A parallel optimization control method for grid-connected energy storage systems based on virtual synchronization

Abstract

The present invention discloses a parallel optimization control method for a grid-connected energy storage system based on virtual synchronization, which relates to the technical field of power system operation control. The parallel optimization control method for a grid-connected energy storage system includes the following steps: setting a virtual synchronous generator control strategy for the energy storage unit based on the active-frequency droop characteristics and the reactive-voltage droop characteristics; introducing a virtual impedance into the virtual synchronous generator control strategy, and setting the output impedance of the virtual synchronous generator to inductive; and selecting the optimal virtual inertia and damping value to optimize the dynamic response of the grid-connected energy storage system. The present invention introduces dynamic virtual impedance into the parallel converter, adjusts the virtual impedance, realizes power decoupling of the VSG parallel control system, and comprehensively considers the optimal system frequency and power dynamic response, utilizes a particle swarm algorithm to complete the optimal configuration of the control parameters, improves the system frequency and power dynamic response, and enhances the stability of the system.

Description

Technical Field

[0001] The present invention relates to the technical field of power system operation control, and in particular to a parallel optimization control method for a grid-connected energy storage system based on virtual synchronization. Background Art

[0002] With the increasing proportion of new energy access such as photovoltaic power generation and wind power generation, higher requirements are placed on the capacity of VSG (virtual synchronous machine). The parallel connection of multiple converters can achieve large-capacity power supply and improve the stability of power supply. When a single device fails and is removed, the parallel system still remains in operation. However, due to the influence of line impedance, there is coupling between the active and reactive power of the parallel system, and the differences in parameters between multiple VSG units will cause power oscillation and other problems, affecting the stability of the parallel system.

[0003] VSGs simulate the inertia and damping characteristics of synchronous generators by introducing a mathematical model into inverter control, enabling the inverter to exhibit electrical characteristics similar to those of traditional synchronous generators. Their goal is to provide inertia support, frequency regulation, and reactive power support in power systems.

[0004] When multiple VSGs operate in parallel, the stability and power quality of the power system are affected by power distribution between generators, load variations, and system disturbances. Optimizing parallel control ensures orderly and stable power distribution and frequency support among multiple VSGs, avoiding problems such as power imbalance, frequency deviation, and voltage fluctuations caused by load fluctuations.

[0005] Currently, no effective solutions have been proposed for the problems in related technologies. Summary of the Invention

[0006] To address the shortcomings of existing technologies, the present invention proposes a parallel optimization control method for grid-connected energy storage systems based on virtual synchronization. This method addresses the aforementioned background art issue, which states that when multiple VSGs operate in parallel, the stability and power quality of the power system are affected by power distribution between generators, load variations, and system disturbances. Optimizing parallel control ensures orderly and stable power distribution and frequency support between multiple VSGs, avoiding power imbalance, frequency deviation, and voltage fluctuations caused by load fluctuations.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0008] A parallel optimization control method for a grid-connected energy storage system based on virtual synchronization, the parallel optimization control method for a grid-connected energy storage system comprising the following steps:

[0009] S1. By simulating the inertia, primary frequency regulation, and excitation regulation of a synchronous generator, the active power-frequency droop characteristics and reactive power-voltage droop characteristics are characterized, and based on the active power-frequency droop characteristics and reactive power-voltage droop characteristics, a virtual synchronous generator control strategy for the energy storage unit is set;

[0010] S2. Modify the energy storage unit using dynamic virtual impedance control, introduce virtual impedance into the virtual synchronous generator control strategy, and set the output impedance of the virtual synchronous generator to inductive;

[0011] S3. Based on the particle swarm algorithm, the control parameters of the virtual synchronous generator are optimized and allocated, and the optimal virtual inertia and damping values ​​are selected to optimize the dynamic response of the grid-connected energy storage system.

[0012] Furthermore, by simulating the inertia, primary frequency regulation, and excitation regulation process of the synchronous generator, the active power-frequency droop characteristics and reactive power-voltage droop characteristics are characterized respectively. Based on the active power-frequency droop characteristics and reactive power-voltage droop characteristics, the virtual synchronous generator control strategy of the energy storage unit is set, including the following steps:

[0013] S11. Based on the rotor motion equation and the frequency modulation controller, the inertial response and primary frequency modulation process of the synchronous generator are simulated respectively to obtain the active power-frequency droop characteristics;

[0014] S12. Based on the voltage control loop, the excitation regulation process of the synchronous generator is simulated by the voltage control loop of the virtual synchronous generator to obtain the reactive power-voltage droop characteristic;

[0015] S13. Setting a virtual synchronous generator control strategy for the energy storage unit according to the obtained active power-frequency droop characteristics and reactive power-voltage droop characteristics.

