Wind power access control method for preassembled equipment compartment

Abstract

The invention relates to the field of wind power access control, and discloses a wind power access control method for a preassembled equipment cabin, which comprises the following steps: dynamically adjusting the power output of a wind turbine generator and the charge and discharge rate of an energy storage system by monitoring the wind speed, the running state of the wind turbine generator and the load demand of a power grid in real time, and combining with an optimal control algorithm and Lyapunov stability analysis to realize the wind power access control of the preassembled equipment cabin. Efficient utilization of wind power resources and stable operation of the system are ensured, and power grid load balance is optimized; the invention discloses a wind power access control system for a preassembled equipment cabin. The system comprises a wind turbine generator module, an energy storage system module, an optimal control module and a stability analysis module. According to the invention, through cooperative scheduling of the wind turbine generator and the energy storage system, Lyapunov stability analysis and an optimal control algorithm, accurate balance between wind power output and power grid load is realized; the wind power utilization rate is improved, the service life of the battery is prolonged, energy waste is reduced, and the problems of wind speed fluctuation, excessive charging and discharging of the battery and the like in the traditional technology are effectively solved.

Description

Technical Field

[0001] The present invention relates to the field of wind power access control, and in particular to a wind power access control method for a prefabricated equipment cabin. Background Art

[0002] At present, the control systems for wind power access to the grid generally adopt control strategies based on fixed scheduling or empirical rules; there is a certain power difference between wind turbines and grid loads, especially when the wind speed changes greatly, traditional systems find it difficult to respond to such changes efficiently.

[0003] First, the scheduling of wind turbines in the prior art mostly relies on simple preset rules or static scheduling strategies. The power output of wind turbines is usually directly linked to changes in wind speed. When the wind speed changes drastically, the system fails to adjust the output power in time to cope with the fluctuations in the grid load. This fixed control strategy makes it difficult for wind turbines to adjust their power in time when faced with sudden changes in wind speed, which leads to unbalanced grid load or waste of wind power resources.

[0004] Secondly, the energy storage control method of traditional systems is generally simple; the charging and discharging strategy of the energy storage system is usually based on the relationship between the current battery power and the grid load, but due to the failure to dynamically monitor the health status of the energy storage system, the energy storage battery is prone to over-charging and over-discharging; especially when the wind speed is high, the energy storage system may charge too fast or the energy storage battery capacity may be too large, resulting in battery performance degradation; traditional systems fail to effectively avoid battery loss under high load, which will significantly reduce the battery life and system stability during long-term operation.

[0005] In addition, most of the existing wind power access control methods lack system stability analysis; although some systems can perform real-time scheduling, they often ignore the stability of the system during wind turbine output fluctuations and grid load changes; the scheduling process of wind turbines and energy storage systems, if lacking stability control, can easily lead to unstable grid loads and even cause power supply interruptions in extreme cases; therefore, the present invention proposes a wind power access control method for a prefabricated equipment cabin to address the deficiencies of the prior art. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides a wind power access control method for a prefabricated equipment cabin, which solves the imbalance problem between the output power of the wind turbine and the grid load, and avoids system instability caused by wind speed fluctuations and grid load changes; by introducing an optimal control algorithm and real-time data monitoring, and combining the Lyapunov stability theory to perform system stability analysis, the present invention can dynamically adjust the power output of the wind turbine and the charge and discharge rate of the energy storage system, ensure grid load balance, and effectively extend the service life of the energy storage battery, thereby improving the efficiency and stability of the wind power access system.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: A wind power access control method for a prefabricated equipment cabin comprises the following steps:

[0008] S1, real-time monitoring of wind speed, wind turbine operating status and grid load demand, and adjustment of wind turbine power output according to monitoring results;

[0009] S2, obtaining the power status of the energy storage system, and adjusting the charge and discharge rate of the energy storage system based on the power output of the wind turbine and the grid demand;

[0010] S3. Use the optimal control algorithm to optimize the coordinated dispatch of wind turbines and energy storage systems, and adjust the operating parameters of wind turbine output and energy storage system charge and discharge rates;

[0011] S4. Based on stability analysis, use Lyapunov stability theory to analyze the dispatching process of wind turbines and energy storage systems and constrain the system;

[0012] S5. When the wind speed is high, increase the power output of the wind turbine and charge the energy storage system;

[0013] S6. When the wind speed is low or the grid load is high, reduce the power output of the wind turbine and release the stored electric energy to balance the grid load.

[0014] Preferably, the optimal control algorithm includes optimizing through the Pontryagin maximum principle and the Hamilton-Jacobi-Bellman equation to calculate the dispatching strategy of the wind turbine output power and the charging and discharging rate of the energy storage system.

[0015] Preferably, the stability analysis uses Lyapunov stability theory so that the Lyapunov function of the system always decreases during the scheduling process to ensure that the system is in a stable state.

[0016] Preferably, the power state of the energy storage system is adjusted in real time based on the grid load demand and the power output of the wind turbine generator set to avoid over-charging and over-discharging of the battery.

