Charge state balance control method for network-forming type energy storage unit and related equipment

Abstract

The invention belongs to the field of networking type energy storage systems, and discloses a state of charge balance control method for a networking type energy storage unit and related equipment, and the method comprises the steps: obtaining a real-time SOC value and charging and discharging power, dynamically judging a preset working condition, determining an optimization target, and carrying out the optimization of a state of charge balance based on an optimization feedback coefficient. And the power is superposed in a power instruction to compensate the power fluctuation of the network-forming type energy storage unit so as to realize SOC balance control. The characteristics of the synchronous generator are simulated through the virtual synchronous generator model, so that the energy storage unit better adapts to the power grid; working conditions are judged according to real-time SOC and power, and different optimization targets are made for different working conditions, so that the control strategy is more targeted; a feedback coefficient is optimized to compensate power fluctuation, and the power is accurately regulated and controlled. By adopting the method, the problem of inaccurate regulation and control under a complex working condition of a traditional static balancing method can be effectively solved, the contradiction between the system efficiency and SOC balancing can be balanced, quick regulation can be realized when the SOC is close to a limit value, overcharge and overdischarge are avoided, safe and stable operation of a power grid is ensured, and the overall efficiency of an energy storage system is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of grid-forming energy storage systems, and particularly relates to a method for controlling the state of charge (SOC) balance of a grid-forming energy storage unit and related equipment. Background Art

[0002] With the continuous transformation of the global energy structure and the rapid development of renewable energy power generation technology, the power system is facing unprecedented challenges and opportunities. In the process of coping with the impact of the intermittency and volatility of new energy power generation on the grid stability, the electrochemical energy storage system, as an efficient and flexible solution for electric energy storage and release, has become one of the key technologies for building a strong and intelligent power grid and promoting the large-scale consumption of renewable energy. The electrochemical energy storage system realizes the storage and conversion of electric energy through a battery pack, and combines core components such as a power electronic interface device (such as a PCS or a bidirectional DC / DC converter), a battery management system (BMS), and an energy management system (EMS) to achieve efficient management and rapid response of electric energy. Its advantages such as short construction period, fast response speed, and high energy density make it an important supplement to traditional mechanical energy storage methods such as pumped storage, and it plays an irreplaceable role in improving the grid regulation ability, ensuring the security of power supply, and promoting the efficient utilization of renewable energy. Especially in the grid-forming energy storage application scenario, this technology further enhances the resilience and self-healing ability of the power system by actively providing frequency support and black start capabilities, and becomes an important technical force for supporting the construction of a new power system.

[0003] Although the electrochemical energy storage technology, especially the grid-forming energy storage system, shows great potential in enhancing the flexibility and stability of the grid, there are still several key problems to be solved in the current technical field, especially the significant deficiency in the control of the state of charge (SOC) balance of energy storage units. First, traditional SOC balancing strategies mostly adopt static balancing methods, that is, hierarchical control based on preset fixed thresholds. This method is difficult to achieve precise regulation and dynamic balance of SOC in the face of complex working conditions such as dynamic changes in grid load and uncertainty of new energy output, which limits the overall efficiency of the energy storage system. Second, there is a significant contradiction between the pursuit of system efficiency and SOC balance in the existing technology. In high-load working conditions, the priority efficiency strategy adopted to reduce system losses often leads to an exacerbation of SOC imbalance; while in low-load periods, excessive pursuit of SOC balance may sacrifice system efficiency and increase operating costs. Finally, the lack of safety is another major challenge. Especially when the SOC is close to the charge and discharge limits, the existing control strategies lack a rapid and effective adjustment mechanism, which is likely to cause the energy storage unit to prematurely exit operation due to overcharge or over-discharge. This not only shortens the equipment life, but also may cause system frequency fluctuations and endanger the safe and stable operation of the grid. Summary of the Invention

[0004] The present invention provides a method and related equipment for charge state equalization control of a network-forming energy storage unit. By dynamically adjusting the feedback coefficient and comprehensively considering the SOC deviation, output demand, and remaining capacity, the proposed control method realizes optimized control under different working conditions, ensuring the efficient operation of the system while extending the lifespan of the energy storage unit. Moreover, through the adaptive feedback coefficient and working condition classification strategy, the method achieves a dynamic balance between efficiency and equalization and enhances the security of the system operation boundary.

[0005] To achieve the above objectives, the present invention adopts the following technical solutions: A method for charge state equalization control of a network-forming energy storage unit, comprising: 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 a synchronous generator; Obtain the real-time SOC value and charge-discharge power of the virtual synchronous generator model; According to the SOC value and charge-discharge power, dynamically determine the preset working conditions corresponding to the network-forming energy storage system, and obtain the optimization objectives corresponding to the current preset working conditions; Optimize the feedback coefficient based on the optimization objectives corresponding to the current preset working conditions to achieve equalization control of the charge state of the network-forming 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 network-forming energy storage unit.

