for an energy storage system based on photovoltaic anechoic louvers, the energy management device for the energy storage system based on photovoltaic anechoic louvers comprising:

[0028] The thermal status management module is used to acquire multi-source sensor data and perform equipment status estimation based on the multi-source sensor data to obtain the current thermal status of the heat load equipment.

[0029] The ventilation volume prediction module is used to predict the temperature of the heat load equipment based on the current thermal state and the historical data of the heat load equipment, and to determine the ventilation volume prediction sequence corresponding to the temperature prediction sequence.

[0030] The rolling optimization module is used to obtain the photovoltaic power generation power, and construct an objective function based on the photovoltaic power generation power, the ventilation volume prediction sequence, the temperature prediction sequence and the current actual parameter value to perform rolling optimization and obtain the energy storage charging and discharging power command.

[0031] The energy storage management module is used to manage the energy storage charging and discharging according to the energy storage charging and discharging power command.

[0032] Furthermore, to achieve the above objectives, this application also proposes an energy management device for an energy storage system based on photovoltaic anechoic louvers. The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. The computer program is configured to implement the steps of the energy management method for an energy storage system based on photovoltaic anechoic louvers as described above.

[0033] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the energy management method for the energy storage system based on photovoltaic silencing louvers as described above.

[0034] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the energy management method for an energy storage system based on photovoltaic silencing louvers as described above.

[0035] One or more technical solutions proposed in this application have at least the following technical effects:

[0036] This application obtains the current thermal state of the heat load equipment by acquiring multi-source sensor data and estimating the equipment state based on this data. It then predicts the temperature of the heat load equipment based on the current thermal state and historical data, and determines the corresponding ventilation volume prediction sequence. The application acquires the photovoltaic power generation and constructs an objective function based on the photovoltaic power generation, ventilation volume prediction sequence, temperature prediction sequence, and current actual parameter values ​​for rolling optimization, resulting in energy storage charging and discharging power commands. Finally, it manages energy storage charging and discharging based on these commands. Because the current thermal state of the heat load equipment is determined through state estimation, the system always operates based on the actual state of the equipment, avoiding prediction deviations caused by parameter inaccuracies. By predicting future temperature and ventilation demands, the system shifts from passive response to active scheduling, improving the energy management accuracy of the photovoltaic-based silencing louver energy storage system. Through multi-objective collaborative optimization, specific energy storage charging and discharging power commands are obtained, enabling overall system charging and discharging power management and improving photovoltaic absorption rate. Attached Figure Description

[0037] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0038] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 This is a flowchart illustrating an embodiment of the energy management method for an energy storage system based on photovoltaic noise-absorbing louvers in this application.

[0040] Figure 2 This is a schematic diagram of the photovoltaic noise-absorbing louver in one implementation of the energy management method for an energy storage system based on photovoltaic noise-absorbing louvers in this application;

[0041] Figure 3 This is a flowchart illustrating Embodiment 2 of the energy management method for an energy storage system based on photovoltaic noise-absorbing louvers in this application.

[0042] Figure 4 This is a flowchart illustrating Embodiment 3 of the energy management method for an energy storage system based on photovoltaic noise-absorbing louvers in this application.

[0043] Figure 5 This is a schematic diagram of the module structure of the energy management device of the energy storage system based on photovoltaic noise-absorbing louvers, as described in an embodiment of this application.

[0044] Figure 6 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the energy management method of the energy storage system based on photovoltaic noise-absorbing louvers in the embodiments of this application.

[0045] Figure labels and descriptions:

[0046]

[0047] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0048] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0049] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0050] The main solution of this application embodiment is as follows: acquire multi-source sensor data, and estimate the equipment state based on the multi-source sensor data to obtain the current thermal state of the heat load equipment; make predictions based on the current thermal state and historical data of the heat load equipment to obtain the temperature prediction sequence of the heat load equipment, and determine the ventilation volume prediction sequence corresponding to the temperature prediction sequence; acquire photovoltaic power generation, and construct an objective function based on the photovoltaic power generation, ventilation volume prediction sequence, temperature prediction sequence and current actual parameter values ​​for rolling optimization to obtain energy storage charging and discharging power commands; and perform energy storage charging and discharging management based on the energy storage charging and discharging power commands.

[0051] This application provides a solution that, by predicting the ventilation needs of heat-loaded equipment and obtaining photovoltaic power generation, continuously optimizes the energy storage charging and discharging power commands. This enables the energy storage system to dynamically match photovoltaic output with equipment load demands, reducing photovoltaic curtailment caused by ventilation constraints and improving renewable energy utilization. By incorporating the current thermal state and temperature prediction sequence of the heat-loaded equipment into the optimization objective, it ensures that the energy storage charging and discharging strategy operates within the equipment's temperature safety boundaries, achieving synergy between energy dispatch and equipment thermal dynamics, and avoiding operational risks caused by excessive power adjustments.

