Optical storage cooperative control strategy for alternating current micro-grid system

Abstract

The invention, which belongs to the technical field of optical storage cooperative control, discloses an optical storage cooperative control strategy for an AC micro-grid system, comprising the following steps: step 1, carrying out model analysis; step 2, establishing an energy storage system based on fuzzy adaptive control; step 3, constructing a photovoltaic maximum power point tracking (MPPT) algorithm based on the SMC; and step 4, setting a photovoltaic system adaptive sliding mode control strategy fused with the frequency support function. On the basis of a traditional 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, and the dynamic performance of the system is improved. The strategy not only can effectively deal with the problem of frequency fluctuation in the micro-grid, but also can adjust inertia according to actual operation conditions, so that the system is more stable under different load and disturbance conditions.

Description

Technical Field

[0001] The present invention belongs to the technical field of photovoltaic-storage collaborative control, and specifically relates to a photovoltaic-storage collaborative control strategy for an AC microgrid system. Background Art

[0002] In recent years, in order to achieve the strategic goal of "carbon peak and carbon neutrality", more and more photovoltaic power sources are connected to AC microgrids (AC MG) through inverters. However, the inverter interface usually lacks the inertia of traditional generator sets, which makes the overall inertia of the microgrid significantly reduced after a large number of photovoltaic systems are connected, resulting in a decrease in anti-interference ability, which in turn affects the frequency stability of the system. In addition, photovoltaic power generation has strong uncertainty and unpredictability, especially in microgrids with a high proportion of photovoltaic penetration. This uncertainty is more prominent, making the microgrid more susceptible to disturbances and facing higher challenges in frequency stability.

[0003] To address this problem, renewable energy is usually connected to the microgrid through an inverter, and the inverter control methods mainly include droop control and virtual synchronous machine (VSG) control. Droop control is a common and simple control strategy. It sets a droop controller on the inverter or distributed generator (DG) of the microgrid. When the grid load 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 inertial support and may not be suitable for more complex frequency control requirements.

[0004] In contrast, the VSG control strategy can give distributed power sources a certain "inertia" by simulating the dynamic behavior of synchronous generators, so that they can provide inertial support similar to that required by traditional power systems when the microgrid is disturbed. This method can better ensure frequency stability in microgrids with a high proportion of new energy penetration. Therefore, in the current environment, the VSG control strategy has certain advantages and helps to improve the dynamic response and anti-interference capabilities of microgrids. Summary of the invention

[0005] The purpose of the present invention is to overcome the deficiencies of the prior art and to provide a photovoltaic-storage collaborative control strategy for an AC microgrid system, thereby solving the problems in the above-mentioned background technology.

[0006] The object of the present invention is achieved by: a photovoltaic storage coordinated control strategy for an AC microgrid system, comprising the following steps:

[0007] Step 1: Conduct model analysis;

[0008] Step 2: Establish an energy storage system based on fuzzy adaptive control;

[0009] Step 3: Construct a photovoltaic maximum power point tracking MPPT algorithm based on SMC;

[0010] Step 4: Set up an adaptive sliding mode control strategy for the photovoltaic system integrating the frequency support function.

[0011] The step 1 includes analyzing the VSG model, studying the effect of rotational inertia on frequency stability, exploring the influence of rotational inertia on frequency stability, and responding to system frequency fluctuations by adjusting the output power of each distributed generation unit DG. When the microgrid is disturbed, the VSG control strategy quickly adjusts the output power of the generation unit to simulate the inertia effect, thereby achieving frequency stability; the VSG strategy includes the relationship between frequency and power, and adjusts the power generation by detecting frequency changes. When the system frequency deviates from the set value, it provides necessary inertial support, reduces frequency fluctuations, and helps maintain the stable operation of the system. The VSG control strategy is,

[0012] P in -PD·(ww 0 )=J·w 0 ·s·(ww 0 )

[0013] Where 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 w 0 Respectively represent the actual angular frequency and nominal angular frequency of the system, s is a complex variable;

[0014] P in =P ref -m w ·(ww 0 )

[0015] Where 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 is derived from the above two equations:

[0017] J.W 0 ·s·(ww 0 )=P ref -m w ·(ww 0 )-PD·(ww 0 )

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

[0019] In the early stage of frequency fluctuation, the moment of inertia is increased to improve 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 the zero point during the frequency recovery process, which is used to accelerate the frequency back 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 microgrid system, and optimizes its 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 status of the frequency and the frequency change rate in the microgrid to ensure that the system reduces the maximum frequency deviation and quickly recovers to the nominal frequency when disturbed.