[0016] Furthermore, based on the rotor motion equation and the frequency modulation controller, the inertial response and the primary frequency modulation process of the synchronous generator are simulated respectively to obtain the active power-frequency droop characteristic, which includes the following steps:

[0017] S111. Use the rotor motion equation to reflect the dynamic behavior of the synchronous generator rotor under the imbalance of mechanical power and electromagnetic power, and simulate the inertial response of the synchronous generator;

[0018] S112. Based on the frequency modulation controller, the primary frequency modulation control simulates the primary frequency modulation process of the synchronous generator by adjusting the input power of the synchronous generator, balancing the active power and stabilizing the frequency;

[0019] S113. Based on the rotor motion equation and the simulation results of the frequency modulation controller, the active power-frequency droop characteristic is obtained.

[0020] Furthermore, the rotor motion equation is expressed as:

[0021]

[0022] Where ω represents the rotor angular velocity; H represents the inertia constant of the generator; P m Represents the mechanical power of the synchronous generator; P e It represents the electromagnetic power output by the generator; D represents the damping coefficient; ω0 represents the rated angular velocity.

[0023] Furthermore, the expression of primary frequency modulation control is:

[0024] P m =P m0 +R f (ω0-ω);

[0025] Where, P m Represents the mechanical power of the synchronous generator; P m0 Indicates the mechanical power in equilibrium state; R f represents the frequency modulation coefficient; ω0-ω represents the frequency deviation.

[0026] Furthermore, based on the voltage control loop, simulating the excitation regulation process of the synchronous generator through the voltage control loop of the virtual synchronous generator to obtain the reactive power-voltage droop characteristic includes the following steps:

[0027] S121. Dynamically adjust the actual terminal voltage through the voltage controller of the virtual synchronous generator and simulate the excitation response of the synchronous generator under the voltage deviation condition;

[0028] S122. Utilize a reactive power regulator to adjust the output voltage amplitude, simulate reactive voltage characteristics, and obtain reactive-voltage droop characteristics.

[0029] Furthermore, the expression for dynamically adjusting the actual terminal voltage through the voltage controller of the virtual synchronous generator is:

[0030] V ref =V set +K q Q;

[0031] E out =K v (V ref -V term )+sT f ;

[0032] Where V set Indicates the set reference voltage; Q indicates reactive power output; K q Represents reactive power-voltage regulation coefficient; V term Indicates the actual terminal voltage; E outIndicates the output equivalent excitation voltage; K v represents the voltage control gain; T f Represents the time constant of the voltage controller; V ref represents the reference voltage of the input energy storage system; s represents the Laplace operator.

[0033] Furthermore, the expression for adjusting the output voltage amplitude using the reactive power regulator is:

[0034] ΔV=K p (Q ref -Q)+K i ∫(Q ref -Q)dt;

[0035] Where Q ref Indicates reactive power reference value; Q indicates reactive power output; K p Indicates the ratio of reactive power regulator; K i It represents the integral gain of the reactive power regulator; ΔV represents the adjustment amount of the output voltage.

[0036] Furthermore, based on the particle swarm algorithm, the control parameters of the virtual synchronous generator are optimized and allocated, and the optimal virtual inertia and damping values ​​are selected to optimize the dynamic response of the grid-connected energy storage system, including the following steps:

[0037] S31. According to the dynamic response requirements of the virtual synchronous generator network energy storage system, the optimization objective function is set to the minimum value of the frequency deviation and the damping ratio;

[0038] S32. Calculate the fitness value of each particle according to the optimization objective function, and update the individual optimal position of the particle and the global optimal position;

[0039] S32, judging whether the current particle swarm has reached the maximum number of iterations, if so, stopping the iteration and outputting the current optimal virtual inertia and damping coefficient as the final result; otherwise, continuing the iteration;

[0040] S33. Apply the obtained optimal virtual inertia and damping coefficient to the actual control of the virtual synchronous generator network energy storage system.

