A light-storage cooperative control strategy for alternating current micro-grid system

Abstract

The application discloses an AC micro-grid system-oriented photovoltaic storage cooperative control strategy and belongs to the technical field of photovoltaic storage cooperative control, which comprises the following steps: step 1, model analysis; step 2, establishment of an energy storage system based on fuzzy adaptive control; step 3, construction of a photovoltaic maximum power point tracking (MPPT) algorithm based on SMC; and step 4, setting of a photovoltaic system adaptive sliding mode control strategy with a frequency support function. On the basis of a conventional VSG control strategy, a fuzzy adaptive VSG control strategy is designed, so that the inertia constant can be adaptively adjusted according to the real-time state of the system, thereby improving the dynamic performance of the system. This strategy can not only effectively deal with the problem of frequency fluctuation in the micro-grid, but also adjust the inertia according to the actual operation condition, so that the system is more stable under different loads and disturbance conditions.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of light storage cooperative control, and particularly relates to a light storage cooperative control strategy for an alternating current micro-grid system. BACKGROUND

[0002] In recent years, in order to achieve the strategic goal of "carbon peak and carbon neutral", more and more photovoltaic power sources are connected to an alternating current micro-grid (AC MG) through inverters. However, the inverter interface usually lacks the inertia of traditional generator sets, which makes the overall inertia of the micro-grid significantly reduced after a large number of photovoltaic systems are connected, resulting in a decrease in anti-interference ability and further affecting the frequency stability of the system. In addition, photovoltaic power generation has strong uncertainty and difficulty in prediction, especially in a micro-grid with a high proportion of photovoltaic penetration, the uncertainty is more prominent, which makes the micro-grid more susceptible to disturbances and the frequency stability faces higher challenges.

[0003] To solve this problem, new energy is usually connected to the micro-grid through inverters, and the control mode of the inverter mainly includes droop control and virtual synchronous machine (VSG) control. Droop control is a common and simple control strategy, which sets a droop controller on the inverter or distributed generator (DG) of the micro-grid, and when the load of the power grid changes, the controller adjusts the output power of each power generation unit to maintain the stability of the system. Although droop control is widely used, it lacks sufficient inertia support and may not be suitable for more complex frequency control requirements.

[0004] In contrast, the VSG control strategy can simulate the dynamic behavior of a synchronous generator and give the distributed power source a certain "inertia", so that it can provide similar inertia support to the traditional power system when the micro-grid is disturbed. This way can better ensure the stability of the frequency in a micro-grid with a high proportion of new energy penetration, so the VSG control strategy has certain advantages in the current environment and helps to improve the dynamic response ability and anti-interference ability of the micro-grid. SUMMARY

[0005] The purpose of the present application is to overcome the shortcomings of the prior art and provide a light storage cooperative control strategy for an alternating current micro-grid system, which solves the problems in the background art.

[0006] The purpose of the present application is achieved by a light storage cooperative control strategy for an alternating current micro-grid system, which comprises the following steps:

[0007] Step 1, model analysis;

[0008] Step 2, establish an energy storage system based on fuzzy adaptive control;

[0009] Step 3, constructing SMC-based photovoltaic maximum power point tracking (MPPT) algorithm;

[0010] Step 4, setting up a photovoltaic system adaptive sliding mode control strategy with fusion frequency support function.

[0011] The step 1 includes analyzing the VSG model, studying the effect of the moment of inertia on the frequency stability, exploring the influence of the moment of inertia on the frequency stability, and responding to the system frequency fluctuation by adjusting the output power of each distributed generation unit (DG), so that when the microgrid is disturbed, the VSG control strategy can simulate the inertia effect by quickly adjusting the output power of the generation unit, and then realize the frequency stability; the VSG strategy includes the relationship between frequency and power, which adjusts the power generation by detecting the frequency change, provides necessary inertia support when the system frequency deviates from the set value, reduces the frequency fluctuation, and helps maintain the stable operation of the system, and the VSG control strategy is,

[0012] P in -P-D·(w-w0)=J·w0·s·(w-w0)

[0013] In the formula, P in is the reference value of active power, P is the active power output of the grid support type inverter, D and J are the damping factor and inertia constant, w and w0 respectively represent the actual angular frequency and the nominal angular frequency of the system, and s is a complex variable;

[0014] P in =P ref -m w ·(w-w0)

[0015] In the formula, P ref represents the active output reference instruction of the microgrid layer, m w is the droop coefficient;

[0016] The coupling relationship between active power and frequency derived from the above two formulas is:

[0017] J·w0·s·(w-w0)=P ref -m w ·(w-w0)-P-D·(w-w0)

[0018] By adjusting the value of the inertia constant, the frequency response curve and the frequency change rate of the microgrid after being disturbed under different inertia constants are analyzed.

[0019] In the initial stage of frequency fluctuation, the moment of inertia is increased to improve the system inertia, reduce the frequency change rate and reduce the maximum frequency deviation; in the frequency recovery stage, the moment of inertia is reduced to reduce the oscillation amplitude near zero during the frequency recovery process, so as to accelerate the frequency to return to the nominal value; while ensuring that the system frequency fluctuation is small, it does not hinder the rapid recovery of the frequency, improves the stability of the micro-grid system, and optimizes the dynamic response performance.

