A method and system for maximizing wind energy utilization of a wind farm configured with distributed energy storage

Abstract

The application discloses a kind of methods and systems for maximizing wind energy utilization of wind farm configured with distributed energy storage, the method of the application includes constructing tail coupling optimization model of wind farm equipped with distributed energy storage to uniformly describe the wake effect between wind turbines of wind turbine unit, internal aerodynamic-mechanical-electrical coupling of wind turbine, dynamic coupling relationship between energy storage systems, construct the problem model and basic objective function of two system modes of wake optimization mode WOP and power tracking mode PTP, power compensation item of various working modes of wind turbine unit;Determine the current mode and current working mode of wind turbine unit;Add the basic objective function under current mode, the power compensation item of current working mode to generate final objective function, solve the problem model of current mode and obtain the control quantity and issue to wind turbine unit.The application aims to solve the problems of low-power operation of wind farm, slow energy storage response, insufficient consideration of wake effect, etc., to improve wind energy capture and grid acceptance efficiency.

Description

Technical Field

[0001] This invention relates to the field of wind power technology, and specifically to a method and system for maximizing wind energy utilization in wind farms equipped with distributed energy storage. Background Technology

[0002] As a core pillar of the renewable energy system, wind power's large-scale, efficient grid-connected operation and its increasing penetration rate in the power system place higher technical demands on grid stability. However, the inherent intermittency and strong randomness of wind energy lead to significant fluctuations in wind farm output power, posing a severe challenge to grid power balance and power quality. Further complicating matters, in large wind farms, the wake effect between multiple wind turbines can trigger significant cascading attenuation, resulting in a 30-40% reduction in downstream inflow wind speed, further restricting the overall power generation capacity of the wind farm. Current technological systems face three core challenges: First, traditional wind farms typically operate in a conservative, power-limited mode with a safety margin when responding to grid dispatch commands. Especially during low-load periods, a significant amount of wind energy is wasted because it cannot be effectively stored. Second, traditional centralized energy storage systems suffer from limited response speed and insufficient flexibility, failing to meet the rapid dynamic response requirements of wind turbines and hindering rapid and accurate power compensation. Third, existing methods often fail to fully consider the wake effect on the overall output of the wind farm and treat wind speed as a linear, invariant system, resulting in the inability to maximize wind energy utilization. Therefore, maximizing wind energy utilization in wind farms has become a critical technical problem that urgently needs to be solved. Summary of the Invention

[0003] The technical problem to be solved by this invention is to provide a method and system for maximizing wind energy utilization in wind farms by configuring distributed energy storage, in response to the above-mentioned problems in the prior art. This invention aims to solve problems such as low-power operation of wind farms, slow energy storage response, and insufficient consideration of wake effect, and improve the efficiency of wind energy capture and grid integration.

[0004] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0005] A method for maximizing wind energy utilization in a wind farm with distributed energy storage configuration includes the following steps:

[0006] S101. Construct a wake coupling optimization model for wind farms equipped with distributed energy storage to uniformly describe the wake effect between wind turbines, the aerodynamic-mechanical-electrical coupling inside the wind turbines, and the dynamic coupling relationship between energy storage systems. Based on the wake coupling optimization model, construct problem models and basic objective functions for two system modes: wake optimization mode (WOP) and power point tracking mode (PTP), as well as power compensation terms for various operating modes of wind turbines.

[0007] S102, determine the current mode of the wind turbine as either wake optimization mode (WOP) or power point tracking mode (PTP) based on whether the state of charge (SOC) of the energy storage system is within the preset optimal range.

[0008] S103, determine the current operating mode of the wind turbine based on the grid command power, power fluctuation, wind curtailment rate and the state of charge (SOC) of the energy storage system;

[0009] S104, the basic objective function of the wind turbine under the current mode and the power compensation term of the current working mode are added together to generate the final objective function, and the problem model of the current mode is solved based on the final objective function;

[0010] S105, the obtained control quantity is sent to the wind turbine, and the process jumps to step S102 in the next cycle.

[0011] Optionally, the basic objective function expression for the wake optimization mode WOP in step S101 is:

[0012] ;

[0013] in, The basic objective function corresponding to the wake optimization mode WOP is: To control the number of steps, This is the rated power of the wind farm. The number of wind turbine units. For wind turbine units The initial power value, For wind turbine units The change in the initial value of active power, and They are respectively and The amount of change of the control item at any time and These are the weights for power fluctuations and voltage fluctuations, respectively; the functional expression of the basic objective function corresponding to the power tracking mode (PTP) in step S101 is:

[0014] ;

[0015] in, The fundamental objective function for Power Tracking Mode (PTP) is... For wind turbine units Power grid command power, For wind turbine units The active power.

[0016] Optionally, the calculation function expression for the power compensation term of the wind turbine in various operating modes in step S101 is as follows:

[0017] ;

[0018] ;

[0019] ;

[0020] in, , and These are the power compensation items for power compensation mode, power smoothing mode, and low power command mode, respectively. Work mode control strategy, work mode control strategy The values ​​are 1, 2, and 3 in power compensation mode, power smoothing mode, and low power command mode, respectively. , and These are the weighting coefficients for power compensation mode, power smoothing mode, and low power command mode, respectively.

[0021] Optionally, step S102, determining whether the current mode of the wind turbine is wake-optimized mode (WOP) or power-point-tracking mode (PTP) based on whether the state of charge (SOC) of the energy storage system is within a preset optimal range, includes: determining whether the SOC of the energy storage system is within a preset optimal range; if it is within the preset optimal range, then determining the current mode as wake-optimized mode (WOP); otherwise, determining the current mode as power-point-tracking mode (PTP).

