Compound energy storage system frequency optimization control method based on virtual synchronous machine

Abstract

The invention relates to the technical field of energy storage and power supply, in particular to a frequency optimization control method of a composite energy storage system based on a virtual synchronous machine. High-frequency disturbance quick response is achieved through the high-power density characteristic of the super-capacitor, through dynamic power distribution, the super-capacitor preferentially bears more than 80% of power fluctuation in a high-frequency small-disturbance scene, frequent charging and discharging of the lithium-iron battery are avoided, the lithium-iron battery participates in long-term adjustment in a low-frequency large-deviation scene, and in combination with assistance of the super-capacitor, high-frequency disturbance quick response is achieved. The system frequency recovery time is shortened, decoupling control of energy type and power type energy storage is achieved through the DC / DC converters in the sub-modules, the direct current bus voltage is kept stable, meanwhile, the discharge depth of the lithium iron battery and the super capacitor is improved, and the overall capacity requirement and cost of the energy storage system are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage power supply, and particularly to a frequency optimization control method for a composite energy storage system based on a virtual synchronous machine. Background Art

[0002] With the continuous expansion of China's power grid capacity, the peak-valley difference is increasing continuously. With the booming development of renewable energy, distributed energy supply, and smart grid, the demand for large-scale development of battery energy storage technology is increasing day by day.

[0003] However, in the prior art, traditional energy storage systems mostly adopt a single type of energy storage element. Although its high energy density characteristic can meet the long-term energy storage requirements, its power response speed is slow, and the charge and discharge current change rate is limited. It is difficult to cope with high-frequency disturbances such as load mutations and renewable energy fluctuations in the power grid, resulting in frequency regulation lag and poor system stability. Existing control algorithms mostly adopt fixed parameter regulation and are difficult to adapt to complex operation scenarios. When the state of charge of the energy storage element is lower than 20%, it still charges and discharges according to the default strategy, which may cause over-discharge and damage the battery. In extreme environments such as high temperature and high humidity, the temperature rise of power devices is likely to exceed the safety threshold, and there is a lack of an adaptive derating mechanism, resulting in a decline in system reliability. Summary of the Invention

[0004] In view of the deficiencies of the prior art, the technical solution adopted by the present invention to solve its technical problems is: a frequency optimization control method for a composite energy storage system based on a virtual synchronous machine, including the following steps:

[0005] Step S1, establish a virtual synchronous machine control model that simulates the external characteristics of a synchronous generator; by simulating the external characteristics of a synchronous generator, align the control characteristics of the composite energy storage system with those of a synchronous generator in a traditional power system, enabling it to participate in the coordinated regulation of power grid frequency and voltage. The active frequency control module suppresses power grid frequency fluctuations and improves system stability by adjusting the active power output of the energy storage element to simulate the inertial characteristics of the synchronous machine. The reactive voltage control module maintains power grid voltage stability and reduces voltage flicker and distortion caused by reactive power fluctuations by adjusting the reactive power output.

[0006] Step S2, according to the power grid frequency fluctuation signal, dynamically allocate the active power of the energy-type energy storage element and the power-type energy storage element through a power distribution controller; in the case of high-frequency small disturbances, the supercapacitor responds quickly and preferentially bears high-frequency power fluctuations to avoid premature battery life loss caused by frequent charging and discharging of lithium iron phosphate batteries. In the case of low-frequency large deviations, the lithium iron phosphate battery participates in continuous power regulation and uses its high energy density characteristic to provide long-term frequency support to ensure system stability. The hierarchical control strategy extends the overall service life of the energy storage element and reduces the operating cost.

[0007] Step S3: By means of instantaneous frequency detection and grid connection criteria, the grid voltage fluctuation is monitored in real time. In combination with the reactive power decoupling controller, the composite energy storage system is controlled to output compensating reactive power to offset the voltage deviation caused by the reactive power fluctuation of the grid. By monitoring the grid voltage fluctuation in real time, the composite energy storage system is controlled to output compensating reactive power to offset the reactive power deficit or surplus of the grid, achieving rapid suppression of the voltage deviation.

