A coupling heat supply control method and system based on wind energy and battery energy storage

Abstract

The application provides a coupling heating control method and system based on wind energy and battery energy storage, which comprises the following steps: obtaining the ambient wind speed, the battery state of charge and the total demand heat power, and determining the mixed heating weight coefficient according to the above parameters, wherein the coefficient is used to define the power sharing ratio of the compressor heating path and the electric heating element heating path; based on the mixed heating weight coefficient and the total demand heat power, the established compressor dynamic model is combined for forward-looking optimization processing to generate a smooth compressor power instruction; the compensation power of the electric heating element is calculated according to the difference between the total demand heat power and the smooth compressor power instruction; and the smooth compressor power instruction and the electric heating element compensation power are respectively converted into control signals to drive the compressor and the electric heating element to cooperate in heating. The method improves the clarity, robustness and maintainability of the control system.

Description

Technical Field

[0001] This invention relates to the field of thermal power and renewable energy utilization technology, and in particular to a coupled heating control method and system based on wind power and battery energy storage. Background Technology

[0002] With the global energy structure transitioning towards cleaner and lower-carbon energy, and the continued growth in demand for stable heating in the construction and industrial sectors, utilizing intermittent renewable energy sources such as wind power for heating has become an important research direction in the fields of new energy utilization and energy conservation. The industry is actively researching hybrid energy systems that combine wind power generation with thermal and energy storage technologies. The goal of this system is to overcome the randomness and volatility of wind energy through spatiotemporal transfer and multi-energy complementarity, thereby achieving continuous and stable heat output. Technological advancements in this field demonstrate a shift from single-source power supply to synergistic thermoelectric power generation and from simple combinations to intelligent coupling.

[0003] A search revealed a patent with publication number CN220436624U, which discloses a wind-solar hybrid direct-drive coupled clean energy heating system. This system includes a concentrating solar collector, a fan-driven mechanical heat pump unit, a coupled thermal storage heating system, a smart energy management system, and a solar-assisted power supply and energy storage system. The concentrating solar collector is adapted to absorb solar energy and is connected to the coupled thermal storage heating system via pipelines. The fan-driven mechanical heat pump unit is adapted to directly convert wind energy into heat energy through mechanical energy and is connected to the coupled thermal storage heating system via pipelines. Additionally, a search revealed a patent with publication number CN117094517A. This paper presents an optimized scheduling method for low-carbon smart building integrated energy systems to promote wind power consumption. Belonging to the technical field of integrated building energy systems, it introduces solid oxide fuel cells-combined heat and power (CHP) equipment and ground source heat pumps considering variable operating conditions to decouple traditional CHP units from the heat-driven power generation mode and reduce system carbon emissions. A system thermal energy equation considering room heat interaction and thermal inertia is constructed, and a combined energy storage system and integrated demand response mechanism are introduced. This achieves interactive synergy between dissimilar energy storage devices while fully stimulating the potential of load to absorb wind power and improving the wind power utilization efficiency of smart building systems. An optimized scheduling model is constructed with the goal of minimizing energy purchase costs, carbon emission costs, comfort costs, and wind curtailment penalty costs.

