Multi-energy storage microgrid droop control strategy based on energy storage charge state

Abstract

The invention discloses a multi-energy-storage micro-grid droop control strategy based on an energy storage charge state. The multi-energy-storage micro-grid droop control strategy comprises the following steps: establishing a power function relationship between the charge state (SOC) of an energy storage unit and a droop coefficient; and a droop coefficient correction factor is dynamically adjusted, and each energy storage unit dynamically adjusts the output of the active power thereof according to the SOC level thereof while maintaining the stability of the bus voltage and frequency, thereby realizing the balanced distribution of the SOC. And carrying out stability analysis on the system by using a small signal disturbance method. And determining suitable value ranges of the parameters m, n and p. By properly adjusting the values of the parameters, the stability of the system frequency can be effectively controlled, and the equalization process of the state of charge (SOC) of the energy storage unit can be promoted. According to the control strategy, the energy storage battery can reasonably adjust the output active power in real time according to the charge state of the energy storage battery. In addition, the control strategy can keep a good control effect when the energy storage charge state is low.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrid droop control, and in particular to a multi-energy storage microgrid droop control strategy based on energy storage charge state. Background Art

[0002] In the independent operation mode, the microgrid may encounter interference, causing the system frequency to fluctuate. Therefore, it is particularly important to design an appropriate controller to maintain frequency stability. For microgrid systems equipped with multiple energy storage units, the control strategy usually includes constant voltage and frequency control, active and reactive power control, and droop control. Droop control technology has become the focus of many researchers due to its advantages such as easy implementation, easy integration, and high reliability. Under the conventional droop control framework, the converter fails to properly adjust its active power output according to the actual power status of the energy storage unit. At the same time, the voltage and frequency are also prone to fluctuations due to external interference, which prompts the need to optimize the traditional droop control method. At present, most improvement measures are based on the concept of secondary control, which compensates for the control of active and reactive power by adjusting the droop curve.

[0003] In traditional droop control strategies, the droop coefficient is usually set to a fixed value, which makes it impossible for the energy storage device to dynamically adjust its active power output according to its current state of charge (SOC) level, making it impossible for traditional droop control to achieve reasonable distribution of active power. Summary of the invention

[0004] In view of the problem that the active power output of energy storage cannot be reasonably distributed in traditional droop control, the present invention provides a multi-energy storage microgrid droop control strategy based on the energy storage charge state, which can make the energy storage battery reasonably adjust the output active power in real time according to its own charge state. In addition, the control strategy can also maintain a good control effect when the energy storage charge state is low.

[0005] The technical solution adopted by the present invention is:

[0006] The multi-energy storage microgrid droop control strategy based on energy storage charge state includes the following steps:

[0007] Step 1: By establishing a power function relationship between the state of charge (SOC) of the energy storage unit and the droop coefficient;

[0008] Step 2: Dynamically adjust the droop coefficient correction factor. While maintaining the stability of the bus voltage and frequency, each energy storage unit dynamically adjusts its active power output according to its SOC level to achieve balanced distribution of SOC.

[0009] In step 1, the droop control expression is:

[0010]

[0011] In formula (3), f aci is the actual frequency of the i-th microgrid, is the reference value of the system frequency, k p is the droop control coefficient, The state of charge of the i-th energy storage after correction by the droop coefficient correction factor m. The exponential expression of the charge state of the i-th energy storage after correction by the droop coefficient correction factor n. is the rated active power of the converter, p acli The active power output of the i-th converter after low-pass filtering, SOC i is the state of charge of the i-th energy storage unit. m, n and p represent the parameters of the modified droop coefficient, which are associated with the state of charge (SOC) of the i-th energy storage unit.

[0012] In step 2, the energy storage unit with a higher state of charge will output more active power, while the energy storage unit with a lower state of charge will reduce its output, thereby achieving a balanced distribution of SOC.

[0013] The method further includes step 3, which is an improved droop control based on energy storage SOC, specifically including:

[0014] The energy storage unit is connected to the AC bus through a converter, and each converter is equipped with a local control unit;

[0015] An LC filter is used at the output end of the converter to ensure the quality of the output signal, and the load is directly connected to the AC bus.

[0016] Before applying the controller, the coupling effect is decoupled to ensure the accuracy and effectiveness of the controller design. To achieve approximate decoupling of the converter output power, a virtual impedance is introduced to distribute the reactive power load before determining the voltage reference value. This design strategy can achieve the decoupling effect by reconfiguring the equivalent output impedance while keeping the control parameters unchanged.

[0017]

[0018] In formula (4), R f is the resistance reference value, s is the Laplace transform coefficient, L f is the inductance reference value, i d (s), i q (s) represent the reference values ​​of the three-phase current on the d-axis and q-axis after dq transformation, u 0d (s),u 0q (s) is the component of voltage in rotating coordinates. ω is the angular frequency, u sd(s),u sq (s) is the component of the voltage after Laplace transformation in the rotating coordinates.