[0021] The step 3 includes

[0022] Step 3.1: Selection of single / double-stage photovoltaic power generation system;

[0023] Step 3.2: Photovoltaic MPPT algorithm based on SMC;

[0024] The photovoltaic power generation system in step 3.1 is divided into two types: unipolar type and bipolar type according to whether it is equipped with a front-stage DC / DC converter;

[0025] The structure of the monopole photovoltaic power generation system includes 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 used to generate a PWM signal, which is input into the inverter to convert the DC power generated by the photovoltaic array into AC power and adjust the output power of the photovoltaic array. The L-type filter is used to filter high-frequency harmonics, reduce voltage fluctuations, ensure voltage stability and suppress external interference signals.

[0026] The bipolar 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 to ensure that the system can achieve maximum power point tracking MPPT under different lighting conditions. By using a DC / DC converter, the bipolar photovoltaic system can cope with changes in light and provide a more stable power output, thereby optimizing energy conversion efficiency and improving overall system performance; the bipolar photovoltaic power generation system is used to ensure that the photovoltaic power generation unit can interact with the AC microgrid for power.

[0027] The MPPT algorithm of step 3.2 is:

[0028]

[0029] Where P pv and V pv 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] In order to ensure that the photovoltaic array can use the sliding mode controller to perform MPPT, the sliding mode surface S pv is set to,

[0031]

[0032] V pv,ref It is defined as the output voltage reference value of the photovoltaic array operating in the MPP state. This reference value will be sent to the voltage loop controller as a voltage reference instruction. Its derivative is Based on the sliding surface S pv The positive and negative properties of the PV array, the working range includes two parts;

[0033] (1) When dP pv / dV pv > 0, taking point A as an example, the output voltage of the photovoltaic array is V A In order to make the photovoltaic array track the MPP, the derivative of the output voltage is a positive value, so that the voltage of the photovoltaic array can be adjusted from V A Arrival V mpp In order to satisfy the convergence of sliding mode control, the derivative of the sliding surface should satisfy

[0034] (2) When dP pv / dV pv When <0, taking point B as an example, the output voltage of the photovoltaic array is V B , in order to enable the PV array to track the MPP, the derivative of the output voltage is a negative value, so that the voltage of the PV array can be increased from V B Arrival V mpp In order to satisfy the convergence of sliding mode control, the derivative of the sliding surface should satisfy

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

[0036]

[0037] In order to ensure the convergence speed, the exponential convergence law is set to make the sliding surface converge at an exponential rate.

[0038]

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

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

[0041] The step 4 includes adjusting the sliding surface value by a sliding surface adjustment item to adjust the output power of the photovoltaic array, thereby providing frequency support for the microgrid system. The sliding surface includes:

[0042]

[0043] Where ΔS is the proposed sliding surface adjustment term, which is used to adjust the output power of the photovoltaic array. When the load in the microgrid decreases and the frequency of the system increases, the output power of the photovoltaic array decreases as ΔS decreases. In order to increase the inertia of the microgrid system and make the photovoltaic array consider df / dt when participating in the frequency regulation, the sliding surface adjustment term includes:

[0044]

[0045] ΔS adj =K d df / dt+K p Δf

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

[0047] Classical frequency response curve after load increase disturbance in microgrid Depending on the value, the frequency response includes two stages;

[0048] Stage 1 (df / dt<0): When the load in the system increases, the frequency of the microgrid system tends to decrease due to the imbalance of active power. The adaptive inertia coefficient is set. In the initial stage of the disturbance, the amplitude of the inertia coefficient of the adjustment item is increased, so that the photovoltaic can provide greater inertia and 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 frequency from further deteriorating.