[0017] Preferably, the difference between the power output of the wind turbine and the power grid demand is adjusted by minimizing an objective function, wherein the objective function comprehensively considers the balance between the power of the wind turbine, the amount of energy storage system and the charge and discharge rate.

[0018] Preferably, the coordinated scheduling of the wind turbine and the energy storage system is optimized based on the following objective function:

[0019] ;

[0020] in, is the objective function, which is used to quantify the optimization target in the process of wind power access control; For wind turbines at time Output power; For the power grid at all times The power demand; For energy storage system at all times Battery level; For energy storage system at all times The optimal power; For energy storage system at all times The charge and discharge rate; is the weight coefficient, which is used to measure the cost of the difference between the output power of the wind turbine and the power demanded by the grid; is the weight coefficient, which is used to measure the cost of the difference between the energy storage system power and the optimal power; is the weight coefficient, which is used to measure the cost of the charging and discharging rate of the energy storage system.

[0021] The present invention also provides a wind power access control system for a prefabricated equipment cabin, comprising:

[0022] Wind turbine module, used to monitor wind speed and wind turbine operating status in real time and adjust wind turbine power output according to grid load demand;

[0023] Energy storage system module, used to monitor the energy storage system power in real time and adjust the charging and discharging rate according to grid demand and wind turbine power output;

[0024] The optimal control module is used to coordinate the wind turbine and the energy storage system through the optimal control algorithm, and calculate the scheduling strategy of the wind turbine power output and the energy storage system charging and discharging rate;

[0025] The stability analysis module is used to perform stability analysis on the dispatching process of wind turbines and energy storage systems.

[0026] Preferably, the optimal control module includes a Pontryagin maximum principle calculation module and a HJB equation solving module, which are used to solve the optimal scheduling strategy for the output power of the wind turbine set and the charging and discharging rate of the energy storage system.

[0027] Preferably, the stability analysis module constrains the scheduling process through Lyapunov stability theory, so that the system can maintain stable operation under dynamic changes.

[0028] Preferably, the wind turbine module and the energy storage system module work in coordination with the cloud dispatching platform through a real-time feedback mechanism to exchange real-time data for adjusting the operating status of the system.

[0029] The present invention provides a wind power access control method for a prefabricated equipment cabin, which has the following beneficial effects:

[0030] 1. The present invention adopts a technical solution of coordinated scheduling of wind turbines and energy storage systems, achieving the effect of achieving a precise balance between wind power output and grid demand; compared with the common single wind turbine control strategy in the prior art, the present invention solves the problem of insufficient adaptability of traditional methods in dealing with wind speed fluctuations and grid load changes through the application of real-time data monitoring and optimal control algorithms.

[0031] 2. The present invention adopts Lyapunov stability theory to analyze the system stability, so as to achieve the effect of always keeping the system running stably during the scheduling process of wind turbines and energy storage systems. Compared with traditional methods, the existing technology relies more on experience scheduling and cannot cope with complex changes in real time. The present invention solves the instability problem that may occur in the system under extreme working conditions through stability analysis.

[0032] 3. The present invention adopts a technical solution combining real-time feedback mechanism with optimal control, which improves the utilization rate of wind power and reduces energy waste. In traditional systems, wind turbines often waste wind energy due to lack of flexible scheduling, while the present invention solves this problem through intelligent scheduling, ensures the efficient utilization of wind power, and enables the energy storage system to be charged in the optimal time period.

[0033] 4. The present invention achieves the effect of extending the battery life by dynamically adjusting the charge and discharge rate of the energy storage system; the prior art usually ignores the health status of the battery, resulting in excessive charge and discharge of the battery, while the present invention accurately controls the charge and discharge rate according to real-time data, solves the problem of excessive battery use, effectively extends the battery life and reduces maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 is a flow chart of the method of the present invention;

[0035] Figure 2 It is a system architecture diagram of the present invention. DETAILED DESCRIPTION

[0036] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0037] See also Figure 1 The embodiment of the present invention provides a wind power access control method for a prefabricated equipment cabin, comprising the following steps:

[0038] S1, real-time monitoring of wind speed, wind turbine operating status and grid load demand, and adjustment of wind turbine power output according to monitoring results;

[0039] During the implementation of the present invention, the efficient scheduling of wind turbines and the optimized operation of energy storage systems are inseparable from the accurate acquisition and processing of real-time data; firstly, by real-time monitoring of wind speed, the operating status of wind turbines and grid load demand, the required basic data can be provided for subsequent steps; this process plays a vital role in the system's responsiveness, scheduling accuracy and overall stability.

[0040] In this embodiment, the system uses multiple sensors and monitoring equipment to obtain wind speed, wind turbine operating status and grid load demand respectively; wind speed information is obtained through a meteorological station or a wind speed sensor, which is deployed near the wind farm or on top of the wind turbine and can monitor wind speed changes in different areas in real time; the operating status of the wind turbine is provided by its internal control system, including speed, voltage, current, temperature and other parameters, to ensure that the operation of the unit at each moment is accurately controllable.

