State of charge balancing control method and related equipment for grid-type energy storage units

Abstract

The present invention belongs to the field of grid-type energy storage systems, and discloses a state of charge balancing control method and related equipment for grid-type energy storage units. By obtaining real-time SOC values ​​and charge and discharge power, the preset working conditions are dynamically judged and the optimization target is determined based on this. The feedback coefficient is then optimized based on this, and superimposed on the power instruction to compensate for the power fluctuation of the grid-type energy storage unit to achieve SOC balancing control. The characteristics of the synchronous generator are simulated by a virtual synchronous generator model, so that the energy storage unit can better adapt to the power grid; the working conditions are judged according to the real-time SOC and power, and different optimization targets are formulated for different working conditions to make the control strategy more targeted; the feedback coefficient is optimized to compensate for power fluctuations and accurately control the power. The use of this method can effectively overcome the problem of inaccurate control of traditional static balancing methods under complex working conditions, balance the contradiction between system efficiency and SOC balance, and can also quickly adjust when the SOC approaches the limit value to avoid overcharging and over-discharging, ensure the safe and stable operation of the power grid, and improve the overall efficiency of the energy storage system.

Description

Technical Field

[0001] The present invention belongs to the technical field of grid-type energy storage systems, and in particular relates to a state of charge balancing control method for a grid-type energy storage unit and related equipment. Background Art

[0002] With the ongoing transformation of the global energy mix and the rapid development of renewable energy generation technologies, the power system is facing unprecedented challenges and opportunities. To address the impact of intermittent and volatile renewable energy generation on grid stability, electrochemical energy storage systems (ECSs), as an efficient and flexible energy storage and release solution, have become a key technology for building a robust smart grid and promoting the large-scale integration of renewable energy. ECSs store and convert energy through battery packs. Combined with core components such as power electronics interfaces (such as PCSs or bidirectional DC / DC converters), battery management systems (BMSs), and energy management systems (EMSs), they enable efficient energy management and rapid response. Their advantages, such as short construction cycles, rapid response times, and high energy density, make them a valuable complement to traditional mechanical energy storage methods such as pumped hydropower storage. They play an irreplaceable role in enhancing grid regulation capabilities, ensuring power supply security, and promoting the efficient use of renewable energy. In particular, in grid-connected energy storage applications, this technology, by proactively providing frequency support and black start capabilities, further enhances the resilience and self-healing capabilities of the power system, becoming a key technological force in supporting the construction of new power systems.

[0003] While electrochemical energy storage technology, particularly grid-connected energy storage systems, demonstrates significant potential for enhancing grid flexibility and stability, several key challenges remain within the current technology landscape, particularly significant deficiencies in the control of the state of charge (SOC) balance within energy storage units. First, conventional SOC balancing strategies often employ static balancing methods, implementing hierarchical control based on preset fixed thresholds. This approach struggles to precisely control and dynamically balance SOC under complex operating conditions, such as dynamic grid load fluctuations and uncertain renewable energy output, limiting the overall effectiveness of energy storage systems. Second, existing technologies exhibit a significant conflict between the pursuit of system efficiency and SOC balance. Under high-load conditions, prioritizing efficiency to reduce system losses often leads to increased SOC imbalance. Meanwhile, during low-load periods, excessive pursuit of SOC balance can compromise system efficiency and increase operating costs. Finally, safety is another significant challenge. Existing control strategies lack a rapid and effective adjustment mechanism when SOC approaches charge and discharge limits, which can easily lead to premature shutdown of energy storage units due to overcharge or over-discharge. This not only shortens device life but can also cause system frequency fluctuations, compromising the safe and stable operation of the grid. Summary of the Invention

[0004] The present invention provides a state of charge balancing control method and related equipment for grid-type energy storage units. This control method dynamically adjusts the feedback coefficient and comprehensively considers SOC deviation, output demand and remaining capacity to achieve optimized control under different operating conditions, ensuring efficient operation of the system while extending the life of the energy storage unit. In addition, this method achieves a dynamic balance between efficiency and balance through adaptive feedback coefficients and operating condition classification strategies, and enhances the safety of the system's operating boundaries.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A method for controlling a state of charge balance for a grid-type energy storage unit, comprising:

[0007] Based on the rated angular frequency, equivalent power angle, active power command and actual power, a virtual synchronous generator model is constructed in combination with the characteristics of the synchronous generator;

[0008] Obtain the real-time SOC value and charge and discharge power of the virtual synchronous generator model;

[0009] According to the SOC value and charge and discharge power, dynamically determine the preset working conditions corresponding to the grid-type energy storage system, and obtain the corresponding optimization target under the current preset working conditions;

[0010] The feedback coefficient is optimized based on the corresponding optimization target under the current preset operating conditions to achieve balanced control of the charge state of the grid-type energy storage unit; wherein the feedback coefficient is superimposed on the power instruction controlled by the virtual synchronous generator model to compensate for the power fluctuation amplitude of the grid-type energy storage unit.

[0011] Furthermore, the virtual synchronous generator model is specifically expressed as follows:

[0012]

[0013] Where, is the output angular frequency of the energy storage converter; w0 is the rated value of the angular frequency; Δw is The difference between w0 and δ is the equivalent generator power angle; the active power command value of the energy storage converter and the actual value P are equivalent to the generator input mechanical power and output electromagnetic power respectively; H is equivalent to the inertia time constant of the unit; D is the proportional coefficient of active power deviation and angular frequency deviation; T is the time constant of the first-order inertia link; t is time.