[0041] Furthermore, the expression of the optimization objective function is:

[0042] F=a·|f d |+b·|D best -D|;

[0043] Where F represents the minimum value of frequency deviation and damping ratio; f d Indicates the frequency deviation of the energy storage system; D bestrepresents the optimal damping ratio; D represents the damping coefficient; a represents the weight coefficient of the frequency deviation; and b represents the weight coefficient of the damping ratio.

[0044] The beneficial effects of the present invention are:

[0045] 1. The present invention introduces dynamic virtual impedance into the parallel converter, adjusts the virtual impedance, realizes power decoupling of the VSG parallel control system, comprehensively considers the optimal system frequency and power dynamic response, and uses the particle swarm algorithm to complete the optimal configuration of control parameters, improve the system frequency and power dynamic response, and enhance the stability of the system.

[0046] 2. The present invention optimizes the frequency deviation and damping characteristics of the grid-connected energy storage system through parameter optimization using the particle swarm algorithm, achieving optimal dynamic response. It can provide stable support for the energy storage system during grid-connected operation, effectively improve frequency drops, reduce system power oscillations, and ensure the safe and stable operation of the system under different operating conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0048] Figure 1 is a flow chart of a parallel optimization control method for a grid-connected energy storage system based on virtual synchronization according to an embodiment of the present invention;

[0049] Figure 2 2. Schematic diagram of a VSG parallel optimization control architecture according to an embodiment of the present invention;

[0050] Figure 3 is a schematic diagram of a virtual impedance VSG control structure according to an embodiment of the present invention;

[0051] Figure 4 2 is a schematic diagram of a voltage-current dual-loop control structure according to an embodiment of the present invention. DETAILED DESCRIPTION

[0052] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0053] In the description of the present invention, unless otherwise specified, "plurality" means two or more. In addition, the terms "first", "second", "third", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0054] According to an embodiment of the present invention, a parallel optimization control method for a grid-connected energy storage system based on virtual synchronization is provided.

[0055] The present invention will now be further described with reference to the accompanying drawings and specific embodiments. Figure 1 As shown, according to an embodiment of the present invention, a parallel optimization control method for a grid-connected energy storage system based on virtual synchronization includes the following steps:

[0056] S1. By simulating the inertia, primary frequency regulation, and excitation regulation of a synchronous generator, the active power-frequency droop characteristics and reactive power-voltage droop characteristics are characterized, and based on the active power-frequency droop characteristics and reactive power-voltage droop characteristics, a virtual synchronous generator control strategy for the energy storage unit is set;

[0057] It should be explained that the inertia and primary frequency regulation process of the synchronous generator are simulated to characterize the active power-frequency droop characteristics; the excitation regulation process of the synchronous generator is simulated to characterize the reactive power-voltage droop characteristics; and each energy storage unit is transformed using dynamic virtual impedance control, the output impedance of the virtual synchronous generator (VSG) is set to inductive, and virtual impedance is introduced to achieve parallel control.

[0058] S2. Modify the energy storage unit using dynamic virtual impedance control, introduce virtual impedance into the virtual synchronous generator control strategy, and set the output impedance of the virtual synchronous generator to inductive;

[0059] It should be explained that the use of virtual impedance can make the output impedance of the parallel system inductive, reduce the oscillation of the system power, and ensure that the system can operate smoothly. When the dynamic virtual impedance control is used to transform each energy storage unit, the impedance is considered to be set to pure inductive. At this time, the active power is related to the voltage phase, and the reactive power is related to the voltage amplitude. In order to improve the stability of the parallel system, the output impedance of the virtual synchronous generator (VSG) can be set to inductive, and parallel control can be achieved by introducing virtual impedance. The structure of adding virtual impedance to the VSG control strategy, such as Figure 2 shown.

[0060] It should be explained that before implementing the transformation, the power system requirements must be clarified, including the grid-connected / off-grid mode, power distribution strategy, and dynamic performance requirements. Then, the impedance characteristics of the circuit are simulated using the virtual impedance formula. The virtual impedance formula is:

[0061] Z v =Rv +jωL v ;

[0062] Where R v Represents the resistance component, which is used to reduce circulating current and improve power distribution in parallel systems; L v Represents the inductance component, which is used to improve the voltage and power dynamic characteristics; Z v Indicates virtual impedance.