[0041] In specific implementation, the wind speed monitoring system can record the changes in wind speed in real time, and predict and adjust the output of the wind turbine according to the changes in wind speed; the operating status data of the wind turbine includes its power generation power, motor speed, vibration, etc. These data are collected by the wind turbine control system and uploaded to the centralized control system for dynamic adjustment of the power output of the wind turbine; the grid load demand is obtained through the grid dispatching system or the power market platform. The grid load demand may change at any time, so real-time monitoring is required so that the control system can respond promptly according to demand fluctuations.

[0042] As an option, these monitoring data can be transmitted to the control center via a wireless communication module. Through the data exchange platform, the output of the wind turbine, the status of the energy storage system and the grid load demand data will be transmitted to the optimal control module in real time for further processing within a short period of time. During the operation of the system, the monitoring equipment will continue to provide real-time feedback to ensure that the system always makes decisions based on the most accurate data.

[0043] Specifically, the frequency of wind speed data collection is usually once per second or higher, ensuring that real-time changes in wind speed can be captured in a very short time; these data are directly transmitted to the control system through the data acquisition system, and the control system predicts and schedules the output of the wind turbine according to the current wind speed and historical data; for the operating status of the wind turbine, in addition to the basic power output, information such as the operating temperature of the unit, lubricating oil status, motor load, etc. will also be collected. These data will help the system more comprehensively evaluate the health of the wind turbine and make corresponding scheduling decisions.

[0044] In one possible implementation, the system builds a prediction model by integrating real-time data on wind speed, unit power output and grid load. Based on the fusion of historical data and real-time data, the model can estimate the output of wind turbines for a period of time in the future. This prediction result will serve as the basis for scheduling decisions, helping the system decide whether to increase or decrease the output power of wind turbines, or whether it is necessary to start the energy storage system to store or release energy.

[0045] In this process, it is crucial to obtain the load demand of the power grid; changes in the load demand data of the power grid directly affect the dispatching decisions of wind turbines and energy storage systems; the power grid load is usually monitored and predicted in real time through the power dispatching center; the real-time changes in load demand and the operating status of the power grid will be fed back to the system as an important basis for dispatching wind turbines and energy storage systems; the real-time data of the power grid load includes not only the current demand power, but also the frequency, stability and future demand forecasts of the power grid.

[0046] In some embodiments, the output power of the wind turbine and the power demand of the power grid It will make adjustments based on the comparison results of real-time data; if the wind speed is high, the system will ensure the stability of the grid load by increasing the power output of the wind turbines; when the wind speed is low, the system will automatically adjust the power output of the wind turbines to avoid excessive dependence on wind power, thereby ensuring the stable operation of the grid.

[0047] There is a certain functional relationship between wind speed and wind turbine power output, specifically:

[0048] ;

[0049] in, For wind turbines at time Output power (unit: kW); is the constant coefficient of the wind turbine, which indicates the conversion ratio between wind speed and power; is the current wind speed (unit: m / s), which comes from meteorological equipment or sensors.

[0050] According to real-time wind speed and grid demand , the control system will calculate the optimal wind turbine output power , ensuring that it matches the grid load demand; real-time monitoring of wind speed and grid demand and adjustment are made, effectively avoiding grid load imbalance and unstable wind turbine output.

[0051] S2, obtaining the power status of the energy storage system, and adjusting the charge and discharge rate of the energy storage system based on the power output of the wind turbine and the grid demand;

[0052] In the above step S1, we provide basic data for the system by real-time monitoring of wind speed, operating status of wind turbines and grid load demand; the collection of this data is crucial for the management of the energy storage system in subsequent steps; the goal of step S2 is to adjust the charge and discharge rate of the energy storage system by obtaining the power status of the energy storage system in real time, combined with the power output of the wind turbines and grid demand; this process not only involves data collection, but also needs to judge the operation strategy of the energy storage system according to the current grid load and wind speed conditions of the system, so as to ensure efficient use of energy and system stability.

[0053] In this embodiment, the power status of the energy storage system is monitored by a storage management system (BMS), which obtains the status information of the energy storage system in real time through sensors and battery monitoring units; the power, health status, charge and discharge rate and other information of the energy storage battery will be fed back to the control system so as to make decisions in real-time scheduling; the system will also consider the output power of the wind turbine and the load demand of the power grid, and dynamically adjust the charge and discharge rate of the energy storage system; specifically, when the power output of the wind turbine is higher than the power grid demand, the energy storage system will enter the charging mode; and when the output power of the wind turbine is lower than the power grid demand, the energy storage system will release electric energy to meet the power grid load demand.

[0054] In the control strategy, the charge and discharge rate of the energy storage system (i.e., the charge and discharge power of the battery) is a key parameter; the charge and discharge rate of the energy storage system It can be dynamically adjusted through the system control algorithm; the system decides whether charging or discharging is needed, as well as the charging and discharging rate, based on the difference between the power output of the wind turbine and the grid demand.