[0020] The step 2 includes a control algorithm and a fuzzy controller, which dynamically adjusts the key parameter of the moment of inertia according to the real-time state of the frequency and the frequency change rate in the micro-grid, so as to ensure that the system reduces the maximum frequency deviation when disturbed and quickly recovers to the nominal frequency.

[0021] The step 3 includes

[0022] Step 3.1: Single / dual-stage photovoltaic power generation system selection;

[0023] Step 3.2: SMC-based photovoltaic MPPT algorithm;

[0024] The photovoltaic power generation system of the step 3.1 is divided into single-stage and dual-stage types according to whether a front-stage DC / DC converter is equipped;

[0025] The structure of the single-stage photovoltaic power generation system includes a photovoltaic array, a DC / AC grid-connected inverter, an inverter control system and an L-type filter; in the system, the control algorithm is used to generate a PWM signal input to the inverter, so as to convert the direct current generated by the photovoltaic array into alternating current, while adjusting the output power of the photovoltaic array; the L-type filter is used to filter high-frequency harmonics, reduce voltage fluctuations, ensure the stability of the voltage and suppress external interference signals;

[0026] The dual-stage photovoltaic power generation system is provided with a front-stage DC / DC converter, which is used to adjust the voltage and current output by the photovoltaic array, so as to ensure that the system can realize maximum power point tracking MPPT under different light conditions; by using the DC / DC converter, the dual-stage photovoltaic system can cope with light changes and provide more stable power output, so as to optimize the energy conversion efficiency and improve the overall system performance; through the dual-stage photovoltaic power generation system, the photovoltaic power generation unit can be ensured to interact with the alternating current micro-grid.

[0027] The MPPT algorithm of the step 3.2 is,

[0028]

[0029] In the formula, P pv and V pv respectively represent the output power and voltage of the photovoltaic array, I pv is the current of the photovoltaic array, dIpv / dV pv is the incremental conductance of the PV array;

[0030] To ensure the PV array can be MPPT by the sliding mode controller, the sliding surface S pv is set as,

[0031]

[0032] V pv,ref is defined as the output voltage reference value of the PV array operating at the MPP, which is sent to the voltage loop controller as the voltage reference command, and its derivative is Based on the positive and negative of the sliding surface S pv , the working interval of the PV array includes two parts;

[0033] (1) When dP pv / dV pv > 0, take point A as an example, at this time the output voltage of the PV array is V A , in order to make the PV array track to the MPP, the derivative of the output voltage is a positive value, so that the voltage of the PV array can reach V A from V mpp , in order to meet the convergence of the sliding mode control, the derivative of the sliding surface should satisfy

[0034] (2) When dP pv / dV pv < 0, take point B as an example, at this time the output voltage of the PV array is V B , in order to make the PV array track to the MPP, the derivative of the output voltage is a negative value, so that the voltage of the PV array can reach V B from V mpp , in order to meet the convergence of the sliding mode control, the derivative of the sliding surface should satisfy

[0035] Based on the above analysis, the derivative of V pv,ref is set as The reaching law of the sliding mode controller is set as,

[0036]

[0037] In order to ensure the convergence speed, the exponential reaching law is set, so that the sliding surface converges at an exponential rate,

[0038]

[0039] Where ε > 0, k > 0, sgn(·) represents the sign function, and the SMC-based MPPT algorithm is set as,

[0040] V pv,ref =∫(ε·sgn(S pv )+k·S pv )dt。

[0041] The step 4 includes adjusting the value of the sliding mode surface by a sliding mode surface adjustment term, for adjusting the output power of the photovoltaic array, thereby providing frequency support for the micro-grid system, the sliding mode surface includes,

[0042]

[0043] In the formula, ΔS is a proposed sliding mode surface adjustment term for adjusting the output power of the photovoltaic array, when the load in the micro-grid decreases to cause the frequency of the system to increase, the output power of the photovoltaic array decreases with the decrease of ΔS, in order to increase the inertia of the micro-grid system, the photovoltaic array considers df / dt when participating in the adjustment of the frequency, the sliding mode surface adjustment term includes,

[0044]

[0045] ΔS adj =K d df / dt+K p Δf

[0046] In the formula, ΔS adj is an adjustment term for adjusting ΔS, K d is an inertia control coefficient, K p is a droop coefficient, and Δf and df / dt respectively refer to the frequency deviation and the frequency change in the system.