[0022] Optionally, in step S103, when determining the current operating mode of the wind turbine based on the grid command power, power fluctuation, wind curtailment rate, and the state of charge (SOC) of the energy storage system, the trigger condition for determining the current operating mode of the wind turbine as power compensation mode is:

[0023] ;

[0024] in, This is the trigger condition for power compensation mode. For the wind farm at the current moment active power gradient, for The Sobolev norm, The preset threshold, For logical operations, State of charge (SOC) To preset the state of charge range; the trigger condition for determining the current operating mode of the wind turbine as power smoothing mode is:

[0025] ;

[0026] in, This is the trigger condition for the power smoothing mode. For the wind farm at the current moment Power grid command power, The preset threshold, To preset the state of charge range; the trigger condition for determining the current operating mode of the wind turbine as low-power command mode is:

[0027] ;

[0028] in, This is the trigger condition for low-power command mode. For the current moment The power grid can dispatch surplus power. Indicates the wind curtailment rate, The preset threshold, For the preset state of charge range, and and equal, Greater than .

[0029] Optionally, the functional expression for constructing the Wake Optimization Mode (WOP) problem model in step S101 is:

[0030] ;

[0031] ;

[0032] in, The problem model for optimizing wake mode WOP. For wind turbine units wind speed, and These represent the wind speed, input terms, and control terms in the wake optimization problem. and The state matrix for the wake optimization problem is... For the nonlinear coupling term of the wake optimization problem, This represents the total number of wind turbines. For the block diagonal matrix direct sum, For wind turbine units The state matrix, Regarding wind turbine units and The identity matrix, which is only in the first... OK The column is set to 1, and the rest are 0. For Kronecker product, For wind turbine units and The wake coupling coefficient vector between them, where:

[0033] ;

[0034] ;

[0035] in, Generator speed The change The rotational torque of the generator rotor The change Pitch angle The change This represents the change in the rate of change of the energy storage system's capacity. and These represent the changes in charging power and discharging power of the energy storage system, respectively. and These represent the changes in charging power and discharging power of the energy storage system, respectively. and These are the charging current and discharging current of the energy storage system, respectively. For wind turbine units Inflow wind speed, The rotational torque of the generator rotor Reference value for the change Pitch angle Reference value for the change and These are reference values ​​for the change in charging power and the change in discharging power of the energy storage system, respectively. and These are binary variables representing the energy flow state of the energy storage system, with values ​​of 1 or 0. A value of 1 indicates charging. A value of 1 indicates discharge, and the following relationship is satisfied. The superscript T indicates transpose.

[0036] Optionally, the functional expression of the problem model for the power point tracking mode (PTP) constructed in step S101 is:

[0037] ;

[0038] in, For the problem model of power point tracking mode (PTP), and These are the input and control terms for the power tracking problem, respectively. and The state matrix for the power tracking problem is... For the nonlinear coupling term of the power tracking problem, where:

[0039] ;

[0040] ;

[0041] in, Generator speed The change The rotational torque of the generator rotor The change Pitch angle The change This represents the change in the rate of change of the energy storage system's capacity. and These represent the changes in charging power and discharging power of the energy storage system, respectively. and These represent the changes in charging power and discharging power of the energy storage system, respectively. and These are the charging current and discharging current of the energy storage system, respectively. The rotational torque of the generator rotor Reference value for the change Pitch angle Reference value for the change and These are reference values ​​for the change in charging power and the change in discharging power of the energy storage system, respectively. and These are binary variables representing the energy flow state of the energy storage system, with values ​​of 1 or 0. A value of 1 indicates charging. A value of 1 indicates discharge, and the following relationship is satisfied. The superscript T indicates transpose.

[0042] Furthermore, the present invention also provides a wind farm maximizing wind energy utilization system with distributed energy storage, comprising a microprocessor and a memory interconnected, wherein the microprocessor is programmed or configured to execute the wind farm maximizing wind energy utilization method with distributed energy storage.

[0043] Furthermore, the present invention also provides a computer-readable storage medium storing a computer program or instructions that are programmed or configured to execute, via a processor, the method for maximizing wind energy utilization in a wind farm with configured distributed energy storage.

[0044] In addition, the present invention also provides a computer program product, including a computer program or instructions, which are programmed or configured to execute, via a processor, the method for maximizing wind energy utilization in a wind farm with configured distributed energy storage.

[0045] Compared with existing technologies, the present invention mainly achieves the following beneficial effects: The method for maximizing wind energy utilization in wind farms with distributed energy storage in the present invention includes the key impact mechanism of wind resource dynamic characteristics on wind farm operating efficiency, considering energy loss caused by wind turbine wake effect, and constructing a problem model of distributed energy storage wind farm. The method of the present invention adopts a dual-mode operation mechanism. When the SOC is normal, the wake effect is optimized to make the wind farm operate at the maximum power point. When the SOC exceeds the limit, the system adjusts its own output and switches to power smoothing mode to track grid commands. The method of the present invention includes three working modes: the power smoothing mode suppresses fluctuations through small charging and discharging of energy storage; the power compensation mode accurately compensates the difference between the total output of the wind turbine group and the grid power command during high command to reduce power fluctuations; and the low command mode stores redundant wind energy to reduce wind curtailment rate. This invention establishes a problem model for wind farms equipped with distributed energy storage. By combining the rapid response characteristics of the energy storage system, it enables optimal energy management of wind farms under different operating conditions. This overcomes the limitations of traditional centralized control and the technical bottleneck of traditional wind farms operating at reduced capacity to mitigate power fluctuations and thus wasting wind energy. By real-time monitoring of the aerodynamic parameters of each wind turbine and the SOC status of the energy storage system, a collaborative optimization method for the operating characteristics of the distributed energy storage system and wind turbines is constructed. This achieves a dynamic balance between wind energy capture efficiency and grid dispatch requirements, thereby improving the overall operating efficiency of the wind farm and the level of wind power absorption. Attached Figure Description

[0046] Figure 1 This is a schematic diagram of the basic process of the method in an embodiment of the present invention.