[0052] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device or virtual device capable of performing the above functions. The following description uses an energy management device for an energy storage system based on photovoltaic noise-absorbing louvers (hereinafter referred to as the management device) as an example to illustrate this embodiment and the following embodiments.

[0053] Based on this, the embodiments of this application provide an energy management method for an energy storage system based on photovoltaic silencing louvers, referring to... Figure 1 , Figure 1 This is a flowchart illustrating an embodiment of the energy management method for an energy storage system based on photovoltaic noise-absorbing louvers, as provided in this application.

[0054] In this embodiment, the energy management method for the energy storage system based on photovoltaic noise-absorbing louvers includes steps S10 to S40:

[0055] Step S10: Acquire multi-source sensor data and perform equipment state estimation based on the multi-source sensor data to obtain the current thermal state of the heat load equipment.

[0056] It is understood that various types of sensors can be deployed at different locations within the system in this embodiment of the application, thereby collecting sensor data that reflects the operating status and environmental parameters of each component of the system. This sensor data is multi-source sensor data, which has diverse sources and types, providing basic information for the monitoring, control, and optimization of the system.

[0057] For example, the temperature of the heat-loaded equipment is collected by a temperature sensor, and the power of the heat-loaded equipment is collected by a power sensor.

[0058] For example, the irradiance of the photovoltaic noise-absorbing louvers can be collected by a radiometer, and the opening and closing angle of the photovoltaic noise-absorbing louvers can be collected by an angle encoder.

[0059] In some embodiments of this application, the photovoltaic noise-absorbing louvers of this application can be as follows: Figure 2 As shown, where, Figure 2 This is a schematic diagram of the photovoltaic noise-absorbing louver 100 in one implementation of the energy management method for an energy storage system based on photovoltaic noise-absorbing louvers in this application.

[0060] It should be noted that by using real-time collected multi-source sensor data, it is possible to determine the internal parameters and state variables of the equipment that cannot be directly measured or change over time. In the embodiments of this application, the thermal state of the heat-loaded equipment can be estimated based on multi-source sensor data to determine the current thermal state of the heat-loaded equipment.

[0061] Understandably, in practical applications, the thermal resistance and heat capacity of heat-loaded equipment (such as refrigeration units and reactors) will change with factors such as operating conditions, equipment aging, and environmental conditions. Using fixed parameters without thermal state estimation will lead to model inaccuracies, resulting in control failure and even losses. By performing state estimation on heat-loaded equipment, the assessment of load regulation capacity can be more realistic, avoiding power regulation exceeding safe limits or wasting regulation potential due to parameter deviations. This reduces unnecessary deep charging and discharging, contributing to more precise energy storage scheduling.

[0062] It should be noted that the aforementioned current thermal state refers to the set of parameters used to describe the thermodynamic state of the heat load equipment at the current moment, which may include parameters such as temperature, thermal inertia, and heat capacity, and can reflect the thermal dynamic response capability of the equipment under the current operating conditions. In the embodiments of this application, the current thermal state estimation of the heat load equipment can be achieved by recursive least squares method, Kalman filtering, particle filtering, or neural networks, deep learning, etc., and the specific implementation method is not limited in the embodiments of this application.

[0063] In its specific implementation, the management device in this application acquires multi-source sensor data and estimates the device's current thermal state based on this data. Since the thermal parameters are identified in real time, the temperature prediction error can be controlled within ±1℃, thus making ventilation demand prediction more accurate. The load regulation capacity assessment is closer to reality, avoiding power regulation exceeding safe limits or wasting regulation potential due to parameter deviations, and reducing unnecessary deep charging and discharging. Simultaneously, it enables the device to maintain good control performance even after aging, requiring no manual intervention and reducing maintenance costs.

[0064] Step S20: Based on the current thermal state and the historical data of the heat load equipment, a temperature prediction sequence for the heat load equipment is obtained, and the ventilation volume prediction sequence corresponding to the temperature prediction sequence is determined.

[0065] It is understood that the aforementioned historical data may be operational data of the heat load equipment obtained from historical system predictions or actual monitoring, such as historical temperature sequences of the heat load equipment, historical temperature sequences of the heat load equipment, historical sequences of photovoltaic irradiance, etc., and this application embodiment does not limit this.

[0066] It should be understood that the above sequence can be a dataset or array composed of consecutive time steps arranged in chronological order, and each element in the sequence can be used to represent the value of the corresponding time step. A temperature prediction sequence is a dataset or array composed of temperature prediction data, and a ventilation volume prediction sequence is a dataset or array composed of ventilation volume prediction data.