[0006] Furthermore, the virtual synchronous generator model is specifically expressed as follows:

[0007] In the formula, is the output angular frequency of the energy storage converter; w0 is the rated value of the angular frequency; Δw is the difference between and w0; δ is the equivalent generator power angle; the active power command value and the actual value P of the energy storage converter are respectively equivalent to the input mechanical power and output electromagnetic power of the generator; H is equivalently the inertia time constant of the unit; D is the proportional coefficient of the active power deviation and the angular frequency deviation; T is the time constant of the first-order inertia link; t is the time.

[0008] Furthermore, the step of dynamically determining the preset working conditions corresponding to the network-forming energy storage system according to the SOC value and charge-discharge power, and obtaining the optimization objectives corresponding to the current preset working conditions includes: Based on the SOC value and charge-discharge power, dynamically determine the preset working conditions corresponding to the network-forming energy storage system, specifically as follows: If the charge-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, it is determined that the grid-forming energy storage system is in the first operating condition, and the corresponding optimization goal for the first operating condition is to reduce system losses; If the charge-discharge power is between the first-level power threshold and the second-level power threshold and the SOC value is between the first SOC threshold and the second SOC threshold, it is determined that the grid-forming energy storage system is in the second operating condition, and the corresponding optimization goal for the second operating condition is to balance the SOC values of the energy storage units while reducing system losses; If the charge-discharge power is less than the second-level power threshold and the SOC value is between the first SOC threshold and the second SOC threshold, it is determined that the grid-forming energy storage system is in the third operating condition, and the corresponding optimization goal for the third operating condition is to adjust the output power at the cost of the lowest losses; 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-forming energy storage system is in the fourth operating condition, and the corresponding optimization goal for the fourth operating condition is to extend the input time of the energy storage system; Among them, the second-level power threshold is less than the first-level power threshold; the first SOC threshold is less than the second SOC threshold.

[0009] Furthermore, the optimization of the feedback coefficient based on the optimization goal corresponding to the current preset operating condition includes: When it is determined that the grid-forming energy storage system is in the first operating condition, the corresponding optimization goal is to reduce system losses; Calculate the feedback coefficient P according to the optimization goal feed , and the specific formula is as follows:

[0010] In the formula, P1 is the first-level power threshold; α1 is the first-level balancing 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 DC side of the energy storage unit; R1 is the equivalent resistance of the AC side of the energy storage unit; U sN is the rated AC voltage of the energy storage converter; is the active power command value.

[0011] Furthermore, the optimization of the feedback coefficient based on the optimization goal corresponding to the current preset operating condition also includes: When it is determined that the grid-forming energy storage system is in the second operating condition, the corresponding optimization goal is to balance the SOC values of the energy storage units while reducing system losses; Calculate the feedback coefficient P according to the optimization goal feed ]>, and the specific formula is as follows:

[0012] Wherein, P2 is the secondary power threshold; α2 is the secondary balancing coefficient; SOC ave is the average value of the SOCs 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 energy storage unit to be balanced; is the nth power of the SOC value of the energy storage unit to be balanced; is the nth power of the average value of the SOCs of all energy storage units in the system.

[0013] Furthermore, the optimization of the feedback coefficient based on the optimization target corresponding to the current preset operating condition further includes: When it is determined that the grid-forming energy storage system is in the third operating condition, the corresponding optimization target is to adjust the output power at the cost of the lowest loss; Calculate the feedback coefficient P feed according to the optimization target, and the specific formula is as follows: .

[0014] Furthermore, the optimization of the feedback coefficient based on the optimization target corresponding to the current preset operating condition further includes: When it is determined that the grid-forming energy storage system is in the fourth operating condition, the corresponding optimization target is to extend the input time of the energy storage system; Calculate the feedback coefficient P feed according to the optimization target, and the specific formula is as follows:

[0015] Wherein, I bat is the battery current.

[0016] A state of charge balancing control system for a grid-forming energy storage unit includes: A model construction module, configured to construct a virtual synchronous generator model based on the rated angular frequency, equivalent power angle, active power command and actual power, in combination with the characteristics of the synchronous generator; A data acquisition module, configured to acquire the real-time SOC value and charge and discharge power of the virtual synchronous generator model; A judgment module, configured to dynamically judge the preset operating condition corresponding to the grid-forming energy storage system according to the SOC value and charge and discharge power, and obtain the optimization target corresponding to the current preset operating condition; An optimization module, configured to optimize the feedback coefficient based on the optimization target corresponding to the current preset operating condition, so as to realize the balanced control of the state of charge of the grid-forming energy storage unit; wherein, the feedback coefficient is superimposed on the power command of the virtual synchronous generator model control to compensate for the power fluctuation amplitude of the grid-forming energy storage unit.