[0047] Figure 2 This is a schematic diagram of the dynamic coupling relationship of the wake coupling optimization model in an embodiment of the present invention.

[0048] Figure 3 This is a comparison of experimental results between the method of this invention in power compensation mode and existing methods.

[0049] Figure 4 This is a comparison of experimental results between the method of this invention in low-power command mode and existing methods.

[0050] Figure 5 This is a comparison of experimental results between the method of this invention in low-power command mode and existing methods.

[0051] Figure 6 This is a comparison of experimental results between the method of the present invention in power smoothing mode and existing methods. Detailed Implementation

[0052] This invention aims to address issues such as low-power operation of wind farms, slow energy storage response, and insufficient consideration of wake effects. By constructing a problem model for wind farms equipped with distributed energy storage, it improves wind energy capture and grid integration efficiency. To enable those skilled in the art to better understand the technical solution of this invention, the following will provide a more detailed description of the technical solution in conjunction with the accompanying drawings of the embodiments of this invention.

[0053] like Figure 1 As shown, the method for maximizing wind energy utilization in a wind farm with distributed energy storage in this embodiment includes the following steps:

[0054] S101. Construct a wake coupling optimization model for wind farms equipped with distributed energy storage to uniformly describe the wake effect between wind turbines, the aerodynamic-mechanical-electrical coupling inside the wind turbines, and the dynamic coupling relationship between energy storage systems. Based on the wake coupling optimization model, construct problem models and basic objective functions for two system modes: wake optimization mode (WOP) and power point tracking mode (PTP), as well as power compensation terms for various operating modes of wind turbines.

[0055] S102, determine the current mode of the wind turbine as either wake optimization mode (WOP) or power point tracking mode (PTP) based on whether the state of charge (SOC) of the energy storage system is within the preset optimal range.

[0056] S103, determine the current operating mode of the wind turbine based on the grid command power, power fluctuation, wind curtailment rate and the state of charge (SOC) of the energy storage system;

[0057] S104, the basic objective function of the wind turbine under the current mode and the power compensation term of the current working mode are added together to generate the final objective function, and the problem model of the current mode is solved based on the final objective function;

[0058] S105, the obtained control quantity is sent to the wind turbine, and the process jumps to step S102 in the next cycle.

[0059] The functional expression for constructing the Wake Optimization Mode (WOP) problem model in step S101 of this embodiment is:

[0060] ;

[0061] ;

[0062] in, The problem model for optimizing wake mode WOP. For wind turbine units wind speed, and These represent the wind speed, input terms, and control terms in the wake optimization problem. and The state matrix for the wake optimization problem is... For the nonlinear coupling term of the wake optimization problem, This represents the total number of wind turbines. For the block diagonal matrix direct sum, For wind turbine units The state matrix, Regarding wind turbine units and The identity matrix, which is only in the first... OK The column is set to 1, and the rest are 0. For Kronecker product, For wind turbine units and The wake coupling coefficient vector between them, where:

[0063] ;

[0064] ;

[0065] in, Generator speed The change The rotational torque of the generator rotor The change Pitch angle The change This represents the change in the rate of change of the energy storage system's capacity. and These represent the changes in charging power and discharging power of the energy storage system, respectively. and These represent the changes in charging power and discharging power of the energy storage system, respectively. and These are the charging current and discharging current of the energy storage system, respectively. For wind turbine units Inflow wind speed, The rotational torque of the generator rotor Reference value for the change Pitch angle Reference value for the change and These are reference values ​​for the change in charging power and the change in discharging power of the energy storage system, respectively. and These are binary variables representing the energy flow state of the energy storage system, with values ​​of 1 or 0. A value of 1 indicates charging. A value of 1 indicates discharge, and the following relationship is satisfied. The superscript T indicates transpose. Where:

[0066] ;

[0067] ;

[0068] ; ;

[0069] in, For equivalent rotating mass, For generator speed, The pitch angle is the propeller angle. The generator filtering time constant is... The time constant for the pitch angle filter is... The loss coefficient is... The time constant of the active power filter in the DC / DC converter. Voltage value of the energy storage system The proportional gain of the outer loop PI controller. The integral gain of the outer loop PI controller. The inner loop filter time constant is... The gearbox speed ratio, For mechanical torque, Let be the generator speed of wind turbine i. Let j be the wind speed of the wind turbine unit. These are the initial charging parameters for the energy flow of the energy storage system. , Initial discharge parameters for the energy flow of the energy storage system , This represents the initial value of the generator's electromagnetic torque. The initial value of the charging power for the energy storage system. This represents the initial value of the discharge power of the energy storage system.

