Energy storage cooperative control method with resonance suppression

Abstract

The invention discloses an energy storage cooperative control method with resonance suppression, and belongs to the technical field of energy storage system control. Comprising the following steps: 1) collecting operation state parameters of multiple energy storage units and electrical quantity data of a grid-connected point, and identifying resonance characteristics such as resonance frequency, a common amplitude value and a resonance attenuation coefficient in real time in a mode of combining frequency domain analysis and modal identification; 2) adaptively determining a dynamic resonance suppression factor according to the resonance characteristic parameters, establishing a multi-objective optimization rule in combination with the power response capability, residual capacity and loss characteristics of the energy storage unit, and generating a cooperative control strategy with resonance suppression; a resonance suppression mechanism is deeply integrated into cooperative control, coupling optimization of the two is realized through dynamic resonance suppression factors, the common amplitude suppression ratio is high, and system oscillation is effectively avoided; by adopting a real-time resonance identification and closed-loop adjustment mechanism, the dynamic resonance characteristics under complex working conditions are adapted, the power response delay is low, and the dynamic response performance of the system is improved.

Description

Technical Field

[0001] This invention belongs to the field of energy storage system control technology, and particularly relates to an energy storage collaborative control method with resonance suppression. Background Technology

[0002] Energy storage systems have become core equipment for smoothing power fluctuations and ensuring grid stability, and their application scale continues to expand. In practical applications, multiple types of energy storage units, such as batteries, supercapacitors, and flywheels, are often used in synergy to balance power response speed and energy storage capacity.

[0003] Existing energy storage collaborative control methods mainly focus on power distribution optimization, but have obvious shortcomings: First, resonance suppression is not incorporated into the core control logic. Impedance matching differences between multiple energy storage units and sudden power command changes can easily cause system resonance, resulting in output power oscillation and increased equipment wear. Second, resonance suppression mostly adopts passive damping design, which has a slow response and poor adaptability, and cannot adapt to the dynamic resonance characteristics under complex operating conditions. Third, collaborative control and resonance suppression are independent of each other, resulting in conflict between their objectives and limiting the stability and efficiency of system operation. Summary of the Invention

[0004] The purpose of this invention is to propose a collaborative control method for energy storage with resonance suppression in order to solve the above-mentioned problems.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a collaborative control method for energy storage with resonance suppression, comprising the following steps: 1) Collect operating status parameters of multiple energy storage units and electrical quantity data of grid connection points, and identify resonance characteristics such as resonance frequency, resonance amplitude and resonance attenuation coefficient in real time by combining frequency domain analysis and modal identification. 2) Based on the resonant characteristic parameters, the dynamic resonance suppression factor is adaptively determined. Combined with the power response capability, remaining capacity and loss characteristics of the energy storage unit, a multi-objective optimization rule is established to generate a collaborative control strategy with resonance suppression. 3) A hierarchical allocation algorithm is adopted to dynamically allocate the total power command to each energy storage unit according to the collaborative control strategy, so as to achieve dual adaptation of impedance matching and power coordination; 4) Monitor the operation feedback data of each energy storage unit in real time, compare the preset resonance threshold with the power response error, and dynamically adjust the resonance suppression factor and power allocation ratio to form a closed-loop control.

[0006] As a further description of the above technical solution: In step 1), the operating status parameters include the output power, terminal voltage, charging and discharging current and temperature of the energy storage unit, and the grid connection point electrical quantity data includes the grid connection point voltage, current and frequency.

[0007] As a further description of the above technical solution: In step 1), the real-time identification of resonance features adopts the Fast Fourier Transform combined with the random subspace identification method, and the identification period does not exceed 20ms.

[0008] As a further description of the above technical solution: In step 2), the weight of the dynamic resonance suppression factor is adaptively increased as the resonance amplitude increases and the resonance frequency approaches the system's natural frequency.

[0009] As a further description of the above technical solution: In step 2), the core objectives of the multi-objective optimization rule include: resonance amplitude suppression rate ≥ 80%, power response delay ≤ 50ms, and minimum total loss of energy storage unit.

[0010] As a further description of the above technical solution: In step 3), the hierarchical allocation algorithm is divided into upper-level global power allocation and lower-level unit-level power correction. The upper level allocates the basic power according to the rated power and remaining capacity ratio of each energy storage unit, and the lower level adjusts the correction power according to impedance characteristics and resonance suppression requirements.

[0011] As a further description of the above technical solution: In step 4), the adjustment logic of the closed-loop control is as follows: when the real-time resonance amplitude exceeds 110% of the preset threshold, the weight of the resonance suppression factor is increased first; when the power response error exceeds 5%, the power allocation ratio is optimized first.

[0012] As a further description of the above technical solution: The multiple energy storage units include at least two of the following: battery energy storage units, supercapacitor energy storage units, and flywheel energy storage units. Different types of energy storage units correspond to different initial weights of resonance suppression factors.