[0008] Step S4: According to the real-time operating state of the composite energy storage system, the moment of inertia, damping coefficient and voltage regulation parameters in the virtual synchronous machine control model are dynamically adjusted to optimize the frequency response characteristics of the system. Based on the parameter adaptive mechanism of model predictive control, the system can quickly adapt to different working conditions, avoiding the lag of traditional fixed parameter control. Through the dynamic matching of the rule base, the frequency stability and dynamic response speed of the system in complex scenarios are improved.

[0009] The composite energy storage system includes an energy-type energy storage element, a power-type energy storage element and an energy storage converter based on a modular multilevel converter. The energy-type energy storage element is a lithium iron phosphate battery, and the power-type energy storage element is a super capacitor. By monitoring the DC bus voltage fluctuation of the modular multilevel converter in real time, the control parameters of the DC / DC converter in the sub-module are dynamically adjusted to maintain the DC bus voltage stable within the range of ±5% of the rated value, while increasing the discharge depth of the battery and the super capacitor.

[0010] The present invention is further configured such that the virtual synchronous machine control model in the step S1 includes an active frequency control module and a reactive voltage control module; the active module focuses on frequency stability, and the reactive module focuses on voltage regulation, simplifying the control logic and improving the regulation efficiency. Compared with the single-function control of the traditional energy storage system, the "frequency-voltage" collaborative optimization is realized, which is closer to the actual operation requirements of the power grid.

[0011] The active frequency control module is based on the rotor motion equation of the synchronous generator. By adjusting the active power output of the energy-type energy storage element and the power-type energy storage element, the moment of inertia and damping characteristics of the synchronous generator are simulated to maintain the system frequency stability.

[0012] The equation established by the active frequency control module is: , where is the output active power, is the rated active power, is the frequency droop coefficient, is the rated angular frequency, is the real-time angular frequency.

[0013] The reactive voltage control module is based on the excitation regulation principle of the synchronous generator. By controlling the reactive power output of the composite energy storage system, the grid voltage stability is maintained.

[0014] The present invention is further configured such that the active power dynamic distribution method in step S2 is as follows:

[0015] Set the frequency threshold to Fc and the predetermined time to Hc. When the frequency deviation F≥Fc, the power-type energy storage element is preferentially used to quickly respond to high-frequency power fluctuations and suppress frequency mutations. When the frequency deviation H≥Hc, the energy-type energy storage element is triggered to participate in low-frequency power regulation to achieve frequency support at different time scales. This avoids the participation of lithium iron phosphate batteries in high-frequency and short-time power fluctuations, prolongs their cycle life, ensures that there is sufficient energy to support frequency regulation in low-frequency large deviation scenarios, and avoids overload of a single energy storage element.

[0016] The present invention is further configured such that the specific operation steps of step S3 are as follows:

[0017] Step A1: Perform coordinate rotation transformation on the grid voltage and current to extract the d-axis and q-axis current components;

[0018] Step A2: Calculate the required reactive power compensation amount through a power decoupling algorithm, and drive the energy storage converter to adjust the output reactive power to achieve voltage fluctuation, flicker, and harmonic suppression.

[0019] The present invention is further configured such that the reactive power decoupling controller in step S3 adopts a repetitive control algorithm. By adjusting the DC / DC converter in the energy storage converter sub-module, the charging and discharging current of the supercapacitor is controlled, and the compensation response time of the reactive power decoupling controller ≤50ms. Repetitive control has a high gain characteristic for periodic interference, can accurately track the reactive power compensation command, improve the compensation accuracy, and the response speed meets the fast regulation requirements of the power grid, which is superior to the lag of traditional PI control in complex harmonic scenarios.

[0020] The present invention is further configured such that step S4 further includes: Based on the model predictive control algorithm, establish a parameter adaptive adjustment rule library, match the adjustment rules according to the real-time monitoring data, and automatically update the control parameters to improve the frequency stability and dynamic response speed of the system under different working conditions.