[0047] The classical frequency response curve after the load disturbance in the micro-grid includes two stages according to the value difference;

[0048] Stage one (df / dt<0): when the load in the system increases, the frequency of the micro-grid system has a downward trend due to the imbalance of active power, the adaptive inertia coefficient is set, the inertia coefficient amplitude of the adjustment term is increased in the initial stage of the disturbance, thereby the photovoltaic can provide greater inertia to reduce the frequency deterioration speed, when the frequency deviation becomes larger and larger, the amplitude of the droop coefficient is increased to increase the output of the photovoltaic to prevent the continuous deterioration of the frequency;

[0049] Stage two (df / dt>0): in the frequency recovery stage, the amplitude of the inertia coefficient is reduced, thereby the frequency can be recovered to the nominal value as soon as possible, two fuzzy controllers are set to adjust the inertia coefficient and the droop coefficient.

[0050] The application has the beneficial effects that: based on the traditional VSG control strategy, the application designs a fuzzy adaptive VSG control strategy, so that the inertia constant can be adaptively adjusted according to the real-time state of the system, thereby improving the dynamic performance of the system. This strategy not only effectively deals with the problem of frequency fluctuation in the micro-grid, but also adjusts the inertia according to the actual operating conditions, so that the system is more stable under different loads and disturbance conditions.

[0051] In addition, the application also deeply analyzes the power generation characteristics of the photovoltaic array, and proposes a maximum power point tracking (MPPT) control strategy based on sliding mode control (SMC). This strategy does not rely on temperature sensors, irradiance sensors or maximum power point estimators, reducing the hardware requirements of the AC MG system and thus reducing the investment cost. Through this method, the system can efficiently track the maximum power point and optimize energy output.

[0052] On this basis, the application designs a fuzzy adaptive sliding mode control strategy, so that the photovoltaic system always maintains at the maximum power point during operation, while retaining a certain power reserve for supporting the frequency stability of the micro-grid. This control strategy not only automatically adjusts the inertia according to the system frequency change, but also enhances the support ability of the photovoltaic system to the low-inertia AC MG, thereby further improving the overall stability and disturbance rejection ability of the light-storage micro-grid. BRIEF DESCRIPTION OF DRAWINGS

[0053] Figure 1 is a traditional VSG control structure diagram;

[0054] Figure 2 is a frequency response curve diagram of the application under different inertia constants;

[0055] Figure 3 is a frequency change rate diagram of the micro-grid under different inertia constants of the application;

[0056] Figure 4 is a VSG control strategy diagram based on fuzzy adaptation of the application;

[0057] Figure 5 is a three-dimensional diagram of the inertia constant J of the application;

[0058] Figure 6 is a single-pole photovoltaic power generation system structure diagram of the application;

[0059] Figure 7 is a bipolar photovoltaic power generation system structure diagram of the application;

[0060] Figure 8 is a P-V characteristic curve diagram of the photovoltaic system of the application;

[0061] Figure 9is a photovoltaic array P-dP of the present application pv / dV pv graph;

[0062] Figure 10 is a frequency response graph of the AC MG of the present application;

[0063] Figure 11 is a three-dimensional graph of the inertia coefficient K d of the present application;

[0064] Figure 12 is a three-dimensional graph of the droop coefficient K p of the present application;

[0065] Figure 13 is a photovoltaic storage system control strategy simulation verification model graph of the present application;

[0066] Figure 14 is a frequency response graph of each control algorithm after a load disturbance occurs of the present application;

[0067] Figure 15 is a change trend graph of the moment of inertia under the VSG control strategy based on fuzzy self-adaption of the present application after a load disturbance occurs;

[0068] Figure 16 is a frequency change rate of the fixed moment of inertia and the self-adaptive moment of inertia after a load disturbance occurs of the present application;

[0069] Figure 17 is a simulation result graph of the micro-grid system after a load disturbance occurs under a low penetration rate of the present application: (a) output power of the energy storage; (b) output power of the photovoltaic; (c) frequency response of the micro-grid system;

[0070] Figure 18 is a simulation result graph of the micro-grid system after a load disturbance occurs under a high penetration rate of the present application: (a) output power of the energy storage; (b) output power of the photovoltaic; (c) frequency response of the micro-grid system. DETAILED DESCRIPTION

[0071] The present application will be further described below in conjunction with the accompanying drawings, which need to be pointed out that it is only for more clearly illustrating and explaining the present application.

[0072] As shown in Figures 1-18 , the embodiment discloses a photovoltaic storage collaborative control strategy for an alternating current micro-grid system, which comprises the following steps: step 1, model analysis; step 2, establishing an energy storage system based on fuzzy self-adaptive control; step 3, constructing a photovoltaic maximum power point tracking (MPPT) algorithm based on SMC; step 4, setting a photovoltaic system self-adaptive sliding mode control strategy with frequency support function fusion.

[0073] Step 1: Analyze the VSG model and study the effect of rotational inertia on frequency stability;

[0074] Step 2: A VSG method for energy storage systems based on fuzzy adaptive control is proposed;

[0075] Step 3: A photovoltaic maximum power point tracking (MPPT) algorithm based on SMC was developed;

[0076] Step 4: An adaptive sliding mode control strategy for a photovoltaic system integrating frequency support function was designed.