[0049] Phase 2 (df / dt>0): In the frequency recovery phase, the amplitude of the inertia coefficient is reduced so that the frequency can be restored to the nominal value as quickly as possible, and two fuzzy controllers are set to adjust the inertia coefficient and the droop coefficient.

[0050] Beneficial effects of the invention: Based on the traditional VSG control strategy, the invention designs a fuzzy adaptive VSG control strategy, which enables the inertia constant to 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 fluctuations in microgrids, but also adjust the inertia according to actual operating conditions, making the system more stable under different load and disturbance conditions.

[0051] In addition, the present invention also conducts an in-depth analysis of 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 investment costs. In this way, the system can efficiently track the maximum power point and optimize energy output.

[0052] On this basis, the present invention designs a fuzzy adaptive sliding mode control strategy to keep the photovoltaic system at the maximum power point during operation, while retaining a certain power reserve to support the frequency stability of the microgrid. This control strategy can not only automatically adjust the inertia according to the system frequency changes, but also enhance the photovoltaic system's support capacity for low-inertia AC MG, thereby further improving the overall stability and anti-disturbance capability of the photovoltaic storage microgrid. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

[0055] Figure 3 is a frequency change rate diagram of the microgrid under different inertia constants of the present invention;

[0056] Figure 4 It is the VSG control strategy diagram based on fuzzy self-adaptation of the present invention;

[0057] Figure 5 is a three-dimensional graph of the inertia constant J of the present invention;

[0058] Figure 6 It is a structural diagram of the monopole photovoltaic power generation system of the present invention;

[0059] Figure 7 It is a structural diagram of the bipolar photovoltaic power generation system of the present invention;

[0060] Figure 8 is a PV characteristic curve diagram of the photovoltaic system of the present invention;

[0061] Fig. 9The photovoltaic array P-dP of the present invention pv / dV pv Graphs;

[0062] Fig.10 is a frequency response curve diagram of the AC MG of the present invention;

[0063] Fig.11 is the inertia coefficient K of the present invention d 3D graph of

[0064] Fig.12 is the droop coefficient K of the present invention p 3D graph of

[0065] Fig.13 It is a simulation verification model diagram of the photovoltaic storage system control strategy of the present invention;

[0066] Fig.14 is a frequency response curve diagram of each control algorithm after a load disturbance occurs in the present invention;

[0067] Fig.15 It is a changing trend diagram of the moment of inertia under the VSG control strategy based on fuzzy self-adaptation after load disturbance occurs in the present invention;

[0068] Fig.16 is the frequency change rate of the fixed moment of inertia and the adaptive moment of inertia after a load disturbance occurs in the present invention;

[0069] Fig.17 The simulation results of the microgrid system under low penetration rate of the present invention after being disturbed by load are as follows: (a) output power of energy storage; (b) output power of photovoltaic; (c) frequency response of the microgrid system;

[0070] Fig.18 These are the simulation results of the microgrid system under high penetration rate of the present invention after being subjected to load disturbance: (a) output power of energy storage; (b) output power of photovoltaics; (c) frequency response of the microgrid system. DETAILED DESCRIPTION

[0071] The present invention is further described in detail below in conjunction with the accompanying drawings. It should be pointed out that it is only for the purpose of more clearly describing and explaining the present invention.

[0072] like Figure 1-18 As shown, this embodiment discloses a photovoltaic storage collaborative control strategy for an AC microgrid system, which includes the following steps: Step 1, perform model analysis; Step 2, establish a storage system based on fuzzy adaptive control; Step 3, construct a photovoltaic maximum power point tracking MPPT algorithm based on SMC; Step 4, set an adaptive sliding mode control strategy for the photovoltaic system with integrated frequency support function.