[0070] The functional expression of the problem model for Power Point Tracking (PTP) constructed in step S101 of this embodiment is as follows:

[0071] ;

[0072] in, For the problem model of power point tracking mode (PTP), and These are the input and control terms for the power tracking problem, respectively. and The state matrix for the power tracking problem is... For the nonlinear coupling term of the power tracking problem, where:

[0073] ;

[0074] ;

[0075] in, Generator speed The change The rotational torque of the generator rotor The change Pitch angle The change This represents the change in the rate of change of the energy storage system's capacity. and These represent the changes in charging power and discharging power of the energy storage system, respectively. and These represent the changes in charging power and discharging power of the energy storage system, respectively. and These are the charging current and discharging current of the energy storage system, respectively. The rotational torque of the generator rotor Reference value for the change Pitch angle Reference value for the change and These are reference values ​​for the change in charging power and the change in discharging power of the energy storage system, respectively. and These are binary variables representing the energy flow state of the energy storage system, with the superscript T indicating transpose. Wherein:

[0076] ;

[0077] ; ;

[0078] in, The gearbox speed ratio, For equivalent rotating mass, For mechanical torque, Generator speed The change The pitch angle is the propeller angle. The generator filtering time constant is... The time constant for the pitch angle filter is... The loss coefficient is... The time constant of the active power filter in the DC / DC converter. This refers to the voltage value of the energy storage system. The proportional gain of the outer loop PI controller. The integral gain of the outer loop PI controller. The inner loop filter time constant is... Initial charging parameters for the energy flow of the energy storage system , The initial charging parameters for the energy flow of the energy storage system , This is the initial value of the mechanical torque. This represents the initial value of the generator's electromagnetic torque. The initial value of the charging power for the energy storage system. This represents the initial value of the discharge power of the energy storage system.

[0079] The function expression of the basic objective function corresponding to the wake optimization mode WOP in step S101 of this embodiment is:

[0080] ;

[0081] in, The basic objective function corresponding to the wake optimization mode WOP is: To control the number of steps, This is the rated power of the wind farm. The number of wind turbine units. For wind turbine units The initial power value, For wind turbine units The change in the initial value of active power, and They are respectively and The amount of change of the control item at any time and The weights for power fluctuations and voltage fluctuations are respectively. It can be achieved in five steps The internal rolling real-time optimization of the wind turbine's pitch angle, shaft torque command, and energy storage system's SOC (State of Charge) command is as follows. The constraints of the wake optimization mode (WOP) are:

[0082] ;

[0083] in, Let i be the electromagnetic torque of the generator of wind turbine i. This represents the change in the maximum value of the generator's electromagnetic torque. This represents the change in the electromagnetic torque of the generator. This represents the maximum value of the generator's electromagnetic torque. Let be the initial value of the pitch angle of wind turbine i. This represents the maximum value of the pitch angle change. Let be the change in pitch angle of wind turbine i. This represents the maximum value of the pitch angle. To optimize the state of charge (SOC) of wind turbine i under wake optimization mode WOP, and These represent the maximum and minimum State of Charge (SOC). Under the wake optimization mode (WOP), the SOC of wind turbine i is within the normal range. The system adjusts the pitch angle and shaft torque to ensure the wind farm operates at its maximum power point. The energy storage system is controlled to operate in power compensation mode, discharging under high command to smooth power fluctuations and charging under low command to store redundant wind energy, thereby maximizing wind energy utilization.

[0084] The function expression of the basic objective function corresponding to the power tracking mode (PTP) in step S101 of this embodiment is as follows:

[0085] ;

[0086] in, The fundamental objective function for Power Tracking Mode (PTP) is... For wind turbine units Power grid command power, For wind turbine units The active power. This allows for the control of wind turbine output, ensuring that the wind farm's power output closely matches the grid command for the wind farm. The constraints of Power Point Tracking (PTP) mode can be expressed as:

[0087] ;

[0088] in, Let i be the available power of wind turbine unit i. Let i be the number of wind turbine units i during the operating time T. The state of charge (SOC) of wind turbine i under power point tracking mode (PTP) is given. and These represent the maximum and minimum values ​​of the state of charge (SOC), respectively.

[0089] The calculation function expression for the power compensation term of the wind turbine in step S101 of this embodiment is as follows:

[0090] ;

[0091] ;

[0092] ;

[0093] in, , and These are the power compensation items for power compensation mode, power smoothing mode, and low power command mode, respectively. Work mode control strategy, work mode control strategy The values ​​are 1, 2, and 3 in power compensation mode, power smoothing mode, and low power command mode, respectively. , and These are the weighting coefficients for power compensation mode, power smoothing mode, and low-power command mode, respectively. In low-power command mode, priority is given to storing curtailed wind energy; in high-power command mode, priority is given to compensating for power deficits. Power smoothing mode controls the energy storage system to perform small-amplitude charging and discharging to suppress power fluctuations. As an optional implementation, the functional expression of the operating mode control strategy in this embodiment is:

[0094] ;

[0095] in, For the set of triggering conditions The indicator function is 1 when the condition is met and 0 when the condition is not met; Membership function of the security region , can be represented as:

[0096] ;

[0097] in For the set of triggering conditions, It can implement soft boundary constraints. These correspond to power compensation mode, power smoothing mode, and low-power command mode, respectively. The triggering conditions can only be met within a security domain; the set of triggering conditions. yes It is triggered only when certain conditions are met.

[0098] In this embodiment, step S102, determining whether the current mode of the wind turbine is Wake Optimization Mode (WOP) or Power Point Tracking Mode (PTP) based on whether the State of Charge (SOC) of the energy storage system is within a preset optimal range, includes: determining whether the SOC of the energy storage system is within a preset optimal range; if it is, determining the current mode as Wake Optimization Mode (WOP); otherwise, determining the current mode as Power Point Tracking Mode (PTP). When the SOC of the energy storage system is within the optimal range, it enters Wake Optimization Mode (WOP) to control the pitch angle and shaft torque so that the wind farm operates at its maximum power point; when the SOC of the energy storage system exceeds the limit, it enters Power Point Tracking Mode (PTP) to adjust the wind turbine output to track the grid command.