[0077] In step 1, the VSG model is analyzed to explore the effect of rotational inertia on frequency stability:

[0078] Traditional VSG control strategies are typically based on the simulation of rotational inertia, responding to system frequency fluctuations by adjusting the output power of each distributed generation unit (DG). When a microgrid (AC MG) is disturbed, the VSG control strategy simulates inertial effects by rapidly adjusting the output power of the generation units, thereby achieving frequency stability. Specifically, the VSG strategy usually involves the relationship between frequency and power, adjusting power generation by detecting frequency changes to provide necessary inertial support when the system frequency deviates from the set value, reducing frequency fluctuations and helping to maintain stable system operation. A traditional VSG control strategy is shown below:

[0079] P in -PD·(w-w0)=J·w0·s·(w-w0) (1)

[0080] In the formula, P in is the reference value of active power, P is the active power output of the grid-supported inverter, D and J are the damping factor and inertia constant, w and w0 represent the actual angular frequency and nominal angular frequency of the system, respectively, and s is a complex variable.

[0081] like Figure 1 It is a traditional VSG control structure with a fixed moment of inertia, which is not conducive to making the energy storage system's discharge flexibility more flexible.

[0082] P in =P ref -m w ·(w-w0) (2)

[0083] In the formula, P ref This represents the active power output reference command for the microgrid layer, m. w It is the droop coefficient.

[0084] From equations (1) and (2), the coupling relationship between active power and frequency can be derived as follows:

[0085] J·w0·s·(w-w0)=P ref -m w ·(w-w0)-P-D·(w-w0) (3)

[0086] Firstly, the VSG control model is established, and its control structure is given (see Figure 1 ). On this basis, by adjusting the value of the inertia constant, the frequency response curve and the frequency change rate of the micro-grid (AC MG) after being disturbed under different inertia constants are analyzed. Figure 2 and Figure 3 respectively show the frequency change and the frequency change rate of the micro-grid after being disturbed under different inertia constants. As can be seen from the figures, larger rotational inertia helps to slow down the rate of frequency fluctuation of the micro-grid and reduce the maximum frequency deviation of the system. However, larger rotational inertia will cause more severe changes in the frequency around the nominal value, resulting in larger oscillation amplitude and affecting the smooth recovery of the frequency to the nominal value.

[0087] Therefore, the ideal approach is to appropriately increase the rotational inertia at the initial stage of frequency fluctuation to improve the system inertia, reduce the frequency change rate and lower the maximum frequency deviation, and to appropriately reduce the rotational inertia at the frequency recovery stage to reduce the oscillation amplitude around zero during the frequency recovery process and accelerate the frequency to return to the nominal value. In this way, the stability of the micro-grid system can be improved while ensuring small frequency fluctuation and not hindering the rapid recovery of the frequency, thereby optimizing the dynamic response performance of the micro-grid system.

[0088] Step 2 includes the following steps:

[0089] Through analysis, the present application proposes a control algorithm aimed at dynamically adjusting the key parameter of rotational inertia according to the real-time state of the frequency and the frequency change rate in the micro-grid, so as to ensure that the system can effectively reduce the maximum frequency deviation and quickly recover to the nominal frequency when being disturbed. Compared with other control methods, fuzzy control has stronger robustness and is particularly suitable for the uncertainty and volatility caused by the high proportion of new energy access in the current micro-grid. Fuzzy control can handle complex nonlinear problems, and for systems that cannot be accurately modeled or described by traditional control methods, fuzzy control provides a more flexible and effective solution. Therefore, the present application selects the fuzzy control strategy to realize the adaptive adjustment of the rotational inertia.

[0090] The control structure of the designed fuzzy adaptive VSG control strategy is as shown in Figure 4where df / dt and Δf are the inputs of the fuzzy controller, which can be measured and calculated. J, as the output of the fuzzy controller, will be continuously adjusted according to the changes in the states of df / dt and Δf to ensure that the maximum frequency deviation is reduced while the speed of frequency recovery to the nominal value is accelerated. For this purpose, a fuzzy controller is designed as shown in Figure 5 As can be seen from Figure 5 When df / dt and Δf are of the same sign, i.e. in the deterioration stage of frequency, J has a larger amplitude, and when df / dt and Δf are of opposite signs, i.e. in the recovery stage of frequency, the value of J is correspondingly reduced, and the proposed control strategy meets the requirements.

[0091] Step 3 includes the following steps:

[0092] Step 3.1: Single / dual-stage photovoltaic power generation system selection

[0093] Photovoltaic power generation systems can be divided into single-stage and dual-stage types according to whether they are equipped with a front-stage DC / DC converter. Figure 6 The structure of a single-stage photovoltaic power generation system is shown, which mainly consists of a photovoltaic array, a DC / AC grid-connected inverter, an inverter control system, and an L-type filter. In this system, the control algorithm is designed to generate PWM signals that are input to the inverter, thereby converting the DC power generated by the photovoltaic array into AC power while adjusting the output power of the photovoltaic array. The L-type filter serves to filter high-frequency harmonics, reduce voltage fluctuations, ensure voltage stability, and effectively suppress external interference signals.