[0019] It also includes step 4, using the small signal perturbation method to perform stability analysis on the microgrid system containing multiple energy storages:

[0020] It can be seen from formula (3) that when two energy storage units are discharged in parallel, their active power output is closely related to their respective energy storage state of charge (SOC). Specifically, the ratio of the active power output deviation of the two energy storage units can be expressed as:

[0021]

[0022] In formula (9), is the rated active power of the converter, p acl1 The active power output of the first low-pass filtered converter, p acl2 The active power output of the second converter after low-pass filtering, The state of charge of the first energy storage after correction by the droop coefficient correction factor m. The state of charge of the second energy storage after correction by the droop coefficient correction factor m. The exponential expression of the charge state of the first energy storage after correction by the droop coefficient correction factor n. The exponential expression of the state of charge of the second energy storage after correction by the droop coefficient correction factor n.

[0023] According to formula (3), the active power output of the energy storage unit is directly proportional to its state of charge (SOC). In addition, the system balancing rate and frequency fluctuation are regulated by factors m, n and p.

[0024] From formula (3), the improved droop expression of two parallel operation energy storage can be obtained as follows:

[0025]

[0026] Where: f ac1 is the actual frequency of the first microgrid, f ac2 is the actual frequency of the second microgrid.

[0027] p acl1 and p acl2 is the active power output by the converter after low-pass filtering; System frequency reference value; is the rated active power of the converter; k p , k q is the droop control coefficient.

[0028] fac1 and f ac2 Perform a small signal perturbation:

[0029]

[0030] In the above formula, '^' represents the small signal disturbance, m, n and p are the droop coefficient correction factors; is the actual frequency small signal disturbance of the first microgrid, is the actual frequency small signal disturbance of the second microgrid, The state of charge of the first energy storage after correction by the droop coefficient correction factor m-1. The state of charge of the second energy storage after correction by the droop coefficient correction factor m-1. The state of charge of the first energy storage after correction by the droop coefficient correction factor n-1. The state of charge of the second energy storage after correction by the droop coefficient correction factor n-1. p Droop control factor. It is the exponential expression of the charge state of the first energy storage after correction by the droop coefficient correction factor n. It is the exponential expression of the charge state of the second energy storage after correction by the droop coefficient correction factor n. is the small signal disturbance of the state of charge of energy storage 1, To perform a small signal disturbance on the charge state of energy storage 2, is the small signal disturbance of active power output by the first converter after low-pass filtering, It is the small signal disturbance of active power output by the second converter after low-pass filtering.

[0031] The small signal perturbation method is used here to evaluate the stability of the system and determine the appropriate value range of parameters m, n and p. By properly adjusting the values ​​of these parameters, the stability of the system frequency can be effectively controlled and the equalization process of the state of charge (SOC) of the energy storage unit can be promoted.

[0032] The present invention provides a multi-energy storage microgrid droop control strategy based on the energy storage charge state. The strategy introduces the power exponential function term of the droop coefficient and the energy storage charge state, so that the energy storage unit can dynamically balance according to its own state of charge level. The power exponential function droop control (PEFCD) does not require communication between energy storage units, and has better control effect than the traditional droop control (CDC) and the power function droop control (PFCD). BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The present invention will be further described below in conjunction with the accompanying drawings and examples;

[0034] Figure 1 The overall framework diagram of improved droop control based on energy storage SOC.

[0035] Figure 2 The main circuit of the AC microgrid consists of two parallel energy storage units.

[0036] Figure 3 This is the simulation circuit diagram of the energy storage module.

[0037] Figure 4 This is the control block diagram of traditional droop control.

[0038] Figure 5 This is the control architecture diagram of the power controller.

[0039] Figure 6 It is the relationship diagram between the three-phase stationary coordinate system and the dq0 rotating coordinate system.

[0040] Figure 7 It is the position and reference direction of the three-phase converter in the corresponding electrical quantity.

[0041] Figure 8 This is the simulation result of mathematical model.

[0042] Fig. 9 The overall control architecture is an improved droop control strategy based on the state of charge (SOC) of the energy storage unit.

[0043] Fig.10 Schematic diagram for reference voltage generation.

[0044] Fig.11 This is the voltage and current dual closed-loop control structure diagram.

[0045] Fig.12 is the eigenvalue distribution diagram.

[0046] Fig.13 Schematic diagram comparing the three methods of power output and SOC difference.