[0099] In step S103 of this embodiment, when determining the current operating mode of the wind turbine based on the grid command power, power fluctuation, wind curtailment rate, and the state of charge (SOC) of the energy storage system, the trigger condition for determining the current operating mode of the wind turbine as the power compensation mode is as follows:

[0100] ;

[0101] in, This is the trigger condition for power compensation mode. For the wind farm at the current moment active power gradient, for The Sobolev norm, The preset threshold, For logical operations, State of charge (SOC) To preset the state of charge range; the trigger condition for determining the current operating mode of the wind turbine as power smoothing mode is:

[0102] ;

[0103] in, This is the trigger condition for the power smoothing mode. For the wind farm at the current moment Power grid command power, The preset threshold, To preset the state of charge range; the trigger condition for determining the current operating mode of the wind turbine as low-power command mode is:

[0104] ;

[0105] in, This is the trigger condition for low-power command mode. For the current moment The power grid can dispatch surplus power. Indicates the wind curtailment rate, The preset threshold, For the preset state of charge range, and and equal, Greater than Power compensation mode (i=1): when the set characteristic function Security domain membership function When the trigger condition is met, the deviation between the grid command and the wind turbine output exceeds a threshold. State of charge The range, that is Entering power compensation mode, controlling the high-power discharge compensation difference of energy storage. Power smoothing mode (i=2): when the set characteristic function Security domain membership function When the trigger condition is met, the power fluctuation gradient exceeds a threshold. State of charge The range, that is Entering power smoothing mode, controlling small charging and discharging of energy storage to suppress fluctuations. Low power command mode (i=3): when the set characteristic function Security domain membership function When the trigger condition is met, the wind curtailment rate Greater than If the grid command is too low, redundant wind energy can be stored, and the state of charge is... The range, that is It enters a low-power command mode to reduce the output of the wind turbine and control the charging of energy storage to store redundant energy.

[0106] In step S101 of this embodiment, when constructing a wake coupling optimization model for a wind farm equipped with distributed energy storage to uniformly describe the wake effect between wind turbines, the aerodynamic-mechanical-electrical coupling within the wind turbines, and the dynamic coupling relationship between energy storage systems, this wake coupling optimization model consists of an inflow wind speed calculation model for each wind turbine under the wake effect in the aerodynamic system, a wind turbine control model, and a control model for the energy storage system. Because the wind turbine captures the inflow wind, significant energy loss occurs behind the turbine rotor. The actual inflow wind to the rear turbines, i.e., the wind speed by which upstream unit i affects downstream unit j, is... Therefore, the functional expression for the inflow wind speed calculation model for each wind turbine under the wake effect is:

[0107] ;

[0108] ;

[0109] ;

[0110] in, , and These are the wind speeds of wind turbine units 0, 1, and 2 respectively. The inflow wind speed of the upstream wind turbine unit. Let be the thrust coefficient of wind turbine j, and the ratio of thrust coefficient to tip speed. and pitch angle Related, Wake flow influencing factor Parameters describing the wake region, =1 indicates the near-wake region. =2 indicates the far wake region. =3 indicates the wake mixing region. The attenuation coefficients for different wake regions of the wind turbine are given. For wind turbine units iThe impact of the wake effect on the rotor coverage area of ​​the wind turbine unit j. The swept area of ​​the wind turbine rotor. For upstream wind turbines i The geographical distance of wind turbine j along the wind direction, for The radius of the wake effect area at a certain distance. R The radius of the wind turbine rotor. Let be the wake expansion coefficient of wind turbine i.

[0111] The functional expression for the wind turbine control model is:

[0112] ;

[0113]

[0114] ;

[0115] in, for The first-order differential, Generator speed The change The gearbox speed ratio, For equivalent rotating mass, For mechanical torque, Pitch angle The change The rotational torque of the generator rotor The change This is the initial value of the mechanical torque. This represents the initial value of the generator rotor's rotational torque. for The first-order differential, for The first-order differential, Pitch angle Reference value for the change (pitch angle command value). is the constant for the propeller pitch angle filter.

[0116] The functional expression of the control model of the energy storage system is:

[0117] ;

[0118] ;

[0119] ;

[0120] ;

[0121] ;

[0122] ;

[0123] ;

[0124] ;

[0125] ;

[0126] in, This represents the change in the generator's power. For the conversion efficiency of the generator, Electromagnetic torque of the generator The change and These represent the rate of capacity change during charging and discharging of the energy storage system, respectively. and These are the power outputs during charging and discharging of the energy storage system, respectively. This represents the initial power of the energy storage system. for The first-order differential, for The first-order differential, and These represent the power changes during charging and discharging of the energy storage system, respectively. It is a time constant. For energy storage system voltage, for The first-order differential, for The first-order differential, and These represent the changes in current during charging and discharging of the energy storage system, respectively. and These are the first-order differentials of the initial power during charging and discharging of the energy storage system, respectively. and These are the initial power outputs during charging and discharging of the energy storage system, respectively. This is a power reference value for the energy storage system. and These are the control parameters for PID control used in the energy storage system. This is the time constant for the inner loop filter.