[0094] Compared with dual-stage or multi-stage photovoltaic power generation systems, single-stage photovoltaic power generation systems do not have a front-stage DC / DC converter, resulting in a more concise system structure and reducing overall complexity, thereby lowering manufacturing and maintenance costs. In addition, by removing the front-stage DC / DC converter, the system can effectively reduce energy loss during energy conversion, helping to improve overall efficiency. However, the performance of single-stage photovoltaic systems may be affected by factors such as shadow shading or uneven illumination, which can result in less optimal maximum power point tracking (MPPT) performance compared to other types of systems, especially under complex lighting conditions.

[0095] Figure 7A structural diagram of a bipolar photovoltaic (PV) power generation system is shown. Compared to a monopolar PV system, the bipolar system adds a front-end DC / DC converter. While adding a DC / DC converter requires more electrical components, undoubtedly increasing the initial investment cost and potentially causing additional energy losses during energy conversion, its advantages are also significant. The front-end DC / DC converter can regulate the voltage and current output of the PV array, ensuring effective maximum power point tracking (MPPT) under different illumination conditions. By using the DC / DC converter, the bipolar PV system can better cope with changes in illumination, providing a more stable power output, thereby optimizing energy conversion efficiency and improving overall system performance. Therefore, this invention selects a bipolar PV system to ensure efficient power interaction between the PV power generation units and the AC microgrid (AC MG).

[0096] Step 3.2: SMC-based photovoltaic MPPT algorithm

[0097] based on Figure 8 The photovoltaic array characteristics shown in the figure indicate that the photovoltaic array can operate in MPP when it satisfies equation (4).

[0098]

[0099] In the formula, P pv and V pv These represent the output power and voltage of the photovoltaic array, respectively. pv This is the current in the photovoltaic array. dI pv / dV pv It is the incremental conductivity of the photovoltaic array.

[0100] To ensure that the photovoltaic array can perform MPPT using a sliding mode controller, the sliding surface S... pv Designed as:

[0101]

[0102] V pv,ref Defined as the output voltage reference value of the photovoltaic array operating in MPP mode, this reference value is sent to the voltage loop controller as a voltage reference command, and its derivative is...

[0103] Depend on Figure 8 It can be seen that, based on the sliding surface S pv The sign of the PV array can divide its working region into two parts. Therefore, the design of the MPPT algorithm based on SMC in this invention needs to consider the following two cases:

[0104] 1) When dP pv / dV pv When >0, withFigure 8 Taking point A in the diagram as an example, the output voltage of the photovoltaic array at this time is V. A In order for the photovoltaic array to track the MPP, the derivative of the output voltage should be a positive value, so that the voltage of the photovoltaic array can be converted from V... A Reaching V mpp In addition, to satisfy the convergence of sliding mode control, the derivative of the sliding surface should satisfy the following:

[0105] 2) When dP pv / dV pv When <0, Figure 8 Taking point B as an example, the output voltage of the photovoltaic array is V at this time. B In order for the photovoltaic array to track the MPP, the derivative of the output voltage should be negative, so that the voltage of the photovoltaic array can be reduced from V. B Reaching V mpp Furthermore, to satisfy the convergence of sliding mode control, the derivative of the sliding surface should satisfy the following condition.

[0106] Based on the above analysis, V pv,ref The derivative is designed as Therefore, the reaching law of the sliding mode controller can be designed as follows:

[0107]

[0108] To ensure a fast convergence rate, an exponential reaching law as shown below is designed to make the sliding surface converge at an exponential rate:

[0109]

[0110] In the formula, ε>0, k>0, and sgn(·) represents the sign function. Therefore, the MPPT algorithm based on SMC can be designed as follows:

[0111] V pv,ref =∫(ε·sgn(S) pv )+k·S pv )dt (8)

[0112] Step 4 includes the following steps:

[0113] The coupling relationship between the active power output of the photovoltaic array and the sliding mode surface is as follows: Figure 9 As shown, when dP pv / dV pv When dP = 0, the photovoltaic array operates in MPP state. pv / dV pvWhen ≠0, the photovoltaic array operates in a state less than MPP, at this time there is a certain power reserve, which can participate in the bidirectional frequency support of the AC MG system to increase the stability of the system. When the photovoltaic array operates in a state less than MPP, the photovoltaic array according to the value of dP pv / dV pv can be divided into two cases of dP pv / dV pv <0 and dP pv / dV pv >0, and it can be known from Figure 9 that when dP pv / dV pv <0, the output power of the photovoltaic array changes slowly, which is more conducive to the design of the controller and the stable operation of the system. Therefore, the photovoltaic array is operated in the case of dP pv / dV pv <0 in the application, and a certain power reserve is reserved to provide frequency response services for the micro-grid system. A sliding mode surface adjustment term is designed to adjust the value of the sliding mode surface, so as to adjust the output power of the photovoltaic array, and then provide frequency support for the AC MG system. The improved sliding mode surface is designed as:

[0114]

[0115] In the formula, ΔS is the proposed sliding mode surface adjustment term, which is used to adjust the output power of the photovoltaic array.