[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 system based on fuzzy adaptive control is proposed;

[0075] Step 3: Developed a photovoltaic maximum power point tracking (MPPT) algorithm based on SMC;

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

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

[0078] Traditional VSG control strategies are usually based on the simulation of rotational inertia, and respond to system frequency fluctuations by adjusting the output power of each distributed generation unit (DG). When the microgrid (AC MG) is disturbed, the VSG control strategy simulates the inertia effect by quickly adjusting the output power of the generation unit to achieve frequency stability. Specifically, the VSG strategy usually includes the relationship between frequency and power, and adjusts the power generation by detecting frequency changes, so as to provide necessary inertial support when the system frequency deviates from the set value, reduce frequency fluctuations, and help maintain the stable operation of the system. The traditional VSG control strategy is as follows:

[0079] P in -PD·(ww 0 )=J·w 0 ·s·(ww 0 ) (1)

[0080] Where 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 w 0 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 value, which is not conducive to the discharge flexibility of the energy storage system.

[0082] P in =P ref -m w ·(ww 0 ) (2)

[0083] Where P ref Represents the active output reference instruction of the microgrid layer, m w is the droop coefficient.

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

[0085] J.W 0 ·s·(ww 0 )=P ref -m w ·(ww 0 )-PD·(ww 0 ) (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 frequency change rate of the microgrid (AC MG) after being disturbed under different inertia constants are analyzed. Figure 2 and Figure 3 The frequency change and frequency change rate of the microgrid after being disturbed under different inertia constant conditions are shown respectively. It can be seen from the figure that a larger moment of inertia helps to slow down the rate of frequency fluctuation of the microgrid and reduce the maximum frequency deviation of the system. However, a larger moment of inertia will cause the frequency to change more drastically near the nominal value, resulting in a larger oscillation amplitude, which will cause oscillation when the frequency returns to the nominal value, affecting the smooth recovery of the frequency.

[0087] Therefore, the ideal approach is to increase the moment of inertia appropriately in the early stage of frequency fluctuation to improve system inertia, reduce the frequency change rate, and reduce the maximum frequency deviation; and in the frequency recovery stage, the moment of inertia should be appropriately reduced to reduce the oscillation amplitude near the zero point during the frequency recovery process and accelerate the frequency back to the nominal value. This can ensure that the system frequency fluctuation is small while not hindering the rapid recovery of the frequency, thereby improving the stability of the microgrid system and optimizing its dynamic response performance.

[0088] Step 2 includes the following steps:

[0089] Through analysis, the present invention proposes a control algorithm, which aims to dynamically adjust the key parameter of moment of inertia according to the real-time status of frequency and frequency change rate in the microgrid, so as to ensure that the system can effectively reduce the maximum frequency deviation and quickly recover to the nominal frequency when 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 current microgrids. Fuzzy control can handle complex nonlinear problems. For some systems that cannot be accurately modeled or described by traditional control methods, fuzzy control provides a more flexible and effective solution. Therefore, the present invention uses a fuzzy control strategy to achieve adaptive adjustment of the moment of inertia.

[0090] The control structure of the designed fuzzy adaptive VSG control strategy is as follows: Figure 4 As shown in the figure, df / dt and Δf are the inputs of the fuzzy controller, which can be obtained by measurement and calculation. J, as the output of the fuzzy controller, will be continuously adaptively adjusted according to the changes in the states of df / dt and Δf to ensure that the speed of frequency recovery to the nominal value is accelerated while reducing the maximum frequency deviation. For this purpose, a fuzzy controller is designed, as shown in Figure 5 As shown. Figure 5 It can be seen that when df / dt and Δf have the same sign, that is, in the frequency deterioration stage, J has a larger amplitude. When df / dt and Δf have different signs, that is, in the frequency recovery stage, the value of J decreases accordingly. The proposed control strategy meets the requirements.

[0091] Step 3 includes the following steps:

[0092] Step 3.1: Selection of single / double-stage photovoltaic power generation system

[0093] Photovoltaic power generation systems can be divided into two types: unipolar and bipolar, depending on whether they are equipped with a front-stage DC / DC converter. Figure 6 The structural schematic diagram of the monopole photovoltaic power generation system is shown. The system 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 a PWM signal, which is input to the inverter to convert the DC power generated by the photovoltaic array into AC power and adjust the output power of the photovoltaic array. The function of the L-type filter is to filter high-frequency harmonics, reduce voltage fluctuations, ensure voltage stability, and effectively suppress external interference signals.