[0014] Furthermore, the method of dynamically determining the preset operating condition corresponding to the grid-type energy storage system based on the SOC value and the charge and discharge power, and obtaining the optimization target corresponding to the current preset operating condition, includes:

[0015] Based on the SOC value and charge and discharge power, the preset operating conditions corresponding to the grid-type energy storage system are dynamically judged as follows:

[0016] If the charge and discharge power is greater than the first-level power threshold, and the SOC value is between the first SOC threshold and the second SOC threshold, the grid-type energy storage system is judged to be in the first operating condition, and the optimization goal corresponding to the first operating condition is to reduce system losses;

[0017] If the charge and discharge power is between the first power threshold and the second power threshold, and the SOC value is between the first SOC threshold and the second SOC threshold, then the grid-type energy storage system is judged to be in the second operating condition. The optimization goal corresponding to the second operating condition is to simultaneously reduce system losses and balance the SOC values ​​of the energy storage units.

[0018] If the charge and discharge power is less than the secondary power threshold, and the SOC value is between the first SOC threshold and the second SOC threshold, the grid-type energy storage system is judged to be in the third operating condition. The optimization goal corresponding to the third operating condition is to adjust the output power at the cost of minimum loss.

[0019] If the SOC value is less than the first SOC threshold or the SOC value is greater than the second SOC threshold, it is determined that the grid-type energy storage system is in the fourth operating condition, and the optimization goal corresponding to the fourth operating condition is to extend the commissioning time of the energy storage system;

[0020] Among them, the second-level power threshold is smaller than the first-level power threshold; the first SOC threshold is smaller than the second SOC threshold.

[0021] Furthermore, the optimization of the feedback coefficient based on the corresponding optimization target under the current preset working condition includes:

[0022] When the grid-type energy storage system is judged to be in the first operating condition, the corresponding optimization goal is to reduce system losses;

[0023] The feedback coefficient P is calculated according to the optimization target feed , the specific formula is as follows:

[0024]

[0025] Where, P1 is the first-level power threshold; α1 is the first-level equalization coefficient; P N is the rated power of the energy storage unit, k is the voltage modulation ratio of the energy storage converter; R is the equivalent resistance of the energy storage unit on the DC side; R1 is the equivalent resistance of the energy storage unit on the AC side; U sN is the rated AC voltage of the energy storage converter; is the active power command value.

[0026] Furthermore, the optimization of the feedback coefficient based on the optimization target corresponding to the current preset working condition further includes:

[0027] When the grid-type energy storage system is judged to be in the second operating condition, the corresponding optimization goal is to simultaneously reduce system losses and balance the SOC values ​​of the energy storage units;

[0028] The feedback coefficient P is calculated according to the optimization target feed , the specific formula is as follows:

[0029]

[0030] Where, P2 is the secondary power threshold; α2 is the secondary equalization coefficient; SOC ave is the average SOC value of all energy storage units in the system; β is the weight coefficient; n is the acceleration power index; SOC is the SOC value of the balanced energy storage unit; is the nth power of the SOC value of the balanced energy storage unit; It is the nth power of the average value of the SOC of all energy storage units in the system.

[0031] Furthermore, the optimization of the feedback coefficient based on the optimization target corresponding to the current preset working condition further includes:

[0032] When the grid-type energy storage system is judged to be in the third operating condition, the corresponding optimization goal is to adjust the output power at the cost of minimum loss;

[0033] The feedback coefficient P is calculated according to the optimization target feed , the specific formula is as follows:

[0034] .

[0035] Furthermore, the optimization of the feedback coefficient based on the optimization target corresponding to the current preset working condition further includes:

[0036] When the grid-type energy storage system is judged to be in the fourth operating condition, the corresponding optimization goal is to extend the commissioning time of the energy storage system;

[0037] The feedback coefficient P is calculated according to the optimization target feed , the specific formula is as follows:

[0038]

[0039] Where, I bat is the battery current.

[0040] A state of charge balancing control system for a grid-type energy storage unit, comprising:

[0041] A model building module is used to build a virtual synchronous generator model based on the rated angular frequency, equivalent power angle, active power command and actual power, combined with the characteristics of the synchronous generator;

[0042] Data acquisition module, used to obtain the real-time SOC value and charge and discharge power of the virtual synchronous generator model;

[0043] The judgment module is used to dynamically judge the preset working condition corresponding to the grid-type energy storage system based on the SOC value and the charge and discharge power, and obtain the corresponding optimization target under the current preset working condition;

[0044] An optimization module is used to optimize the feedback coefficient based on the corresponding optimization target under the current preset operating conditions to achieve balanced control of the charge state of the grid-type energy storage unit; wherein the feedback coefficient is superimposed on the power instruction controlled by the virtual synchronous generator model to compensate for the power fluctuation amplitude of the grid-type energy storage unit.

[0045] An electronic device, comprising:

[0046] memory for storing computer programs;

[0047] A processor is used to implement the steps of the above-mentioned charge state balancing control method for grid-type energy storage units when executing the computer program.

[0048] A computer-readable storage medium stores a computer program, which, when executed by a processor, is used to implement the steps of the above-mentioned charge state balancing control method for a grid-type energy storage unit.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] The present invention provides a state of charge balancing control method for a grid-type energy storage unit. This method obtains the real-time SOC value and charge and discharge power, dynamically judges the preset working condition and determines the optimization target based on this, and then optimizes the feedback coefficient based on this, and superimposes it in the power instruction to compensate for the power fluctuation of the grid-type energy storage unit to achieve SOC balancing control. The characteristics of the synchronous generator are simulated by a virtual synchronous generator model, so that the energy storage unit can better adapt to the power grid; the working condition is judged according to the real-time SOC and power, and different optimization targets are formulated for different working conditions to make the control strategy more targeted; the feedback coefficient is optimized to compensate for power fluctuations and accurately control the power. The use of this method can effectively overcome the problem of inaccurate control of traditional static balancing methods under complex working conditions, balance the contradiction between system efficiency and SOC balance, and can also quickly adjust when the SOC approaches the limit value to avoid overcharging and over-discharging, ensure the safe and stable operation of the power grid, and improve the overall efficiency of the energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 A control block diagram of the improved virtual synchronous machine provided by an embodiment of the present invention, used to illustrate the VSG core module and feedback coefficient superposition logic;