[0017] An electronic device includes: A memory, configured to store a computer program; A processor for implementing the steps of the above-mentioned charge state equalization control method for a network-forming energy storage unit when executing the computer program.

[0018] A computer-readable storage medium storing a computer program, which is used to implement the steps of the above-mentioned charge state equalization control method for a network-forming energy storage unit when executed by a processor.

[0019] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a charge state equalization control method for a network-forming energy storage unit. This method obtains the real-time SOC value and charge-discharge power, dynamically judges the preset working conditions based on this and determines the optimization objectives, and then based on this, optimizes the feedback coefficient and superimposes it on the power command to compensate for the power fluctuation of the network-forming energy storage unit to achieve SOC equalization control. By simulating the characteristics of a synchronous generator through a virtual synchronous generator model, the energy storage unit can better adapt to the power grid; judge the working conditions according to the real-time SOC and power, and formulate different optimization objectives for different working conditions to make the control strategy more targeted; optimize the feedback coefficient to compensate for power fluctuations and accurately control the power. Using this method can effectively overcome the problem of inaccurate regulation of traditional static equalization methods under complex working conditions, balance the contradiction between system efficiency and SOC equalization, and can also quickly adjust when the SOC is close to 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

[0020] Figure 1 It is a control block diagram of an improved virtual synchronous machine provided by an embodiment of the present invention, used to show the VSG core module and the feedback coefficient superposition logic; Figure 2 It is a photovoltaic energy storage load simulation architecture diagram provided by an embodiment of the present invention; Figure 3 It is a comparison diagram of simulation results provided by an embodiment of the present invention; among them, (a) is the active power output by the energy storage converter; (b) is the charge state of the BESS; showing the SOC equalization and power distribution effects under different working conditions; Figure 4 It is a flowchart of a charge state equalization control method for a network-forming energy storage unit provided by an embodiment of the present invention; Figure 5 It is a schematic structural diagram of a charge state equalization control system for a network-forming energy storage unit provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] To further understand the content of the present invention, the following describes the present invention in detail with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments are only for explaining the present invention and not for limiting it.

[0022] The following is an explanation of the technical terms related to the present invention: Electrochemical energy storage: refers to an energy storage system that completes the storage, release, and management of electrical energy through batteries.

[0023] Energy density: the energy stored per unit volume.

[0024] PCS: Power Converter System, energy storage converter.

[0025] BMS: Battery Management System, battery management system.

[0026] EMS: Energy Management System, energy management system.

[0027] SOC: State of Charge, state of charge.

[0028] BESS: Battery Energy Storage System, electrochemical energy storage system.

[0029] VSG: Virtual Synchronous Generator, virtual synchronous generator.

[0030] PV: Photovoltaic, photovoltaic.

[0031] AC: Alternating Current, alternating current.

[0032] DC: Direct Current, direct current.

[0033] In order to overcome the defects existing in the traditional SOC balancing method for electrochemical energy storage units, this embodiment provides a state-of-charge balancing control method for grid-forming 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 a balancing coefficient accordingly to adapt to various working conditions in the grid-forming scenario. This method is very intelligent and flexible. When the SOC margin is large and the load is large, it preferentially reduces the system loss to ensure efficient operation; when the SOC margin is large but the load is small, it focuses on making the SOC tend to be consistent to lay a solid foundation for operation; when the SOC margin is small, it accelerates the output decline to prevent the energy storage unit from exiting prematurely. The specific steps are as follows: 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 a synchronous generator. Among them, the virtual synchronous generator (VSG) technology can simulate the damping characteristics of a synchronous generator compared with traditional droop control, and at the same time maintains the ability of seamless switching between grid-connected and off-grid, and is widely used in the control of grid-forming energy storage.

[0034]

[0035] In the formula, w0 is the rated value of the angular frequency, which is usually consistent with the grid rated frequency; 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 PCS active power P are respectively equivalent to the input mechanical power and output electromagnetic power of the generator; H is equivalently the inertia time constant of the unit, enabling the frequency regulation of the PCS to have an inertial characteristic; D is the proportional coefficient of the active power deviation and the angular frequency deviation, equivalently the damping coefficient; T is the time constant of the first-order inertial link.

[0036] 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. A feedback coefficient P is superimposed on the power command feed , which is used to compensate for the power fluctuation amplitude of the energy storage system and achieve the expected control effect without affecting the damping ability. The control block diagram of the energy storage system is as Figure 1 shown, where is the argument of the VSG output electromotive force, corresponding to the initial phase angle of V s , where V s is the PCS AC side port voltage; s is the Laplace variable after transforming the time domain to the complex frequency domain. Figure 1 It can be seen that by adding a first-order inertial link on the basis of the outer-loop conventional active power-frequency droop control, the converter has the power angle characteristics similar to those of a synchronous generator, thus having the ability to provide virtual inertia. However, the conventional VSG control uses a constant power command and cannot compensate for the power fluctuations of the energy storage system under different working conditions. Therefore, this embodiment proposes an improved VSG control.