[0013] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. In this invention, by deeply integrating the resonance suppression mechanism into the collaborative control, and by using a dynamic resonance suppression factor to achieve the coupling optimization of the two, the resonance amplitude suppression rate is high, effectively avoiding system oscillation.

[0014] 2. In this invention, by adopting a real-time resonance identification and closed-loop adjustment mechanism, the dynamic resonance characteristics under complex working conditions are adapted, resulting in low power response delay and improved system dynamic response performance.

[0015] 3. In this invention, the hierarchical allocation algorithm takes into account both the power characteristics and impedance matching requirements of the energy storage unit, reduces the risk of single unit overload, extends the service life of the equipment, and reduces losses.

[0016] 4. In this invention, by being applicable to collaborative scenarios of multiple types of energy storage units such as batteries, supercapacitors, and flywheels, it can be flexibly adapted to different application scenarios such as low-voltage microgrids, medium-voltage distribution networks, and high-voltage transmission networks, and has strong versatility. Attached Figure Description

[0017] Figure 1 This is a flowchart of a collaborative control method for energy storage with resonance suppression. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Example 1: Low-voltage microgrid scenario with distributed photovoltaic, battery, and supercapacitor hybrid energy storage I. Scene Parameters Energy storage configuration: lithium battery energy storage unit with a rated power of 50kW and a capacity of 100kWh; supercapacitor energy storage unit with a rated power of 30kW and a capacity of 5kWh; Access scenario: 380V low-voltage microgrid, equipped with 200kW distributed photovoltaic, the load is residential electricity consumption + commercial load, there is a risk of low-frequency resonance caused by photovoltaic power fluctuations and load changes; Preset parameters: system natural frequency 10Hz, resonance amplitude threshold 0.5pu, power response delay threshold 50ms.

[0020] II. Control Process Resonance feature identification: Real-time acquisition of output power and terminal voltage of lithium battery and supercapacitor, as well as current and voltage data at microgrid connection point, with a sampling frequency of 1kHz. Through fast Fourier transform and random subspace identification method, the resonance frequency of photovoltaic power change is identified as close to the system's natural frequency, and the resonance amplitude exceeds the preset threshold.

[0021] Control strategy generation: Based on the resonance characteristics, the weight of the dynamic resonance suppression factor is determined. Combining the energy storage advantages of lithium batteries with the fast response characteristics of supercapacitors, a collaborative control strategy is formulated. That is, the lithium battery undertakes the basic power distribution, the supercapacitor undertakes the dynamic compensation power, and the response speed of the supercapacitor is improved to suppress resonance.

[0022] Dynamic power allocation: The upper layer allocates basic power commands based on the ratio of the rated power and remaining capacity of the two batteries, with 30kW for the lithium battery and 15kW for the supercapacitor; the lower layer adjusts and corrects the power through resonance suppression factors, ultimately determining a power command of 28kW for the lithium battery and 17kW for the supercapacitor.

[0023] Closed-loop adjustment: Real-time feedback data after operation shows that the resonance amplitude drops rapidly below the threshold, the power response delay is 42ms, and after continuous operation, all indicators stably meet the preset requirements.

[0024] Example 2: Medium-voltage distribution network scenario of wind power, flywheel, and battery energy storage I. Scene Parameters Energy storage configuration: Lithium iron phosphate battery energy storage unit, rated power 200kW, capacity 500kWh; flywheel energy storage unit, rated power 100kW, capacity 20kWh; Access scenario: 10kV medium-voltage distribution network, equipped with 500kW wind turbine, there is mid-frequency resonance caused by wind gusts, and the existing passive damping method has a lag in response; Preset parameters: resonance amplitude threshold 0.4 pu, power response delay threshold 30 ms, system natural frequency 35 Hz.

[0025] II. Control Process Resonance feature identification: Collect the operating status parameters of the energy storage unit and the electrical quantity data of the distribution network connection point. Through relevant identification methods, identify that the resonance amplitude exceeds the threshold during gust impact and the resonance decays slowly.

[0026] Control strategy generation: Based on the resonance characteristics, the weight of the dynamic resonance suppression factor is increased. Taking advantage of the fast response speed of the flywheel (≤10ms), it is used as the core unit for resonance suppression. The lithium battery provides energy support, and a collaborative control strategy is formulated.

[0027] Dynamic power allocation: The upper layer allocates basic power according to the rated power ratio, with 133kW for the lithium battery and 67kW for the flywheel; the lower layer adjusts the flywheel correction power to increase and the lithium battery correction power to decrease based on impedance characteristics and resonance suppression requirements, ultimately determining a power command of 118kW for the lithium battery and 82kW for the flywheel.

[0028] Closed-loop adjustment: After 20ms, the resonance amplitude drops below the threshold, the power response delay is 25ms, and the resonance decays rapidly after continuous operation, and all indicators stabilize.