[0052] Figure 2 This is a diagram of the optical load storage simulation architecture provided by an embodiment of the present invention;

[0053] Figure 3 Comparison chart of simulation results provided by an embodiment of the present invention; (a) is the active power output of the energy storage converter; (b) is the charge state of the BESS; showing the SOC balancing and power distribution effects under different operating conditions;

[0054] Figure 4 A flow chart of a method for controlling the state of charge balance of a grid-type energy storage unit provided by an embodiment of the present invention;

[0055] Figure 5 A schematic structural diagram of a state of charge balancing control system for a grid-type energy storage unit provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0056] In order to further understand the content of the present invention, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the embodiments are only for explaining the present invention and are not intended to limit it.

[0057] The following is an explanation of the technical terms involved in the present invention:

[0058] Electrochemical energy storage: refers to an energy storage system that uses batteries to store, release and manage electrical energy.

[0059] Energy density: The amount of energy stored per unit volume.

[0060] PCS: Power Converter System, energy storage converter.

[0061] BMS: Battery Management System, battery management system.

[0062] EMS: Energy Management System.

[0063] SOC: State of Charge.

[0064] BESS: Battery Energy Storage System, electrochemical energy storage system.

[0065] VSG: Virtual Synchronous Generator, virtual synchronous generator.

[0066] PV: Photovoltaic.

[0067] AC:Alternating Current, alternating current.

[0068] DC: Direct Current, direct current.

[0069] In order to overcome the defects of the traditional SOC balancing method of electrochemical energy storage units, this embodiment provides a state of charge balancing control method for grid-type energy storage units. This method comprehensively considers the SOC deviation, the output of the energy storage power station and the remaining charge and discharge capacity, and designs the balancing coefficient accordingly to adapt to various working conditions in the grid-type scenario. This method is very intelligent and flexible. When the SOC margin is large and the load is heavy, it prioritizes reducing system losses to ensure efficient operation; when the SOC margin is large but the load is small, it focuses on making the SOC consistent to lay a solid foundation for operation; when the SOC margin is small, it accelerates the output reduction to prevent the energy storage unit from exiting prematurely. It specifically includes the following steps:

[0070] S1. Based on the rated angular frequency, equivalent power angle, active power command and actual power, a virtual synchronous generator model is constructed in combination with the characteristics of the synchronous generator. Among them, the virtual synchronous generator (VSG) technology can simulate the damping characteristics of the synchronous generator compared to traditional droop control, while maintaining the ability to seamlessly switch between on-grid and off-grid, and is widely used in grid-connected energy storage control.

[0071]

[0072] Where w0 is the rated value of the angular frequency, which is usually consistent with the rated frequency of the power grid; is the output angular frequency of the energy storage converter; δ is the equivalent generator power angle; PCS active power command value and the actual value of PCS active power P are equivalent to the generator input mechanical power and output electromagnetic power respectively; H is equivalent to the inertia time constant of the unit, which makes the frequency regulation of PCS have inertial characteristics; D is the proportional coefficient of active power deviation and angular frequency deviation, which is equivalent to the damping coefficient; T is the time constant of the first-order inertia link.

[0073] In this embodiment, in order to make the energy storage unit operate continuously, the SOC of the battery needs to be controlled within a reasonable range. The feedback coefficient P is superimposed on the power instruction. feed , used to compensate for the power fluctuation amplitude of the energy storage system and achieve the expected control effect without affecting the damping capacity. The control block diagram of the energy storage system is as follows Figure 1 As shown, is the argument of the VSG output electromotive force, corresponding to V s The initial phase angle, where V s is the voltage at the PCS AC side port; s is the Laplace variable after transforming from the time domain to the complex frequency domain. Figure 1It can be seen that by adding a first-order inertia link to the conventional outer-loop active power-frequency droop control, the converter has power angle characteristics similar to those of a synchronous generator, thus enabling the provision of virtual inertia. However, conventional VSG control uses a constant power command and cannot compensate for power fluctuations in the energy storage system under different operating conditions. Therefore, this embodiment proposes an improved VSG control.

[0074] In this embodiment, in the virtual synchronous generator model, the key parameters of the energy storage system are equated with the relevant parameters of the synchronous generator, providing a theoretical basis and an accurate model for subsequent SOC balancing control based on this model, ensuring that the control strategy can accurately simulate the operating characteristics of the synchronous generator and improve the energy storage system's response to grid frequency and power fluctuations.

[0075] S2. Obtain the real-time SOC value and charge / discharge power of the virtual synchronous generator model. Here, a monitoring terminal is used to collect and upload real-time data of the current SOC value and charge / discharge power of the virtual synchronous generator model.

[0076] S3. Based on the SOC value and the charge and discharge power, dynamically determine the preset operating conditions corresponding to the grid-type energy storage system, and obtain the corresponding optimization target under the current preset operating conditions.

[0077] In this embodiment, the following is an analysis of the preset working conditions:

[0078] (1) First operating condition: |P|>P1, SOC>SOC1&SOC<SOC2.

[0079] The SOC is far from the limit, and the energy storage unit still has sufficient operating margin. Considering that the voltage level is determined by the grid, a large energy storage output power (i.e., charging and discharging power) means a large output current. In this case, the priority should be to control the system to reduce system losses.