[0037] In this embodiment, in the virtual synchronous generator model, the key parameters of the energy storage system are equivalent to 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 improving the response ability of the energy storage system to grid frequency and power fluctuations.

[0038] S2. Obtain the real-time SOC value and charge-discharge power of the virtual synchronous generator model; here, the monitoring terminal is used to collect and upload the real-time data of the current SOC value and charge-discharge power of the virtual synchronous generator model.

[0039] S3. Dynamically judge the preset working conditions corresponding to the network-forming energy storage system according to the SOC value and charge-discharge power, and obtain the corresponding optimization objectives under the current preset working conditions.

[0040] In this embodiment, the following is an analysis of the preset working conditions: (1) The first working condition: |P| > P1, SOC > SOC1 & SOC < SOC2.

[0041] The SOC is far from the limit value, and the energy storage unit still has sufficient operating margin. Considering that the voltage level is determined by the power grid, a large value of the energy storage output power (i.e., charge-discharge power) means a large output current. At this time, the control should be prioritized with the goal of reducing system losses.

[0042] (3-2) (3-3) In the formula, P1 is the first-level power threshold; α1 is the first-level balancing coefficient; P N is the rated power of the energy storage unit, that is, when the charge-discharge power exceeds the first-level power threshold, it enters this working condition, and the main control goal is to reduce system losses; k is the voltage modulation ratio of the PCS, which is used for conversion from the DC resistance to the AC side; R is the equivalent resistance of the DC side of the energy storage unit; R1 is the equivalent resistance of the AC side of the energy storage unit; U sN is the rated AC voltage of the PCS, and 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, so as to undertake less power distribution and reduce system losses.

[0043] (2) The second working condition: P2 < |P| < P1, SOC > SOC1 & SOC < SOC2.

[0044] The SOC is far from the limit value, and the energy storage unit still has sufficient operating margin. Considering that the output power is between the first-level power threshold and the second-level power threshold, at this time, both reducing system losses and balancing the SOC of the unit should be taken into account. In order to facilitate dual-objective control, a weight coefficient β with a gradual change characteristic is introduced, and the control method is as follows:

[0045]

[0046]

[0047] 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.

[0048] (3) The third operating condition: |P|<P2, SOC>SOC1&SOC<SOC2.

[0049] 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.

[0050]

[0051] (4) Fourth operating condition: SOC<SOC1||SOC>SOC2.

[0052] 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.

[0053]

[0054] Where, I bat is the battery current.

[0055] 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.

[0056] 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 reciprocal 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-level and second-level power thresholds, the feedback coefficient is equal to the weighted average of the feedback coefficients in 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-level 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 power command of the energy storage unit and an exponential function, where 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 value of the SOCs of all energy storage units; 4) When the SOC margin is small, the feedback coefficient is equal to the feedback coefficient in the third working condition multiplied by the ratio of the remaining SOC value to the second-level balance coefficient.

[0057] In this embodiment, the working conditions of the energy storage system are subdivided into four types according to the SOC value and the charge-discharge power, and different optimization objectives are set for different working conditions. This refined division of working conditions and objective setting enables the energy storage system to adopt the most appropriate control strategy under different operating states, specifically addressing problems of the system under different working conditions, such as reducing losses, balancing SOC, and extending the input time, thereby improving the adaptability and operating efficiency of the energy storage system.

[0058] Among them, in the first working condition (high power and SOC within the safe range), the feedback coefficient is calculated according to the optimization objective of reducing system losses. This method comprehensively considers various parameters. By accurately calculating the feedback coefficient, it can reasonably compensate for the power fluctuation of the energy storage unit while ensuring relatively low system losses, effectively balancing system efficiency and SOC balance, and avoiding the exacerbation of SOC imbalance caused by pursuing efficiency.

[0059] In the second working condition (medium power and SOC within the safe range), the optimization objective is to balance reducing system losses and SOC value. The feedback coefficient calculation formula introduces parameters such as the average SOC value, balance coefficient, and weight coefficient, which can dynamically adjust the feedback coefficient according to the SOC state, promote SOC balance while reducing losses, avoid the exacerbation of SOC imbalance under high-load working conditions, and improve the overall performance of the energy storage system.

[0060] In the third working condition (low power and SOC within the safe range), the output power is adjusted at the cost of the lowest loss. The feedback coefficient calculation formula combines the active power command value and the SOC value, which can reasonably adjust the power output during low-load periods, ensure the normal operation of the energy storage system while reducing system losses, avoid sacrificing system efficiency in the pursuit of SOC balance, and reduce the operating cost.