[0094] Compared with bipolar or multi-stage photovoltaic power generation systems, monopolar photovoltaic power generation systems do not have a front-stage DC / DC converter, and the system structure is simpler, reducing the overall complexity, thereby reducing the cost of manufacturing and maintenance. In addition, after removing the front-stage DC / DC converter, the system can effectively reduce energy loss during the energy conversion process, which helps to improve the overall efficiency. However, the performance of the monopolar photovoltaic system may be affected by factors such as shadowing or uneven illumination, which may cause its maximum power point tracking (MPPT) performance to be inferior to other types of systems, especially under complex lighting conditions.

[0095] Figure 7The structural diagram of the bipolar photovoltaic power generation system is shown. Compared with the unipolar photovoltaic power generation system, the bipolar system adds a front-stage DC / DC converter. Although the addition of the DC / DC converter requires more power components, which will undoubtedly increase the initial investment cost of the system and may generate additional energy losses during the energy conversion process, its advantages are also very obvious. The front-stage DC / DC converter can adjust the voltage and current output by the photovoltaic array to ensure that the system can achieve effective maximum power point tracking (MPPT) under different lighting conditions. By using a DC / DC converter, the bipolar photovoltaic system can better cope with changes in light and provide a more stable power output, thereby optimizing energy conversion efficiency and improving overall system performance. Therefore, the present invention selects a bipolar photovoltaic power generation system to ensure that the photovoltaic power generation unit can perform efficient power interaction with the AC microgrid (AC MG).

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

[0097] based on Figure 8 The photovoltaic array characteristics shown in , the photovoltaic array can operate at MPP when satisfying equation (4).

[0098]

[0099] Where P pv and V pv Represent the output power and voltage of the photovoltaic array respectively. pv is the current of the photovoltaic array. dI pv / dV pv is the incremental conductance of the PV array.

[0100] In order to ensure that the photovoltaic array can use the sliding mode controller to perform MPPT, the sliding mode surface S pv Designed to:

[0101]

[0102] V pv,ref It is defined as the output voltage reference value of the photovoltaic array operating in the MPP state. This reference value will be sent to the voltage loop controller as a voltage reference instruction. Its derivative is

[0103] Depend on Figure 8 It can be seen that based on the sliding surface S pv The positive and negative nature of the PV array can be divided into two parts. Therefore, the design of the MPPT algorithm based on SMC in the present invention needs to consider the following two situations:

[0104] 1) When dP pv / dV pv >0, Figure 8 Take point A in the figure as an example. At this time, the output voltage of the photovoltaic array is V A In order for the PV array to track the MPP, the derivative of the output voltage should be a positive value, so that the voltage of the PV array can be adjusted from V A Arrival V mpp In addition, in order to satisfy the convergence of sliding mode control, the derivative of the sliding surface should satisfy

[0105] 2) When dP pv / dV pv When <0, Figure 8 Take point B in the figure as an example. At this time, the output voltage of the photovoltaic array is V B In order for the PV array to track the MPP, the derivative of the output voltage should be a negative value, so that the voltage of the PV array can be adjusted from V B Arrival V mpp In addition, in order to satisfy the convergence of sliding mode control, the derivative of the sliding surface should satisfy

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

[0107]

[0108] In order to ensure the convergence speed, the exponential convergence law shown below is designed to make the sliding surface converge at an exponential rate:

[0109]

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

[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 output of the photovoltaic array and the sliding surface is as follows: Fig. 9 As shown, when dP pv / dV pv = 0, the PV array operates in the MPP state. pv / dV pv≠0, the PV array operates at a state less than the MPP. At this time, there is a certain power reserve and it can participate in the two-way frequency support of the AC MG system to increase the stability of the system. When the PV array operates below the MPP state, the PV array is based on dP pv / dV pv The value of dP can be divided into pv / dV pv <0 and dP pv / dV pv >0, there are two cases, Fig. 9 It can be seen that in dP pv / dV pv When dP < 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. pv / dV pv When <0, a certain power reserve is reserved to provide frequency response service for the microgrid system. The present invention designs a sliding surface adjustment item to adjust the value of the sliding surface, so as to adjust the output power of the photovoltaic array, thereby providing frequency support for the AC MG system. The improved sliding surface is designed as:

[0114]

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

[0116] ΔS nom It is defined as the nominal value of the sliding surface adjustment term, that is, when the microgrid system is operating normally, ΔS = ΔS nom , leaving a certain amount of power reserve for frequency regulation of AC MG. Fig. 9 As shown, ΔS nom =ΔS A , when the load in the microgrid decreases and the frequency of the system increases, the output power of the photovoltaic array can decrease as ΔS decreases. Similarly, when the load in the microgrid increases and the frequency of the system decreases, the output power of the photovoltaic array increases as ΔS increases. In order to increase the inertia of the microgrid system and make the photovoltaic array consider df / dt when participating in the frequency regulation, the sliding 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 the adjustment term used to adjust ΔS, Kd is the inertia control coefficient, K p is the droop coefficient, Δf and df / dt refer to the frequency deviation and frequency change in the system respectively.

[0120] Fig.10 It is the classic frequency response curve after load increase disturbance in AC MG. According to the different df / dt values, the frequency response can be divided into two stages.

[0121] Stage 1 (df / dt<0): When the load in the system increases, the frequency of the AC MG system will tend to decrease due to the imbalance of active power. In the current AC MG, since the high proportion of new energy does not have inertia, the microgrid will experience a sharp drop in frequency in the initial stage of frequency change. To this end, the present invention designs an adaptive inertia coefficient. In the initial stage of disturbance, the amplitude of the inertia coefficient of the adjustment item is increased, so that photovoltaics can provide greater inertia and reduce the frequency deterioration rate. When the frequency deviation becomes larger and larger, the amplitude of the droop coefficient is increased to increase the output of photovoltaics and prevent the frequency from continuing to deteriorate.

[0122] Stage 2 (df / dt>0): In the frequency recovery stage, the present invention reduces the amplitude of the inertia coefficient so that the frequency can be restored to the nominal value as quickly as possible.

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

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

[0125] Verification of fuzzy adaptive VSG control algorithm for energy storage system, Fig.14 , Fig.15 and Fig.16 This is a simulation result diagram of the microgrid after a load disturbance occurs. When the load increases, the active balance of the system is broken and the frequency decreases. In the initial stage of the decline, if the inertia constant is not considered, that is, the droop control method is adopted, the system has a very large frequency change rate, resulting in a large drop in frequency. When the VSG control strategy is adopted, the frequency change rate can be effectively reduced, thereby reducing the maximum frequency deviation. Compared with the VSG control strategy with fixed parameters, the VSG control strategy based on fuzzy adaptation designed in this paper can reduce the change rate of the frequency change in the initial stage by increasing the value of the moment of inertia, and further reduce the maximum frequency deviation.

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

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

[0128] Verification of the frequency support algorithm of photovoltaic system based on fuzzy adaptive SMC. To verify the effectiveness of the proposed control algorithm in photovoltaic system under load fluctuation, the load of AC MG test system increases at t = 3s and decreases at t = 6s. In addition, simulation tests are carried out under high and low photovoltaic penetration to verify the universality of the proposed control algorithm. Fig.17 and Fig.18 The simulation results for low and high penetration cases are shown in Table 1. The maximum frequency deviation during load fluctuations for low and high penetration cases are recorded in Table 1.

[0129] like Fig.17 and Fig.18 As shown in the figure, at t=3s, the load increases, the energy storage increases power, and the photovoltaic system operating at MPP has the largest frequency deviation of the microgrid because it has no additional power. When the control strategy proposed in this paper is adopted, a certain power reserve can be reserved to participate in the frequency regulation of the system, providing inertial 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, under low PV penetration, compared with the PV system operating in MPPT mode, the proposed frequency support algorithm based on fuzzy adaptive sliding mode control reduces the maximum frequency deviation by 20% and 30% in low-frequency events and high-frequency events, respectively. Under high PV penetration, the control strategy proposed in this paper reduces the maximum frequency deviation by 31% in low-frequency events and 42% in high-frequency events compared with the PV system operating in MPPT mode. It can be seen that the control algorithm proposed in this paper is more suitable for microgrid systems with a high proportion of PV power sources, and can better adapt to future AC MGs with high PV penetration.