[0080] (3-2)

[0081] (3-3)

[0082] Where, P1 is the first-level power threshold; α1 is the first-level equalization coefficient; P N is the rated power of the energy storage unit, that is, when the charging and discharging power exceeds the first-level power threshold, the system enters this operating mode, with the main control goal of reducing system losses; k is the voltage modulation ratio of the PCS, which is used to convert the DC resistance to the AC side; R is the equivalent resistance of the energy storage unit on the DC side; R1 is the equivalent resistance of the energy storage unit on the AC side; U sNis the rated AC voltage of the PCS. The value of the feedback coefficient is determined by the ratio of the power command to the percentage of the equivalent resistance on the AC side. The larger the output current, the smaller the compensation of the energy storage unit for the power command, thereby assuming less power distribution and reducing system losses.

[0083] (2) Second operating condition: P2<|P|<P1, SOC>SOC1&SOC<SOC2.

[0084] The SOC is far from the limit, and the energy storage unit still has sufficient operating margin. Considering that the output power is between the first and second power thresholds, it is necessary to simultaneously reduce system losses and balance the unit SOC. To facilitate dual-objective control, a weight coefficient β with a gradual characteristic is introduced. The control method is as follows:

[0085]

[0086]

[0087]

[0088] Where n is the acceleration power index, which is used to achieve SOC balanced acceleration based on the exponential function; P2 is the secondary power threshold; α2 is the secondary balancing coefficient; SOC ave It is the average SOC of all energy storage units in the system. Through dynamic correction of the weight coefficient β, the closer to the primary power threshold, the more the second operating condition behaves like the first operating condition, with optimal system loss as the control objective. The closer to the secondary power threshold, the more the second operating condition behaves like the third operating condition, with SOC balance as the control objective, taking into account the dual-objective control effects in the gradual transition zone.

[0089] (3) The third operating condition: |P|<P2, SOC>SOC1&SOC<SOC2.

[0090] The SOC is far from the limit, and the energy storage unit still has sufficient operating margin. Considering the low output current, the output power can be quickly adjusted at the cost of minimal loss to balance the SOC of each energy storage unit, preventing the energy storage unit from automatically shutting down due to overcharge or overdischarge, and expanding the operating margin.

[0091]

[0092] (4) Fourth operating condition: SOC<SOC1||SOC>SOC2.

[0093] Considering the limited balancing capability of a single exponential function, when the SOC approaches the charge and discharge cutoff limit, the energy storage unit has insufficient operating margin. At this time, the SOC balancing effect should be enhanced to ensure that all energy storage units reach the limit at the same time as much as possible, thereby extending the commissioning time of the energy storage system and avoiding the network impact and frequency fluctuations caused by the premature exit of the energy storage units.

[0094]

[0095] Where, I bat is the battery current.

[0096] The second-level power threshold P2 is smaller than the first-level power threshold P1; and the first SOC threshold SOC1 is smaller than the second SOC threshold SOC2.

[0097] It can be seen that in this embodiment, the calculation of the feedback coefficient is divided into the following four working conditions: 1) When the SOC margin is large and the active power value exceeds the first-level power threshold, the feedback coefficient is equal to the inverse of the per-unit value of the equivalent AC resistance of the energy storage unit; 2) When the SOC margin is large and the active power value is between the first and second-level power thresholds, the feedback coefficient is equal to the weighted average of the feedback coefficients of the first and third working conditions, and the weight is equal to the ratio of the difference between the first-level power threshold and the active power to the difference between the first and second-level power thresholds; 3) When the SOC margin is large and the active power value is less than the second-level power threshold, the feedback coefficient is equal to the product of the energy storage unit power command and the exponential function, the base of the exponential function is the natural logarithm, and the exponent is the difference between the accelerated power of the SOC of the energy storage unit and the accelerated power of the average SOC of all energy storage units; 4) When the SOC margin is small, the feedback coefficient is equal to the third working condition feedback coefficient multiplied by the ratio of the SOC residual value to the second-level balancing coefficient.

[0098] In this embodiment, the energy storage system operating conditions are subdivided into four categories based on SOC value and charge / discharge power, and different optimization targets are set for each operating condition. This refined classification of operating conditions and target setting enables the energy storage system to adopt the most appropriate control strategy under different operating conditions, specifically addressing system issues under different operating conditions, such as reducing losses, balancing SOC, and extending the operating life, thereby improving the adaptability and operational efficiency of the energy storage system.

[0099] Under the first operating condition (high power and SOC within a safe range), the feedback coefficient is calculated based on the optimization goal of reducing system losses. This method comprehensively considers multiple parameters and accurately calculates the feedback coefficient. This method can ensure low system losses while reasonably compensating for energy storage unit power fluctuations, effectively balancing system efficiency and SOC equilibrium, and avoiding the exacerbation of SOC imbalances caused by the pursuit of efficiency.

[0100] Under the second operating condition (medium power and SOC within a safe range), the optimization goal is to balance system losses with SOC balance. The feedback coefficient calculation formula incorporates parameters such as the average SOC value, the balancing coefficient, and the weight coefficient. This dynamically adjusts the feedback coefficient based on the SOC state, minimizing losses while promoting SOC balance. This prevents SOC imbalances from worsening under high-load conditions and improves the overall performance of the energy storage system.

[0101] In the third operating condition (low power and SOC within a safe range), output power is adjusted to minimize losses. The feedback coefficient calculation formula, combined with the active power command value and SOC value, can rationally adjust power output during low-load periods, reducing system losses while ensuring the normal operation of the energy storage system. This avoids excessive pursuit of SOC balance at the expense of system efficiency and reduces operating costs.