[0061] And under the fourth operating condition (SOC approaching the charge and discharge limits), the optimization goal is to extend the input time of the energy storage system. The calculation formula of the feedback coefficient introduces parameters such as battery current, which can quickly adjust the feedback coefficient when the SOC approaches the limit, avoid overcharging or over-discharging of the energy storage unit, extend the equipment life, reduce the system frequency fluctuation caused by the early withdrawal of the equipment from operation, and ensure the safe and stable operation of the power grid.

[0062] S4. Optimize the feedback coefficient based on the optimization goal corresponding to the current preset operating condition to achieve the balanced control of the state of charge of the network-forming energy storage unit.

[0063] The following combines specific examples to specifically implement the method provided in this embodiment. The implementation process is as follows: Taking the distribution of typical industrial loads as the background, a photovoltaic-storage-load system is built in the electromagnetic transient simulation software PSCAD / EMTDC as Figure 2 shown; among them, PSCAD / EMTDC is a set of powerful electromagnetic transient simulation software, which has extensive applications in the field of power system analysis, research and design. The 2MW distributed photovoltaic system is connected to the 10kV bus 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, and is connected to the grid through a converter-booster integrated machine; 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 balanced control method described in this embodiment. Considering the SOC balance effect comprehensively, in this embodiment, n = 2, α1 = 0.8, α2 = 0.6, SOC1 = 0.2, and SOC2 = 0.8 are taken.

[0064] It should be noted that in order to improve the simulation efficiency, the charge and discharge rate of the energy storage unit is set relatively large in this embodiment. In actual engineering, it is mostly 0.25, 0.5 or 1, and the controller volatility is lower. Therefore, the control method proposed in this embodiment is also applicable.

[0065] Assume that the initial state is at 1s, the state of charge of BESS1 and BESS2 are 0.45 and 0.55 respectively, the photovoltaic irradiance is 500W / m 2 , the constant impedance part of the comprehensive load is 0.6p.u., the constant current part is 0.1p.u., and the constant power part is 0.3p.u. At 2s, the load drops from 1p.u. to 0.6p.u. At 4s, due to the influence of weather, the irradiance rises from 500W / m 2 to 1200W / m 2 . The simulation results are as Figure 3 shown, specifically as shown in Figure 3 Figures (a) and (b) therein.

[0066] Since BESS1 has a smaller equivalent resistance than BESS2, in the initial state, although BESS1 has a smaller capacity, its active power output reaches 0.98 p.u., while the active power output of BESS2 is 0.81 p.u. At this time, power distribution can effectively reduce system losses, which is in line with the theoretical analysis results of the first working condition. At 2 s, due to the reduction of load, in order to balance power, the energy storage units simultaneously reduce the active power and enter the second working condition interval. The SOC balance speed increases, and the per-unit value of the active power output of BESS1 is still greater than that of BESS2, taking into account both system losses and balance speed in the transition zone. At 4 s, due to the increase in photovoltaic power, in order to balance power, the energy storage units reduce the active power again, and the output of the energy storage units drops below the secondary power threshold. At this time, the SOC balance speed significantly accelerates, the active power output of BESS1 decreases compared with BESS2, and BESS1 and BESS2 quickly tend to be consistent. And due to the relatively small power, the system losses... At 5.2 s, the state of charge of BESS2 drops below 0.2 and enters the fourth working condition, and is determined to be close to the safe operation boundary. In order to reduce the grid-forming impact, the SOC balance effect is further strengthened. At 6.9 s, the SOC reaches consistency, and there is still a certain margin until the energy storage units reach the cut-off, effectively improving the system safety.

[0067] Compared with the prior art, the present embodiment provides a method for controlling the state of charge balance of a grid-forming energy storage unit, which has the following advantages: Aiming at the SOC balance problem of the grid-forming energy storage unit, the present embodiment proposes an improved dynamic balance control method. This control method fully considers the SOC deviation, the output of the energy storage power station and the remaining charge-discharge capacity, and designs the balance coefficient, which is more suitable for multi-condition operation in the grid-forming scenario. When the SOC margin is large and the load is large, the system losses are preferentially reduced. When the SOC margin is large and the load is small, the SOC is preferentially made to tend to be consistent. When the SOC margin is small, the output decline is accelerated to avoid the early withdrawal of the energy storage unit from operation. By self-regulating the balance coefficient, the control target is optimized, and the safety of the operation boundary is improved. The balance coefficient can be self-regulated, which not only optimizes the control target, makes the control more accurate, but also improves the safety of the energy storage unit under complex boundary conditions, making the grid-forming energy storage more reliable and stable.

[0068] Exemplarily, as Figure 4 shown, the present embodiment provides a method for controlling the state of charge balance of a grid-forming energy storage unit, including the following steps: 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-discharge power of the virtual synchronous generator model; Dynamically determine the preset operating conditions corresponding to the grid-forming energy storage system according to the SOC value and the charge-discharge power, and obtain the optimization objectives corresponding to the current preset operating conditions; Optimize the feedback coefficient based on the optimization objectives corresponding to the current preset operating conditions to achieve balanced control of the state of charge of the grid-forming energy storage unit; wherein, the feedback coefficient is superimposed on the power command of the virtual synchronous generator model to compensate for the power fluctuation amplitude of the grid-forming energy storage unit.