[0047] Fig.14 Changes in ESU output power and fluctuations in system frequency when m=3, p=5 and n factor take different values. DETAILED DESCRIPTION

[0048] The droop control strategy of a multi-energy storage microgrid based on the energy storage charge state first establishes a power exponential function relationship between the droop coefficient and the energy storage unit charge state; then, the small signal disturbance analysis method is used to evaluate the stability of the system; then, the droop coefficient function is deeply analyzed to guide the selection of factors. In addition, the present invention also constructs a model simulation environment to compare and analyze the proposed power exponential function droop control strategy with the conventional droop control strategy and the power function droop control strategy. The study explores the specific effects of parameters m, n and p on system stability and control performance. Finally, the effectiveness of the proposed control strategy is verified by simulation results. It includes the following steps:

[0049] Step 1: Implementation of droop control:

[0050] The traditional voltage drop control method includes three aspects: power controller, voltage and current dual closed-loop controller, and PWM generator. The basic function of the power controller is to test the voltage and current at the common point, find out the instantaneous power of the converter, and perform low-pass filter, noise reduction method and other processing to obtain the system's frequency, voltage and other parameters. The voltage and current dual closed-loop controller consists of two parts: the voltage outer loop and the circuit inner loop. The voltage outer loop obtains the voltage of the DC side and the DC side through dq conversion, and then obtains the voltage of the DC side and the DC side through PI control. By performing PI regulation on the current and decoupling it, the voltage modulation waveform can be obtained. Finally, the voltage modulation waveform is input into the pulse generator to generate a pulse width modulation waveform to achieve control of the converter.

[0051] The expression of traditional droop control of high-voltage microgrid is:

[0052]

[0053] Where: f ac and V ac are the actual frequency and voltage of the microgrid respectively; p acl1 and q acl1 are respectively the active power and reactive power output by the converter after low-pass filtering; and are the system frequency and voltage reference values ​​respectively, and are the rated active power and reactive power of the converter, respectively, p and k q is the droop control coefficient. As can be seen from formula (1), in the traditional droop control, the droop coefficient is a constant, and the energy storage output active power cannot dynamically adjust the output active power according to its own state of charge (SOC).

[0054] Step 2: Introduce the power exponential function model:

[0055] In the classic droop control strategy, the droop coefficient is usually set to a fixed value, which makes it impossible for the energy storage device to dynamically adjust its active power output according to its current state of charge (SOC) level. The present invention proposes a novel control method, which realizes the dynamic adjustment of the droop coefficient by establishing a power function relationship between the state of charge (SOC) of the energy storage device and the droop coefficient. The improved droop control expression is:

[0056]

[0057] Where: m, n and p are droop coefficient correction factors, SOC i is the state of charge of the ith energy storage.

[0058] m, n, and p represent the parameters of the modified droop coefficient, which are associated with the state of charge (SOC) of the i-th energy storage unit. By adjusting these parameters, each energy storage module can be allowed to dynamically adjust its active power output according to its SOC level while maintaining the stability of the bus voltage and frequency. Specifically, energy storage modules with higher state of charge will output more active power, while modules with lower state of charge will reduce their output, thereby achieving a balanced distribution of SOC.

[0059] Step 3: Implementation of the improved control strategy:

[0060] After obtaining the reference voltage, the Park transform is used to convert it into a voltage component in the dq0 coordinate system. However, according to the analysis of formula (4), there is a coupling phenomenon between the direct-axis and quadrature-axis voltages, which may have an adverse effect on the design of the controller. Therefore, before applying the controller, this coupling effect must be decoupled to ensure the accuracy and effectiveness of the controller design. In order to achieve approximate decoupling of the converter output power, a large virtual impedance is introduced to distribute the reactive power load before determining the voltage reference value. This design strategy allows the decoupling effect to be achieved by reconfiguring the equivalent output impedance while keeping the control parameters unchanged.

[0061]

[0062] The overall framework of the proposed improved droop control based on energy storage SOC is as follows: Figure 1 As shown. The energy storage battery is connected to the AC bus through the converter, and each converter is equipped with a local control unit. In addition, an LC filter is used at the output of the converter to ensure the quality of the output signal, and the load is directly connected to the AC bus.

[0063] Step 4: Use the small signal perturbation method to analyze the stability of the system:

[0064] From formula (3), the improved droop expression of two parallel operation energy storage can be obtained as follows:

[0065]

[0066] Where: f ac is the actual frequency of the microgrid; p acl1 and p acl2 is the active power output by the converter after low-pass filtering; System frequency reference value, is the rated active power of the converter, k p k q is the droop control coefficient.

[0067] f ac1 and f ac2 Perform a small signal perturbation:

[0068]

[0069] Wherein: capital letters represent steady-state quantities, '^' represents small signal disturbance quantities, and m, n, and p are droop coefficient correction factors.

[0070] Small signal analysis is used here to evaluate the stability of the system and determine the appropriate range of values ​​for the parameters m, n, and p. By properly adjusting the values ​​of these parameters, the stability of the system frequency can be effectively controlled and the equalization process of the state of charge (SOC) of the energy storage unit can be promoted.

[0071] By applying the small signal perturbation technique to analyze the system stability, it can be observed from formula (1) that when two energy storage units are discharged in parallel, their active power output is closely related to their respective energy storage state of charge (SOC). Specifically, the ratio of the active power output deviation of the two energy storage units can be expressed as:

[0072]

[0073] According to the analysis results of formula (3), it is found that the active power output of the energy storage unit is proportional to its state of charge (SOC). In addition, the system balancing rate and frequency fluctuation are mainly regulated by factors m, n and p. The small signal perturbation method is used to analyze the stability of the system using the above strategy.