[0067] For example, temperature prediction sequences The value in Used to represent the future... The predicted temperature value corresponding to each time step. .

[0068] For example, ventilation volume prediction sequence The value in Used to represent the future... The predicted ventilation volume for each time step .

[0069] In some embodiments of this application, the duration of the time step can be 15 minutes, and the number of time steps in each sequence can be 4, i.e., N=4. Currently, the duration of the time step and the number of time steps in each sequence can be selected according to actual applications, and this application does not impose any limitations on this.

[0070] It is understandable that, since cooling towers / building vents can remove the heat generated by the operation of heat load equipment, for a specific heat load equipment, there is a corresponding relationship between the required ventilation volume and the equipment temperature under certain operating conditions. This relationship can be obtained through heat exchange experiments or on-site calibration when the equipment leaves the factory (for example, by measuring the minimum ventilation volume required to maintain thermal balance at different temperatures), or it can be measured by neural networks, deep learning, etc. The embodiments of this application do not limit this.

[0071] Step S30: Obtain the photovoltaic power generation power, and construct an objective function based on the photovoltaic power generation power, the ventilation volume prediction sequence, the temperature prediction sequence and the current actual parameter value for rolling optimization to obtain the energy storage charging and discharging power command;

[0072] Step S40: Perform energy storage charging and discharging management according to the energy storage charging and discharging power command.

[0073] It should be noted that the aforementioned photovoltaic power generation can be obtained directly or determined based on photovoltaic irradiance prediction. Specifically, photovoltaic irradiance can be the solar radiation power received per unit area, which can be measured in real time by an irradiance sensor installed near the photovoltaic silencing louvers. By obtaining photovoltaic irradiance, future photovoltaic power generation can be predicted, and the objective function can be continuously optimized to obtain energy storage charging and discharging power commands. Based on these energy storage charging and discharging power commands, energy storage charging and discharging management can be achieved.

[0074] It should be understood that the above-mentioned current actual parameter values ​​are the real-time state data of the system obtained at the current moment by direct measurement by sensors or by state estimation or other means. These values ​​may include the current equipment temperature, thermal resistance, and thermal capacity of the heat load equipment, as well as the state of charge and health status of the energy storage system. They may also include the louver angle of the photovoltaic sound-absorbing louvers, the actual ventilation volume of the cooling tower, and the ambient temperature. This application embodiment does not limit these values.

[0075] This application embodiment acquires multi-source sensor data and estimates the device state based on this data to obtain the current thermal state of the heat load device. It then predicts the temperature of the heat load device based on the current thermal state and historical data, and determines the corresponding ventilation volume prediction sequence. The system acquires photovoltaic power generation and constructs an objective function based on the photovoltaic power generation, ventilation volume prediction sequence, temperature prediction sequence, and current actual parameter values ​​for rolling optimization, resulting in energy storage charging and discharging power commands. Finally, it manages energy storage charging and discharging based on these commands. Because the current thermal state of the heat load device is determined through state estimation, the system is always based on the actual state of the device, avoiding prediction deviations caused by parameter inaccuracies. By predicting future temperature and ventilation needs, the system shifts from passive response to active scheduling, improving the energy management accuracy of the photovoltaic-based silencing louver energy storage system. Through multi-objective collaborative optimization, specific energy storage charging and discharging power commands are obtained, enabling overall system charging and discharging power management and improving photovoltaic absorption rate.

[0076] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 3 , Figure 3 This is a flowchart illustrating Embodiment 2 of the energy management method for an energy storage system based on photovoltaic noise-absorbing louvers in this application.

[0077] like Figure 3 As shown in the embodiment of this application, the step of constructing an objective function based on the photovoltaic power generation, the ventilation volume prediction sequence, the temperature prediction sequence, and the current actual parameter values, and performing rolling optimization to obtain the energy storage charging and discharging power command, includes:

[0078] Step S31: Construct an objective function based on the photovoltaic power generation, the ventilation volume prediction sequence, the temperature prediction sequence, and the current actual parameter values;

[0079] Step S32: The thermal dynamic constraints, the power adjustment range constraints of the heat load equipment, the energy storage constraints, the power balance constraints, and the louver angle range constraints are used as the conditional constraints of the objective function. The objective function is then subjected to rolling optimization to obtain the decision quantity. The decision quantity includes at least the power charging and discharging command.

[0080] It should be noted that the above-mentioned thermal dynamic constraints can be a type of constraint used to describe the temperature change of thermal load equipment over time. Through thermal dynamic constraints, the optimizer can be forced to conform to the balance of "heat intake-heat dissipation" when making decisions.