[0127] like Figure 2 As shown, the dynamic coupling relationships between wind turbine units, including the wake effect between turbines, the aerodynamic-mechanical-electrical coupling within the turbines, and the energy storage system, include: the inflow wind speed of each wind turbine unit due to the wake effect. Unlike other systems, this system collects the inflow wind speed of the first upstream unit in real time. The inflow velocity of the downstream unit is calculated using a small-signal model of the aerodynamic system. The blades of a wind turbine capture wind energy and convert it into mechanical torque. The pneumatic torque is transmitted through the transmission system. On the power generation system side, when the generator rotor (magnetic field) rotates, the stator windings (conductors) cut the magnetic field lines, generating an induced electromotive force. The alternating current induced in the stator windings is adjusted by the converter to power compatible with the power grid. Indicates rotor-side power. This represents the stator-side power. The unit controller receives the power status of the wind turbine and energy storage, and through the method of this embodiment, it can perform rolling real-time optimization of the small-signal model, including: real-time data acquisition: upstream wind turbine inflow, thrust coefficient, pitch angle, shaft torque, and state of charge (SOC); and calculation of the safety domain function. The value is used to determine the current mode. If the State of Charge (SOC) is within the optimal finite range, the Wake Optimization (WOP) mode is selected; otherwise, the Power Point Tracking (PTP) mode is selected. Calculations are based on grid commands, power fluctuations, and wind curtailment rates. It determines which operating mode to trigger. If the power deviation exceeds a threshold, then... This triggers the power compensation mode; when the power fluctuation exceeds the threshold, then... If the wind curtailment rate exceeds the threshold, then the power smoothing mode is triggered. This triggers a low-power command. The unit controller solves the optimization problem to obtain the pitch angle reference value. Shaft torque reference value and energy storage system power reference value Equal control variables. Pitch angle reference value. Influence on wind energy utilization coefficient and the thrust coefficient of downstream unit j Control power capture. And shaft torque command. The power output is related to the generator speed; the faster the speed, the more electricity is generated. The energy storage system stores the energy as a reference value. The error between the two signals is compared with the actual measured value and used as a reference for the inner loop current of the PI controller. The PI controller is then used to design an energy storage power control loop to control the duty cycle of the two switching circuits and control the rise and fall of the inductor current on the DC bus voltage, thereby achieving energy storage power control.

[0128] Pneumatic systems are used to capture the mechanical power of the fan. , can be represented as:

[0129] ;

[0130] in, air mass density, For fan speed, For the radius of the wind turbine, The wind energy utilization coefficient is the ratio of the wind energy utilization coefficient to the blade tip speed. and pitch angle Related.

[0131] As a key coupling link in the pneumatic-electric energy conversion, the transmission system achieves pneumatic torque through multi-stage gear transmission. Transmission and generator speed Matching, aerodynamic torque It can be represented as:

[0132] ;

[0133] in, This refers to the low-speed shaft torque of the gearbox. For the rotating mass of the impeller rotor, The rotor speed, For equivalent rotating mass, The gearbox speed ratio, The moment of inertia of the generator rotor. This refers to the rotor torque of the generator. The grid-connected output power of the power generation system. It can be represented as:

[0134] ;

[0135] in, This refers to the generator's conversion efficiency. The pitch angle servo system dynamically controls the pitch angle. Pitch angle reference value It can be represented as:

[0136] ;

[0137] in, For the rated speed of power generation, This is the gain coefficient. This is the proportionality coefficient. The integral coefficient is... This is the actual speed of the generator. This is the generator's rated speed. Considering the energy loss characteristics during battery charging and discharging, the energy state of the energy storage system (ESS) is... It can be represented as:

[0138] ;

[0139] in, The initial energy state, and The charging efficiency coefficient and discharging efficiency coefficient of the energy storage system are both related to the energy storage battery loss coefficient. Related, and These are the charging and discharging power of the energy storage system. and For the energy flow state of the energy storage system in A binary variable at time t, where the binary variable takes the value 1 or 0. A value of 1 indicates charging. A value of 1 indicates discharge, and the relationship between the two can be expressed as follows: , For time.

[0140] To provide a unified description of the dynamic coupling relationships among multiple interacting physical processes such as wake effects between wind turbines, aerodynamic-mechanical-electrical coupling within wind turbines, and energy storage systems, for example, the wake effect of an upstream wind turbine affects the wind speed of a downstream wind turbine, and the wind speed of each wind turbine directly affects its mechanical power. aerodynamic torque and generator output power The dynamic response will also affect the pitch angle reference value of the pitch angle control. Meanwhile, the energy state of the energy storage system The power fluctuations of the entire system also need to be coordinated. Therefore, considering multiphysics coupling effects and random perturbations, a high-order nonlinear state-space equation is established. It consists of a two-mode microfractal flow system, which can be represented as:

[0141]

[0142] in, for The first-order differential, For the problem model under mode i, These are input items and control items, respectively. For the system's output items, This represents the output function that maps system state terms to output terms. Here, mode index i=1 represents the wake optimization mode (WOP), which optimizes pitch angle and shaft torque to maximize turbine output; i=2 represents the power point tracking mode (PTP), which adjusts turbine output to match the wind farm's grid command.

[0143] The bimodal operation satisfies the following operating states:

[0144] ;

[0145] ;

[0146] in, For the prediction cycle, For the number of wind turbines, For the capacity of the energy storage system, This is a coefficient representing the energy capacity of the energy storage system. The rated capacity of the energy storage system, This is the power reference value (power command) for the wind turbine unit. For wind turbine units The initial power value, For wind turbine units The change in the initial value of active power, and These are the charging and discharging power of the energy storage system. and For the binary variable representing the energy flow state of the energy storage system, For the weighting coefficient of the energy storage system, This is a weighting factor for wind turbine power. The available energy utilization coefficient, and These represent the maximum and minimum values ​​of the state of charge (SOC), respectively. The state of charge (SOC) of the energy storage system in wind turbine i. The available capacity of energy storage system i, For time intervals, and These represent the available charging and discharging power of energy storage system i, respectively. For the power output limitation of energy storage system i, and These represent the maximum charging and discharging power, respectively. The above operating states, by coordinating the operation of distributed energy storage and wind turbines, achieve efficient tracking of grid dispatch commands and maximize wind energy utilization. The system first calculates the total power demand based on dispatch commands and then dynamically allocates the power increments of each wind turbine using the MPC collaborative optimization algorithm. Simultaneously, the energy storage system prioritizes response to high-frequency power fluctuations, and its charging and discharging states are switched in real time by a binary data stream to obtain the charging and discharging power of the energy storage system. / The two work together to ensure rapid and accurate power tracking. Secondly, the system uses a capacity balancing mechanism. Optimize energy storage operation to ensure each energy storage unit operates at a reasonable proportion of its rated capacity, avoiding overcharging or over-discharging and improving overall reliability. Finally, a five-step model predictive control (MPC) approach is employed. Rolling real-time optimization further coordinates short-term power fluctuation mitigation with long-term energy balance, ensuring that the wind-storage system achieves optimal economic operation while meeting power requirements.