[0055] As an option, the charging and discharging rate adjustment of the energy storage system depends not only on the real-time power difference between the wind turbine and the grid, but also on the current charge of the energy storage battery and its health status. To ensure that the energy storage battery is not overcharged and overdischarged, the system sets an upper limit on the charging and discharging rate based on the battery's charge status, maximum charge / discharge capacity, and battery health status. The system monitors these parameters in real time through the battery management system (BMS) and flexibly adjusts the charging and discharging strategy based on actual conditions.

[0056] Specifically, in a possible implementation, assuming that the current power output of the wind turbine is , the grid load demand is , the current power of the energy storage system is The goal is to enable the energy storage system to maintain a reasonable battery charge while meeting the needs of the grid; the charging and discharging rate It can be dynamically calculated using the following formula:

[0057] ;

[0058] in: The wind turbine is at the time Power output (in kW); The power grid is at Required power (unit: kW); The energy storage system is at the moment The charge and discharge rate (unit: kW).

[0059] In one possible implementation, when the wind turbine output power Greater than grid load demand When the wind turbine power output is lower than the grid demand, the energy storage system will discharge to supplement the grid demand, and the discharge rate is the difference between the grid demand and the wind power output. If the wind power output is equal to the grid demand, the energy storage system remains static and does not perform charging or discharging operations.

[0060] As a further option, the charge and discharge rates of the energy storage system can also be constrained based on the health of the battery to avoid overcharging or over-discharging the battery; for example, when the battery reaches its maximum charge state, the system will automatically reduce the charge rate to prevent overcharging; similarly, when the battery approaches its minimum state, the system will also limit the discharge rate.

[0061] In another implementation, the battery management system (BMS) can also dynamically adjust the charge and discharge rate based on the battery's charge and discharge history, temperature and other parameters; the BMS ensures that the battery does not operate in an unsafe state during operation, thereby extending the battery life and ensuring the stability of the system.

[0062] Generally speaking, the goal of the system is to maximize the utilization of wind power and effectively schedule the charging and discharging process of the energy storage system to avoid battery damage while meeting the grid load. To achieve this goal, the control system continuously adjusts the charging and discharging rate of the energy storage system based on real-time monitoring data to ensure that the difference between wind turbine output and grid demand is balanced, and to avoid system instability caused by wind speed fluctuations or excessive changes in grid load.

[0063] S3. Use the optimal control algorithm to optimize the coordinated dispatch of wind turbines and energy storage systems, and adjust the operating parameters of wind turbine output and energy storage system charge and discharge rates;

[0064] In steps S1 and S2, we have obtained the wind speed, the operating status of the wind turbine and the grid load demand through real-time monitoring, and adjusted the power output of the wind turbine and the charge and discharge rate of the energy storage system based on these data; at this point, the system already has the required real-time data basis; next, the goal of step S3 is to optimize the scheduling of wind turbines and energy storage systems through the optimal control algorithm to achieve more efficient energy utilization and ensure the load balance of the grid; the application of the optimal control algorithm can not only maximize the efficiency of wind power generation, but also ensure that the energy storage system operates within the appropriate power range, thereby avoiding excessive charging and discharging of the battery and improving the stability and reliability of the system.

[0065] In this embodiment, the optimal control algorithm is optimized based on the Pontryagin maximum principle and the Hamilton-Jacobi-Bellman (HJB) equation; specifically, the goal of the optimal control problem is to adjust the output power of the wind turbine and the charging and discharging rate of the energy storage system , so that the system can maximize the utilization of wind power while meeting the grid load demand and avoid overcharging and discharging of the energy storage system; to achieve this goal, the control system optimizes the operating parameters of the wind turbine and the energy storage system through real-time data and model prediction, ensuring that the output of the wind turbine matches the grid load as much as possible and accurately dispatches the energy storage system.

[0066] Generally speaking, the output power of a wind turbine is Power demanded by the grid The difference between them needs to be compensated by the charging and discharging rate of the energy storage system; the charging and discharging rate calculated by the optimal control algorithm The control algorithm relies on this difference; based on the output of the control algorithm, the energy storage system will adjust the charging and discharging rate in real time to maximize the efficiency of wind power while ensuring the needs of the power grid.

[0067] Objective Function : Specifically, the optimal control algorithm solves the variational problem of Pontryagin's maximum principle to find a set of optimal control inputs to ensure that the system reaches the optimal state under multiple constraints; the Hamiltonian function in Pontryagin's maximum principle is usually expressed as:

[0068] ;

[0069] in, is the objective function, which is used to quantify the optimization target in the process of wind power access control; For wind turbines at time Output power; For the power grid at all times The power demand; For energy storage system at all times Battery level; For energy storage system at all times The optimal power; For energy storage system at all times The charge and discharge rate; is the weight coefficient, which is used to measure the cost of the difference between the output power of the wind turbine and the power demanded by the grid; is the weight coefficient, which is used to measure the cost of the difference between the energy storage system power and the optimal power; is the weight coefficient, which is used to measure the cost of the charging and discharging rate of the energy storage system.