[0081] In one example of an embodiment of this application, thermal dynamic constraints can be expressed as:

[0082] ;

[0083] in, Indicates the first The equipment temperature of the heat load equipment at each time step; Indicates the duration of each time step; This indicates the heat capacity of the equipment (i.e., how much heat is required to raise the temperature by 1°C). Indicates the energy efficiency ratio of the heat load equipment; Indicates the first The actual power of the equipment under heat load at each time step The day-ahead reference value representing the actual power of the equipment; Indicates the first The ambient temperature value at each time step. The term is used to represent the heat input. Indicates heat dissipation. Indicates thermal resistance.

[0084] It is understandable that the power adjustment range constraint of the aforementioned heat load equipment is a constraint condition determined based on the current thermal state of the heat load equipment. The power limit and thermal safety limit of the equipment itself can be determined through the power adjustment range constraint of the heat load equipment.

[0085] In some embodiments of this application, the power adjustment range constraint of the heat load equipment can be expressed as:

[0086] ;

[0087] in, Indicates the heat load equipment in the first Power adjustment amount per hour step; Indicates the maximum adjustable power. This indicates the maximum adjustable power.

[0088] It should be noted that the maximum adjustable power downwards and maximum adjustable power upwards mentioned above can be determined based on the current thermal state of the heat load equipment in the actual application. Specifically, it can be as follows:

[0089] ;

[0090] ;

[0091] in, Indicates the current time The power of the heat load equipment Indicates the current time Equipment temperature of heat load equipment; This indicates the minimum allowable power for heat-loaded equipment. Indicates the maximum allowable power of the heat load equipment. Indicates the maximum allowable temperature for the heat load equipment. Indicates the minimum allowable temperature for the heat load equipment; Indicates the current time The heat capacity of the heat load equipment Indicates the current time Thermal inertia of heat-loaded equipment.

[0092] For example, in the current heat load equipment Temperature at any time With an upper temperature limit of 50℃, a thermal inertia of 30 minutes, and a heat capacity of 800 kJ per degree Celsius, the corresponding maximum adjustable power is 53.3 kW.

[0093] It should be noted that the aforementioned energy storage constraints are constraints related to the energy storage system, specifically including three parts: state of charge recursion, state of charge limit, and power limit. Specifically, the state of charge recursion constraint stipulates that changes in the state of charge of the energy storage system must comply with the law of conservation of energy; the state of charge limit protects the energy storage system from becoming too high or too low (i.e., it cannot exceed the upper and lower limits of the state of charge); and the power limit prevents the charging and discharging power from exceeding the hardware physical limits of the energy storage system (i.e., it cannot exceed the upper and lower limits of the charging and discharging power).

[0094] In some embodiments of this application, the recursive constraint of the state of charge can be as follows:

[0095] ;

[0096] in, Used to indicate the first The state of charge of the energy storage system at each time step; Used to represent energy storage system, representing the first The charging and discharging power of the time-phase energy storage system; This indicates the rated capacity of the energy storage system.

[0097] It should be noted that the upper and lower limits (such as the upper and lower limits of state of charge and the upper and lower limits of charge and discharge power) can be set according to the needs of actual applications, or they can be determined in real time based on the battery health status of the energy storage system. This application embodiment does not impose any restrictions on this.

[0098] It should be understood that the above-mentioned power balance constraint is a constraint used to constrain the power generation and power consumption in the system to be balanced. This application embodiment does not impose specific restrictions on it, and it can be determined according to the load power demand, photovoltaic power generation, energy storage output parameters and grid input parameters in actual applications.

[0099] It should be noted that the aforementioned angle range constraint of the photovoltaic noise-absorbing louver is a constraint used to limit the rotation range of the photovoltaic noise-absorbing louver, such as an angle range of [0, 90].

[0100] In some embodiments of this application, the objective function constructed in the embodiments of this application is... It can be as follows:

[0101] ;

[0102] in, Indicates the first The required ventilation volume for each time step (e.g., ventilation volume forecast data). Indicates the first The angle of the photovoltaic noise-absorbing louvers at each time step is: The corresponding actual ventilation volume at that time The weight corresponding to the ventilation volume; Indicates the first The adjustment amount of the equipment power of the time-step heat load equipment relative to the reference power. The weight corresponding to the device power; Indicates the first The charging and discharging power corresponding to each time step energy storage system The weights corresponding to the charging and discharging power.

[0103] It should be understood that the weights of the above items can be set according to the needs of actual applications, and the embodiments of this application do not impose any restrictions on this.