[0004] Existing technologies, which focus on macroscopic scheduling and optimal allocation, may lead to a deeply overlooked but serious technical problem affecting system stability and energy efficiency in practical engineering applications. When a system includes heating units with drastically different dynamic response characteristics, such as air-source heat pumps and thermal storage electric boilers, the mechanical compression path is constrained by compressor start-up and shutdown inertia, refrigerant circulation, and thermal inertia of the heat exchange process. Its heating power response exhibits significant lag and inertia, typically requiring tens of seconds to several minutes to reach the command requirement. In contrast, the power output of the direct resistance heating path can respond almost instantaneously, with delays in the millisecond range. Existing scheduling strategies, after calculating and issuing power commands to each unit, usually assume that each unit can instantaneously track the command without difference. This control method, which ignores the dynamic characteristics of physical objects, will cause severe internal dynamic mismatch in actual operation, especially under conditions where rapid fluctuations in wind speed cause frequent changes in total heating demand commands. The fast-responding resistance heating path immediately adapts to changes in total demand, while the slow-responding mechanical compression path's actual output lags significantly behind its assigned command value, resulting in a large instantaneous power deficit or surplus during the transition process. This deficit or surplus forces the control system to continuously and drastically adjust the power of the resistance heating path to compensate, thereby generating continuous, low-frequency power oscillations throughout the system. These oscillations not only directly cause fluctuations in the heating outlet temperature, impairing heating comfort and stability, but also cause the compressor to operate under unstable transient conditions for extended periods, accelerating equipment wear and aging. Furthermore, the frequent high-power compensation actions of the resistance heaters significantly reduce the overall energy efficiency of the system. Summary of the Invention

[0005] In view of this, the present invention provides a coupled heating control method and system based on wind energy and battery energy storage to solve the above problems.

[0006] This invention provides a coupled heating control method based on wind energy and battery energy storage, comprising: acquiring ambient wind speed, battery state of charge, and total demand heat power, and determining a hybrid heating weight coefficient accordingly, wherein the coefficient is used to define the power sharing ratio of the compressor heating path and the electric heating element heating path; performing look-ahead optimization processing based on the hybrid heating weight coefficient and the total demand heat power, combined with an established compressor dynamic model, to generate a smooth compressor power command; calculating the compensation power of the electric heating element according to the difference between the total demand heat power and the smooth compressor power command; and converting the smooth compressor power command and the electric heating element compensation power into control signals respectively to drive the compressor and the electric heating element to provide coordinated heating.

[0007] In another implementation of the present invention, the method further includes: obtaining the heating demand based on the real-time temperature of the medium at the outlet of the heating system, and calculating the real-time temperature deviation and its rate of change; and calculating the total required heat power to eliminate the real-time temperature deviation using a heat demand calculation model.

[0008] In another implementation of the present invention, the heat demand calculation model is characterized as follows:

[0009] in, For total demand heat power, This is the proportionality coefficient. The integral coefficient is... The equivalent heat capacity coefficient of the heating system; This represents the real-time temperature deviation. The change rate is denoted as .

[0010] In another implementation of the present invention, the step of acquiring ambient wind speed, battery state of charge, and total demanded heat power, and determining the hybrid heating weight coefficient accordingly, includes: acquiring ambient wind speed, battery state of charge, and total demanded heat power; filtering the ambient wind speed to obtain a stable wind speed value; querying the fan characteristic curve based on the stable wind speed value to calculate the theoretical mechanical power that the current wind energy can capture and use to directly drive the compressor; evaluating the discharge capacity of the battery that can be used for auxiliary heating based on the battery state of charge, and determining the battery state coefficient characterizing this capacity; and calculating the hybrid heating weight coefficient based on the theoretical mechanical power, the total demanded heat power, and the battery state coefficient.

[0011] In another implementation of the present invention, the formula for calculating the mixed heating weight coefficient is as follows:

[0012]

[0013]

[0014] in, This refers to the battery state coefficient. Theoretical mechanical power; This represents the total required thermal power.

[0015] In another implementation of the present invention, the compressor dynamic model takes the compressor drive power command as input and the compressor estimated output heat power as output, adopts the form of first-order inertia plus pure delay, and its transfer function is characterized as follows:

[0016] in, It is a time constant. This is the pure delay time.

[0017] In another implementation of the present invention, the method further includes: monitoring the actual operating status of the compressor and feeding back the operating parameters to the compressor dynamic model for online adaptive fine-tuning.