[0102] Under the fourth operating condition (SOC approaching the charge and discharge limits), the optimization goal is to extend the energy storage system's operational life. The feedback coefficient calculation formula incorporates parameters such as battery current, enabling rapid adjustment of the feedback coefficient when the SOC approaches the limit. This prevents overcharging or over-discharging of the energy storage unit, extends equipment life, reduces system frequency fluctuations caused by premature equipment shutdown, and ensures safe and stable grid operation.

[0103] S4. Optimize the feedback coefficient based on the corresponding optimization target under the current preset working conditions to achieve balanced control of the charge state of the grid-type energy storage unit.

[0104] The following is a specific example of the method provided in this embodiment. The implementation process is as follows:

[0105] Taking typical industrial load distribution as the background, a photovoltaic load storage system is built in the electromagnetic transient simulation software PSCAD / EMTDC. Figure 2 As shown; PSCAD / EMTDC is a powerful electromagnetic transient simulation software with a wide range of applications in the fields of power system analysis, research, and design. The 2MW distributed photovoltaic system is connected to the 10kV busbar through a photovoltaic inverter; the energy storage system includes two energy storage units, with configurations of 2MW / 0.0014MWh and 2.5MW / 0.0017MWh respectively, which are connected to the grid through a converter and booster; the rated capacity of the load is 4.5MW, and a comprehensive load model is used for modeling. BESS1 and BESS2 both adopt the state of charge balancing control method described in this embodiment. Taking into account the SOC balancing effect, this embodiment takes n=2, α1=0.8, α2=0.6, SOC1=0.2, and SOC2=0.8.

[0106] It should be noted that, in order to improve simulation efficiency, the charge and discharge rate of the energy storage unit is set relatively high in this embodiment. In actual projects, it is mostly 0.25, 0.5 or 1. The controller fluctuation rate is lower. Therefore, the control method proposed in this embodiment is also applicable.

[0107] Assume that the initial test state is 1s, the charge states of BESS1 and BESS2 are 0.45 and 0.55 respectively, and the photovoltaic irradiance is 500W / m 2 The constant impedance part of the comprehensive load is 0.6pu, the constant current part is 0.1pu, and the constant power part is 0.3pu. At 2s, the load drops from 1p.u. to 0.6pu. At 4s, the irradiance is affected by the weather from 500W / m 2 Increased to 1200W / m 2 The simulation results are as follows. Figure 3 As shown, specifically Figure 3 As shown in Figures (a) and (b).

[0108] Because BESS1 has a smaller equivalent resistance than BESS2, in the initial state, despite its smaller capacity, BESS1's active output reaches 0.98 pu, while BESS2's active output is 0.81 pu. At this point, power allocation effectively reduces system losses, consistent with the theoretical analysis results for the first operating condition. At 2 seconds, due to a decrease in load, the energy storage unit simultaneously reduces its active power to balance power, entering the second operating range. SOC balancing accelerates, and the per-unit active output of BESS1 remains greater than that of BESS2, balancing system losses and balancing speed in the transition zone. At 4 seconds, due to increased photovoltaic power generation, the energy storage unit reduces its active power again to balance power, causing its output to fall below the secondary power threshold. At this point, SOC balancing speed accelerates significantly, and BESS1's active output decreases compared to BESS2. BESS1 and BESS2 quickly converge, and due to their relatively low power, system losses are minimized. At 5.2s, the state of charge of BESS2 fell below 0.2, entering the fourth operating condition and being judged to be close to the safe operating boundary. In order to reduce the impact on the network, the SOC balancing effect was further enhanced. At 6.9s, the SOC reached consistency. At this time, there was still a certain margin before the energy storage unit reached the cut-off point, which effectively improved the system safety.

[0109] Compared with the prior art, this embodiment provides a method for controlling the state of charge balance of a grid-type energy storage unit, which has the following advantages:

[0110] In response to the SOC balancing problem of grid-type energy storage units, this embodiment proposes an improved dynamic balancing control method. This control method fully considers the SOC deviation, the output of the energy storage power station and the remaining charge and discharge capacity, and designs the balancing coefficient, which is more suitable for multi-operating conditions in grid-type scenarios. When the SOC margin is large and the load is large, priority is given to reducing system losses. When the SOC margin is large and the load is small, priority is given to making the SOC tend to be consistent. When the SOC margin is small, the output is accelerated to avoid premature exit of the energy storage unit from operation. The control target is optimized and the safety of the operating boundary is improved through the self-regulatory adjustment of the balancing coefficient. The self-regulatory adjustment of the balancing coefficient not only optimizes the control target and makes the control more precise, but also improves the safety of the energy storage unit under complex boundary conditions, making the grid-type energy storage more reliable and stable.

[0111] For example, Figure 4 As shown, this embodiment provides a charge state balancing control method for a grid-type energy storage unit, comprising the following steps:

[0112] Based on the rated angular frequency, equivalent power angle, active power command and actual power, a virtual synchronous generator model is constructed in combination with the characteristics of the synchronous generator;

[0113] Obtain the real-time SOC value and charge and discharge power of the virtual synchronous generator model;

[0114] According to the SOC value and charge and discharge power, dynamically determine the preset working conditions corresponding to the grid-type energy storage system, and obtain the corresponding optimization target under the current preset working conditions;

[0115] The feedback coefficient is optimized based on the corresponding optimization target under the current preset operating conditions to achieve balanced control of the charge state of the grid-type energy storage unit; wherein the feedback coefficient is superimposed on the power instruction controlled by the virtual synchronous generator model to compensate for the power fluctuation amplitude of the grid-type energy storage unit.