[0063] It needs to be explained that, Figure 2 As shown in the figure, virtual impedance is added to the control strategy of the energy storage unit VSG to ensure the smooth operation of the parallel system. It is known that the selection of dynamic virtual impedance parameters is related to the ratio of the reactive power of the two VSGs. The value of the virtual impedance can be obtained by the optimal virtual impedance value formula, which is:

[0064] Q1 / Q2=m;

[0065]

[0066] Where m represents the ratio of the reactive power of the two VSGs. When m is greater than 1, the converter will adaptively adjust the virtual impedance value through m to reduce the difference in output impedance between converters. m represents virtual inductance; Z v represents virtual impedance; dt represents the integral over time; s represents the Laplace operator; R n Represents a virtual resistor, which is offset by the resistive part of the output impedance of the VSG by taking a negative value. At the same time, let R n Slightly smaller than R, the parallel system becomes weakly resistive, which can reduce system oscillations. By introducing a virtual impedance link and increasing the inductive part of the VSG impedance, power decoupling can be achieved.

[0067] After combining the virtual impedance and the voltage-current double closed loop after dq decoupling, a simplified voltage-current double loop control structure diagram can be obtained, as shown in Figure 4 shown.

[0068] Specifically, such as Figure 4 As shown, Z L represents load impedance; s represents Laplace operator; L represents inductance; C represents capacitance; u o Represents the output voltage signal; i L Indicates the current after PWM modulation; i o Represents the output current signal; i ref represents reference current; r represents resistance; u ref Represents the input reference voltage signal; Z v (s) represents virtual impedance; G u (s) represents the voltage outer loop control function; Gi (s) represents the current inner loop control function; K pwm represents the pulse width modulation gain. Combining the basic expression of the rotor motion equation, the expression of the equivalent output impedance including virtual impedance can be obtained as follows:

[0069] Z * (s)=G(s)Z v (s)+Z(s);

[0070] Where Z v (s) represents the virtual impedance; G(s) represents the transfer function of the controller; Z(s) represents the original impedance function of the system; Z * (s) represents the equivalent output impedance.

[0071] From the above expression of equivalent output impedance including virtual impedance, it can be seen that after the introduction of virtual impedance, the output impedance of the converter is equal to the sum of the filter output impedance and the virtual impedance.

[0072] It should be explained that the modeling analysis of three-phase virtual impedance is:

[0073]

[0074] Where u ref Indicates the voltage amplitude after adding virtual impedance; Indicates the voltage amplitude before application; L m In the control of three-phase converters, dual-loop control is often performed in the dq coordinate system to reduce current disturbances and improve system stability. Therefore, the virtual impedance model based on the dq coordinate system is:

[0075]

[0076] Where u dref Indicates the d-axis reference voltage after compensation; u qref Represents the q-axis reference voltage after compensation; Indicates the d-axis reference voltage before compensation; Indicates the q-axis reference voltage before compensation.

[0077] S3. Based on the particle swarm algorithm, the control parameters of the virtual synchronous generator (such as virtual inertia and damping coefficient) are optimized and distributed, and the optimal virtual inertia and damping values ​​are selected to optimize the dynamic response of the grid-connected energy storage system.

[0078] Preferably, by simulating the inertia and primary frequency regulation process and excitation regulation process of the synchronous generator, the active power-frequency droop characteristics and the reactive power-voltage droop characteristics are characterized respectively, and based on the active power-frequency droop characteristics and the reactive power-voltage droop characteristics, setting the virtual synchronous generator control strategy of the energy storage unit includes the following steps:

[0079] S11. Based on the rotor motion equation and the frequency modulation controller, the inertial response and primary frequency modulation process of the synchronous generator are simulated respectively to obtain the active power-frequency droop characteristics;

[0080] S12. Based on the voltage control loop, the excitation regulation process of the synchronous generator is simulated by the voltage control loop of the virtual synchronous generator to obtain the reactive power-voltage droop characteristic;

[0081] S13. Setting a virtual synchronous generator control strategy for the energy storage unit according to the obtained active power-frequency droop characteristics and reactive power-voltage droop characteristics.

[0082] It's important to note that the VSG's active power-frequency control, which includes a frequency controller and rotor motion equations, simulates the synchronous generator's inertia and primary frequency modulation, and is used to characterize the active power-frequency droop characteristic. In the synchronous generator's active power-frequency control, the frequency controller and rotor motion equations jointly simulate the generator's inertial response and primary frequency modulation.