[0018] Another aspect of the present invention provides a coupled heating control system based on wind energy and battery energy storage, comprising: a fan, a compressor, a battery energy storage module, a heating circuit, an electric heating element, a wind speed sensor, and a controller; the fan is used to capture wind energy and output mechanical energy; the power input shaft of the compressor is mechanically connected to the output shaft of the fan for compressing the working fluid; the battery energy storage module is used to store and release electrical energy; the heating circuit includes a condenser, the working fluid inlet of the condenser is connected to the outlet of the compressor for transferring the heat energy of the compressed high-temperature and high-pressure working fluid to an external heat-consuming end; the electric heating element is disposed in the heating circuit. On the road, it is electrically connected to the battery energy storage module to convert electrical energy into heat energy; the wind speed sensor is used to detect the ambient wind speed; the signal input terminal of the controller is connected to the wind speed sensor, and its control output terminal is electrically connected to the compressor, the battery energy storage module and the electric heating element respectively; wherein, the controller is configured to: control the compressor to be directly driven by the fan or driven by the battery energy storage module according to the wind speed signal detected by the wind speed sensor, and control the start and stop of the electric heating element, so as to switch or couple the first heating path driven by the compressor and the second heating path formed by the electric heating element.

[0019] In another aspect, the present invention provides an electronic device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of a coupled heating control method based on wind energy and battery energy storage as described in any of the preceding claims. In another aspect, the present invention provides a computer storage medium storing a computer program, which, when executed by a processor, implements the steps of a coupled heating control method based on wind energy and battery energy storage as described in any of the preceding claims.

[0020] The coupled heating control method based on wind energy and battery energy storage of the present invention clearly divides the control task into a macro-level energy scheduling layer and a dynamic collaborative compensation layer. The macro-level layer calculates the mixed heating weight coefficient based on wind speed, energy storage status and heating demand to complete static power allocation; the dynamic collaborative layer focuses on dealing with the dynamic mismatch problem caused by differences in physical characteristics, and performs model prediction, command buffering and real-time compensation, thereby achieving decoupling of control objectives. This allows the system to take into account global optimization objectives such as maximizing wind energy utilization and battery life, while also focusing on solving the local key problem of dynamic stability, thus improving the clarity, robustness and maintainability of the control system. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. By reading the detailed description of the embodiments below, the advantages and benefits of the solutions will become clear to those skilled in the art. The accompanying drawings are only for illustrating preferred embodiments and are not intended to limit the present invention. In the accompanying drawings: Figure 1 This is a schematic diagram of the overall process of a coupled heating control method based on wind power and battery energy storage according to an embodiment of the present invention.

[0022] Figure 2 This is a detailed flowchart illustrating a method for coupled heating control based on wind power and battery energy storage according to an embodiment of the present invention.

[0023] Figure 3 This is a control block diagram of a coupled heating control system based on wind power and battery energy storage, according to an embodiment of the present invention.

[0024] Figure 4 This is a schematic diagram of the internal processing flow of the dynamic mismatch suppression module according to an embodiment of the present invention.

[0025] Figure 5 This is a schematic diagram of closed-loop execution and model parameter fine-tuning according to an embodiment of the present invention. Detailed Implementation

[0026] To enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and thoroughly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art should fall within the protection scope of the present invention.

[0027] Figure 1This is a schematic flowchart of a coupled heating control method based on wind power and battery energy storage provided in an embodiment of the present invention, as shown below. Figure 1 As shown, this embodiment mainly includes: S101. Obtain the ambient wind speed, battery state of charge, and total required thermal power, and determine the hybrid heating weighting coefficient accordingly. The coefficient is used to define the power sharing ratio between the compressor heating path and the electric heating element heating path.

[0028] S102. Based on the hybrid heating weight coefficient and the total demand heat power, and combined with the established compressor dynamic model, forward optimization processing is performed to generate a smooth compressor power command.

[0029] For example, the controller receives a mixed heating weighting coefficient. To determine the total required thermal power, a dynamic model of the compressor is first established, followed by the generation of a forward buffer command for the compressor, and finally the calculation of the resistance compensation power to generate a smooth final power command.

[0030] S103. Calculate the compensation power of the heating element based on the difference between the total required heat power and the power command of the smoothing compressor.