[0131] Maximum power deviation of microgrid after disturbance at high and low photovoltaic penetration rates

[0132]

[0133]

[0134] Based on the traditional VSG control strategy, the present invention 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 can not only effectively deal with the problem of frequency fluctuation in the microgrid, but also adjust the inertia according to the actual operating conditions, so that the system is more stable under different load and disturbance conditions. On this basis, the present invention designs a fuzzy adaptive sliding mode control strategy, so that the photovoltaic system is always kept at the maximum power point during operation, while retaining a certain power reserve to support the frequency stability of the microgrid. This control strategy can not only automatically adjust the inertia according to the system frequency change, but also enhance the photovoltaic system's support capacity for low-inertia AC MG, thereby further improving the overall stability and anti-disturbance ability of the photovoltaic storage microgrid. In addition, the present invention also conducts an in-depth analysis of 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, reduces the hardware requirements of the AC MG system, and thus reduces the investment cost. In this way, the system can efficiently track the maximum power point and optimize energy output.

[0135] The above are only preferred specific implementation modes of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical solutions and concepts of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A photovoltaic-storage collaborative control strategy for AC microgrid systems, characterized in that: The following steps are involved: Step 1: Conduct model analysis; Step 2: Establish an energy storage system based on fuzzy adaptive control; Step 3: Construct a photovoltaic maximum power point tracking MPPT algorithm based on SMC; Step 4: Set up an adaptive sliding mode control strategy for the photovoltaic system integrating the frequency support function.

2. The photovoltaic-storage coordinated control strategy for AC microgrid system according to claim 1 is characterized by: The step 1 includes analyzing the VSG model, studying the effect of rotational inertia on frequency stability, exploring the influence of rotational inertia on frequency stability, and responding to system frequency fluctuations by adjusting the output power of each distributed generation unit DG. When the microgrid is disturbed, the VSG control strategy quickly adjusts the output power of the generation unit to simulate the inertia effect, thereby achieving frequency stability; the VSG strategy includes the relationship between frequency and power, and adjusts the power generation by detecting frequency changes. When the system frequency deviates from the set value, it provides necessary inertial support, reduces frequency fluctuations, and helps maintain the stable operation of the system. The VSG control strategy is, P in -PD·(w-w0)=J·w0·s·(w-w0) Where 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; P in =P ref -m w ·(w-w0) Where P ref Represents the active output reference instruction of the microgrid layer, m w is the droop coefficient; The coupling relationship between active power and frequency is derived from the above two equations: J·w0·s·(w-w0)=P ref -m w ·(w-w0)-P-D·(w-w0) By adjusting the value of the inertia constant, the frequency response curve and frequency change rate of the microgrid after being disturbed under different inertia constants are analyzed.

3. The photovoltaic-storage coordinated control strategy for AC microgrid system according to claim 2 is characterized by: In the early stage of frequency fluctuation, the moment of inertia is increased to improve 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 the zero point during the frequency recovery process, which is used to accelerate the frequency back 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 microgrid system, and optimizes its dynamic response performance.

4. The photovoltaic-storage coordinated control strategy for AC microgrid system according to claim 1 is characterized by: 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 status of the frequency and the frequency change rate in the microgrid to ensure that the system reduces the maximum frequency deviation and quickly recovers to the nominal frequency when disturbed.