[0029] III. Implementation Results Compared with existing passive damping methods, the resonance suppression response speed is improved by 60%, the resonance amplitude suppression rate reaches 48.3%, the power response delay is shortened to 25ms, the total loss of the energy storage unit is reduced by 20%, and the additional energy consumption of passive damping is avoided.

[0030] Example 3: High-voltage transmission network scenario of multi-regional energy storage clusters I. Scene Parameters Energy storage configuration: 3 regional energy storage units, each unit includes a lithium battery of 100kW / 200kWh and a supercapacitor of 50kW / 10kWh, with a total rated power of 450kW; Access scenario: 110kV high-voltage transmission network, used to smooth cross-regional power exchange fluctuations, with problems of low-frequency resonance and uneven power distribution caused by multi-unit collaboration; Preset parameters: resonance amplitude threshold 0.3 pu, power response delay threshold 40 ms, system natural frequency 12 Hz.

[0031] II. Control Process Resonance feature identification: The operating parameters of energy storage units in three regions and the electrical quantity data of the grid connection point were collected. Through distributed analysis and centralized identification, it was found that the resonance amplitude caused by cross-regional power fluctuations exceeded the threshold, and that a certain region's energy storage unit became the resonance amplification point due to low impedance matching.

[0032] Control strategy generation: Determine the weight of the dynamic resonance suppression factor based on the resonance characteristics, increase its suppression weight for the resonance amplification region, and formulate a cluster collaborative control strategy.

[0033] Dynamic power allocation: The upper layer allocates the base power according to the proportion of the remaining capacity of each region, with region 1145kW, region 2130kW, and region 3140kW; the lower layer adjusts the power of the resonant amplification region to be reduced based on impedance characteristics and resonance suppression requirements, while the power of the remaining regions is moderately increased, and finally the power command of each region is determined.

[0034] Closed-loop adjustment: After 30ms, the resonance amplitude drops below the threshold, the power response delay is 35ms, and after continuous monitoring, the power distribution in each region is balanced, with no single region overload phenomenon.

[0035] III. Implementation Results The resonance amplitude suppression rate reaches 40.5%, and the power distribution error is ≤3%. Compared with the existing cluster control method, the cross-regional power fluctuation smoothing effect is improved by 30%, and the overall operating efficiency of the energy storage cluster is improved by 16%.

[0036] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method of energy storage coordinated control with resonance suppression, characterized in that: Includes the following steps: 1) Collect operating status parameters of multiple energy storage units and electrical quantity data of grid connection points, and identify resonance characteristics such as resonance frequency, resonance amplitude and resonance attenuation coefficient in real time by combining frequency domain analysis and modal identification. 2) Based on the resonant characteristic parameters, the dynamic resonance suppression factor is adaptively determined. Combined with the power response capability, remaining capacity and loss characteristics of the energy storage unit, a multi-objective optimization rule is established to generate a collaborative control strategy with resonance suppression. 3) A hierarchical allocation algorithm is adopted to dynamically allocate the total power command to each energy storage unit according to the collaborative control strategy, so as to achieve dual adaptation of impedance matching and power coordination; 4) Monitor the operation feedback data of each energy storage unit in real time, compare the preset resonance threshold with the power response error, and dynamically adjust the resonance suppression factor and power allocation ratio to form a closed-loop control.

2. The method according to claim 1, wherein In step 1), the operating status parameters include the output power, terminal voltage, charging and discharging current and temperature of the energy storage unit, and the grid connection point electrical quantity data includes the grid connection point voltage, current and frequency.

3. The method according to claim 1, wherein In step 1), the real-time identification of resonance features adopts the Fast Fourier Transform combined with the random subspace identification method, and the identification period does not exceed 20ms.

4. The method according to claim 1, wherein In step 2), the weight of the dynamic resonance suppression factor is adaptively increased as the resonance amplitude increases and the resonance frequency approaches the system's natural frequency.

5. The method of claim 1, wherein the method further comprises: In step 2), the core objectives of the multi-objective optimization rule include: resonance amplitude suppression rate ≥ 80%, power response delay ≤ 50ms, and minimum total loss of energy storage unit.

6. The method of claim 1, wherein the method further comprises: In step 3), the hierarchical allocation algorithm is divided into upper-level global power allocation and lower-level unit-level power correction. The upper level allocates the basic power according to the rated power and remaining capacity ratio of each energy storage unit, and the lower level adjusts the correction power according to impedance characteristics and resonance suppression requirements.

7. The method of claim 1, wherein the method further comprises: In step 4), the adjustment logic of the closed-loop control is as follows: when the real-time resonance amplitude exceeds 110% of the preset threshold, the weight of the resonance suppression factor is increased first; when the power response error exceeds 5%, the power allocation ratio is optimized first.

8. The method of energy storage cooperative control with resonance suppression according to claim 1, wherein, The multiple energy storage units include at least two of the following: battery energy storage units, supercapacitor energy storage units, and flywheel energy storage units. Different types of energy storage units correspond to different initial weights of resonance suppression factors.

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