[0031] S104. The power command of the smooth compressor and the compensation power of the electric heating element are converted into control signals to drive the compressor and the electric heating element to provide heat in a coordinated manner.

[0032] For example, the processed compressor power command reference trajectory is converted into a specific control signal for the compressor drive unit; at the same time, the resistance compensation power is converted into a specific control signal for the resistance heating element.

[0033] The coupled heating control method based on wind energy and battery energy storage of the present invention clearly divides the control task into a macro-level energy scheduling layer and a dynamic collaborative compensation layer. The macro-level layer calculates the mixed heating weight coefficient based on wind speed, energy storage status and heating demand to complete static power allocation; the dynamic collaborative layer focuses on dealing with the dynamic mismatch problem caused by differences in physical characteristics, and performs model prediction, command buffering and real-time compensation, thereby achieving decoupling of control objectives. This allows the system to take into account global optimization objectives such as maximizing wind energy utilization and battery life, while also focusing on solving the local key problem of dynamic stability, thus improving the clarity, robustness and maintainability of the control system.

[0034] In another implementation of the present invention, the method further includes: obtaining the heating demand based on the real-time temperature of the medium at the outlet of the heating system, and calculating the real-time temperature deviation and its rate of change; and calculating the total required heat power to eliminate the real-time temperature deviation using a heat demand calculation model.

[0035] In another implementation of the present invention, the heat demand calculation model is characterized as follows:

[0036] in, For total demand heat power, This is the proportionality coefficient. The integral coefficient is... The equivalent heat capacity coefficient of the heating system; This represents the real-time temperature deviation. The change rate is denoted as .

[0037] For example, the controller receives real-time temperature deviations. In each control cycle Perform difference operations to calculate its real-time rate of change. The controller internally establishes a heat demand calculation model, which incorporates real-time temperature deviations. and its rate of change Input the model, the integral term in the model Through history The values ​​are accumulated and calculated. The controller executes the model calculation and outputs the total required heat power of the system to eliminate the current temperature deviation and maintain the desired temperature change trend.

[0038] In another implementation of the present invention, the step of acquiring ambient wind speed, battery state of charge, and total demanded heat power, and determining the hybrid heating weight coefficient accordingly, includes: acquiring ambient wind speed, battery state of charge, and total demanded heat power; filtering the ambient wind speed to obtain a stable wind speed value; querying the fan characteristic curve based on the stable wind speed value to calculate the theoretical mechanical power that the current wind energy can capture and use to directly drive the compressor; evaluating the discharge capacity of the battery that can be used for auxiliary heating based on the battery state of charge, and determining the battery state coefficient characterizing this capacity; and calculating the hybrid heating weight coefficient based on the theoretical mechanical power, the total demanded heat power, and the battery state coefficient.

[0039] For example, the raw voltage or frequency signal of the ambient wind speed is acquired by a wind speed sensor, and the raw signal is converted into a digital wind speed sequence via an analog-to-digital converter. For digital wind speed sequences A first-order low-pass digital filter with a cutoff frequency of 0.1 Hz is used for filtering to remove high-frequency turbulent fluctuation components and output a stable wind speed value. .

[0040] Thermocouple or resistance temperature detector (RTD) signals of the outlet medium temperature of the heating system are acquired by temperature sensors and converted into real-time temperature values ​​by signal conditioning circuits. Read the target temperature setpoint from the user interface. According to the formula Calculate real-time temperature deviation .

[0041] The controller obtains battery voltage, current, and temperature data, as well as the battery's state of charge (SOC) value, through the communication bus of the battery management system (BMS).

[0042] The controller receives steady wind speed values. The controller has a pre-stored fan characteristic data table, which defines the correspondence between wind speed and the extractable mechanical power of the fan. The current wind speed can be obtained by looking up the table or by interpolation. The theoretical maximum mechanical power output of the downdraft fan Introduce a mechanical transmission efficiency coefficient According to the formula The theoretical mechanical power that can be used to directly drive the compressor was calculated. .