[0083] Preferably, based on the rotor motion equation and the frequency modulation controller, respectively simulating the inertial response and the primary frequency modulation process of the synchronous generator to obtain the active power-frequency droop characteristic includes the following steps:

[0084] S111. Use the rotor motion equation to reflect the dynamic behavior of the synchronous generator rotor under the imbalance of mechanical power and electromagnetic power, and simulate the inertial response of the synchronous generator;

[0085] S112. Based on the frequency modulation controller, the primary frequency modulation control simulates the primary frequency modulation process of the synchronous generator by adjusting the input power of the synchronous generator, balancing the active power and stabilizing the frequency;

[0086] S113. Based on the rotor motion equation and the simulation results of the frequency modulation controller, the active power-frequency droop characteristic is obtained.

[0087] Preferably, the rotor motion equation is expressed as:

[0088]

[0089] Where ω represents the rotor angular velocity; H represents the inertia constant of the generator (i.e., the kinetic energy stored in the generator); P m Represents the mechanical power provided by the synchronous generator; P eIt represents the electromagnetic power output by the generator; D represents the damping coefficient; ω0 represents the rated angular velocity.

[0090] It's important to explain that when a power system experiences an active power disturbance (such as a load increase or decrease), the synchronous generator's rotor angular velocity changes instantaneously. The release or absorption of stored kinetic energy slows the rate of frequency change through inertial response. Inertial response is the frequency support over a short period of time and is directly reflected in the rotor's equations of motion.

[0091] Preferably, the expression of primary frequency modulation control is:

[0092] P m =P m0 +R f (ω0-ω);

[0093] Where, P m Represents the mechanical power of the synchronous generator; P m0 Indicates the mechanical power in equilibrium state; R f It represents the frequency modulation coefficient (the rate of power change caused by frequency deviation); ω0-ω represents the frequency deviation (the difference between the actual frequency and the rated frequency).

[0094] It should be explained that the frequency regulation controller dynamically adjusts the output power of the synchronous generator according to the frequency deviation to reduce the frequency deviation and achieve a new balance point.

[0095] At the beginning of the disturbance, the inertial response slows down the rapid frequency change by releasing / absorbing the rotor kinetic energy, which is manifested as the effect of the rotor motion equation on the frequency dynamics.

[0096] As time goes by, the primary frequency controller gradually adjusts the mechanical power P m , offset the frequency deviation and make the system frequency stable.

[0097] Preferably, based on the voltage control loop, simulating the excitation regulation process of the synchronous generator through the voltage control loop of the virtual synchronous generator to obtain the reactive power-voltage droop characteristic includes the following steps:

[0098] S121. Dynamically adjust the actual terminal voltage through the voltage controller of the virtual synchronous generator and simulate the excitation response of the synchronous generator under the voltage deviation condition;

[0099] S122. Utilize a reactive power regulator to adjust the output voltage amplitude, simulate reactive voltage characteristics, and obtain reactive-voltage droop characteristics.

[0100] Preferably, the expression for dynamically adjusting the actual terminal voltage by the voltage controller of the virtual synchronous generator is:

[0101] V ref =Vset +K q Q;

[0102] E out =K v (V ref -V term )+sT f ;

[0103] Where V set Indicates the set reference voltage; Q indicates reactive power output; K q Represents reactive power-voltage regulation coefficient, simulating reactive voltage characteristics; V term Indicates the actual terminal voltage; E out Indicates the output equivalent excitation voltage; K v represents the voltage control gain; T f Represents the time constant of the voltage controller, simulating the dynamic response of the excitation system; V ref represents the reference voltage of the input energy storage system; s represents the Laplace operator.

[0104] It should be explained that, through the control of the voltage controller, the VSG can dynamically adjust the output voltage and simulate the excitation response of the synchronous generator under voltage deviation conditions.

[0105] Preferably, the expression for adjusting the output voltage amplitude using a reactive power regulator (i.e., a PI controller) is:

[0106] ΔV=K p (Q ref -Q)+K i ∫(Q ref -Q)dt;

[0107] Where Q ref Indicates reactive power reference value; Q indicates reactive power output; K p Indicates the ratio of reactive power regulator; K i represents the integral gain of the reactive power regulator; ΔV represents the adjustment amount of the output voltage; dt represents the integral of time; and p represents a part of the proportional expression of the reactive power regulator.