[0074] Step 5: Build a simulation to verify the effectiveness of the improved control strategy:

[0075] In order to verify the effectiveness of the improved droop control strategy proposed in this invention, a parallel system model of two energy storage units was constructed in the MATLAB / Simulink platform, and a simulation test was carried out on an independently operated AC microgrid. By comparing the application effects of three different droop control strategies in AC microgrids, the results show that the droop control method based on power index proposed in this invention surpasses the traditional method in performance and is suitable for AC microgrid environments containing multiple energy storage units.

[0076] In the simulation, the carrier frequency of the converter is set to 6000 Hz, the output frequency is 50 Hz, and the simulation time step is set to 30 μs. Figure 2 The main circuit design of the AC microgrid consisting of two parallel energy storage units is shown. Other simulation parameters are shown in Table 1.

[0077] Table 1 Simulation parameters

[0078]

[0079] The present invention provides an improved droop control strategy considering the state of charge of a microgrid multi-energy storage system, which has the following characteristics:

[0080] ①. In step one of the present invention: First, it ensures the stable operation of the microgrid system through the coordinated work of the power controller, the voltage and current dual closed-loop controller and the PWM generator; further, the control strategy can accurately monitor the voltage and current values ​​of the common connection point of the power grid, and then calculate the instantaneous power output of the converter. Subsequently, by applying a low-pass filter and a droop control algorithm, the set values ​​of frequency and voltage are generated to ensure that the frequency and voltage of the power grid are maintained at the desired level. In addition, the design of the voltage and current dual closed-loop controller can achieve precise control of the current through the PI control strategy, and simplifies the design of the controller through decoupling, thereby improving the response speed and stability of the system. Overall, the control strategy of step one not only improves the operating stability of the microgrid, but also enhances the anti-interference ability of the system, laying a solid foundation for the implementation of subsequent improved control strategies.

[0081] ②. In step 2 of the present invention: The power exponential function model introduced in step 2 of the present invention is innovative and practical. By constructing a power exponential function relationship between the droop coefficient and the energy storage state of charge (SOC), the model can dynamically adjust the active power output of the energy storage unit, so that the energy storage unit can reasonably allocate the output power according to its own SOC level. This adaptive control strategy not only improves the energy efficiency of the system, but also enhances the adaptability of the microgrid to the SOC changes of different energy storage units. In addition, by introducing correction factors m, n and p, the model provides more adjustment parameters for the system, making the system design more flexible and able to be optimized according to different operating conditions and requirements. This improved droop control strategy does not require communication between energy storage units, simplifies the system structure, reduces costs, and improves the reliability of the system. In general, the power exponential function model of step 2 provides a more intelligent and efficient control method for the microgrid, which helps to achieve SOC balance of energy storage units and enhances the stability and economy of the system.

[0082] ③. The improved control strategy in step three of the present invention realizes the precise control of the output power of the energy storage unit. By introducing the virtual impedance and reconstructing the equivalent output impedance, the approximate decoupling of the converter output power is realized. This design not only simplifies the design process of the controller, but also improves the stability and response speed of the system. Before determining the voltage reference value, a larger virtual impedance is introduced to distribute reactive power. This strategy can effectively promote the balanced distribution of power between energy storage units without adjusting the control parameters. In addition, the improved control strategy also enhances the anti-interference ability of the system, so that a better control effect can be maintained even when the energy storage charge state is low, which is crucial to improving the operational stability and reliability of the microgrid. Overall, the implementation of step three provides a more efficient and flexible control method for the microgrid, which helps to optimize the utilization of energy storage resources and improve the economy and environmental adaptability of the entire system.

[0083] ④. In step 4 of the present invention, the small signal perturbation method is used to perform stability analysis on the system, which has significant theoretical and practical value. Through the small signal perturbation method, the present invention can accurately analyze and predict the dynamic behavior of the system after being subjected to a small disturbance, thereby evaluating the stability of the system. This method not only provides a quantitative evaluation of the stability of the system, but also can determine the key parameters that affect the stability of the system, such as the droop coefficient correction factors m, n and p. By analyzing the impact of these parameters on the system frequency fluctuations and the SOC equalization rate, the present invention can provide guidance for system design to ensure that the system can remain stable under various operating conditions. In addition, the application of the small signal perturbation method also helps to optimize the control strategy, improve the system's robustness to disturbances, and reduce the system's sensitivity to external interference. In short, the stability analysis of step 4 provides a solid theoretical basis for the reliable operation of the microgrid, enhances the scientific nature of the system design and the safety of the system operation.