[0043] The controller receives the battery's state of charge (SOC), and the controller internally defines the battery state coefficient. A piecewise linear function that sets the first state-of-charge threshold. With the second state of charge threshold ;when At that time, the battery state factor ;when At that time, the battery state factor ;when At that time, the battery state factor .

[0044] In another implementation of the present invention, the formula for calculating the mixed heating weight coefficient is as follows:

[0045]

[0046]

[0047] in, This refers to the battery state coefficient. Theoretical mechanical power; This represents the total required thermal power.

[0048] For example, the controller receives theoretical mechanical power. Battery state coefficient and total demand for heat power The controller first calculates the theoretical mechanical power. Total demand heat power ratio The controller The value is subjected to amplitude limiting, with the upper limit set to 1 and the lower limit set to 0, resulting in the amplitude-limited ratio. Then calculate the product term. Finally, the dynamic mixed heating weight coefficient is calculated based on the formula. .

[0049] In another implementation of the present invention, the compressor dynamic model takes the compressor drive power command as input and the compressor estimated output heat power as output, adopts the form of first-order inertia plus pure delay, and its transfer function is characterized as follows:

[0050] in, It is a time constant. This is the pure delay time.

[0051] For example, such as Figure 4 As shown, establishing the compressor dynamic model specifically involves creating a simplified dynamic model of the compressor's thermal path within the controller. This model uses the compressor's drive power command. As input, the compressor's estimated output thermal power is used. For output; the model adopts the form of first-order inertia plus pure delay, and its transfer function is characterized as follows: The time constant The pure delay time is calculated using the heat capacity and thermal resistance parameters of the compressor, related piping, and heat exchanger. The parameters were calculated based on the refrigerant flow velocity and path length in the loop; during the initialization phase, the model's basic parameters were loaded from non-volatile memory. and During operation, the controller receives feedback on the actual operating status parameters of the compressor. Using the deviation between these actual parameters and the model's predicted output, it identifies and updates the model's time constant online through recursive least squares method. With delay time .

[0052] In another implementation of the invention, generating the compressor look-ahead buffer command further includes the controller receiving a mixed heating weighting coefficient. Total demand for heat power First, calculate the compressor's base command power. , will base instruction power The data is input to the compressor dynamic model; the model takes the current control cycle as the starting point and calculates the values ​​for the next N control cycles. Simulations predict the changing trend; simultaneously, the maximum allowable power increase slope of the compressor is set. and maximum allowable power drop slope As a constraint, a model predictive control algorithm is used. With the objective of ensuring the predicted output of the first N cycles is smooth and satisfies the slope constraint, an optimal compressor power command reference trajectory is solved in reverse. ,in This indicates the k-th control cycle in the future; the instruction value output in the current control cycle is... .

[0053] The controller receives the total required thermal power. and the current cycle compressor power command reference value According to the formula The feedforward component of the resistance compensation power is calculated; signals from the compressor's actual operating status sensors are received, and the dynamic model is used to predict the thermal power that the compressor should output in the current cycle based on historical commands. The controller acquires the measured value or high-confidence estimate of the actual output thermal power of the compressor. Deviation between the controller's computational model prediction and the actual result. The controller will record the deviation. Through a proportional feedback gain The feedback correction component of the resistance compensation power is calculated. The controller adds the feedforward component and the feedback correction component to obtain the final resistance-compensated power. .

[0054] The controller receives the compressor power command reference value. The controller has a pre-stored performance mapping table for the compressor drive unit, which establishes a correspondence between output thermal power commands and motor torque, speed, or inverter frequency commands. The controller uses table lookup or interpolation to... This is converted into a specific motor control signal, which is sent to the compressor drive inverter or servo driver via analog output or pulse width modulation (PWM) port; the controller also receives the resistance compensation power. The controller is based on the formula. The duty cycle calculation instruction, where The rated maximum power of the resistance heating element is given; the controller loads this duty cycle command into its internal PWM generator to generate a pulse sequence with the corresponding duty cycle, and controls the on / off state of the solid-state relay connected to the resistance heater through the digital output port.