[0069] In this embodiment, the virtual synchronous generator model is specifically expressed as follows:

[0070] In the formula, is the output angular frequency of the energy storage converter; w0 is the rated value of the angular frequency; Δw is the difference between and w0; δ is the equivalent generator power angle; the active power command value and the actual value P of the energy storage converter are respectively equivalent to the input mechanical power and the output electromagnetic power of the generator; H is equivalently the inertia time constant of the unit; D is the proportional coefficient of the active power deviation and the angular frequency deviation; T is the time constant of the first-order inertia link; t is the time.

[0071] In this embodiment, the dynamically determining the preset operating conditions corresponding to the grid-forming energy storage system according to the SOC value and the charge-discharge power, and obtaining the optimization objectives corresponding to the current preset operating conditions includes: Dynamically determine the preset operating conditions corresponding to the grid-forming energy storage system based on the SOC value and the charge-discharge power, specifically as follows: If the charge-discharge power is greater than the first power threshold and the SOC value is between the first SOC threshold and the second SOC threshold, it is determined that the grid-forming energy storage system is in the first operating condition, and the optimization objective corresponding to the first operating condition is to reduce system losses; If the charge-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 less than the second SOC threshold, it is determined that the grid-forming energy storage system is in the second operating condition, and the optimization objective 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-discharge power is less than the second power threshold and the SOC value is between the first SOC threshold and the second SOC threshold, it is determined that the grid-forming energy storage system is in the third operating condition, and the optimization objective corresponding to the third operating condition is to adjust the output power at the cost of the lowest 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-forming energy storage system is in the fourth operating condition, and the optimization objective corresponding to the fourth operating condition is to extend the input time of the energy storage system; Among them, the secondary power threshold is less than the primary power threshold; the first SOC threshold is less than the second SOC threshold.

[0072] In this embodiment, optimizing the feedback coefficient based on the optimization objective corresponding to the current preset working condition includes: When it is determined that the grid-forming energy storage system is in the first working condition, the corresponding optimization objective is to reduce the system loss; Calculate the feedback coefficient P according to the optimization objective feed , and the specific formula is as follows:

[0073] In the formula, P1 is the primary power threshold; α1 is the primary balancing 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 DC side of the energy storage unit; R1 is the equivalent resistance of the AC side of the energy storage unit; U sN is the rated AC voltage of the energy storage converter; is the active power command value.

[0074] In this embodiment, optimizing the feedback coefficient based on the optimization objective corresponding to the current preset working condition further includes: When it is determined that the grid-forming energy storage system is in the second working condition, the corresponding optimization objective is to simultaneously reduce the system loss and balance the SOC values of the energy storage units; Calculate the feedback coefficient P according to the optimization objective feed , and the specific formula is as follows:

[0075] In the formula, P2 is the secondary power threshold; α2 is the secondary balancing coefficient; SOC ave is the average value of the SOCs 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.

[0076] In this embodiment, optimizing the feedback coefficient based on the optimization objective corresponding to the current preset working condition further includes: When it is determined that the grid-forming energy storage system is in the third working condition, the corresponding optimization objective is to adjust the output power at the cost of the lowest loss; Calculate the feedback coefficient P according to the optimization objective feed , and the specific formula is as follows: .

[0077] In this embodiment, optimizing the feedback coefficient based on the optimization objective corresponding to the current preset working condition further includes: When it is determined that the grid-forming energy storage system is in the fourth operating condition, the corresponding optimization goal is to extend the input time of the energy storage system; The feedback coefficient P is calculated according to the optimization goal feed , and the specific formula is as follows:

[0078] In the formula, I bat is the battery current.

[0079] Exemplarily, as Figure 5 shown, this embodiment also provides a state of charge equalization control system for a grid-forming energy storage unit, including: a model construction module for constructing a virtual synchronous generator model based on the rated angular frequency, equivalent power angle, active power command and actual power, in combination with the characteristics of a synchronous generator; a data acquisition module for obtaining the real-time SOC value and charge-discharge power of the virtual synchronous generator model; a judgment module for dynamically judging the preset operating condition corresponding to the grid-forming energy storage system according to the SOC value and charge-discharge power, and obtaining the corresponding optimization goal under the current preset operating condition; an optimization module for optimizing the feedback coefficient based on the optimization goal corresponding to the current preset operating condition to achieve equalization control of the state of charge of the grid-forming 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-forming energy storage unit.

[0080] The present invention also provides an electronic device, including: a memory for storing a computer program; a processor for implementing the steps of the state of charge equalization control method for the grid-forming energy storage unit when executing the computer program.