[0084] ⑤. Step 5 of the present invention verifies the effectiveness of the improved control strategy by building a simulation model under the MATLAB / Simulink environment. The advantage of this step is that it is highly empirical and intuitive, and can intuitively demonstrate the performance of the proposed control strategy in practical applications. The establishment of the simulation model allows researchers to test and optimize the control algorithm without physical risks and costs, ensuring the consistency between theoretical analysis and practical applications. By comparing traditional droop control, power function droop control and the power exponential function droop control of the present invention, step five not only proves the superiority of the present invention in reasonable power distribution, SOC balance and system stability, but also demonstrates its adaptability and robustness under different operating conditions. In addition, the analysis of the simulation results provides an important reference for the design and operation of the actual microgrid system, helps to guide practical engineering applications, and reduces the risks and costs of actual deployment. In short, the simulation verification of step five not only enhances the credibility of the present invention, but also provides valuable experience and data support for research and practice in the field of microgrids.

[0085] ⑥. The present invention realizes efficient collaborative management of multiple energy storage units in a microgrid through an improved droop control strategy. The power exponential function is introduced to dynamically adjust the active power output of the energy storage unit, optimize the balance of the energy storage state of charge, and improve the system stability. Simulation verification shows that this strategy is superior to traditional methods in power allocation, SOC balancing rate and system stability, and does not require communication between energy storage units, reducing system complexity and cost. In addition, the stability analysis of the small signal perturbation method provides theoretical support for system design, ensuring the reliability and economy of the microgrid under various operating conditions.

[0086] Example:

[0087] (1): The specific implementation of traditional drooping is as follows Figure 4As shown, it mainly includes three parts: power controller, voltage and current dual closed-loop controller and PWM generator:

[0088] The key functions of the power controller include: monitoring the voltage and current at the common connection point of the power grid, and calculating the instantaneous power of the converter based on this. After undergoing low-pass filtering and droop control algorithm, the instantaneous power is used to determine the set values ​​of voltage and frequency. In addition, the voltage and current dual closed-loop control system consists of two parts: an outer voltage control loop and an inner current control loop. The voltage outer loop controller receives the voltage reference value from the controller and converts it into direct-axis and quadrature-axis voltage components using dq transformation. Through the action of the PI regulator, the corresponding current components can be calculated. After further PI regulation and decoupling, these current components form a control waveform for regulating the voltage. Finally, a pulse width modulation (PWM) generator is used to generate a PWM signal to control the converter. For details of the architecture of the power controller, see Figure 5 .

[0089] The power controller consists of four parts: dq conversion, instantaneous power calculation, low-pass filtering and droop control; od 、v oq 、i od 、i oq They are the four components of voltage and current in rotating coordinates, which are used to calculate the instantaneous power output by the converter; ω c is the cut-off frequency of the low-pass filter. The filtered power value determines the amplitude and frequency of the reference voltage through the droop control process. In this process, the selection of the droop coefficient depends on the rated capacity of the converter and the maximum range of voltage-frequency fluctuation allowed by the system.

[0090] Next, the design principle of the voltage and current dual closed-loop control strategy will be discussed in detail from the two perspectives of Park transformation and mathematical modeling of inverter circuits. Park transformation realizes the equivalent representation of three-phase electrical quantities by converting the voltage and current in the abc coordinate system of the three-phase system into the dq0 coordinate system. In the actual control process, since PID control has a better control effect on DC, it is necessary to convert the static αβ coordinate system to the dq0 coordinate system after Clark transformation. From a physical point of view, projecting the three-phase voltage and current onto the rotating coordinate axis is equivalent to equating the AC quantity to the DC quantity and equating the voltage and current to the direct axis and the quadrature axis. From the perspective of steady state, after the transformation, the voltage and current components of the direct axis and the quadrature axis are constants, which just meets the requirements of PID control. There are four main types of Park transformation matrices. The present invention selects a Park transformation commonly used in MATLAB / Simulink for in-depth analysis to explore the design method of the voltage and current dual closed-loop control system.

[0091] The three-phase voltage of the AC microgrid system can be expressed as:

[0092]

[0093] Where: E is the fundamental amplitude of the three-phase voltage, ω is the angular frequency. The system three-phase voltage v b The relationship between the three-phase stationary coordinate system and the dq0 rotating coordinate system is as follows Figure 6 shown.

[0094] Depend on Figure 6 We can get:

[0095]

[0096] Combining equation (10) and equation (11), we can get:

[0097]

[0098] From equations (12) to (14), we can get the Clark transform:

[0099]

[0100] Clark's inverse transform is:

[0101]

[0102] according to Figure 6 , projecting the dq0 axis voltage onto the αβ coordinate axis, we can get:

[0103]

[0104] Formula (17) can be expressed by a matrix as:

[0105]

[0106] Equation (16) and (19) can be used to obtain the Park transform:

[0107]

[0108] Simplifying, we get:

[0109]

[0110] Therefore, the Park transformation matrix P is:

[0111]

[0112] Correspondingly, the Park inverse transformation matrix P can be obtained from equations (16) and (19): -1 for:

[0113]

[0114] The position and reference direction of the three-phase converter in the corresponding electrical quantity are as follows Figure 7 shown.