[0116] ΔS nom is defined as the nominal value of the sliding mode surface adjustment term, that is, ΔS=ΔS nom under the normal operation of the micro-grid system, and a certain power reserve is reserved for the frequency regulation of the AC MG. As Figure 9 shown, ΔS nom =ΔS A , when the load of the micro-grid is reduced to cause the frequency of the system to increase, the output power of the photovoltaic array can be reduced with the decrease of ΔS. Similarly, when the load of the micro-grid is increased to cause the frequency of the system to decrease, the output power of the photovoltaic array is increased with the increase of ΔS. In order to increase the inertia of the micro-grid system, when the photovoltaic array participates in the frequency regulation, the sliding mode surface adjustment term can be designed as:

[0117]

[0118] ΔS adj =K d df / dt+K p Δf (11)

[0119] In the formula, ΔS adj is an adjustment term for adjusting ΔS, Kd is the inertia control coefficient, K p is the droop coefficient, Δf and df / dt represent the frequency deviation and the frequency variation in the system, respectively.

[0120] Figure 10 is the classical frequency response curve of the AC MG after the load increase disturbance. According to the value of df / dt, the frequency response can be divided into two stages.

[0121] Stage one (df / dt < 0): When the load in the system increases, the frequency of the AC MG system will have a downward trend due to the imbalance of active power. In the current AC MG, due to the high proportion of new energy contained, the microgrid will have a large frequency drop in the initial stage of frequency change. Therefore, the application designs an adaptive inertia coefficient, which increases the amplitude of the inertia coefficient of the regulation term in the initial stage of disturbance, so that the photovoltaic can provide larger inertia to reduce the frequency deterioration speed. When the frequency deviation becomes larger and larger, the amplitude of the droop coefficient is increased, and the output of the photovoltaic is increased to prevent the continuous deterioration of the frequency.

[0122] Stage two (df / dt > 0): In the recovery stage of the frequency, the application reduces the amplitude of the inertia coefficient, so that the frequency can be restored to the nominal value as soon as possible.

[0123] According to the above analysis, the application designs two fuzzy controllers to adjust the inertia coefficient and the droop coefficient. As shown in Figure 11 and Figure 12 , Δf and df / dt are the inputs of the fuzzy controller, and the values of K d and K p will be dynamically adjusted according to whether the frequency response is in different stages, so as to better provide fast frequency response service.

[0124] In order to verify the effectiveness of the control strategy, an AC MG system of 4.16kV / 60Hz IEEE13 bus is established in MATLAB / Simulink, as shown in Figure 13 , which includes 13 buses, one energy storage system, two photovoltaic power generation units and 10 loads. In order to better verify the effectiveness of the control strategy, the microgrid system is divided into high penetration and low penetration according to the penetration rate of photovoltaic. In the case of low penetration, the capacity of the energy storage is 6MW, the capacity of PV1 and PV2 is 0.8MW, the maximum load is 4.6MW, and the penetration rate of PV is 35%; in the case of high penetration, the capacity of the energy storage is 3MW, the capacity of PV1 and PV2 is 0.8MW, the maximum load is 2.3MW, and the penetration rate of PV is 70%.

[0125] Verification of fuzzy adaptive VSG control algorithm for energy storage system, Figure 14 , Figure 15 and Figure 16 are simulation results of microgrid after load disturbance. When the load increases, the active power balance of the system is broken, and the frequency drops. In the initial stage of the drop, if the inertia constant is not considered, i.e. the droop control method is used, the system has a large frequency change rate, resulting in a large drop in frequency. When the VSG control strategy is used, the frequency change rate can be effectively reduced, thereby reducing the maximum deviation of the frequency. Compared with the fixed parameter VSG control strategy, the fuzzy adaptive VSG control strategy designed in this paper can reduce the frequency change rate in the initial stage by increasing the value of the rotational inertia, further reducing the maximum deviation of the frequency.

[0126] As shown in Figure 15 and Figure 16 , the proposed control strategy reduces the oscillation amplitude of the frequency change rate around the nominal value by adaptively adjusting the value of the inertia constant, thereby reducing the fluctuation of the frequency around the nominal value and enhancing the dynamic performance of the AC MG.

[0127] As shown in Figure 15 and Figure 16 , the proposed control strategy reduces the oscillation amplitude of the frequency change rate around the nominal value by adaptively adjusting the value of the inertia constant, thereby reducing the fluctuation of the frequency around the nominal value and enhancing the dynamic performance of the AC MG.

[0128] Verification of frequency support algorithm based on fuzzy adaptive SMC for photovoltaic system To verify the effectiveness of the proposed control algorithm in the photovoltaic system under load fluctuation, the load of the AC MG test system is increased at t = 3s and decreased at t = 6s. In addition, simulation tests are conducted under high and low photovoltaic penetration rates to verify the universality of the proposed control algorithm. Figure 17 and Figure 18 are simulation results under low and high penetration rate cases respectively. The maximum frequency deviation during load fluctuation under low and high penetration rate cases is recorded in Table 1.