[0081] When the processor executes the computer program, it implements the above steps of state of charge equalization control for the grid-forming energy storage unit, for example: constructing a virtual synchronous generator model based on the rated angular frequency, equivalent power angle, active power command and actual power, in combination with the characteristics of a synchronous generator; obtaining the real-time SOC value and charge-discharge power of the virtual synchronous generator model; dynamically judging the preset operating condition corresponding to the grid-forming energy storage system according to the SOC value and charge-discharge power, and obtaining the corresponding optimization goal under the current preset operating condition; optimizing the feedback coefficient based on the optimization goal corresponding to the current preset operating condition to achieve equalization control of the state of charge of the grid-forming 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-forming energy storage unit.

[0082] Alternatively, when the processor executes the computer program, it implements the functions of each module in the above 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 command, and actual power, in combination with the characteristics of a synchronous generator; a data acquisition module, which is used to obtain the real-time SOC value and charge-discharge power of the virtual synchronous generator model; a judgment module, which is used to dynamically judge the preset working conditions corresponding to the grid-forming energy storage system according to the SOC value and charge-discharge power, and obtain the corresponding optimization objectives under the current preset working conditions; an optimization module, which is used to optimize the feedback coefficient based on the corresponding optimization objectives under the current preset working conditions to achieve balanced control of the state of charge of the grid-forming 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-forming energy storage unit.

[0083] Exemplarily, the computer program can be divided into one or more modules / units. The one or more modules / units are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can 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 grid-forming energy storage unit state of charge balance control device. 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 command, and actual power, in combination with the characteristics of a synchronous generator; the data acquisition module is used to obtain the real-time SOC value and charge-discharge power of the virtual synchronous generator model; the judgment module is used to dynamically judge the preset working conditions corresponding to the grid-forming energy storage system according to the SOC value and charge-discharge power, and obtain the corresponding optimization objectives under the current preset working conditions; the optimization module is used to optimize the feedback coefficient based on the corresponding optimization objectives under the current preset working conditions to achieve balanced control of the state of charge of the grid-forming 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-forming energy storage unit.

[0084] The state of charge equalization control device for the network-forming energy storage unit may be a computing device such as a desktop computer, a notebook, a palm computer, or a cloud server. The state of charge equalization control device for the network-forming energy storage unit may include, but is not limited to, a processor and a memory. Those skilled in the art can understand that the above are examples of the state of charge equalization control device for the network-forming energy storage unit, and do not constitute a limitation on the state of charge equalization control device for the network-forming energy storage unit. It may include more components than the above, or combine some components, or different components. For example, the state of charge equalization control device for the network-forming energy storage unit may also include input / output devices, network access devices, buses, etc.

[0085] The so-called processor may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), 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 also be any conventional processor, etc. The processor is the control center of the state of charge equalization control for the network-forming energy storage unit, and connects various parts of the entire state of charge equalization control device for the network-forming energy storage unit through various interfaces and lines.

[0086] The memory can be used to store the computer programs and / or modules. By running or executing the computer programs and / or modules stored in the memory, and by calling the data stored in the memory, the processor realizes various functions of the state of charge equalization control device for the network-forming energy storage unit.

[0087] The memory may mainly include a program storage area and a data storage area. Among them, the program storage area may store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area may store data created according to the use of the mobile phone (such as audio data, phone book, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0088] The present invention also provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps of the method for controlling the state of charge balance of a network-forming energy storage unit are implemented.

[0089] If the modules / units integrated in the system for controlling the state of charge balance of a network-forming energy storage unit are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.

[0090] Based on such understanding, all or part of the processes in the method for controlling the state of charge balance of a network-forming energy storage unit of the present invention can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the method for controlling the state of charge balance of a network-forming energy storage unit can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or a preset intermediate form, etc.

[0091] The computer-readable storage medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disc, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0092] It should be noted that the content included in the computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable storage medium does not include electrical carrier signals and telecommunication signals.

[0093] The above embodiments are only one of the implementation manners capable of implementing the technical solution of the present invention. The scope of protection required by the present invention is not limited only by this embodiment, but also includes any changes, substitutions, and other implementation manners that are easily conceivable by those skilled in the art within the technical scope disclosed by the present invention.

[0094] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: it is still possible to modify the specific implementation manners of the present invention or make equivalent substitutions, and any modification or equivalent substitution that does not depart from the spirit and scope of the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for controlling the state of charge balance of a network-forming energy storage unit, characterized in that including: constructing a virtual synchronous generator model based on the rated angular frequency, equivalent power angle, active power command, and actual power, in combination with the characteristics of a synchronous generator; obtaining the real-time SOC value and charge-discharge power of the virtual synchronous generator model; dynamically determining the preset operating conditions corresponding to the network-forming energy storage system according to the SOC value and charge-discharge power, and obtaining the corresponding optimization objective under the current preset operating conditions; optimizing the feedback coefficient based on the optimization objective corresponding to the current preset operating conditions to achieve balanced control of the state of charge of the network-forming 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 network-forming energy storage unit.