[0116] In this embodiment, the virtual synchronous generator model is specifically represented as follows:

[0117]

[0118] Where, is the output angular frequency of the energy storage converter; w0 is the rated value of the angular frequency; Δw is The difference between w0 and δ is the equivalent generator power angle; the active power command value of the energy storage converter and the actual value P are equivalent to the generator input mechanical power and output electromagnetic power respectively; H is equivalent to the inertia time constant of the unit; D is the proportional coefficient of active power deviation and angular frequency deviation; T is the time constant of the first-order inertia link; t is time.

[0119] In this embodiment, dynamically determining the preset operating condition corresponding to the grid-type energy storage system based on the SOC value and the charge and discharge power, and obtaining the optimization target corresponding to the current preset operating condition, includes:

[0120] Based on the SOC value and charge and discharge power, the preset operating conditions corresponding to the grid-type energy storage system are dynamically judged as follows:

[0121] If the charge and discharge power is greater than the first-level power threshold, and the SOC value is between the first SOC threshold and the second SOC threshold, the grid-type energy storage system is judged to be in the first operating condition, and the optimization goal corresponding to the first operating condition is to reduce system losses;

[0122] If the charge and discharge power is between the first power threshold and the second power threshold, and the SOC value is greater than the first SOC threshold, and the SOC value is less than the second SOC threshold, then the grid-type energy storage system is judged to be in the second operating condition. The optimization goal corresponding to the second operating condition is to simultaneously reduce system losses and balance the SOC values ​​of the energy storage units.

[0123] If the charge and discharge power is less than the secondary power threshold, and the SOC value is between the first SOC threshold and the second SOC threshold, the grid-type energy storage system is judged to be in the third operating condition. The optimization goal corresponding to the third operating condition is to adjust the output power at the cost of minimum loss.

[0124] If the SOC value is less than the first SOC threshold or the SOC value is greater than the second SOC threshold, it is determined that the grid-type energy storage system is in the fourth operating condition, and the optimization goal corresponding to the fourth operating condition is to extend the commissioning time of the energy storage system;

[0125] Among them, the second-level power threshold is smaller than the first-level power threshold; the first SOC threshold is smaller than the second SOC threshold.

[0126] In this embodiment, the optimization of the feedback coefficient based on the optimization target corresponding to the current preset working condition includes:

[0127] When the grid-type energy storage system is judged to be in the first operating condition, the corresponding optimization goal is to reduce system losses;

[0128] The feedback coefficient P is calculated according to the optimization target feed , the specific formula is as follows:

[0129]

[0130] Where, P1 is the first-level power threshold; α1 is the first-level equalization coefficient; P N is the rated power of the energy storage unit, k is the voltage modulation ratio of the energy storage converter; R is the equivalent resistance of the energy storage unit on the DC side; R1 is the equivalent resistance of the energy storage unit on the AC side; U sN is the rated AC voltage of the energy storage converter; is the active power command value.

[0131] In this embodiment, the optimization of the feedback coefficient based on the optimization target corresponding to the current preset working condition further includes:

[0132] When the grid-type energy storage system is judged to be in the second operating condition, the corresponding optimization goal is to simultaneously reduce system losses and balance the SOC value of the energy storage unit;

[0133] The feedback coefficient P is calculated according to the optimization target feed , the specific formula is as follows:

[0134]

[0135] Where, P2 is the secondary power threshold; α2 is the secondary equalization coefficient; SOC ave is the average SOC value of all energy storage units in the system; β is the weight coefficient; n is the acceleration power exponent; SOC is the SOC value of the balanced energy storage unit.

[0136] In this embodiment, the optimization of the feedback coefficient based on the optimization target corresponding to the current preset working condition further includes:

[0137] When the grid-type energy storage system is judged to be in the third operating condition, the corresponding optimization goal is to adjust the output power at the cost of minimum loss;

[0138] The feedback coefficient P is calculated according to the optimization target feed , the specific formula is as follows:

[0139] .

[0140] In this embodiment, the optimization of the feedback coefficient based on the optimization target corresponding to the current preset working condition further includes:

[0141] When the grid-type energy storage system is judged to be in the fourth operating condition, the corresponding optimization goal is to extend the commissioning time of the energy storage system;

[0142] The feedback coefficient P is calculated according to the optimization target feed , the specific formula is as follows:

[0143]

[0144] Where, I bat is the battery current.

[0145] For example, Figure 5As shown, this embodiment also provides a state of charge balancing control system for a grid-type energy storage unit, including: a model construction module, which is used to construct a virtual synchronous generator model based on the rated angular frequency, equivalent power angle, active power instruction and actual power in combination with the characteristics of the synchronous generator; a data acquisition module, which is used to obtain the real-time SOC value and charge and discharge power of the virtual synchronous generator model; a judgment module, which is used to dynamically judge the preset operating condition corresponding to the grid-type energy storage system based on the SOC value and charge and discharge power, and obtain the corresponding optimization target under the current preset operating condition; an optimization module, which is used to optimize the feedback coefficient based on the corresponding optimization target under the current preset operating condition to achieve balanced control of the state of charge of the grid-type energy storage unit; wherein, the feedback coefficient is superimposed on the power instruction controlled by the virtual synchronous generator model to compensate for the power fluctuation amplitude of the grid-type energy storage unit.

[0146] The present invention also provides an electronic device, comprising: a memory for storing a computer program; and a processor for implementing the steps of the charge state balancing control method for a grid-type energy storage unit when executing the computer program.