[0115] Depend on Figure 7 The mathematical model of the three-phase inverter circuit can be obtained:

[0116]

[0117] in,

[0118]

[0119] By simplifying formula (24) and multiplying both sides by the Park matrix P, we can obtain:

[0120]

[0121] According to the four operations of derivatives, we can get:

[0122]

[0123] From equation (26) and equation (27), we can get:

[0124]

[0125] According to Park transformation, formula (28) can be transformed into:

[0126]

[0127] According to the matrix operation rules, we can get:

[0128]

[0129] Combining equation (29) and equation (30), we can get:

[0130]

[0131] in,

[0132]

[0133] Therefore, formula (31) can be written as:

[0134]

[0135] From equation (22) and equation (23), we can get:

[0136]

[0137] in, From equation (35) and equation (36), we can get:

[0138]

[0139] Simplified:

[0140]

[0141] Equation (36) represents the mathematical model of the voltage and current dual closed-loop control. By analyzing the model, it can be observed that each equation contains both direct-axis and quadrature-axis components, indicating the existence of coupling. This coupling is not conducive to the direct application of the PID controller. Therefore, before adopting the PID control strategy, decoupling must be performed first.

[0142] By performing Laplace transform on equation (36), we can obtain:

[0143]

[0144] Therefore, the controller can be designed as:

[0145]

[0146] Introduce the power exponential function model:

[0147] The proposed control measurement constructs a power exponential function relationship between the droop coefficient and the energy storage charge state. The improved droop control expression is:

[0148]

[0149] Where m, n and p are droop coefficient correction factors, SOC i is the charge state of the i-th energy storage. The following mathematical model is established to analyze the impact of the proposed strategy on the balance of energy storage charge state.

[0150] The SOC of lithium-ion batteries is usually estimated by the coulomb counting method, and the calculation formula is:

[0151]

[0152] Among them, SOC i0 is the initial charge state of the lithium battery, C e is the capacity of the battery, and the energy storage output power is:

[0153] P aci =U bati i bati (41);

[0154] Where U bati and i batiare the output voltage and current of the energy storage battery respectively. Therefore, the SOC of the two energy storage batteries can be expressed as:

[0155]

[0156]

[0157] P ac1 and P ac2 are the energy storage output powers of energy storage batteries 1 and 2 respectively. Since frequency is a global variable, it can be seen from equation (39) that the active power output of the converter is related to the droop coefficient, so equations (42) and (43) can be further derived as follows:

[0158]

[0159] In this article, P load Represents the active power of the load. Next, mathematical modeling and simulation analysis will be performed on equations (44) and (45). Figure 8 The simulation results are shown, from which it can be observed that as time goes by, the state of charge (SOC) of the two energy storage units gradually decreases and tends to be consistent. This phenomenon shows that the established mathematical model can effectively achieve the balance of the state of charge of the energy storage units.

[0160] (2): Implementation of the proposed improvement strategy:

[0161] Fig. 9 The overall control architecture of the improved droop control strategy based on the state of charge (SOC) of the energy storage unit is presented. The energy storage battery is connected to the AC bus through the corresponding converter. Each converter is equipped with a local controller and an LC filter is set at its output. The load is directly connected to the AC bus.

[0162] If the phase and amplitude of the voltage are known, the reference value of the voltage can be obtained by voltage synthesis:

[0163]

[0164] Where E is the voltage amplitude and ωt is the voltage phase. The reference voltage generation process is as follows: Fig.10 shown.

[0165] After the reference voltage is determined, the voltage is converted to the components in the dq0 coordinate system using Park transformation. According to equation (39), it can be found that there is a coupling phenomenon between the direct-axis and quadrature-axis voltages, which is not conducive to the construction of the controller. Therefore, a decoupling step is necessary before applying the controller. Before obtaining the voltage reference value, a large virtual impedance is introduced to share the reactive power. By reconstructing the equivalent output impedance without adjusting the control parameters, an approximate decoupling of the converter output power is achieved. Fig.11 The architecture of the voltage and current dual closed-loop control system is shown. In this system, the actual measured voltage value is compared with the set voltage value and then processed by the PI controller to generate the rated current. Subsequently, the rated current is used for the current inner loop control to finally generate the PWM modulation signal

[0166] (3): Small signal stability analysis:

[0167] Perform small signal perturbation on equation (44) and equation (45), and the frequency domain results are expressed as:

[0168]

[0169] The capital letters represent steady-state quantities, and '^' represents small-signal disturbance quantities.