[0055] This invention establishes a simplified dynamic model with first-order inertia and pure delay for the mechanical compression heating path, which has significant thermal inertia and response delay. Based on this model, future demand is simulated and predicted, and the original macroscopic power allocation command is smoothed and optimized in advance to generate a physically realizable compressor power command reference trajectory. This avoids imposing abrupt power commands on the compressor, a slow-dynamic component, which it cannot follow, thus eliminating a major excitation that causes system oscillations.

[0056] This invention, after generating a smooth compressor command trajectory, uses the instantaneous difference between the total demand heat power and this trajectory as a feedforward compensation amount. Simultaneously, it compares the predicted output of the compressor dynamic model with the actual output in real time, and uses a proportional feedback gain to form a feedback correction amount. The two are then superimposed to form the final resistance heater power command. This allows the rapidly responding resistance heating path to accurately and instantly compensate for the power output lag and prediction errors of the slow-dynamic compressor path, thereby ensuring that the system's total output power tracks the total demand in real time without steady-state error, effectively suppressing power fluctuations and temperature fluctuations when two paths are connected in parallel.

[0057] In another implementation of the present invention, such as Figure 2 As shown, it also includes: monitoring the actual operating status of the compressor and feeding back the operating parameters to the compressor dynamic model for online adaptive fine-tuning.

[0058] For example, such as Figure 5 As shown, the controller sends control signals and drives the actuator to work. After executing the sent control signals, it monitors and obtains the actual output thermal power of the compressor by using a high-precision power sensor or by calculating the fusion of current, voltage, and working fluid flow and temperature. The controller obtains the compressor suction pressure. Exhaust pressure and compressor speed The actual operating status parameters; the controller will output the actual thermal power. The predicted output of the dynamic model under the current input and historical states The power prediction error is obtained through comparison. The controller employs a recursive least squares estimation algorithm with a forgetting factor to minimize the power prediction error and adjusts the dynamic model transfer function online. The time constant in and delay time The controller stores the updated model parameters and applies them to the dynamic model prediction in the next control cycle.

[0059] Another aspect of the present invention, such as Figure 3 As shown, a coupled heating control system based on wind power and battery energy storage is provided, including: Fan, compressor, battery energy storage module, heating circuit, electric heating element, wind speed sensor, controller.

[0060] The wind turbine is used to capture wind energy and output mechanical energy.

[0061] The power input shaft of the compressor is mechanically connected to the output shaft of the fan for compressing the working fluid.

[0062] The battery energy storage module is used to store and release electrical energy.

[0063] The heating circuit includes a condenser, the working fluid inlet of which is connected to the outlet of the compressor, for transferring the heat energy of the compressed high-temperature and high-pressure working fluid to the external heat-consuming end.

[0064] The electric heating element is disposed on the heating circuit and electrically connected to the battery energy storage module, and is used to convert electrical energy into heat energy.

[0065] The wind speed sensor is used to detect ambient wind speed.

[0066] The signal input terminal of the controller is connected to the wind speed sensor, and its control output terminal is electrically connected to the compressor, the battery energy storage module and the electric heating element respectively.

[0067] The controller is configured as follows: Based on the wind speed signal detected by the wind speed sensor, the compressor is controlled to be driven directly by the fan or powered by the battery energy storage module, and the start and stop of the electric heating element is controlled to switch or couple the first heating path driven by the compressor with the second heating path formed by the electric heating element.

[0068] The coupled heating control system based on wind energy and battery energy storage of this invention clearly divides the control task into a macro-level energy scheduling layer and a dynamic collaborative compensation layer. The macro-level layer calculates the mixed heating weight coefficient based on wind speed, energy storage status and heating demand to complete static power allocation; the dynamic collaborative layer focuses on dealing with the dynamic mismatch problem caused by differences in physical characteristics, and performs model prediction, command buffering and real-time compensation, thereby achieving decoupling of control objectives. This allows the system to take into account global optimization objectives such as maximizing wind energy utilization and battery life, while also focusing on solving the local key problem of dynamic stability, thus improving the clarity, robustness and maintainability of the control system.