[0147] To verify the method for maximizing wind energy utilization in wind farms with distributed energy storage in this embodiment, the method of this embodiment (energy storage-based active wake control) is compared with traditional control methods (including active wake control and greedy control). Experimental results under power compensation mode are compared with those of existing methods. Figure 3 As shown, experimental results in low-power command mode and existing methods are compared, for example... Figure 4 and Figure 5 As shown, the experimental results in power smoothing mode and existing methods are compared, for example... Figure 6 As shown. (Through) Figure 3 It is known that active wake control, compared to greedy control, can significantly reduce wake losses and increase the total power generation of wind farms. However, the power output fluctuation of active wake control is still relatively significant, affecting the tracking accuracy of grid dispatch commands. The method in this embodiment (energy storage-based active wake control) triggers the power compensation mode of the energy storage system and controls the discharge of the energy storage system to smooth out power fluctuations, thereby maximizing power point tracking. Figure 4 It is evident that under low-power command mode, the power generation from active wake control far exceeds the power generation required by TSO, resulting in a significant waste of wind resources. Through... Figure 5 It is known that active wake control requires a significant reduction in wind turbine output under low-power commands to meet grid dispatch requirements, resulting in large power fluctuations. Greedy control also exhibits large power fluctuations when tracking grid commands. However, the method in this embodiment (energy storage-based active wake control) controls the energy storage system to charge under low-power command mode. The energy storage absorbs redundant wind energy to reduce wind curtailment and improve wind energy utilization, achieving smooth power tracking and demonstrating superior control performance. Figure 6 It is known that when the system detects that the energy storage SOC value exceeds the optimal operating range, the wind turbine side operates at reduced capacity by dynamically adjusting the pitch angle and generator torque. However, the power fluctuation of active wake control is relatively large, while the method of this embodiment (energy storage active wake control) achieves the goal of smoothing power fluctuations by controlling the energy storage system to charge and discharge in small amplitudes, thus triggering the energy storage system to enter the power smoothing mode.

[0148] In summary, the wind energy utilization maximization method for wind farms equipped with distributed energy storage in this embodiment achieves optimal energy management of the wind farm under different operating conditions by establishing a wake coupling optimization model for the wind farm equipped with distributed energy storage and combining it with the rapid response characteristics of the energy storage system. This method overcomes the limitations of traditional centralized control by real-time monitoring of the aerodynamic parameters of each wind turbine and the SOC status of the energy storage system, and constructs a collaborative optimization method for the operating characteristics of the distributed energy storage system and the wind turbine units. This achieves a dynamic balance between wind energy capture efficiency and grid dispatch requirements, effectively improving the overall operating efficiency and wind power absorption level of the wind farm. It can solve problems such as low-power operation of the wind farm, slow energy storage response, and insufficient consideration of wake effects. By constructing a wake coupling optimization model for the wind farm equipped with distributed energy storage and solving its problem model, the wind energy capture and grid acceptance efficiency can be effectively improved.

[0149] Furthermore, this embodiment also provides a wind farm maximizing wind energy utilization system with distributed energy storage, including a microprocessor and a memory interconnected, wherein the microprocessor is programmed or configured to execute the wind farm maximizing wind energy utilization method with distributed energy storage. Additionally, this embodiment provides a computer-readable storage medium storing a computer program or instructions programmed or configured to execute the wind farm maximizing wind energy utilization method with distributed energy storage via a processor. Furthermore, this embodiment also provides a computer program product including a computer program or instructions programmed or configured to execute the wind farm maximizing wind energy utilization method with distributed energy storage via a processor.

[0150] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A method for maximizing wind energy utilization in a wind farm with distributed energy storage, characterized in that, Includes the following steps: S101. Construct a wake coupling optimization model for wind farms equipped with distributed energy storage to uniformly describe the wake effect between wind turbines, the aerodynamic-mechanical-electrical coupling inside the wind turbines, and the dynamic coupling relationship between energy storage systems. Based on the wake coupling optimization model, construct problem models and basic objective functions for two system modes: wake optimization mode (WOP) and power point tracking mode (PTP), as well as power compensation terms for various operating modes of wind turbines. S102, determine the current mode of the wind turbine as either wake optimization mode (WOP) or power point tracking mode (PTP) based on whether the state of charge (SOC) of the energy storage system is within the preset optimal range. S103, determine the current operating mode of the wind turbine based on the grid command power, power fluctuation, wind curtailment rate and the state of charge (SOC) of the energy storage system; S104, the basic objective function of the wind turbine under the current mode and the power compensation term of the current working mode are added together to generate the final objective function, and the problem model of the current mode is solved based on the final objective function; S105, the control quantity obtained from the solution is sent to the wind turbine, and the process jumps to step S102 in the next cycle; The basic objective function expression for the wake optimization mode WOP in step S101 is as follows: ； in, The basic objective function corresponding to the wake optimization mode WOP is: To control the number of steps, This is the rated power of the wind farm. The number of wind turbine units. For wind turbine units The initial power value, For wind turbine units The change in the initial value of active power, and They are respectively and The amount of change of the control item at any time and These are the weights for power fluctuations and voltage fluctuations, respectively; the functional expression of the basic objective function corresponding to the power tracking mode (PTP) in step S101 is: ； in, The fundamental objective function for Power Tracking Mode (PTP) is... For wind turbine units Power grid command power, For wind turbine units The active power.