[0170] The energy storage output power needs to be low-pass filtered:

[0171]

[0172] Among them, H l is the transfer function of a second-order low-pass filter:

[0173]

[0174] Where ζ is the damping coefficient, ω 0 is the cut-off frequency. Substituting equation (49) into equation (47) and equation (48), we can eliminate and You can get:

[0175]

[0176] Substitute equation (51) into equation (7) and equation (52) into equation (8) and eliminate and You can get:

[0177]

[0178] In order to simplify equations (53) and (54), let g im1 and g im2 for:

[0179]

[0180] Then equation (53) and equation (54) can be expressed as:

[0181]

[0182]

[0183] In a stable microgrid, the load power is borne by each energy storage, and the system frequency is the same, then:

[0184] P acl1 +P acl2 =P load (59);

[0185] f ac1 =f ac2 (60);

[0186] Applying small signal perturbations to equations (59) and (60), and solving them simultaneously with (53) and (54), the characteristic equation of the system is:

[0187] As 2 +Bs+C=0 (61);

[0188] in,

[0189] A=g im1 +g im2 (62);

[0190]

[0191] The stability of the power exponential droop control system can be analyzed by the characteristic equation. Energy storage 1 and energy storage 2 have the same rated voltage and capacity, 500V and 20Ah respectively. The initial droop factor k p is 0.000001. The cutoff frequency of the low-pass filter is ω 0 It is 157rad / s, and the damping factor ζ is 0.707.

[0192] (4): System frequency deviation:

[0193] It can be seen from formula (1) that in the island operation mode, the AC microgrid will have frequency deviation. Rewrite formula (1) as:

[0194]

[0195] By analyzing equation (65), it can be observed that the frequency deviation of the AC microgrid when operating in island mode is closely related to the initial droop coefficient and the parameters m, n, and p. In order to ensure that the system frequency deviation is maintained within an acceptable range, the values ​​of the initial droop coefficient and the parameters m, n, and p must be carefully selected. According to equation (3), the droop coefficient of the converter should satisfy a specific relationship:

[0196] 0≤k p ≤k pi e p (66);

[0197] In order to ensure that the system can operate stably within the specified frequency range, it is necessary to ensure that the product of the maximum value of the droop coefficient of the energy storage unit and the difference between the output power and the rated power is less than the maximum frequency deviation allowed by the AC microgrid system to meet the requirements of equation (65).

[0198]

[0199] (5): Comparison of three types of droop control simulation:

[0200] a: Load power distribution:

[0201] Next, the present invention will conduct a comparative analysis on three different droop control strategies, namely: "conventional droop control (CDC)", "power function droop control (PFDC)" and "power exponential function droop control (PEFDC)". Fig.13 Part a of the middle figure shows the comparison of the active power output of the energy storage unit (ESU). The blue, green and red lines in the figure correspond to the active power output of the ESU after applying the traditional droop control (CDC), power function droop control (PFDC) and power exponential function droop control (PEFDC), respectively. Analysis of the graphical data shows that although the traditional droop control strategy can achieve uniform distribution of active power, it cannot adjust the output active power of the distributed energy storage unit according to its actual state of charge. By observing the red and blue lines, it can be found that both the power function droop control (PFDC) and the power exponential function droop control (PEFDC) can adjust the active power output of the energy storage unit (ESU) according to the state of charge (SOC). However, the newly proposed power exponential function droop control strategy is more significant in the regulation effect. Fig.13Part b of the middle figure depicts the difference in the state of charge (SOC) of the two energy storage units. It can be clearly seen from the direction of the green line in the figure that it is difficult to achieve balance in the SOC between the energy storage units when the traditional droop control strategy is adopted. In addition, as the SOC of the energy storage units decreases, the SOC difference between them tends to increase. Through the analysis of the red lines, it can be observed that the power function droop control (PEDC) strategy helps to reduce the difference in the state of charge (SOC) between the energy storage units, but its balancing process is relatively slow. In contrast, the power exponential function droop control (PEFDC) strategy not only achieves rapid balance of SOC, but also maintains a high control efficiency when the SOC difference is small.

[0202] b: Comparison of simulation results:

[0203] Table 2 shows the comparison results of three droop control methods applied in AC microgrids. It can be clearly seen from the comparison that the power index-based droop control method proposed in the present invention outperforms other existing methods in performance. In addition, this method is also applicable to AC microgrid systems containing multiple energy storage units.

[0204] Table 2 Comparison of three droop control methods

[0205]

[0206] (6): Comparison of three types of droop control simulation:

[0207] Fig.14 The change of ESU output power and the fluctuation of system frequency when m=3, p=5 and n factor take different values. Fig.14 Part a of the middle figure and Fig.13 As can be seen in part b of the middle figure, the n factor has little effect on the active power output of the energy storage, but mainly affects the frequency of the system. The larger n is, the smaller the frequency fluctuation of the system. Therefore, the frequency of the system can be stabilized by adjusting the value of n.

Claims

1. The droop control strategy of multi-energy storage microgrid based on energy storage charge state is characterized by The following steps are involved: Step 1: By establishing a power function relationship between the state of charge (SOC) of the energy storage unit and the droop coefficient; Step 2: Dynamically adjust the droop coefficient correction factor. While maintaining the stability of the bus voltage and frequency, each energy storage unit dynamically adjusts its active power output according to its SOC level to achieve balanced distribution of SOC.