[0147] When the processor executes the computer program, it implements the steps of charge state balancing control for the above-mentioned grid-type energy storage unit, for example: based on the rated angular frequency, equivalent power angle, active power instruction and actual power, combined with the characteristics of the synchronous generator, a virtual synchronous generator model is constructed; the real-time SOC value and charge and discharge power of the virtual synchronous generator model are obtained; according to the SOC value and charge and discharge power, the preset operating condition corresponding to the grid-type energy storage system is dynamically determined, and the corresponding optimization target under the current preset operating condition is obtained; based on the optimization target corresponding to the current preset operating condition, the feedback coefficient is optimized to achieve balanced control of the charge state of the grid-type energy storage unit; wherein, the feedback coefficient is superimposed on the power instruction controlled by the virtual synchronous generator model to compensate for the power fluctuation amplitude of the grid-type energy storage unit.

[0148] Alternatively, when the processor executes the computer program, it implements the functions of each module in the above-mentioned system, for example: a model construction module, which is used to construct a virtual synchronous generator model based on the rated angular frequency, equivalent power angle, active power instruction and actual power in combination with the characteristics of the synchronous generator; a data acquisition module, which is used to obtain the real-time SOC value and charge and discharge power of the virtual synchronous generator model; a judgment module, which is used to dynamically judge the preset operating condition corresponding to the grid-type energy storage system according to the SOC value and charge and discharge power, and obtain the corresponding optimization target under the current preset operating condition; an optimization module, which is used to optimize the feedback coefficient based on the corresponding optimization target under the current preset operating condition, so as to achieve balanced control of the charge state of the grid-type energy storage unit; wherein, the feedback coefficient is superimposed on the power instruction controlled by the virtual synchronous generator model, and is used to compensate for the power fluctuation amplitude of the grid-type energy storage unit.

[0149] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of completing preset functions, and the instruction segments are used to describe the execution process of the computer program in the state of charge balancing control device for the grid-type energy storage unit. For example, the computer program can be divided into a model construction module, a data acquisition module, a judgment module and an optimization module; the model construction module is used to construct a virtual synchronous generator model based on the rated angular frequency, equivalent power angle, active power instruction and actual power, combined with the characteristics of the synchronous generator; the data acquisition module is used to obtain the real-time SOC value and charge and discharge power of the virtual synchronous generator model; the judgment module is used to dynamically judge the preset operating conditions corresponding to the grid-type energy storage system according to the SOC value and charge and discharge power, and obtain the corresponding optimization target under the current preset operating conditions; the optimization module is used to optimize the feedback coefficient based on the corresponding optimization target under the current preset operating conditions, so as to achieve balanced control of the charge state of the grid-type energy storage unit; wherein, the feedback coefficient is superimposed on the power instruction controlled by the virtual synchronous generator model, and is used to compensate for the power fluctuation amplitude of the grid-type energy storage unit.

[0150] The state of charge balancing control device for the grid-type energy storage unit can be a computing device such as a desktop computer, a notebook, a PDA, and a cloud server. The state of charge balancing control device for the grid-type energy storage unit can include, but is not limited to, a processor and a memory. Those skilled in the art will understand that the above is an example of a state of charge balancing control device for a grid-type energy storage unit and does not constitute a limitation on the state of charge balancing control device for a grid-type energy storage unit. It can include more components than the above, or a combination of certain components, or different components. For example, the state of charge balancing control device for the grid-type energy storage unit can also include input and output devices, network access devices, buses, etc.

[0151] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor, or the processor may be any conventional processor, etc. The processor serves as the control center for the state of charge balancing control for the grid-type energy storage unit, and utilizes various interfaces and lines to connect various parts of the entire state of charge balancing control device for the grid-type energy storage unit.

[0152] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the charge state balancing control device for the grid-type energy storage unit by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory.

[0153] The memory may primarily include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function (such as sound playback or image playback); the data storage area may store data generated based on the use of the mobile phone (such as audio data and a phone book). Furthermore, the memory may include high-speed random access memory (RAM) and non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0154] The present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the charge state balancing control method for a grid-type energy storage unit.

[0155] If the module / unit integrated in the state of charge balancing control system for the grid-type energy storage unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.

[0156] Based on this understanding, the present invention implements all or part of the process steps of the above-mentioned method for balancing the state of charge for a grid-type energy storage unit, and can also be completed by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, the computer program can implement the steps of the above-mentioned method for balancing the state of charge for a grid-type energy storage unit. The computer program includes computer program code, which can be in source code form, object code form, executable file, or a preset intermediate form.

[0157] The computer-readable storage medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0158] It should be noted that the content contained in the computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practices in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practices, computer-readable storage media do not include electrical carrier signals and telecommunication signals.

[0159] The above embodiment is only one of the implementation methods that can realize the technical solution of the present invention. The scope of protection claimed by the present invention is not limited only to this embodiment, but also includes changes, replacements and other implementation methods that can be easily thought of by any technician familiar with this technical field within the technical scope disclosed by the present invention.

[0160] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the present invention.