[0021] The present invention is further configured such that the establishment steps of the rule library are as follows:

[0022] Step B1: Determine the key parameters in the virtual synchronous machine control model and monitor the system state in real time; the parameters include but are not limited to moment of inertia, damping coefficient, and power distribution ratio, and the monitored system states are the power of the lithium iron phosphate battery and the supercapacitor, the grid frequency deviation, the frequency change rate, and the voltage fluctuation of the energy storage system;

[0023] Step B2: Divide the system operating state into several typical operating conditions according to different ranges of influencing factors; the operating conditions include high-frequency small-disturbance operating conditions where the grid frequency fluctuates slightly but changes rapidly; low-frequency large-deviation operating conditions where the grid frequency deviates significantly from the rated value but changes slowly; energy storage low-power operating conditions where the power of the lithium iron phosphate battery or supercapacitor is lower than the safety threshold.

[0024] Step B3: For each operating condition, set the core goal of parameter adjustment and formulate specific parameter adjustment rules for each operating condition.

[0025] In high-frequency small-disturbance operating conditions, give priority to using the supercapacitor for rapid response to reduce the burden on the lithium iron phosphate battery. When high-frequency small disturbances are detected and the supercapacitor has sufficient power, increase the power distribution ratio of the supercapacitor to quickly suppress frequency fluctuations.

[0026] In low-frequency large-deviation operating conditions, let the lithium iron phosphate battery participate in continuous adjustment to stabilize the frequency. When the frequency deviation is large and changes slowly, reduce the moment of inertia parameter to make the system respond faster to low-frequency deviation.

[0027] In energy storage low-power operating conditions, limit the charge and discharge power to avoid excessive power consumption or equipment damage. When the power of any energy storage element is lower than 20%, automatically reduce the power output amplitude and increase the damping coefficient to suppress charge and discharge oscillations.

[0028] Step B4: Store all adjustment rules in the rule base of the controller in the form of logical statements. The system collects data in real time, automatically matches the rules corresponding to the current operating condition, and dynamically adjusts the control parameters of the virtual synchronous machine; store all adjustment rules in the rule base of the controller in the form of logical statements of "if xx operating condition - then xx parameter adjustment".

[0029] Step B5: Verify the effectiveness of the rules and optimize the parameters through actual operation data feedback. If the adjustment effect is not good under a certain operating condition, adjust the parameter threshold or adjustment amplitude of the corresponding rule, and add complex operating conditions not covered, and supplement new rules to the library to improve the system adaptability.

[0030] The beneficial effects of the present invention are as follows:

[0031] 1. The present invention provides continuous power support by setting up complementary energy-type and power-type energy storage, taking advantage of the high energy density characteristics of lithium iron phosphate batteries to provide continuous power support, and the high power density characteristics of supercapacitors to achieve rapid response to high-frequency disturbances. Through dynamic power distribution, in the "high-frequency small disturbance" scenario, the supercapacitor undertakes more than 80% of the power fluctuations preferentially, avoiding frequent charge and discharge of lithium iron phosphate batteries. In the "low-frequency large deviation" scenario, the lithium iron phosphate battery participates in long-term regulation, combined with the assistance of the supercapacitor, the system frequency recovery time is shortened. The decoupling control of the energy-type and power-type energy storage is realized through the DC / DC converter in the sub-module, maintaining the stability of the DC bus voltage, while increasing the discharge depth of the lithium iron phosphate battery and the supercapacitor, reducing the overall capacity requirement and cost of the energy storage system.

[0032] 2. The present invention simulates the moment of inertia and excitation characteristics of a synchronous generator through a virtual synchronous machine, enabling the composite energy storage system to have the inertial response ability of a traditional synchronous machine. When disturbances such as short-circuit faults occur in the power grid, the peak energy can be instantaneously released and absorbed, providing adjustment time for the system regulating device and avoiding power grid instability. The reactive power voltage control module independently regulates the reactive power output, combined with the active power frequency control module, to achieve the dual-objective optimization of frequency stability + voltage stability. Compared with the single power control mode of traditional energy storage systems, the power quality index is reduced. Specific implementation manners

[0033] The following further details the present invention in conjunction with specific implementation manners. The embodiments of the present invention are given for the purposes of illustration and description, and are not exhaustive or limit the present invention to the disclosed form. Many modifications and variations are obvious to those of ordinary skill in the art. The embodiments are selected and described to better illustrate the principles and practical applications of the present invention, and enable those of ordinary skill in the art to understand the present invention and thus design various embodiments with various modifications suitable for specific purposes.