2. According to claim 1, the multi-energy storage microgrid droop control strategy based on energy storage charge state is characterized in that : In step 1, the droop control expression is: In formula (3), f aci is the actual frequency of the i-th microgrid, is the reference value of the system frequency, k p is the droop control coefficient, The state of charge of the i-th energy storage after correction by the droop coefficient correction factor m; The exponential expression of the charge state of the i-th energy storage after correction by the droop coefficient correction factor n; is the rated active power of the converter, p acli The active power output by the i-th converter after low-pass filtering; m, n and p represent the parameters for correcting the droop coefficient.

3. The multi-energy storage microgrid droop control strategy based on energy storage charge state according to claim 1 is characterized in that: In step 2, the energy storage unit with a higher state of charge will output more active power, while the energy storage unit with a lower state of charge will reduce its output, thereby achieving a balanced distribution of SOC.

4. The multi-energy storage microgrid droop control strategy based on energy storage charge state according to claim 1 is characterized in that: The method further includes step 3, which is an improved droop control based on energy storage SOC, specifically including: The energy storage unit is connected to the AC bus through a converter, and each converter is equipped with a local control unit; The output end of the converter uses an LC filter to ensure the quality of the output signal, and the load is directly connected to the AC bus; The coupling effect is decoupled before the controller is applied. To achieve the approximate decoupling of the converter output power, a virtual impedance is introduced to distribute the reactive power load before the voltage reference is determined. This design strategy can achieve the decoupling effect by reconfiguring the equivalent output impedance while keeping the control parameters unchanged. In formula (4), R f is the resistance reference value, s is the Laplace transform coefficient, L f is the inductance reference value, i d (s), i q (s) represent the reference values ​​of the three-phase current on the d-axis and q-axis after dq transformation, u 0d (s),u 0q (s) is the component of voltage in rotating coordinates; ω is the angular frequency, u sd (s),u sq (s) is the component of the voltage after Laplace transformation in the rotating coordinates.

5. The multi-energy storage microgrid droop control strategy based on energy storage charge state according to claim 1 is characterized by: It also includes step 4, using the small signal perturbation method to perform stability analysis on the microgrid system containing multiple energy storages: It can be seen from formula (3) that when two energy storage units are discharged in parallel, their active power output is closely related to their respective energy storage state of charge (SOC); specifically, the ratio of the active power output deviation of the two energy storage units can be expressed as: In formula (9), is the rated active power of the converter, p acl1 The active power output of the first low-pass filtered converter, p acl2 The active power output of the second converter after low-pass filtering, The state of charge of the first energy storage after correction by the droop coefficient correction factor m; The state of charge of the second energy storage after correction by the droop coefficient correction factor m; The exponential expression of the state of charge of the first energy storage after correction by the droop coefficient correction factor n; The exponential expression of the state of charge of the second energy storage after correction by the droop coefficient correction factor n; According to formula (3), the active power output of the energy storage unit is directly proportional to its state of charge (SOC); in addition, the system balancing rate and frequency fluctuation are regulated by factors m, n and p; From formula (3), the improved droop expression of two parallel operation energy storage can be obtained as follows: Where: f ac1 is the actual frequency of the first microgrid, f ac2 is the actual frequency of the second microgrid; p acl1 and p acl2 is the active power output by the converter after low-pass filtering; System frequency reference value; is the rated active power of the converter; k p , k q is the droop control coefficient; f ac1 and f ac2 Perform a small signal perturbation: In the above formula, '^' represents the small signal disturbance, m, n and p are the droop coefficient correction factors; is the actual frequency small signal disturbance of the first microgrid, is the actual frequency small signal disturbance of the second microgrid, The state of charge of the first energy storage after correction by the droop coefficient correction factor m-1; The state of charge of the second energy storage after correction by the droop coefficient correction factor m-1; The state of charge of the first energy storage after correction by the droop coefficient correction factor n-1; The state of charge of the second energy storage after correction by the droop coefficient correction factor n-1; k p Droop control coefficient; It is the exponential expression of the charge state of the first energy storage after being corrected by the droop coefficient correction factor n; It is the exponential expression of the state of charge of the second energy storage after being corrected by the droop coefficient correction factor n; is the small signal disturbance of the state of charge of energy storage 1, To perform a small signal disturbance on the charge state of energy storage 2, is the small signal disturbance of active power output by the first converter after low-pass filtering, is the small signal disturbance of active power output by the second converter after low-pass filtering; The small signal perturbation method is used here to evaluate the stability of the system and determine the appropriate value range of the parameters m, n and p; by adjusting the values ​​of these parameters, the stability of the system frequency can be effectively controlled and the equalization process of the state of charge SOC of the energy storage unit can be promoted.

Images