[0069] In another aspect of the present invention, the electronic device includes: a processor, a memory, and a communication bus and a communication interface.

[0070] in: The processor, memory, and communication interface communicate with each other via a communication bus.

[0071] A communication interface is used to communicate with other electronic devices or servers.

[0072] The processor is used to execute programs, specifically, to execute any of the steps of the coupled heating control method based on wind energy and battery energy storage in the above embodiments.

[0073] Specifically, the program may include program code, which includes computer operation instructions.

[0074] The processor may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application. The one or more processors included in the smart device may be processors of the same type, such as one or more CPUs; or they may be processors of different types, such as one or more CPUs and one or more ASICs.

[0075] Memory is used to store programs. Memory may include high-speed RAM, and may also include non-volatile memory, such as at least one disk drive.

[0076] Specifically, the program can be used to cause the processor to execute the steps of any of the coupled heating control methods based on wind power and battery storage described in the embodiments. The specific implementation of each step in the program can be found in the corresponding descriptions of the steps and units executed in any of the coupled heating control methods based on wind power and battery storage described above, and will not be repeated here. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the devices and modules described above can be referred to the corresponding process descriptions in the foregoing method embodiments.

[0077] An exemplary embodiment of this application also provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to perform the methods of various embodiments of this application.

[0078] The methods described above according to embodiments of the present invention can be implemented in hardware, firmware, or as software or computer code that can be stored in a recording medium (such as a CD-ROM, RAM, floppy disk, hard disk, or magneto-optical disk), or as computer code originally stored on a remote recording medium or a non-transitory machine-readable medium and subsequently stored on a local recording medium, downloaded via a network. Thus, the methods described herein can be processed by software stored on a recording medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware (such as an ASIC or FPGA). It is understood that the computer, processor, microprocessor controller, or programmable hardware includes storage components (e.g., RAM, ROM, flash memory, etc.) capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods described herein. Furthermore, when a general-purpose computer accesses code used to implement the methods shown herein, the execution of the code transforms the general-purpose computer into a dedicated computer for executing the methods shown herein.

[0079] Specific embodiments of the present invention have now been described. Other embodiments are within the scope of the appended claims. In some cases, the actions described in the claims can be performed in a different order and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result.

[0080] It should be noted that all directional indications (such as up, down, left, right, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship between the components in a certain order (as shown in the figure). If the specific order changes, the directional indication will also change accordingly.

[0081] In the description of this invention, the terms "first" and "second" are used only for convenience in describing different components or names, and should not be construed as indicating or implying a sequential relationship, relative importance, or implicitly specifying the number of technical features indicated. Thus, a feature defined with "first" and "second" may explicitly or implicitly include at least one of that feature.

[0082] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0083] It should be noted that although specific embodiments of the present invention have been described in detail with reference to the accompanying drawings, this should not be construed as limiting the scope of protection of the present invention. Various modifications and variations that can be made by those skilled in the art without inventive effort within the scope described in the claims still fall within the scope of protection of the present invention.

[0084] The examples of the embodiments of the present invention are intended to concisely illustrate the technical features of the embodiments of the present invention, so that those skilled in the art can intuitively understand the technical features of the embodiments of the present invention, and are not intended to be an improper limitation of the embodiments of the present invention.

[0085] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A coupled heating control method based on wind energy and battery energy storage, characterized in that, include: The ambient wind speed, battery state of charge, and total required thermal power are obtained, and a hybrid heating weighting coefficient is determined accordingly. The coefficient is used to define the power sharing ratio between the compressor heating path and the electric heating element heating path. Based on the aforementioned hybrid heating weighting coefficient and total demand heat power, and combined with the established compressor dynamic model, forward optimization processing is performed to generate smooth compressor power commands. The compensation power of the heating element is calculated based on the difference between the total required thermal power and the power command of the smoothing compressor. The power command of the smooth compressor and the compensation power of the electric heating element are converted into control signals to drive the compressor and the electric heating element to provide heat in a coordinated manner.