[0034] Embodiment:

[0035] The present invention provides a technical solution: a frequency optimization control method for a composite energy storage system based on a virtual synchronous machine, including the following steps:

[0036] Step S1, establish a virtual synchronous machine control model that simulates the external characteristics of a synchronous generator;

[0037] Step S2, according to the power grid frequency fluctuation signal, dynamically distribute the active power of the energy-type energy storage element and the power-type energy storage element through a power distribution controller;

[0038] Step S3, through instantaneous frequency detection and grid connection criteria, monitor the power grid voltage fluctuation in real time, and combine with a reactive power decoupling controller to control the composite energy storage system to output compensating reactive power to offset the voltage deviation caused by the reactive power fluctuation of the power grid;

[0039] Step S4: Dynamically adjust the moment of inertia, damping coefficient, and voltage regulation parameters in the virtual synchronous machine control model according to the real-time operating state of the composite energy storage system to optimize the system frequency response characteristics.

[0040] The virtual synchronous machine control model in Step S1 includes an active frequency control module and a reactive voltage control module;

[0041] The active frequency control module is based on the rotor motion equation of a synchronous generator. By adjusting the active power output of the energy-type energy storage element and the power-type energy storage element, it simulates the moment of inertia and damping characteristics of the synchronous generator to maintain system frequency stability;

[0042] The reactive voltage control module is based on the excitation regulation principle of a synchronous generator. By controlling the reactive power output of the composite energy storage system, it maintains grid voltage stability.

[0043] The active power dynamic distribution method in Step S2 is as follows:

[0044] Set the frequency threshold as Fc and the predetermined time as Hc. When the frequency deviation F≥Fc, the power-type energy storage element is given priority to quickly respond to high-frequency power fluctuations and suppress frequency mutations. When the frequency deviation H≥Hc, the energy-type energy storage element is triggered to participate in low-frequency power regulation to achieve frequency support at different time scales.

[0045] The specific operation steps in Step S3 are as follows:

[0046] Step A1: Perform coordinate rotation transformation on the grid voltage and current to extract the d-axis and q-axis current components;

[0047] Step A2: Calculate the required reactive power compensation amount through a power decoupling algorithm, and drive the energy storage converter to adjust the output reactive power to achieve voltage fluctuation, flicker, and harmonic suppression.

[0048] In Step S3, the reactive power decoupling controller adopts a repetitive control algorithm. By adjusting the DC / DC converter in the energy storage converter sub-module, it controls the charging and discharging current of the supercapacitor. The compensation response time of the reactive power decoupling controller ≤50ms.

[0049] Step S4 also includes: Based on the model predictive control algorithm, establish a parameter adaptive adjustment rule library, match the adjustment rules according to the real-time monitoring data, and automatically update the control parameters to improve the frequency stability and dynamic response speed of the system under different working conditions.

[0050] The steps for establishing the rule library are as follows:

[0051] Step B1: Determine the key parameters in the virtual synchronous machine control model and monitor the system state in real time;

[0052] Step B2: Divide the system operating state into several typical operating conditions according to different ranges of influencing factors;

[0053] Step B3: For each operating condition, set the core objective of parameter adjustment and formulate specific parameter adjustment rules for each operating condition;

[0054] Step B4: Store all adjustment rules in the rule base of the controller in the form of logical statements. The system collects data in real time, automatically matches the rules corresponding to the current operating condition, and dynamically adjusts the control parameters of the virtual synchronous machine;

[0055] Step B5: Verify the effectiveness of the rules and optimize the parameters through the feedback of actual operation data.