[0104] It should be noted that the embodiments of this application constitute a constrained quadratic programming problem through the objective function and constraints. By solving this problem with the optimal sequence, the corresponding parameters such as energy storage power command, louver angle control command, and equipment operating power can be obtained.

[0105] This application's embodiments construct an objective function based on photovoltaic power generation, ventilation volume prediction sequence, temperature prediction sequence, and current actual parameter values. Thermal dynamic constraints, power adjustment range constraints for heat load equipment, energy storage constraints, power balance constraints, and louver angle range constraints are used as conditional constraints on the objective function. The objective function is then continuously optimized to obtain decision quantities, which at least include energy storage charging and discharging power commands. Since the optimization of energy storage charging and discharging power commands involves predicting the ventilation demand of heat load equipment and obtaining photovoltaic power generation, the energy storage system can dynamically match photovoltaic output with equipment load demand, reducing photovoltaic curtailment due to ventilation constraints and improving renewable energy utilization. Incorporating the current thermal state and temperature prediction sequence of the heat load equipment into the optimization objective ensures that the energy storage charging and discharging strategy operates within the equipment temperature safety boundary, achieving synergy between energy dispatch and equipment thermal dynamics, and avoiding equipment operational risks caused by excessive power adjustment.

[0106] Based on the first and / or second embodiments of this application, in the third embodiment of this application, the content that is the same as or similar to the first and / or second embodiments described above can be referred to the above description and will not be repeated hereafter. Based on this, please refer to... Figure 4 , Figure 4 This is a flowchart illustrating Embodiment 3 of the energy management method for an energy storage system based on photovoltaic noise-absorbing louvers in this application.

[0107] like Figure 4 As shown in the embodiment of this application, the step of predicting the temperature prediction sequence of the heat load equipment based on the current thermal state and the historical data of the heat load equipment includes:

[0108] Step S21: Obtain the day-ahead reference value of the device power;

[0109] Step S22: Based on the thermal dynamic equation, the daytime reference value and the current thermal state are recursively extrapolated to obtain the temperature prediction sequence of the heat load equipment.

[0110] It should be noted that the aforementioned day-ahead reference value can be a reference value for equipment power predicted based on historical data. By further extrapolating and adjusting based on this predicted reference, parameters such as the energy storage power command, louver angle control command, and equipment operating power actually used for control can be obtained. In this embodiment of the application, the prediction method of the aforementioned day-ahead reference value is not limited; it can be determined using a function similar to the aforementioned objective function, or it can be predicted based on neural networks, deep learning, or other methods.

[0111] Specifically, the embodiments of this application can be based on the current time. Temperature of heat load equipment Thermal resistance and heat capacity The following thermal dynamic equations are constructed based on the current thermal state, the day-ahead reference value, and the ambient temperature:

[0112] ;

[0113] in, Indicates the first The equipment temperature of the heat load equipment at each time step; Indicates the duration of each time step; Indicates the heat load equipment at time Heat capacity (i.e. how much heat is required to raise the temperature by 1°C). Indicates the energy efficiency ratio of the heat load equipment; Indicates the first The day-ahead reference value of the equipment power of the heat load equipment at each time step; Indicates the first The ambient temperature value at each time step.

[0114] It is understandable that, based on the above thermal dynamic equation, the equipment temperature at different time steps can be recursively calculated, thereby determining the temperature prediction sequence of the heat load equipment.

[0115] In some embodiments of this application, the step of determining the ventilation volume prediction sequence corresponding to the temperature prediction sequence includes: obtaining the heat dissipation characteristic curve of the heat load equipment; and determining the corresponding ventilation volume prediction sequence based on the heat dissipation characteristic curve and the temperature prediction sequence.

[0116] It should be noted that the aforementioned heat dissipation characteristic curve is a function used to describe the relationship between equipment temperature and required ventilation volume. Specifically, it can be measured or predicted according to the needs of actual applications, or it can be calibrated by the manufacturer. This application embodiment does not impose any limitations on this. By substituting the temperatures corresponding to the equipment's heat load at different time steps into the heat dissipation characteristic curve, the required ventilation volume at that time can be determined.

[0117] In some embodiments of this application, the step of estimating the current thermal state of the heat load device based on the multi-source sensor data includes: obtaining the device energy efficiency ratio of the heat load device; constructing a thermal dynamic model based on the device energy efficiency ratio and the multi-source sensor data; determining the thermal inertia and thermal capacity of the thermal dynamic model based on the dynamic model; and determining the current thermal state of the heat load device based on the thermal inertia and the thermal capacity.