[0070] According to the Pontryagin maximum principle, the optimal control strategy can be obtained by solving the partial derivatives of the Hamiltonian function; specifically, by solving the following equation:

[0071] ;

[0072] in, is the Hamiltonian function, which represents the objective function of the optimal control problem; For wind turbines at time Power output (in kW); For energy storage system at all times The charge and discharge rate (unit: kW); This expression represents the Hamiltonian function Power output of wind turbines The partial derivative of represents the influence of wind turbine power output on the objective function. The optimal control algorithm finds the optimal power output by solving this derivative. This expression represents the Hamiltonian function Charging and discharging rate of energy storage system By calculating this derivative, the system can derive the optimal charging and discharging rate of the energy storage system, thereby ensuring a balance between grid demand and wind turbine output.

[0073] The optimal wind turbine power output can be obtained and the energy storage system charge and discharge rate , thus achieving the optimal scheduling of the system.

[0074] In one possible implementation, the optimal control algorithm can be further optimized by combining the Hamilton-Jacobi-Bellman equation; the HJB equation is a partial differential equation that describes the optimal control problem and can help solve the optimal control strategy at different times and states; the equation is usually expressed as:

[0075] ;

[0076] in, is the value function of the system, which represents the expected benefit or cost of the system under a given state; is the time variable; is the partial derivative of the system value function with respect to time, which represents the change in value over time; is the power output rate of the wind turbine, that is, the change in wind turbine power per unit time; is the charge and discharge rate of the energy storage system, that is, the change in the charge and discharge power of the energy storage system per unit time; is a Hamiltonian function, which represents the total cost or benefit of the system under a given state, including the power output of the wind turbine, the charging and discharging rate of the energy storage system, and other related constraints and objectives.

[0077] By solving this equation, the optimal operation strategy at each time point can be obtained; the solution of the HJB equation provides a precise control strategy for the system, thereby ensuring that the wind turbine and energy storage system can work together under various dynamic changes.

[0078] As an option, the optimal control algorithm can also be combined with a deep learning model; deep learning can help the system better predict future wind speed changes and grid demand, thereby providing more accurate input data for optimal control; through the training of deep learning models, the system can continuously optimize its predictive capabilities and improve the accuracy of control decisions.

[0079] In some embodiments, the relationship between the power output of the wind turbine and the charge and discharge rate of the energy storage system may not only be determined by the grid demand and wind speed, but may also be affected by other factors; for example, factors such as the health of the battery and the ambient temperature may also affect the charge and discharge rate of the energy storage system; therefore, the system should consider these additional constraints to ensure that the best decision can still be made under various complex conditions.

[0080] S4. Based on stability analysis, use Lyapunov stability theory to analyze the dispatching process of wind turbines and energy storage systems and constrain the system;

[0081] In the above steps, the system adjusts the power output of the wind turbine and the charging and discharging rate of the energy storage system by real-time monitoring of the wind speed, the operating status of the wind turbine and the grid load demand; next, the goal of step S4 is to use the Lyapunov stability theory to analyze the scheduling process of the wind turbine and the energy storage system to ensure the stable operation of the entire system, especially in a dynamically changing external environment (such as wind speed changes, grid load fluctuations, etc.), to maintain the stability of the system and avoid instability or excessive load caused by scheduling imbalance.

[0082] In this embodiment, the system first needs to select a suitable Lyapunov function to represent the energy state of the system or the stability of the system; the Lyapunov function, as a stability indicator of the system, can reflect the gap between the current state and the stable state of the system; on this basis, the system can perform stability analysis to ensure that the system is always in a stable state during the scheduling process of the wind turbine power output and the charging and discharging rate of the energy storage system.

[0083] Generally speaking, when Lyapunov stability theory is applied to the system, it is necessary to first determine the state variables of the system; the power output of the wind turbine , power demand of the power grid , the state of charge of the energy storage system and the charging and discharging rate of the energy storage system These are all key parameters that affect system stability; by monitoring these variables, the control system can adjust the scheduling strategy in real time;

[0084] Lyapunov function Designed for:

[0085] ;

[0086] in: For wind turbines at time Power output (in kW); is the power demand of the power grid (unit: kW); is the current power of the energy storage system (unit: kWh); is the optimal power of the energy storage system (unit: kWh), which is optimized based on grid demand and wind turbine output.

[0087] The Lyapunov function The meaning of is: it measures the difference between the output power of wind turbines and the demand of the power grid, and the difference between the current power of the energy storage system and the optimal power. By minimizing this Lyapunov function, the system can adjust its operating state in different time periods to ensure the balance of power grid load and the reasonable working state of the energy storage system.