[0056] Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art and related fields based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention. Structures, devices, and operation methods not specifically described and explained in the present invention shall be implemented according to the conventional means in the art unless otherwise specified and limited.

Claims

1. A frequency optimization control method for a composite energy storage system based on a virtual synchronous machine, characterized in that, It includes the following steps: Step S1: Establish a virtual synchronous machine control model that simulates the external characteristics of a synchronous generator; Step S2: According to the power grid frequency fluctuation signal, dynamically distribute the active power of the energy storage element and the power storage element through a power distribution controller; Step S3: Through instantaneous frequency detection and grid connection criteria, monitor the power grid voltage fluctuation in real time, and combine with a reactive power decoupling controller to control the composite energy storage system to output compensating reactive power to offset the voltage deviation caused by the reactive power fluctuation of the power grid; Step S4: According to the real-time operation state of the composite energy storage system, dynamically adjust the moment of inertia, damping coefficient and voltage regulation parameters in the virtual synchronous machine control model to optimize the system frequency response characteristics.

2. The frequency optimization control method for the composite energy storage system based on a virtual synchronous machine according to claim 1, wherein: In step S1, the virtual synchronous machine control model includes an active frequency control module and a reactive voltage control module; The active frequency control module is based on the rotor motion equation of the synchronous generator. By adjusting the active power output of the energy storage element and the power storage element, it simulates the moment of inertia and damping characteristics of the synchronous generator to maintain the system frequency stability; The reactive voltage control module is based on the excitation regulation principle of the synchronous generator. By controlling the reactive power output of the composite energy storage system, it maintains the power grid voltage stability.

3. The frequency optimization control method of the composite energy storage system based on a virtual synchronous machine according to claim 1, wherein: The active power dynamic distribution method in step S2 is as follows: Set the frequency threshold as Fc and the predetermined time as Hc. When the frequency deviation F≥Fc, the power storage element is preferentially used to quickly respond to high-frequency power fluctuations and suppress frequency mutations. When the frequency deviation H≥Hc, the energy storage element is triggered to participate in low-frequency power regulation to achieve frequency support at different time scales.

4. The frequency optimization control method for the composite energy storage system based on the virtual synchronous machine according to claim 1, wherein: The specific operation steps of step S3 are as follows: Step A1: Perform coordinate rotation transformation on the power grid voltage and current to extract the d-axis and q-axis current components; Step A2: Calculate the required reactive power compensation amount through a power decoupling algorithm, and drive the energy storage converter to adjust the output reactive power to achieve voltage fluctuation, flicker and harmonic suppression.

5. The frequency optimization control method of the composite energy storage system based on a virtual synchronous machine according to claim 1, characterized in that: In step S3, the reactive power decoupling controller adopts a repetitive control algorithm. By adjusting the DC / DC converter in the energy storage converter sub-module, it controls the charging and discharging current of the super capacitor. The compensation response time of the reactive power decoupling controller ≤50ms.

6. The frequency optimization control method for the composite energy storage system based on a virtual synchronous machine according to claim 1, wherein: Step S4 also includes: Based on the model predictive control algorithm, establish a parameter adaptive adjustment rule library, match the adjustment rules according to the real-time monitoring data, automatically update the control parameters, and improve the frequency stability and dynamic response speed of the system under different working conditions.

7. The frequency optimization control method for the composite energy storage system based on a virtual synchronous machine according to claim 6, wherein: The establishment steps of the rule library are as follows: Step B1: Determine the key parameters in the virtual synchronous machine control model and monitor the system state in real time; Step B2: Divide the system operation state into several typical working conditions according to different ranges of influencing factors; Step B3: For each working condition, set the core goal of parameter adjustment and formulate specific parameter adjustment rules for each working condition; Step B4: Store all adjustment rules in the rule library of the controller in the form of logical statements. The system collects data in real time, automatically matches the rules corresponding to the current working condition, and dynamically adjusts the virtual synchronous machine control parameters; Step B5: Verify the effectiveness of the rules and optimize the parameters through the feedback of actual operation data.