[0118] It is understandable that the energy efficiency ratio of a device is the proportion of heat power converted from every unit of power consumed by the heat load device. Based on the thermal dynamic model, the heat dissipation thermal resistance and heat capacity of the heat load device can be determined. Based on the heat dissipation thermal resistance and heat capacity, the thermal inertia and current thermal state of the heat load device can be determined.

[0119] It should be understood that the aforementioned thermal resistance refers to the resistance to heat dissipation from the heat-loaded equipment to the environment; the greater the thermal resistance, the slower the heat dissipation. Based on parameters such as the equipment's energy efficiency ratio, thermal resistance, heat capacity, and ambient temperature data from multiple sensors, a thermal dynamic model can be constructed.

[0120] It should be noted that the thermal dynamic model constructed in the embodiments of this application can take the form shown in the above thermal dynamic equation or thermal dynamic constraint, and will not be elaborated further in the embodiments of this application.

[0121] It needs to be explained that thermal inertia It can be represented as Current thermal state It can be represented as .

[0122] This application embodiment obtains the day-ahead reference value of equipment power; based on the thermal dynamic equation, it recursively extrapolates the day-ahead reference value and the current thermal state to obtain a temperature prediction sequence for the heat load equipment. Since it generates both temperature prediction sequences and ventilation volume prediction sequences, it achieves the prediction of future temperature and ventilation volume, providing direction for subsequent optimization.

[0123] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the energy management method of the energy storage system based on photovoltaic silencing louvers. Any simple modifications based on this technical concept are within the protection scope of this application.

[0124] This application also provides an energy management device for an energy storage system based on photovoltaic noise-absorbing louvers; please refer to [reference needed]. Figure 5 , Figure 5 This is a schematic diagram of the module structure of the energy management device for the energy storage system based on photovoltaic anechoic louvers, according to an embodiment of this application. The energy management device for the energy storage system based on photovoltaic anechoic louvers includes:

[0125] The thermal status management module 10 is used to acquire multi-source sensor data and perform equipment status estimation based on the multi-source sensor data to obtain the current thermal status of the thermal load equipment.

[0126] The ventilation volume prediction module 20 is used to predict the temperature of the heat load equipment based on the current thermal state and the historical data of the heat load equipment, and to determine the ventilation volume prediction sequence corresponding to the temperature prediction sequence.

[0127] The rolling optimization module 30 is used to obtain the photovoltaic power generation power, and construct an objective function based on the photovoltaic power generation power, the ventilation volume prediction sequence, the temperature prediction sequence and the current actual parameter value to perform rolling optimization and obtain the energy storage charging and discharging power command.

[0128] The energy storage management module 40 is used to perform energy storage charging and discharging management according to the energy storage charging and discharging power command.

[0129] The energy management device for a photovoltaic-based sound-absorbing louver energy storage system provided in this application employs the energy management method for a photovoltaic-based sound-absorbing louver energy storage system described in the above embodiments. This addresses the technical problem of low photovoltaic power generation utilization caused by existing solutions failing to consider actively altering the temporal distribution of ventilation demand by adjusting the power of heat load equipment during energy management. Compared to the prior art, the beneficial effects of the energy management device for a photovoltaic-based sound-absorbing louver energy storage system provided in this application are the same as those of the energy management method for a photovoltaic-based sound-absorbing louver energy storage system provided in the above embodiments. Furthermore, other technical features of the energy management device for a photovoltaic-based sound-absorbing louver energy storage system are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0130] This application provides an energy management device for an energy storage system based on photovoltaic noise-absorbing louvers. The energy management device for an energy storage system based on photovoltaic noise-absorbing louvers includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the energy management method for an energy storage system based on photovoltaic noise-absorbing louvers in the above embodiment 1.

[0131] The following is for reference. Figure 6 The diagram illustrates a structural schematic of an energy management device for a photovoltaic-based sound-absorbing louvered energy storage system suitable for implementing embodiments of this application. The energy management device for the photovoltaic-based sound-absorbing louvered energy storage system in embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 6 The energy management device of the energy storage system based on photovoltaic silencing louvers shown is merely an example and should not impose any limitation on the function and scope of use of the embodiments of this application.

[0132] like Figure 6As shown, the energy management device of the photovoltaic-based sound-absorbing louver energy storage system may include a processing unit 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 1002 or a program loaded from storage device 1003 into random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the photovoltaic-based sound-absorbing louver energy storage system energy management device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the photovoltaic-based louvered energy storage system energy management device to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows a photovoltaic-based louvered energy storage system energy management device with various systems, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems can be implemented alternatively.