[0108] It's important to note that a VSG regulates reactive power by adjusting the output voltage amplitude, a mechanism similar to that of a synchronous generator. The VSG's reactive power regulator can be designed as a PI controller, and the dynamic response model is the expression for the reactive power regulator's output voltage amplitude.

[0109] It should be explained that in the mechanical motion simulation control equation of VSG, the basic expression of the rotor motion equation is:

[0110]

[0111] Where, T m Represents the mechanical torque of the synchronous generator; T e Represents the electromagnetic torque of the synchronous generator; P m Represents the mechanical power of the synchronous generator; P e represents the electromagnetic power of the synchronous generator; ω0 represents the rated angular velocity of the rotor; ω represents the angular velocity of the rotor; θ * represents the generator power angle; D represents the damping coefficient; J represents the virtual inertia.

[0112] It should be explained that the expression of the frequency modulation controller is:

[0113] P m =P ref +k p (ω0-ω);

[0114] Where, P m Represents the mechanical power of the synchronous generator; P ref represents the active power reference value; ω represents the angular velocity of the rotor; ω0 represents the rated angular velocity of the rotor; k p represents the active power regulation coefficient; combined with the rotor motion equation expression, the rotor motion equation expression after frequency modulation can be obtained as follows:

[0115]

[0116] Where, P ref represents the active power reference value; ω represents the angular velocity of the rotor; ω0 represents the rated angular velocity of the rotor; D represents the damping coefficient; J represents the virtual inertia; k p Indicates active power regulation coefficient; T e Represents electromagnetic torque.

[0117] It should be explained that the expression obtained by combining the frequency modulation controller equation and the rotor motion equation after frequency modulation is:

[0118]

[0119] Where, τ=Jω0 / (Dω0+k p ), expressed as the inertia time constant; m=1 / (Dω0+k p ), represents the active power droop coefficient; ΔP represents the active power deviation; Δω represents the frequency deviation; m represents the active power droop coefficient; J represents the virtual inertia; k p Indicates the active power regulation coefficient.

[0120] From the expression obtained by combining the frequency modulation controller equation and the rotor motion equation after frequency modulation, it can be seen that the inertia link is added to the active power-frequency control of VSG, where k p The active power regulation coefficient changes the virtual mechanical power output according to the detected active power deviation, thereby achieving frequency regulation and rotational inertia control.

[0121] It should be explained that, by combining the active power-frequency regulation relationship of the synchronous generator with its rotor motion equation, a VSG virtual speed regulator can be obtained to achieve active power-frequency regulation.

[0122] The reactive power-voltage control of VSG simulates the excitation regulation process of synchronous generator and is used to characterize the reactive power-voltage droop characteristics. The reactive power-voltage control equation of VSG is:

[0123] E=d q (U ref -U)+k q (Q ref -Q);

[0124] Where, U represents the voltage output by the converter; U ref Indicates the reference voltage output by the converter; d q Indicates the voltage regulation coefficient; Q ref Represents the reactive power reference value; Q represents the reactive power output by the converter; k q represents the reactive power regulation coefficient; E represents the internal potential of the virtual synchronous generator.

[0125] It should be explained that based on the above analysis of the basic principles of VSG, a VSG parallel optimization control architecture is built, such as Figure 3 As shown, each energy storage unit is connected to the bidirectional converter PCS and the battery management system BMS. The PCS and BMS are connected to the I / O controller of the energy management system EMS. The I / O controller is connected to the coordination controller of the EMS and connected to the backend monitoring system via optical fiber. The PCS is connected in parallel to the power grid, with the PCC serving as the grid connection point. Measurement devices are used to measure power and frequency at the PCC. The energy management system (EMS) of the power station-level energy storage system is a key part of the control architecture. It consists of an I / O controller and a coordination controller. Its main functions include selecting optimal values ​​through optimization algorithms, assigning optimal instructions to each energy storage system, data collection, and operational monitoring.