[0129] As shown in Figure 17 and Figure 18 , at t = 3s, the load increases, the energy storage increases the power, and the photovoltaic system operating at MPP has no power to increase, so the frequency deviation of the microgrid is the largest. When the control strategy proposed in this paper is used, a certain power reserve can be left to participate in the frequency regulation of the system, providing inertia support for the microgrid system, so the maximum deviation of the frequency after the system is disturbed is the smallest.

[0130] As shown in Table 1, compared with the photovoltaic system running in the MPPT mode under the condition of low photovoltaic penetration, the frequency support algorithm based on the fuzzy adaptive sliding mode control reduces the maximum frequency deviation by 20% and 30% in low-frequency events and high-frequency events, respectively, and under the condition of high photovoltaic penetration, the control strategy reduces the maximum frequency deviation by 31% in low-frequency events and by 42% in high-frequency events compared with the photovoltaic system running in the MPPT mode. It can be seen that the control algorithm is more suitable for the micro-grid system with high proportion of photovoltaic power supply, and can better adapt to the AC MG with high photovoltaic penetration in the future.

[0131] Maximum power deviation of micro-grid under the disturbance of high and low photovoltaic penetration

[0132]

[0133]

[0134] On the basis of the traditional VSG control strategy, the application designs a fuzzy adaptive VSG control strategy, so that the inertia constant can be adaptively adjusted according to the real-time state of the system, thereby improving the dynamic performance of the system. This strategy not only effectively deals with the problem of frequency fluctuation in the micro-grid, but also adjusts the inertia according to the actual operating conditions, so that the system is more stable under different loads and disturbance conditions. On this basis, the application designs a fuzzy adaptive sliding mode control strategy, so that the photovoltaic system always runs at the maximum power point while retaining a certain power reserve for supporting the frequency stability of the micro-grid. This control strategy not only automatically adjusts the inertia according to the system frequency change, but also enhances the support ability of the photovoltaic system to the low-inertia AC MG, thereby further improving the overall stability and anti-disturbance ability of the light-storage micro-grid. In addition, the application also analyzes the power generation characteristics of the photovoltaic array and proposes a maximum power point tracking (MPPT) control strategy based on sliding mode control (SMC). This strategy does not rely on temperature sensors, irradiance sensors or maximum power point estimators, reducing the hardware requirements of the AC MG system and thereby reducing the investment cost. Through this method, the system can efficiently track the maximum power point and optimize energy output.

[0135] The above is only a preferred specific embodiment of the application, but the protection scope of the application is not limited thereto, and any person skilled in the art can make equivalent replacements or changes to the technical solution and concept of the application within the technical scope disclosed by the application, which should be covered within the protection scope of the application.

Claims

1. A photovoltaic storage cooperative control strategy for an alternating current microgrid system, characterized in that, The method comprises the following steps: Step 1, VSG model analysis is performed; Step 2, a fuzzy adaptive control-based energy storage system is established; Step 3, a SMC-based photovoltaic maximum power point tracking (MPPT) algorithm is constructed; Step 4, a photovoltaic system adaptive sliding mode control strategy with fusion frequency support function is set; The step 2 comprises a control algorithm and a fuzzy controller, and the key parameter of rotational inertia is dynamically adjusted according to the real-time state of the frequency and the frequency change rate in the microgrid, so that the maximum frequency deviation is reduced and the nominal frequency is quickly restored when the system is disturbed; The step 3 comprises: Step 3.1: single / dual-stage photovoltaic power generation system selection; Step 3.2: SMC-based photovoltaic MPPT algorithm; The photovoltaic power generation system of the step 3.1 is divided into single-stage and dual-stage types according to whether a front-stage DC / DC converter is equipped; The structure of the single-stage photovoltaic power generation system comprises a photovoltaic array, a DC / AC grid-connected inverter, an inverter control system and an L-type filter; in the system, the control algorithm is used to generate a PWM signal input to the inverter, so that the direct current generated by the photovoltaic array is converted into alternating current, and the output power of the photovoltaic array is adjusted; the L-type filter is used to filter high-frequency harmonics, reduce voltage fluctuation, ensure the stability of the voltage and suppress external interference signals; The dual-stage photovoltaic power generation system is provided with a front-stage DC / DC converter, which is used to adjust the voltage and current output by the photovoltaic array, so that the system can realize maximum power point tracking (MPPT) under different illumination conditions; by using the DC / DC converter, the dual-stage photovoltaic system can cope with illumination changes, provide more stable power output, thereby optimizing energy conversion efficiency and improving overall system performance; the dual-stage photovoltaic power generation system is used to ensure that the photovoltaic power generation unit can interact with the alternating current microgrid in power. The step 4 comprises a sliding mode surface adjustment term for adjusting the value of the sliding mode surface, which is used to adjust the output power of the photovoltaic array and provide frequency support for the microgrid system; the sliding mode surface comprises, In the formula, ΔS is the proposed sliding mode surface adjustment term, which is used to adjust the output power of the photovoltaic array; when the load in the microgrid is reduced to cause the frequency of the system to increase, the output power of the photovoltaic array is reduced with the decrease of ΔS; in order to increase the inertia of the microgrid system, the photovoltaic array considers df / dt when participating in frequency regulation; the sliding mode surface adjustment term comprises, ΔS adj = K d df / dt + K p Δf where ΔS adj is an adjustment term for adjusting ΔS, K d is an inertia control coefficient, K p is a droop coefficient, and Δf and df / dt respectively indicate a frequency deviation and a frequency change amount in the system.

