SOC adaptive constraint double-layer fuzzy primary frequency modulation control method

Abstract

The invention discloses an SOC adaptive constraint double-layer fuzzy primary frequency modulation control method, which comprises the following steps: S1, analyzing a control model of an energy storage system responding to system primary frequency modulation, and respectively using virtual droop control, virtual inertia control and virtual negative inertia control to cooperate with output for different output stages; s2, designing double-layer fuzzy control containing a system fluctuation state and SOH (state of health), and performing depth constraint on energy storage units of different SOHs; s3, designing different SOC constraint outputs by adopting a piecewise function; and S4, performing frequency modulation control through SOH double-layer fuzzy control and SOC constraint output. According to the method, output coefficients of system fluctuation and SOH constraints are determined by adopting double-layer fuzzy control; and meanwhile, the real-time SOC of the energy storage unit is considered, the output depth is adjusted by adopting a piecewise function, the response capability is improved, and energy storage over-charging and over-discharging are avoided.

Description

Technical Field

[0001] The present invention relates to a double-layer fuzzy primary frequency modulation control method with SOC adaptive constraint for an SOC. Background Art

[0002] The penetration rate of clean energy is continuously increasing, but the uncertainty and low inertia of new energy output pose great challenges to the stability of the power grid. The characteristics of rapid response and two-way regulation of the battery energy storage system can make up for the deficiencies of clean energy and effectively assist the stable operation of the power grid.

[0003] Regarding the control strategies for energy storage to participate in the primary frequency modulation of the power grid, domestic and foreign scholars have carried out different studies. For example, the frequency modulation performance of battery energy storage using virtual inertia control has been studied; a virtual droop control strategy for coordinated frequency modulation of thermal power units and energy storage has been proposed, realizing the adaptive adjustment of the output of thermal power units and energy storage; virtual droop control and virtual inertia control (K&M) are jointly applied to energy storage frequency modulation, and the maximum value of the system frequency deviation is used as the switching point to achieve the complementary advantages of control methods. The above only considers the coordinated cooperation of battery control strategies and does not consider the limitation of the output by the battery SOC (state of charge of the energy storage unit). Overcharging or over-discharging will seriously damage the battery life, and the energy storage can exert its maximum frequency modulation ability when in a good SOC state.

[0004] For another example, it has been studied and proposed to consider the constraint of SOC on the output of energy storage during the energy storage frequency modulation stage to avoid overcharging and over-discharging; a fuzzy controller is introduced to smooth the virtual droop and virtual inertia output and reduce the impact on the system caused by mode switching; a self-recovery coefficient of SOC is proposed to enable the energy storage SOC to be maintained at a better level during frequency modulation, but the control model design is relatively complex; the output of energy storage is controlled by fuzzy logic, reducing the complexity of system modeling; it is considered that the dead zone of energy storage frequency modulation should be smaller than that of traditional unit frequency modulation, and the energy storage system advantage is utilized to act in advance to reduce the system frequency fluctuation. Although the above considers SOC constraint and self-recovery control, the influence of the battery health state (SOH) on the output of energy storage is not considered.

[0005] Therefore, a double-layer fuzzy primary frequency modulation control method with SOC adaptive constraint is provided. Summary of the Invention

[0006] The purpose of the present invention is to provide a double-layer fuzzy primary frequency modulation control method with SOC adaptive constraint to overcome the existing defects. By adopting double-layer fuzzy control, the output coefficient of system fluctuation and SOH constraint is determined; at the same time, considering the real-time SOC of the energy storage unit, a piecewise function is used to adjust the output depth, improving the response ability while avoiding overcharging and over-discharging of the energy storage.

[0007] The technical solution to achieve the above purpose is as follows:

[0008] A double - layer fuzzy primary frequency modulation control method with SOC adaptive constraint, including:

[0009] Step S1, analyze the control model of the energy storage system responding to the primary frequency modulation of the system, and use virtual droop control, virtual inertia control, and virtual negative inertia control to cooperate with the output for different output stages;

[0010] Step S2, design a double - layer fuzzy control including the system fluctuation state and SOH to deeply constrain the output of energy storage units with different SOH;

[0011] Step S3, design different SOC - constrained outputs using piece - wise functions;

[0012] Step S4, perform frequency modulation control through the double - layer fuzzy control of SOH and SOC - constrained output.