[0126] Preferably, based on the particle swarm algorithm, optimizing the distribution of control parameters of the virtual synchronous generator and selecting the optimal virtual inertia and damping value to optimize the dynamic response of the grid-connected energy storage system include the following steps:

[0127] S31. According to the dynamic response requirements of the virtual synchronous generator network energy storage system, the optimization objective function is set to the minimum value of the frequency deviation and the damping ratio;

[0128] S32. Calculate the fitness value of each particle according to the optimization objective function, and update the individual optimal position of the particle and the global optimal position;

[0129] It is important to explain that the particle swarm parameter initialization includes the number of particles, the initial position of each particle (corresponding to a random combination of virtual inertia and damping coefficient), and the velocity. The initial parameter range is based on the operating range of the VSG parallel system to ensure search efficiency and accuracy. In each round of iteration, the particle position and velocity are updated to gradually approach the optimal solution. The update formula is:

[0130]

[0131] The location is updated to:

[0132]

[0133] Where ω represents the inertia weight, which is used to balance the global and local search; c1 and c2 are learning factors, which are used to adjust the weight of the particle tending to the individual and global optimal positions;

[0134] S32, determine whether the current particle swarm has reached the maximum number of iterations. If so, stop the iteration and output the current optimal virtual inertia and damping coefficient as the final result; otherwise, continue the iteration; γ1 and γ2 both represent random numbers between 0 and 1 to enhance the randomness of the search; p best represents the optimal position of an individual; p best represents the global optimal position; represents the position of particle i at the tth iteration; represents the position of particle i at the t+1th iteration.

[0135] S33. Apply the obtained optimal virtual inertia and damping coefficient to the actual control of the virtual synchronous generator network energy storage system.

[0136] Preferably, the expression of the optimization objective function is:

[0137] F=a·|f d |+b·|D best -D|;

[0138] Where F represents the minimum value of frequency deviation and damping ratio (fitness function); f d Indicates the frequency deviation of the energy storage system; D best represents the optimal damping ratio; D represents the damping coefficient; a represents the weight coefficient of the frequency deviation; and b represents the weight coefficient of the damping ratio.

[0139] In summary, with the help of the above technical solution of the present invention, the present invention introduces dynamic virtual impedance in the parallel converter, adjusts the virtual impedance, realizes power decoupling of the VSG parallel control system, and comprehensively considers the optimal dynamic response of the parallel system frequency and power, and uses the particle swarm algorithm to complete the optimal configuration of the control parameters, improve the dynamic response of the parallel system frequency and power, and improve the stability of the system. Through the parameter optimization of the particle swarm algorithm, the present invention realizes the optimized control of the frequency deviation and damping characteristics of the grid-connected energy storage system, achieves the optimal dynamic response effect, and can provide stable support for the energy storage system during grid-connected operation, effectively improve frequency drop, reduce system power oscillation, and ensure the safe and stable operation of the system under different working conditions.

[0140] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A parallel optimization control method for grid-connected energy storage systems based on virtual synchronization, characterized in that: The parallel optimization control method of the grid-connected energy storage system includes the following steps: S1. By simulating the inertia, primary frequency regulation, and excitation regulation of a synchronous generator, the active power-frequency droop characteristics and reactive power-voltage droop characteristics are characterized, and based on the active power-frequency droop characteristics and reactive power-voltage droop characteristics, a virtual synchronous generator control strategy for the energy storage unit is set; S2. Modify the energy storage unit using dynamic virtual impedance control, introduce virtual impedance into the virtual synchronous generator control strategy, and set the output impedance of the virtual synchronous generator to inductive; S3. Based on the particle swarm algorithm, the control parameters of the virtual synchronous generator are optimized and allocated, and the optimal virtual inertia and damping values ​​are selected to optimize the dynamic response of the grid-connected energy storage system; The control strategy of the virtual synchronous generator of the energy storage unit is set based on the active power-frequency droop characteristics and the reactive power-voltage droop characteristics by simulating the inertia and primary frequency modulation process and the excitation regulation process of the synchronous generator, including: S11. Based on the rotor motion equation and the frequency modulation controller, the inertial response and primary frequency modulation process of the synchronous generator are simulated respectively to obtain the active power-frequency droop characteristics; S12. Based on the voltage control loop, the excitation regulation process of the synchronous generator is simulated by the voltage control loop of the virtual synchronous generator to obtain the reactive power-voltage droop characteristic; S13. Setting a virtual synchronous generator control strategy for the energy storage unit according to the obtained active power-frequency droop characteristics and reactive power-voltage droop characteristics; The voltage control loop is based on the voltage control loop, and the excitation regulation process of the synchronous generator is simulated by the voltage control loop of the virtual synchronous generator to obtain the reactive power-voltage droop characteristics, including: S121. Dynamically adjust the actual terminal voltage through the voltage controller of the virtual synchronous generator and simulate the excitation response of the synchronous generator under the voltage deviation condition; S122, using a reactive power regulator to adjust the output voltage amplitude, simulate reactive voltage characteristics, and obtain reactive-voltage droop characteristics; The expression for dynamically adjusting the actual terminal voltage by the voltage controller of the virtual synchronous generator is: V ref =V set +K q Q; E out =K v (In ref -V term )+sT f ; Where V set Indicates the set reference voltage; Q indicates reactive power output; K q Represents reactive power-voltage regulation coefficient; V term Indicates the actual terminal voltage; E out Indicates the output equivalent excitation voltage; K v represents the voltage control gain; T f Represents the time constant of the voltage controller; V ref represents the reference voltage of the input energy storage system; s represents the Laplace operator.