2. The photovoltaic storage cooperative control strategy for AC micro-grid system according to claim 1, characterized in that: The step 1 comprises analyzing the VSG model; the output power of each distributed power generation unit (DG) is adjusted to respond to system frequency fluctuations; when the microgrid is disturbed, the VSG control strategy adjusts the output power of the power generation unit to simulate the inertia effect, thereby realizing frequency stability; the VSG strategy comprises the relationship between frequency and power; the power generation capacity is adjusted by detecting the frequency change; when the system frequency deviates from the set value, the necessary inertia support is provided to reduce the frequency fluctuation and help maintain the stable operation of the system; the VSG control strategy is P in P - D - (w - w0) = J - w0- s - (w - w0) where P in is a reference value for active power, P is the active power output of the grid-supporting inverter, D and J are damping factor and inertia constant, w and w0 represent the actual and nominal angular frequency of the system, respectively, and s is a complex variable; P in = P ref - m w · (w - w0) In the formula, P ref represents the active power output reference instruction of the microgrid layer, m w is the droop coefficient; The coupling relationship between active power and frequency derived from the above two formulas is: J • w0 • s • (w - w0) = P ref - m w • (w - w0) - P • D • (w - w0) By adjusting the value of inertia constant, the frequency response curve and the frequency change rate of micro-grid under different inertia constants are analyzed.

3. The photovoltaic storage cooperative control strategy for AC microgrid system according to claim 2, characterized in that: In the initial stage of frequency fluctuation, the moment of inertia is increased to improve the system inertia, reduce the frequency change rate and the maximum frequency deviation; in the frequency recovery stage, the moment of inertia is reduced to reduce the oscillation amplitude near zero in the frequency recovery process, so as to accelerate the frequency to return to the nominal value; while ensuring small frequency fluctuation of the system, the frequency recovery is not hindered, the stability of the micro-grid system is improved, and the dynamic response performance is optimized.

4. The photovoltaic storage cooperative control strategy for AC microgrid system according to claim 1, characterized in that: The MPPT algorithm of step 3.2 is, where P pv and V pv represent the output power and voltage of the photovoltaic array, respectively, I pv is the current of the photovoltaic array, dI pv / dV pv is the incremental conductance of the photovoltaic array; To ensure that the photovoltaic array can use the sliding mode controller for MPPT, the sliding surface S pv is set to, V pv,ref The reference value of output voltage of the photovoltaic array operating in the MPP state is defined as V Based on the positive and negative of the sliding surface S pv The working interval of the PV array includes two parts; (1) when dP pv / dV pv > 0, take point A as an example, at this time the output voltage of the photovoltaic array is V A , in order to make the photovoltaic array be able to track to the MPP, the derivative of the output voltage is a positive value, so that the voltage of the photovoltaic array can reach V A from V mpp , in order to meet the convergence of the sliding mode control, the derivative of the sliding surface should meet (2) When dP pv / dV pv < 0, for example, point B, at this time the output voltage of the photovoltaic array is V B , in order to make the photovoltaic array be able to track to the MPP, the derivative of the output voltage is a negative value, so that the voltage of the photovoltaic array can reach V B from V mpp , in order to meet the convergence of the sliding mode control, the derivative of the sliding surface should satisfy Based on the above analysis, the derivative of V pv,ref is set to The reaching law of the sliding mode controller is set to, In order to ensure the convergence speed, the exponential approach law is set, so that the sliding mode surface converges at an exponential rate, Wherein, ε>0, k>0, sgn(·) represents the sign function, the SMC-based MPPT algorithm is set as, V pv,ref = ∫(ε · sgn(S pv )+ k · S pv )dt.

5. The photovoltaic storage coordinated control strategy for AC microgrid system according to claim 1, characterized in that: The classical frequency response curve of the micro-grid after the load increase disturbance includes two stages according to different values. Stage one (df / dt<0): when the load in the system increases, the frequency of the micro-grid system decreases due to the imbalance of active power, the adaptive inertia coefficient is set, in the initial stage of disturbance, the inertia coefficient of the adjustment term is increased, so that the photovoltaic can provide larger inertia, and the frequency deterioration speed is reduced, when the frequency deviation is larger and larger, the amplitude of droop coefficient is increased, the output of photovoltaic is increased, and the continuous deterioration of frequency is prevented; Stage two (df / dt>0): in the frequency recovery stage, the amplitude of the inertia coefficient is reduced, so that the frequency can recover to the nominal value as soon as possible, two fuzzy controllers are set to adjust the inertia coefficient and the droop coefficient.

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