5. The photovoltaic-storage coordinated control strategy for AC microgrid system according to claim 1 is characterized by: The step 3 includes Step 3.1: Selection of single / double-stage photovoltaic power generation system; Step 3.2: Photovoltaic MPPT algorithm based on SMC; The photovoltaic power generation system in step 3.1 is divided into two types: unipolar type and bipolar type according to whether it is equipped with a front-stage DC / DC converter; The structure of the monopole photovoltaic power generation system includes 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 used to generate a PWM signal, which is input into the inverter to convert the DC power generated by the photovoltaic array into AC power and adjust the output power of the photovoltaic array. The L-type filter is used to filter high-frequency harmonics, reduce voltage fluctuations, ensure voltage stability and suppress external interference signals. The bipolar 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 to ensure that the system can achieve maximum power point tracking MPPT under different lighting conditions. By using a DC / DC converter, the bipolar photovoltaic system can cope with changes in light and provide a more stable power output, thereby optimizing energy conversion efficiency and improving overall system performance; the bipolar photovoltaic power generation system is used to ensure that the photovoltaic power generation unit can interact with the AC microgrid for power.

6. The photovoltaic-storage coordinated control strategy for AC microgrid system according to claim 5 is characterized by: The MPPT algorithm of step 3.2 is: Where P pv and V pv Represent the output power and voltage of the photovoltaic array, I pv is the current of the photovoltaic array, dI pv / dV pv is the incremental conductance of the PV array; In order to ensure that the photovoltaic array can use the sliding mode controller to perform MPPT, the sliding mode surface S pv is set to, V pv,ref It is defined as the output voltage reference value of the photovoltaic array operating in the MPP state. This reference value will be sent to the voltage loop controller as a voltage reference instruction. Its derivative is Based on the sliding surface S pv The positive and negative properties of the PV array, the working range includes two parts; (1) When dP pv / dV pv > 0, taking point A as an example, the output voltage of the photovoltaic array is V A In order to make the photovoltaic array track the MPP, the derivative of the output voltage is a positive value, so that the voltage of the photovoltaic array can be adjusted from V A Arrival V mpp In order to satisfy the convergence of sliding mode control, the derivative of the sliding surface should satisfy (2) When dP pv / dV pv When <0, taking point B as an example, the output voltage of the photovoltaic array is V B , in order to enable the PV array to track the MPP, the derivative of the output voltage is a negative value, so that the voltage of the PV array can be increased from V B Arrival V mpp In order to satisfy the convergence of sliding mode control, the derivative of the sliding surface should satisfy Based on the above analysis, V pv,ref The derivative of is set to The reaching law of the sliding mode controller is set as, In order to ensure the convergence speed, the exponential convergence law is set to make the sliding surface converge at an exponential rate. Where ε>0, k>0, sgn(·) represents the sign function, and the SMC-based MPPT algorithm is set to, V pv,ref =∫(ε·sgn(S pv )+k·S pv )dt。 7. The photovoltaic-storage coordinated control strategy for AC microgrid system according to claim 1 is characterized by: The step 4 includes adjusting the sliding surface value by a sliding surface adjustment item to adjust the output power of the photovoltaic array, thereby providing frequency support for the microgrid system. The sliding surface includes: Where ΔS is the proposed sliding surface adjustment term, which is used to adjust the output power of the photovoltaic array. When the load in the microgrid decreases and the frequency of the system increases, the output power of the photovoltaic array decreases as ΔS decreases. In order to increase the inertia of the microgrid system and make the photovoltaic array consider df / dt when participating in the frequency regulation, the sliding surface adjustment term includes: ΔS adj =K d df / dt+K p Δf In the formula, ΔS adj is the adjustment term used to adjust ΔS, K d is the inertia control coefficient, K p is the droop coefficient, Δf and df / dt refer to the frequency deviation and frequency change in the system respectively.

8. The photovoltaic-storage coordinated control strategy for AC microgrid system according to claim 7 is characterized by: Classical frequency response curve after load increase disturbance in microgrid Depending on the value, the frequency response includes two stages; Stage 1 (df / dt<0): When the load in the system increases, the frequency of the microgrid system tends to decrease due to the imbalance of active power. The adaptive inertia coefficient is set. In the initial stage of the disturbance, the amplitude of the inertia coefficient of the adjustment item is increased, so that the photovoltaic can provide greater inertia and 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 frequency from further deteriorating. Phase 2 (df / dt>0): In the frequency recovery phase, the amplitude of the inertia coefficient is reduced so that the frequency can be restored to the nominal value as quickly as possible, and two fuzzy controllers are set to adjust the inertia coefficient and the droop coefficient.

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