2. The method according to claim 1, characterized in that, Also includes: The heating demand is obtained based on the real-time temperature of the medium at the outlet of the heating system, and the real-time temperature deviation and its rate of change are calculated. The total required heat power to eliminate the real-time temperature deviation is calculated using a heat demand calculation model.

3. The method according to claim 2, characterized in that, The heat demand calculation model is characterized as follows: in, For total demand heat power, This is the proportionality coefficient. The integral coefficient is... The equivalent heat capacity coefficient of the heating system; This represents the real-time temperature deviation. The change rate is denoted as .

4. The method according to claim 2, characterized in that, The process of acquiring ambient wind speed, battery state of charge, and total demand for heat power, and determining the hybrid heating weighting coefficient accordingly, includes: Obtain ambient wind speed, battery state of charge, and total required thermal power; The ambient wind speed is filtered to obtain a stable wind speed value. Based on the stable wind speed value, the wind turbine characteristic curve is queried to calculate the theoretical mechanical power that the current wind energy can capture and be used to directly drive the compressor. The battery's state of charge is used to assess its discharge capacity for auxiliary heating, and a battery state coefficient characterizing this capacity is determined. The hybrid heating weighting coefficient is calculated based on the theoretical mechanical power, the total demanded thermal power, and the battery state coefficient.

5. The method of claim 4, wherein, The formula for calculating the weighting coefficient of the mixed heating system is as follows: wherein, is the battery state coefficient; is the theoretical mechanical power; is the total demanded thermal power.

6. The method of claim 1, wherein, The compressor dynamic model takes the compressor drive power command as input and the compressor's estimated output thermal power as output. It adopts a first-order inertia plus pure delay form, and its transfer function is characterized as follows: wherein is a time constant, is a pure delay time.

7. The method of claim 6, wherein, Also includes: The actual operating status of the compressor is monitored, and the operating parameters are fed back to the compressor dynamic model for online adaptive fine-tuning.

8. A coupling heat supply control system based on wind energy and battery energy storage, characterized in that, include: Fans, compressors, battery energy storage modules, heating circuits, electric heating elements, wind speed sensors, and controllers; The wind turbine is used to capture wind energy and output mechanical energy; The power input shaft of the compressor is mechanically connected to the output shaft of the fan for compressing the working fluid; The battery energy storage module is used to store and release electrical energy; The heating circuit includes a condenser, the working fluid inlet of which is connected to the outlet of the compressor, for transferring the heat energy of the compressed high-temperature and high-pressure working fluid to the external heat-consuming end. The electric heating element is disposed on the heating circuit and electrically connected to the battery energy storage module, and is used to convert electrical energy into heat energy. The wind speed sensor is used to detect ambient wind speed; The signal input terminal of the controller is connected to the wind speed sensor, and its control output terminal is electrically connected to the compressor, the battery energy storage module and the electric heating element respectively. The controller is configured as follows: According to the wind speed signal detected by the wind speed sensor, the compressor is controlled to be directly driven by the fan or powered by the battery energy storage module, and the start and stop of the electric heating element are controlled to switch or couple the first heat supply path driven by the compressor and the second heat supply path formed by the electric heating element.

9. An electronic device, comprising: The application also provides a computer program product, comprising a computer program and a computer readable storage medium, wherein the computer program is stored in the computer readable storage medium, and the computer program is configured to be executed by a processor to implement the steps of the wind energy and battery energy coupling heat supply control method according to any one of claims 1 to 7. The computer storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the wind energy and battery energy coupling heat supply control method according to any one of claims 1 to 7.

10. A computer storage medium, characterized in that, ​

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