Claims

1. A method for controlling state of charge balancing for a grid-type energy storage unit, characterized in that: include: Based on the rated angular frequency, equivalent power angle, active power command and actual power, a virtual synchronous generator model is constructed in combination with the characteristics of the synchronous generator; Obtain the real-time SOC value and charge and discharge power of the virtual synchronous generator model; According to the SOC value and charge and discharge power, dynamically determine the preset working conditions corresponding to the grid-type energy storage system, and obtain the corresponding optimization target under the current preset working conditions; The feedback coefficient is optimized based on the corresponding optimization target under the current preset operating conditions to achieve balanced control of the state of charge of the grid-type energy storage unit; wherein the feedback coefficient is superimposed on the power command controlled by the virtual synchronous generator model to compensate for the power fluctuation amplitude of the grid-type energy storage unit; The method of dynamically determining the preset operating condition corresponding to the grid-type energy storage system based on the SOC value and the charge and discharge power, and obtaining the optimization target corresponding to the current preset operating condition, includes: Based on the SOC value and charge and discharge power, the preset operating conditions corresponding to the grid-type energy storage system are dynamically judged as follows: If the charge and discharge power is greater than the first-level power threshold, and the SOC value is between the first SOC threshold and the second SOC threshold, the grid-type energy storage system is judged to be in the first operating condition, and the optimization goal corresponding to the first operating condition is to reduce system losses; If the charge and discharge power is between the first power threshold and the second power threshold, and the SOC value is between the first SOC threshold and the second SOC threshold, then the grid-type energy storage system is judged to be in the second operating condition. The optimization goal corresponding to the second operating condition is to simultaneously reduce system losses and balance the SOC values ​​of the energy storage units. If the charge and discharge power is less than the secondary power threshold, and the SOC value is between the first SOC threshold and the second SOC threshold, the grid-type energy storage system is judged to be in the third operating condition. The optimization goal corresponding to the third operating condition is to adjust the output power at the cost of minimum loss. If the SOC value is less than the first SOC threshold or the SOC value is greater than the second SOC threshold, it is determined that the grid-type energy storage system is in the fourth operating condition, and the optimization goal corresponding to the fourth operating condition is to extend the commissioning time of the energy storage system; Among them, the second-level power threshold is smaller than the first-level power threshold; the first SOC threshold is smaller than the second SOC threshold.

2. The method for controlling the state of charge balancing of a grid-type energy storage unit according to claim 1, wherein: The virtual synchronous generator model is specifically expressed as follows: Where, is the output angular frequency of the energy storage converter; w0 is the rated value of the angular frequency; Δw is The difference between w0 and δ is the equivalent generator power angle; the active power command value of the energy storage converter and the actual value P are equivalent to the generator input mechanical power and output electromagnetic power respectively; H is equivalent to the inertia time constant of the unit; D is the proportional coefficient of active power deviation and angular frequency deviation; T is the time constant of the first-order inertia link; t is time.

3. The method for controlling state of charge balancing for a grid-type energy storage unit according to claim 1, wherein: The optimization of the feedback coefficient based on the optimization target corresponding to the current preset working condition includes: When the grid-type energy storage system is judged to be in the first operating condition, the corresponding optimization goal is to reduce system losses; The feedback coefficient P is calculated according to the optimization target feed , the specific formula is as follows: Where, P1 is the first-level power threshold; α1 is the first-level equalization coefficient; P N is the rated power of the energy storage unit, k is the voltage modulation ratio of the energy storage converter; R is the equivalent resistance of the energy storage unit on the DC side; R1 is the equivalent resistance of the energy storage unit on the AC side; U sN is the rated AC voltage of the energy storage converter; is the active power command value.

4. The method for controlling state of charge balancing for a grid-type energy storage unit according to claim 3, wherein: The optimizing of the feedback coefficient based on the optimization target corresponding to the current preset working condition further includes: When the grid-type energy storage system is judged to be in the second operating condition, the corresponding optimization goal is to simultaneously reduce system losses and balance the SOC value of the energy storage unit; The feedback coefficient P is calculated according to the optimization target feed , the specific formula is as follows: Where, P2 is the secondary power threshold; α2 is the secondary equalization coefficient; SOC ave is the average SOC value of all energy storage units in the system; β is the weight coefficient; n is the acceleration power index; SOC is the SOC value of the balanced energy storage unit; is the nth power of the SOC value of the balanced energy storage unit; It is the nth power of the average value of the SOC of all energy storage units in the system; P is the actual value of the active power of the energy storage converter.

5. The method for controlling state of charge balancing for a grid-type energy storage unit according to claim 4, wherein: The optimizing of the feedback coefficient based on the optimization target corresponding to the current preset working condition further includes: When the grid-type energy storage system is judged to be in the third operating condition, the corresponding optimization goal is to adjust the output power at the cost of minimum loss; The feedback coefficient P is calculated according to the optimization target feed , the specific formula is as follows: 。 6. The method for controlling state of charge balancing for a grid-type energy storage unit according to claim 5, characterized in that: The optimizing of the feedback coefficient based on the optimization target corresponding to the current preset working condition further includes: When the grid-type energy storage system is judged to be in the fourth operating condition, the corresponding optimization goal is to extend the commissioning time of the energy storage system; The feedback coefficient P is calculated according to the optimization target feed , the specific formula is as follows: Where, I bat is the battery current.

7. A state of charge balancing control system for a grid-type energy storage unit, used to implement the steps of the state of charge balancing control method for a grid-type energy storage unit according to any one of claims 1 to 6, characterized in that: include: A model building module is used to build a virtual synchronous generator model based on the rated angular frequency, equivalent power angle, active power command and actual power, combined with the characteristics of the synchronous generator; Data acquisition module, used to obtain the real-time SOC value and charge and discharge power of the virtual synchronous generator model; The judgment module is used to dynamically judge the preset working condition corresponding to the grid-type energy storage system based on the SOC value and the charge and discharge power, and obtain the corresponding optimization target under the current preset working condition; An optimization module is used to optimize the feedback coefficient based on the corresponding optimization target under the current preset operating conditions to achieve balanced control of the charge state of the grid-type energy storage unit; wherein the feedback coefficient is superimposed on the power instruction controlled by the virtual synchronous generator model to compensate for the power fluctuation amplitude of the grid-type energy storage unit.

8. An electronic device, characterized in that: include: memory for storing computer programs; A processor, configured to implement the steps of the method for controlling the state of charge balancing of a grid-type energy storage unit according to any one of claims 1 to 6 when executing the computer program.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, it is used to implement the steps of the charge state balancing control method for a grid-type energy storage unit according to any one of claims 1 to 6.

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