2. The method for maximizing wind energy utilization in a wind farm with distributed energy storage as described in claim 1, characterized in that, The calculation function expressions for the power compensation terms of the wind turbine in various operating modes in step S101 are as follows: ； ； ； in, , and These are the power compensation items for power compensation mode, power smoothing mode, and low power command mode, respectively. Work mode control strategy, work mode control strategy The values ​​are 1, 2, and 3 in power compensation mode, power smoothing mode, and low power command mode, respectively. , and These are the weighting coefficients for power compensation mode, power smoothing mode, and low power command mode, respectively.

3. The method for maximizing wind energy utilization in a wind farm with distributed energy storage as described in claim 1, characterized in that, Step S102, determining whether the current mode of the wind turbine is wake optimization mode (WOP) or power point tracking mode (PTP) based on whether the state of charge (SOC) of the energy storage system is within a preset optimal range, includes: determining whether the SOC of the energy storage system is within a preset optimal range; if it is within the preset optimal range, then the current mode is determined to be wake optimization mode (WOP); otherwise, the current mode is determined to be power point tracking mode (PTP).

4. The method for maximizing wind energy utilization in a wind farm with distributed energy storage as described in claim 1, characterized in that, In step S103, when determining the current operating mode of the wind turbine based on the grid command power, power fluctuation, wind curtailment rate, and the state of charge (SOC) of the energy storage system, the trigger condition for determining the current operating mode of the wind turbine as power compensation mode is: ； in, This is the trigger condition for power compensation mode. For the wind farm at the current moment active power gradient, for The Sobolev norm, The preset threshold, For logical operations, State of charge (SOC) To preset the state of charge range; the trigger condition for determining the current operating mode of the wind turbine as power smoothing mode is: ； in, This is the trigger condition for the power smoothing mode. For the wind farm at the current moment Power grid command power, The preset threshold, To preset the state of charge range; the trigger condition for determining the current operating mode of the wind turbine as low-power command mode is: ； in, This is the trigger condition for low-power command mode. For the current moment The power grid can dispatch surplus power. Indicates the wind curtailment rate, The preset threshold, For the preset state of charge range, and and equal, Greater than .

5. The method for maximizing wind energy utilization in a wind farm with distributed energy storage as described in claim 1, characterized in that, The functional expression for constructing the Wake Optimization Mode (WOP) problem model in step S101 is: ； ； in, The problem model for optimizing wake mode WOP. For wind turbine units wind speed, and These represent the wind speed, input terms, and control terms in the wake optimization problem. and The state matrix for the wake optimization problem is... For the nonlinear coupling term of the wake optimization problem, This represents the total number of wind turbines. For the direct sum of block diagonal matrices, For wind turbine units The state matrix, Regarding wind turbine units and The identity matrix, which is only in the first... OK The column is set to 1, and the rest are 0. For Kronecker product, For wind turbine units and The wake coupling coefficient vector between them, where: ； ； in, Generator speed The change The rotational torque of the generator rotor The change Pitch angle The change This represents the change in the rate of change of the energy storage system's capacity. and These represent the changes in charging power and discharging power of the energy storage system, respectively. and These represent the changes in charging power and discharging power of the energy storage system, respectively. and These are the charging current and discharging current of the energy storage system, respectively. For wind turbine units Inflow wind speed, The rotational torque of the generator rotor Reference value for the change Pitch angle Reference value for the change and These are reference values ​​for the change in charging power and the change in discharging power of the energy storage system, respectively. and These are binary variables representing the energy flow state of the energy storage system, with values ​​of 1 or 0. A value of 1 indicates charging. A value of 1 indicates discharge, and the following relationship is satisfied. The superscript T indicates transpose.

6. The method for maximizing wind energy utilization in a wind farm with distributed energy storage as described in claim 1, characterized in that, The functional expression of the problem model for Power Point Tracking (PTP) constructed in step S101 is: ； in, For the problem model of power point tracking mode (PTP), and These are the input and control terms for the power tracking problem, respectively. and The state matrix for the power tracking problem is... For the nonlinear coupling term of the power tracking problem, where: ； ； in, Generator speed The change The rotational torque of the generator rotor The change Pitch angle The change This represents the change in the rate of change of the energy storage system's capacity. and These represent the changes in charging power and discharging power of the energy storage system, respectively. and These represent the changes in charging power and discharging power of the energy storage system, respectively. and These are the charging current and discharging current of the energy storage system, respectively. The rotational torque of the generator rotor Reference value for the change Pitch angle Reference value for the change and These are reference values ​​for the change in charging power and the change in discharging power of the energy storage system, respectively. and These are binary variables representing the energy flow state of the energy storage system, with values ​​of 1 or 0. A value of 1 indicates charging. A value of 1 indicates discharge, and the following relationship is satisfied. The superscript T indicates transpose.

7. A wind farm system for maximizing wind energy utilization with distributed energy storage, comprising a microprocessor and a memory interconnected, characterized in that, The microprocessor is programmed or configured to execute the method for maximizing wind energy utilization in a wind farm with distributed energy storage as described in any one of claims 1 to 6.

8. A computer-readable storage medium storing a computer program or instructions, characterized in that, The computer program or instructions are programmed or configured to execute, via a processor, the method for maximizing wind energy utilization in a wind farm with distributed energy storage as described in any one of claims 1 to 6.

9. A computer program product, comprising a computer program or instructions, characterized in that, The computer program or instructions are programmed or configured to execute, via a processor, the method for maximizing wind energy utilization in a wind farm with distributed energy storage as described in any one of claims 1 to 6.

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