[0088] In one possible implementation, to ensure stable operation of the system, the control system calculates the derivative of the Lyapunov function , and use it to judge the stability of the system; according to Lyapunov stability theory, the stability condition of the system requires:

[0089] ;

[0090] if If the derivative is less than zero, it means that the system state is converging to the equilibrium point, that is, the system is stable; if the derivative is greater than zero, it means that the system has an unstable trend, which may cause a large difference between the power output of the wind turbine and the grid load, or an unreasonable charging and discharging rate of the energy storage system, which in turn leads to an imbalance in the grid load.

[0091] Specifically, the derivative of the Lyapunov function The calculation involves the power output of the wind turbine , Grid load demand and the state of charge of the energy storage system , and the charging and discharging rate of the energy storage system ; According to the scheduling results of the control system, the derivative It can be expressed as:

[0092] ;

[0093] in: is the rate of change of wind turbine power output; is the optimal power of the energy storage system (unit: kWh); is the rate of change of the charging and discharging rate of the energy storage system.

[0094] By calculating this derivative, the system can determine whether it is necessary to adjust the dispatching strategy to maintain system stability; specifically, the system will adjust the charging and discharging rate in real time according to the changes in real-time wind speed, wind turbine output, energy storage system power and grid load. and wind turbine output , ensuring that the derivative It is always less than zero, thus achieving stable operation.

[0095] As an option, in order to further improve the stability of the system, a multi-level Lyapunov stability analysis method can be used to evaluate in multiple time dimensions to prevent the system from becoming unstable during long-term fluctuations; through this method, the system can not only handle short-term wind speed fluctuations and load changes, but also maintain high efficiency and stability during long-term operation.

[0096] S5. When the wind speed is high, increase the power output of the wind turbine and charge the energy storage system;

[0097] In the above steps, we have completed the status monitoring of the wind turbine and the energy storage system and the adjustment of the charging and discharging rate; next, the goal of step S5 is to adjust the power output of the wind turbine based on the real-time wind speed change, and use the energy storage system for charging operations when the wind speed is high; through this step, the system can ensure that the output of the wind turbine is balanced with the grid demand, and avoid energy waste when the wind speed is high, while providing backup power for the grid load demand when the wind speed is lower in the future.

[0098] In this embodiment, based on real-time wind speed monitoring, when the wind speed is high, the power output of the wind turbine will increase with the increase of wind speed; at this time, the power generation of the wind turbine may exceed the immediate demand of the power grid. In order to avoid power waste, the system will store the excess power through the energy storage system; the charging rate of the energy storage system It will be dynamically adjusted according to the difference between the wind turbine output and the grid load; when the wind turbine output power exceeds the grid demand, the energy storage system will enter charging mode.

[0099] In general, the charging process of the energy storage system is scheduled by the control system according to the real-time wind speed and grid demand; the charging rate of the energy storage system usually changes dynamically, and the control system will calculate and optimize the charging power of the energy storage system in real time to avoid damage to the battery due to overcharging; the charging rate It will vary in different situations. The specific charging rate depends on the power output of the wind turbine. and grid load demand .

[0100] Specifically, the charging rate of the energy storage system can be calculated by the following formula:

[0101] ;

[0102] in: For wind turbines at time Power output (in kW); For the power grid at all times Required power (unit: kW); For energy storage system at all times The charging rate (in kW).

[0103] In one possible implementation, the difference between the power output of the wind turbine and the grid load is adjusted according to the change in real-time wind speed; when the wind speed is high, the power output of the wind turbine increases, and the system first determines the current grid load; if the grid demand is lower than the output of the wind turbine, the system stores the additional electrical energy in the energy storage system; at this time, the charging rate of the energy storage system is directly related to the difference between the wind turbine and the grid load.

[0104] As an option, the charging rate of the energy storage system is also limited by the current charge of the battery and the health of the battery; to avoid overcharging the battery, the system monitors the current charge of the energy storage system. , and set a maximum charge value , ensuring that the battery operates within a safe power range; when the energy storage capacity approaches the upper limit, the system will appropriately reduce the charging rate.

[0105] In some embodiments, the system can also introduce an additional control parameter to further optimize the charging process of the energy storage system; this control parameter takes into account factors such as the charging efficiency and ambient temperature of the energy storage system to prevent the energy storage system from charging too fast under extreme conditions; by controlling the charging process, the system can improve the long-term stability of the energy storage system and the battery life.

[0106] Specifically, the dynamic adjustment of the energy storage system's charging rate at high wind speeds is based on the following conditions:

[0107] Wind turbine output power and grid load demand The difference between

[0108] Current capacity of the energy storage system And the maximum power ;

[0109] The health of the battery, i.e. whether the battery can be charged safely and quickly to avoid overcharging or battery damage.

[0110] In one possible implementation, the system can also be combined with a predictive model to adjust the charging strategy in advance based on the future wind speed forecast of the wind farm and the grid load forecast. By predicting wind speed and load demand, the system can more accurately determine the timing and charging rate of the energy storage system, thereby improving the efficiency of the entire system.