[0133] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0134] The energy management device for a photovoltaic (PV) sound-absorbing louver-based energy storage system provided in this application employs the energy management method for a PV-based energy storage system described in the above embodiments. This addresses the technical problem of low PV power generation utilization caused by existing solutions failing to consider actively altering the temporal distribution of ventilation demand by adjusting the power of heat load equipment during energy management. Compared to the prior art, the beneficial effects of the energy management device for a PV-based energy storage system provided in this application are the same as those of the energy management method for a PV-based energy storage system provided in the above embodiments. Furthermore, other technical features of this energy management device for a PV-based energy storage system are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0135] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0136] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0137] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, which are used to execute the energy management method of the energy storage system based on photovoltaic silencing louvers in the above embodiments.

[0138] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), or flash memory, optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0139] The aforementioned computer-readable storage medium may be included in the energy management equipment of the photovoltaic-based sound-absorbing louver energy storage system; or it may exist independently and not assembled into the energy management equipment of the photovoltaic-based sound-absorbing louver energy storage system.

[0140] The aforementioned computer-readable storage medium carries one or more programs that, when executed by the energy management device of the photovoltaic-based sound-absorbing louver energy storage system, cause the energy management device of the photovoltaic-based sound-absorbing louver energy storage system to:

[0141] Acquire multi-source sensor data and perform equipment state estimation based on the multi-source sensor data to obtain the current thermal state of the heat load equipment;

[0142] Based on the current thermal state and historical data of the heat load equipment, a temperature prediction sequence for the heat load equipment is obtained, and a ventilation volume prediction sequence corresponding to the temperature prediction sequence is determined.

[0143] The photovoltaic power generation is obtained, and an objective function is constructed based on the photovoltaic power generation, the ventilation volume prediction sequence, the temperature prediction sequence, and the current actual parameter value for rolling optimization to obtain the energy storage charging and discharging power command.

[0144] Energy storage charging and discharging management is performed according to the energy storage charging and discharging power command.

[0145] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0146] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0147] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0148] The readable storage medium provided in this application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the energy management method of the energy storage system based on photovoltaic anechoic louvers described above. This solves the technical problem of low photovoltaic power generation utilization caused by existing solutions failing to consider actively changing the temporal distribution of ventilation demand by adjusting the power of heat load equipment during energy management. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the energy management method of the energy storage system based on photovoltaic anechoic louvers provided in the above embodiments, and will not be repeated here.

[0149] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the energy management method for an energy storage system based on photovoltaic silencing louvers as described above.

[0150] The computer program product provided in this application can solve the technical problem of low photovoltaic power generation utilization caused by the failure of existing solutions to consider actively changing the time distribution of ventilation demand by adjusting the power of heat load equipment during energy management. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of the energy management method of the energy storage system based on photovoltaic sound-absorbing louvers provided in the above embodiments, and will not be repeated here.

[0151] The above description is only a part of the embodiments of this application and does not limit the scope of protection of this application. All equivalent structural transformations made under the technical concept of this application and using the content of this application specification and drawings, or direct / indirect applications in other related technical fields, are included in the scope of protection of this application.

Claims

1. An energy management method for an energy storage system based on photovoltaic silencing louvers, characterized in that, The method includes: Acquire multi-source sensor data and perform equipment state estimation based on the multi-source sensor data to obtain the current thermal state of the heat load equipment; Based on the current thermal state and historical data of the heat load equipment, a temperature prediction sequence for the heat load equipment is obtained, and a ventilation volume prediction sequence corresponding to the temperature prediction sequence is determined. The photovoltaic power generation is obtained, and an objective function is constructed based on the photovoltaic power generation, the ventilation volume prediction sequence, the temperature prediction sequence, and the current actual parameter values ​​for rolling optimization to obtain the energy storage charging and discharging power command. Energy storage charging and discharging management is performed according to the energy storage charging and discharging power command; The step of constructing an objective function based on the photovoltaic power generation, the ventilation volume prediction sequence, the temperature prediction sequence, and the current actual parameter values, and performing rolling optimization to obtain the energy storage charging and discharging power command, includes: A target function is constructed based on the photovoltaic power generation, the ventilation volume prediction sequence, the temperature prediction sequence, and the current actual parameter values. The thermal dynamic constraints, the power adjustment range constraints of the thermal load equipment, the energy storage constraints, the power balance constraints, and the louver angle range constraints are used as the conditional constraints of the objective function. The objective function is then subjected to rolling optimization to obtain the decision quantity, which includes at least the energy storage charging and discharging power command. The objective function is: ； in, Indicates the first The required ventilation volume for each step Indicates the first The angle of the photovoltaic noise-absorbing louvers at each time step is: The corresponding ventilation volume at that time The weight corresponding to the ventilation volume; Indicates the first The adjustment amount of the equipment power of the time-step heat load equipment relative to the reference power. The weight corresponding to the device power; Indicates the first The charging and discharging power corresponding to each time step energy storage system Weights corresponding to charging and discharging power; The thermal dynamic constraint is: ； in, Indicates the first The equipment temperature of the heat load equipment at each time step; Indicates the duration of each time step; Indicates the heat capacity of the equipment under heat load. Indicates the energy efficiency ratio of the heat load equipment; Indicates the first The actual power of the equipment under heat load at each time step The day-ahead reference value representing the actual power of the equipment; Indicates the first The ambient temperature value at each time step; The term is used to represent the heat input. Indicates heat dissipation. Indicates thermal resistance.