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

3. The charge state equalization control method for the network-forming energy storage unit according to claim 1, wherein The dynamically determining the preset operating conditions corresponding to the network-forming energy storage system according to the SOC value and charge-discharge power, and obtaining the corresponding optimization objective under the current preset operating conditions includes: dynamically determining the preset operating conditions corresponding to the network-forming energy storage system based on the SOC value and charge-discharge power, specifically as follows: If the charge-discharge power is greater than the first power threshold and the SOC value is between the first SOC threshold and the second SOC threshold, it is determined that the network-forming energy storage system is in the first operating condition, and the optimization objective corresponding to the first operating condition is to reduce system losses; If the charge-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, it is determined that the network-forming energy storage system is in the second operating condition, and the optimization objective 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-discharge power is less than the second power threshold and the SOC value is between the first SOC threshold and the second SOC threshold, it is determined that the network-forming energy storage system is in the third operating condition, and the optimization objective corresponding to the third operating condition is to adjust the output power at the cost of the lowest losses; 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 network-forming energy storage system is in the fourth operating condition, and the optimization objective corresponding to the fourth operating condition is to extend the input time of the energy storage system; wherein, the second power threshold is less than the first power threshold; the first SOC threshold is less than the second SOC threshold.

4. The method for controlling the state of charge balance of the network-forming energy storage unit according to claim 3, wherein The optimizing the feedback coefficient based on the optimization objective corresponding to the current preset operating conditions includes: When it is determined that the network-forming energy storage system is in the first operating condition, the corresponding optimization objective is to reduce system losses; The feedback coefficient P is calculated according to the optimization objective feed , and the specific formula is as follows: Wherein, P1 is the first-level power threshold; α1 is the first-level balance 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 DC side of the energy storage unit; R1 is the equivalent resistance of the AC side of the energy storage unit; U sN is the rated AC voltage of the energy storage converter; is the active power command value.

5. The method for balancing the state of charge of a network-forming energy storage unit according to claim 4, characterized in that The optimizing the feedback coefficient based on the optimization objective corresponding to the current preset operating conditions further includes: When it is determined that the network-forming energy storage system is in the second operating condition, the corresponding optimization objective is to simultaneously reduce system losses and balance the SOC values of the energy storage units; The feedback coefficient P is calculated according to the optimization objective feed , and the specific formula is as follows: Wherein, P2 is the secondary power threshold; α2 is the secondary balance coefficient; SOC ave is the average value of the SOCs 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; is the nth power of the SOC value of the balanced energy storage unit; is the nth power of the average value of the SOCs of all energy storage units in the system.

6. The method for controlling the state of charge equalization of the network-forming energy storage unit according to claim 5, wherein The optimizing the feedback coefficient based on the optimization objective corresponding to the current preset operating conditions further includes: When it is determined that the network-forming energy storage system is in the third operating condition, the corresponding optimization objective is to adjust the output power at the cost of the lowest losses; The feedback coefficient P is calculated according to the optimization objective feed , and the specific formula is as follows: 。 7. The method for controlling the state of charge balance of the network-forming energy storage unit according to claim 6, wherein The optimizing the feedback coefficient based on the optimization objective corresponding to the current preset operating conditions further includes: When it is determined that the network-forming energy storage system is in the fourth operating condition, the corresponding optimization objective is to extend the input time of the energy storage system; The feedback coefficient P is calculated according to the optimization objective feed , and the specific formula is as follows: Where I bat is the battery current.

8. A state of charge equalization control system for a network-forming energy storage unit, characterized in that including: A model construction module, configured to construct a virtual synchronous generator model based on the rated angular frequency, equivalent power angle, active power command, and actual power, in combination with the characteristics of a synchronous generator; A data acquisition module, configured to obtain the real-time SOC value and charge-discharge power of the virtual synchronous generator model; A judgment module, configured to dynamically judge the preset working conditions corresponding to the network-forming energy storage system according to the SOC value and charge-discharge power, and obtain the optimization target corresponding to the current preset working conditions; An optimization module, configured to optimize the feedback coefficient based on the optimization target corresponding to the current preset working conditions, so as to achieve balanced control of the state of charge of the network-forming energy storage unit; wherein, the feedback coefficient is superimposed on the power command controlled by the virtual synchronous generator model and is used to compensate for the power fluctuation amplitude of the network-forming energy storage unit.

9. An electronic device, characterized in that, Comprising: A memory, configured to store a computer program; A processor, configured to implement the steps of the method for balanced control of the state of charge of the network-forming energy storage unit according to any one of claims 1-7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it is used to implement the steps of the method for balanced control of the state of charge of the network-forming energy storage unit according to any one of claims 1-7.

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