[0111] S6. When the wind speed is low or the grid load is high, reduce the power output of the wind turbines and release the stored electric energy to balance the grid load;

[0112] In the previous step S5, the system has increased the power output of the wind turbines and charged the energy storage system when the wind speed is high; at this time, the energy storage system begins to store excess electrical energy for effective energy release during future demand peaks or when the wind speed is low; in step S6, when the system detects that the wind speed is low or the grid load is high, the control strategy will automatically reduce the power output of the wind turbines and use the energy storage system to release the stored electrical energy to supplement the grid load; this operation ensures that the grid load is always balanced, avoiding grid load imbalance or power shortages caused by insufficient wind turbine power.

[0113] In this embodiment, when the wind speed is low, the output power of the wind turbine will usually be lower than the grid demand. At this time, the system releases the stored electric energy through the energy storage system to make up for the shortfall in wind power output. The real-time data of the grid load will be transmitted to the control system, and the control system will determine whether it is necessary to make up for the shortfall in wind power through the energy storage system based on the grid load demand and the output power of the wind turbine. Specifically, the system will calculate the difference between the grid demand and the wind power output, and adjust the discharge rate of the energy storage system according to the difference.

[0114] Generally speaking, the difference between the output power of wind turbines and the load demand of the grid will trigger the discharge of the energy storage system; the discharge rate of the energy storage system Adjustments will be made based on the energy differences calculated by the system.

[0115] As an option, when the grid load is high, the discharge operation of the energy storage system will be further strengthened; this is because when the wind speed is low, the output of the wind turbine is insufficient, and the increase in grid load will lead to greater power demand; in this case, the energy storage system needs to release more electricity to ensure that the grid load does not experience power outages or instability; at this time, the control system will dynamically adjust the discharge rate of the energy storage system by calculating the demand difference of the grid load.

[0116] Specifically, when the grid load demand Exceeding the output of wind turbines When the load is too high, the control system will provide supplementary power through the energy storage system; at this time, the discharge rate of the energy storage system will be adjusted as needed to ensure that the grid load is always met and prevent load gaps; in order to avoid excessive discharge of the energy storage system, the system will also be adjusted according to the health status of the battery and the current power to ensure that the battery does not experience severe discharge in a short period of time.

[0117] In one possible implementation, the discharge rate of the energy storage system is affected not only by the grid load demand and the power output of the wind turbine, but also by the current charge of the energy storage battery. When the charge of the energy storage system approaches the minimum limit, the system will automatically reduce the discharge rate to avoid excessive discharge of the battery and extend the battery life. At this time, the control system will dynamically adjust the charge and discharge rate according to the battery charge and discharge status and grid load requirements to ensure a balance between battery health and grid demand.

[0118] In some embodiments, the system may also adopt a feedback mechanism based on grid frequency and grid stability to further optimize the discharge strategy; for example, when the grid load increases, the system may adjust the discharge rate of the energy storage system according to the change in grid frequency; if the grid frequency changes abnormally, the system may compensate for the grid load fluctuations and prevent grid instability by increasing the discharge rate of the energy storage system.

[0119] As an option, the discharge of the energy storage system can also take into account the maximum output power of the battery to prevent the battery from releasing too much electrical energy under high load conditions; the battery's discharge capacity usually has an upper limit, and the control system will set a reasonable discharge rate based on this upper limit to ensure that the battery will not be damaged by excessive discharge.

[0120] See also Figure 2 , a wind power access control system for a prefabricated equipment cabin, comprising:

[0121] Wind turbine module: The wind turbine module is mainly used to monitor wind speed and wind turbine operating status in real time. By integrating wind speed sensors, wind turbine monitoring systems and data acquisition devices, the module can obtain real-time working status information of wind turbines, including speed, power output, voltage, current, temperature and other parameters. The system will adjust the power output of wind turbines according to grid load requirements. Especially when the wind speed is high, the wind turbine module can increase power output to maximize wind energy. When the wind speed is low, the system will adjust the working status of wind turbines to avoid excessive operation or inefficient power generation, so as to achieve a balance with grid demand.

[0122] Energy storage system module: The energy storage system module ensures that the battery is charged and discharged within a safe and efficient range by monitoring the power of the energy storage system in real time. The module integrates a battery management system (BMS) to track the health status, remaining power, and charging and discharging rate of the energy storage battery in real time. When the output power of the wind turbine is excessive, the energy storage system module will adjust the charging rate according to the grid demand and the power output of the wind turbine to store the excess power. When the output of the wind turbine is insufficient or the grid load increases, the energy storage system will release power to meet the grid demand. The energy storage system module achieves energy balance between wind turbines and grid demand through precise scheduling, and avoids excessive charging and discharging of batteries.

[0123] Optimal control module: The optimal control module is responsible for the coordinated dispatch of wind turbines and energy storage systems through the optimal control algorithm. This module uses real-time data such as wind speed, wind turbine output power, and grid demand to calculate the optimal wind turbine power output and energy storage system charge and discharge rate. The optimal control algorithm is based on the Pontryagin maximum principle and the Hamilton-Jacobi-Bellman (HJB) equation to dynamically calculate the optimal operation plan of the system under different working conditions to maximize the power generation efficiency of the wind turbine and maintain the balance of the grid load. Through the implementation of the optimal control algorithm, this module ensures the efficient operation of the system and the reasonable dispatch of energy.