2. The energy management method for an energy storage system based on photovoltaic silencing louvers as described in claim 1, characterized in that, The step of predicting the temperature prediction sequence of the heat load equipment based on the current thermal state and historical data of the heat load equipment includes: Obtain the day-ahead reference value for the equipment power; Based on the thermal dynamic equation, the daytime baseline value and the current thermal state are recursively extrapolated to obtain the temperature prediction sequence of the heat load equipment.

3. The energy management method for an energy storage system based on photovoltaic silencing louvers as described in claim 1, characterized in that, The step of determining the ventilation volume prediction sequence corresponding to the temperature prediction sequence includes: Obtain the heat dissipation characteristic curve of the heat load equipment; The corresponding ventilation volume prediction sequence is determined based on the heat dissipation characteristic curve and the temperature prediction sequence.

4. The energy management method for an energy storage system based on photovoltaic silencing louvers as described in claim 1, characterized in that, The step of estimating the equipment state based on the multi-source sensor data to obtain the current thermal state of the heat load equipment includes: Obtain the energy efficiency ratio of the equipment under heat load; A thermal dynamic model is constructed based on the device energy efficiency ratio and the multi-source sensor data. The thermal inertia and thermal capacity of the thermal dynamic model are determined based on the dynamic model. The current thermal state of the heat load equipment is determined based on the thermal inertia and the heat capacity.

5. An energy management device for an energy storage system based on photovoltaic silencing louvers, characterized in that, The energy management device of the energy storage system based on photovoltaic silencing louvers includes: The thermal status management module is used to acquire multi-source sensor data and perform equipment status estimation based on the multi-source sensor data to obtain the current thermal status of the heat load equipment. The ventilation volume prediction module is used to predict the temperature of the heat load equipment based on the current thermal state and the historical data of the heat load equipment, and to determine the ventilation volume prediction sequence corresponding to the temperature prediction sequence. The rolling optimization module is used to obtain the photovoltaic power generation power, and construct an objective function based on the photovoltaic power generation power, the ventilation volume prediction sequence, the temperature prediction sequence and the current actual parameter value to perform rolling optimization and obtain the energy storage charging and discharging power command. The energy storage management module is used to manage the energy storage charging and discharging according to the energy storage charging and discharging power command; The rolling optimization module is also used to construct an objective function based on the photovoltaic power generation, the ventilation volume prediction sequence, the temperature prediction sequence, and the current actual parameter values; The thermal dynamic constraints, the power adjustment range constraints of the thermal load equipment, the energy storage constraints, the power balance constraints, and the louver angle range constraints are used as the conditional constraints of the objective function. The objective function is then subjected to rolling optimization to obtain the decision quantity, which includes at least the energy storage charging and discharging power command. The objective function is: ； in, Indicates the first The required ventilation volume for each step Indicates the first The angle of the photovoltaic noise-absorbing louvers at each time step is: The corresponding ventilation volume at that time The weight corresponding to the ventilation volume; Indicates the first The adjustment amount of the equipment power of the time-step heat load equipment relative to the reference power. The weight corresponding to the device power; Indicates the first The charging and discharging power corresponding to each time step energy storage system Weights corresponding to charging and discharging power; The thermal dynamic constraint is: ； in, Indicates the first The equipment temperature of the heat load equipment at each time step; Indicates the duration of each time step; Indicates the heat capacity of the equipment under heat load. Indicates the energy efficiency ratio of the heat load equipment; Indicates the first The actual power of the equipment under heat load at each time step The day-ahead reference value representing the actual power of the equipment; Indicates the first The ambient temperature value at each time step; The term is used to represent the heat input. Indicates heat dissipation. Indicates thermal resistance.

6. An energy management device for an energy storage system based on photovoltaic silencing louvers, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the energy management method for an energy storage system based on photovoltaic anechoic louvers as described in any one of claims 1 to 4.

7. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the energy management method for an energy storage system based on photovoltaic silencing louvers as described in any one of claims 1 to 4.

8. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the energy management method for an energy storage system based on photovoltaic silencing louvers as described in any one of claims 1 to 4.

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