2. A parallel optimization control method for grid-connected energy storage systems based on virtual synchronization according to claim 1, characterized in that: The method of simulating the inertial response and the primary frequency modulation process of the synchronous generator based on the rotor motion equation and the frequency modulation controller to obtain the active power-frequency droop characteristic includes the following steps: S111. Use the rotor motion equation to reflect the dynamic behavior of the synchronous generator rotor under the imbalance of mechanical power and electromagnetic power, and simulate the inertial response of the synchronous generator; S112. Based on the frequency modulation controller, the primary frequency modulation control simulates the primary frequency modulation process of the synchronous generator by adjusting the input power of the synchronous generator, balancing the active power and stabilizing the frequency; S113. Based on the rotor motion equation and the simulation results of the frequency modulation controller, the active power-frequency droop characteristic is obtained.

3. The method for parallel optimization control of a grid-connected energy storage system based on virtual synchronization according to claim 2, characterized in that: The rotor motion equation is expressed as: Where ω represents the rotor angular velocity; H represents the inertia constant of the generator; P m Represents the mechanical power provided by the synchronous generator; P e It represents the electromagnetic power output by the generator; D represents the damping coefficient; ω0 represents the rated angular velocity.

4. The method for parallel optimization control of a grid-connected energy storage system based on virtual synchronization according to claim 3, characterized in that: The expression of the primary frequency modulation control is: P m =P m0 +R f (ω0-ω); Where, P m Represents the mechanical power of the synchronous generator; P m0 Indicates the mechanical power in equilibrium state; R f represents the frequency modulation coefficient; ω0-ω represents the frequency deviation.

5. The method for parallel optimization control of grid-connected energy storage systems based on virtual synchronization according to claim 4, characterized in that: The expression for adjusting the output voltage amplitude by using the reactive power regulator is: ΔV=K p (Q ref -Q)+K i ∫(Q ref -Q)dt; Where Q ref Indicates reactive power reference value; Q indicates reactive power output; K p Indicates the ratio of reactive power regulator; K i It represents the integral gain of the reactive power regulator; ΔV represents the adjustment amount of the output voltage.

6. The method for parallel optimization control of grid-connected energy storage systems based on virtual synchronization according to claim 1, characterized in that: The method of optimizing the control parameters of the virtual synchronous generator based on the particle swarm algorithm and selecting the optimal virtual inertia and damping value to optimize the dynamic response of the grid-connected energy storage system includes the following steps: S31. According to the dynamic response requirements of the virtual synchronous generator network energy storage system, the optimization objective function is set to the minimum value of the frequency deviation and the damping ratio; S32. Calculate the fitness value of each particle according to the optimization objective function, and update the individual optimal position of the particle and the global optimal position; S32, judging whether the current particle swarm has reached the maximum number of iterations, if so, stopping the iteration and outputting the current optimal virtual inertia and damping coefficient as the final result; otherwise, continuing the iteration; S33. Apply the obtained optimal virtual inertia and damping coefficient to the actual control of the virtual synchronous generator network energy storage system.

7. The method for parallel optimization control of grid-connected energy storage systems based on virtual synchronization according to claim 6, characterized in that: The expression of the optimization objective function is: F=a·|f d |+b·|D best -D|; Where F represents the minimum value of frequency deviation and damping ratio; f d Indicates the frequency deviation of the energy storage system; D best represents the optimal damping ratio; D represents the damping coefficient; a represents the weight coefficient of the frequency deviation; and b represents the weight coefficient of the damping ratio.

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