[0124] Stability analysis module, the stability analysis module is used to perform stability analysis on the dispatching process of wind turbines and energy storage systems; this module ensures that the system can maintain stable operation under dynamic conditions such as different wind speed fluctuations and grid load changes by applying Lyapunov stability theory; by designing appropriate Lyapunov functions, this module monitors the stability of the system in real time, and avoids grid load imbalance or equipment damage caused by unstable operation by optimizing the scheduling strategy of the constraint system; the stability analysis module provides necessary guarantees for the stable operation of the entire system, enabling the system to maintain an efficient and stable state during long-term operation.

[0125] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A wind power access control method for a prefabricated equipment cabin, characterized in that: The following steps are involved: S1, real-time monitoring of wind speed, wind turbine operating status and grid load demand, and adjustment of wind turbine power output according to monitoring results; S2, obtaining the power status of the energy storage system, and adjusting the charge and discharge rate of the energy storage system based on the power output of the wind turbine and the grid demand; S3. Use the optimal control algorithm to optimize the coordinated dispatch of wind turbines and energy storage systems, and adjust the operating parameters of wind turbine output and energy storage system charge and discharge rates; S4. Based on stability analysis, use Lyapunov stability theory to analyze the dispatching process of wind turbines and energy storage systems and constrain the system; S5. When the wind speed is high, increase the power output of the wind turbine and charge the energy storage system; S6. When the wind speed is low or the grid load is high, reduce the power output of the wind turbine and release the stored electric energy to balance the grid load.

2. A wind power access control method for a prefabricated equipment cabin according to claim 1, characterized in that: The optimal control algorithm includes optimizing through the Pontryagin maximum principle and the Hamilton-Jacobi-Bellman equation to calculate the dispatching strategy of the wind turbine output power and the charging and discharging rate of the energy storage system.

3. A wind power access control method for a prefabricated equipment cabin according to claim 1, characterized in that: The stability analysis uses Lyapunov stability theory to make the system's Lyapunov function always decrease during the scheduling process, so as to ensure that the system is in a stable state.

4. A wind power access control method for a prefabricated equipment cabin according to claim 1, characterized in that: The power state of the energy storage system is adjusted in real time based on the load demand of the power grid and the power output of the wind turbine generator set to avoid overcharging and discharging of the battery.

5. A wind power access control method for a prefabricated equipment cabin according to claim 1, characterized in that: The difference between the power output of the wind turbine and the power grid demand is adjusted by minimizing an objective function, which comprehensively considers the balance between the power of the wind turbine, the amount of energy storage system and the charging and discharging rate.

6. A wind power access control method for a prefabricated equipment cabin according to claim 1, characterized in that: The coordinated dispatch of wind turbines and energy storage systems is optimized based on the following objective function: ； in, is the objective function, which is used to quantify the optimization target in the process of wind power access control; For wind turbines at time Output power; For the power grid at all times The power demand; For energy storage system at all times Battery level; For energy storage system at all times The optimal power; For energy storage system at all times The charge and discharge rate; is the weight coefficient, which is used to measure the cost of the difference between the output power of the wind turbine and the power demanded by the grid; is the weight coefficient, which is used to measure the cost of the difference between the energy storage system power and the optimal power; is the weight coefficient, which is used to measure the cost of the charging and discharging rate of the energy storage system.

7. A wind power access control system for a prefabricated equipment cabin, applied to a wind power access control method for a prefabricated equipment cabin as claimed in any one of claims 1 to 6, characterized in that: include: Wind turbine module, used to monitor wind speed and wind turbine operating status in real time and adjust wind turbine power output according to grid load demand; Energy storage system module, used to monitor the energy storage system power in real time and adjust the charging and discharging rate according to grid demand and wind turbine power output; The optimal control module is used to coordinate the wind turbine and the energy storage system through the optimal control algorithm, and calculate the scheduling strategy of the wind turbine power output and the energy storage system charging and discharging rate; The stability analysis module is used to perform stability analysis on the dispatching process of wind turbines and energy storage systems.

8. A wind power access control system for a prefabricated equipment cabin according to claim 7, characterized in that: The optimal control module includes a Pontryagin maximum principle calculation module and a HJB equation solving module, which are used to solve the optimal scheduling strategy for the output power of the wind turbine set and the charging and discharging rate of the energy storage system.

9. A wind power access control system for a prefabricated equipment cabin according to claim 7, characterized in that: The stability analysis module constrains the scheduling process through Lyapunov stability theory, so that the system can maintain stable operation under dynamic changes.

10. A wind power access control system for a prefabricated equipment cabin according to claim 7, characterized in that: The wind turbine module and the energy storage system module work in coordination with the cloud dispatching platform through a real-time feedback mechanism to exchange real-time data for adjusting the operating status of the system.

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