[0013] Preferably, in the step S1, establish a typical energy storage participation system frequency response model, and analyze the energy storage system's response to the system's primary frequency modulation. Among them, the dynamic equation of the traditional unit participating in frequency modulation is:

[0014]

[0015] In the formula, ΔP g is the change in the output of the traditional unit responding to the primary frequency modulation, K g is the regulation coefficient of the traditional unit, Δf is the real - time system frequency deviation, s is, F HP is the reheater gain, T RH is the reheater time constant, T g is the governor time constant of the traditional unit, T HC is the turbine time constant;

[0016] The primary frequency modulation method adopted by the energy storage system is virtual droop control, virtual inertia control, and virtual negative inertia control. The primary frequency modulation output ΔP b is as follows:

[0017]

[0018] K b = ε;

[0019] M b = K S ε;

[0020] In the formula, G BESS_VD and G BESS_VI are the virtual droop control transfer function and the virtual inertia control transfer function respectively, T b is the energy storage device time constant, K b is the virtual droop regulation coefficient of the energy storage participating in the primary frequency modulation, M bis the virtual inertia regulation coefficient, 1 (*) is an exponential function, and its output value is 0 or 1. When Δf * d(Δf) / dt > 0, virtual inertia control is adopted. When Δf * d(Δf) / dt < 0, virtual negative inertia control is adopted. ε is the energy storage output depth coefficient, K S is the energy storage output SOC feedback constraint coefficient.

[0021] Preferably, in the step S2, a primary frequency regulation strategy for the energy storage participating in the system considering frequency fluctuation, SOC, and SOH is designed; among them,

[0022] The first - layer fuzzy control:

[0023] When the system frequency fluctuates, the system frequency deviation Δf and the rate of change of frequency deviation d(Δf) / dt are normalized and used as the first - layer input of the fuzzy control. The frequency fluctuation factor ξ is used as the output, reflecting the degree of fluctuation of the current system frequency. Among them, the input variable universe is taken as [-0.5, 0.5], the output variable universe is [0, 1], the membership function of the input variable is selected as a triangular function, and the membership function of the output variable is a Gaussian membership function;

[0024] The second - layer fuzzy control:

[0025] The SOH of the energy storage battery and the first - layer output frequency fluctuation factor ξ are used as the input of the second - layer fuzzy control, and the energy storage output depth coefficient ε is used as the output. Among them, the membership function of the input of the second - layer fuzzy control is a triangular membership function, the membership function of the output is a Gaussian membership function, the input - output universe is both [0, 1], and the fuzzy subsets are all {MIN (negative large), NB (negative small), ZO (zero), PB (positive small), MAX (positive large)}.

[0026] Preferably, in the step S3, a piece - wise function is selected to design the SOC - constrained energy storage output, as follows:

[0027]

[0028] In the formula, K S is the energy storage output SOC feedback constraint coefficient, S C and S D are the charging and discharging power regulation coefficients respectively. When Δf > 0, the energy storage charges to absorb the excess power of the system. When Δf < 0, the energy storage discharges to fill the power shortage of the system, and are the maximum charging coefficient and the maximum discharging coefficient of the energy storage respectively, S OC is.

[0029] Preferably, in step S4, when the system frequency deviation Δf exceeds the energy storage frequency modulation dead zone, that is, Δf > 0.02, through step S2, based on the double-layer fuzzy logic control, the energy storage output depth ε is confirmed; through step S3, considering the real-time state value of the energy storage SOC, the SOC feedback constraint coefficient K is determined S ; comprehensively calculate the virtual droop coefficient K b and the virtual inertia coefficient M b , and then perform frequency modulation control on the energy storage system. After frequency modulation, judge again whether the system frequency deviation Δf is restored, that is, Δf ≤ 0.02. If so, end the frequency modulation control; if not, re-perform the process of step S2-step S4.

[0030] The beneficial effects of the present invention are as follows: The present invention combines the performance advantages of virtual droop control, virtual inertia control and virtual negative inertia control, takes into account the SOH, constructs a primary frequency modulation control strategy for energy storage, and further considers the energy storage SOC feedback to smooth the energy storage output and avoid overcharging and over-discharging of the energy storage. The method proposed by the present invention has a better frequency modulation effect and can perform more refined adjustment on the energy storage output unit, thus ensuring the stability of the power grid frequency. Description of the Drawings

[0031] Figure 1 is a flowchart of a double-layer fuzzy primary frequency modulation control method with SOC adaptive constraint of the present invention;

[0032] Figure 2 is a schematic diagram of the primary frequency modulation dynamic model of a traditional energy storage system;

[0033] Figure 3 is a schematic diagram of the input membership function of the real-time system frequency deviation Δf in the present invention;

[0034] Figure 4 is a schematic diagram of the input membership function of the frequency deviation change rate d(Δf) / dt in the present invention;

[0035] Figure 5 is a schematic diagram of the output membership function of the frequency fluctuation factor ξ in the present invention;

[0036] Figure 6 is a schematic diagram of the input membership function of the frequency fluctuation factor ξ in the present invention;

[0037] Figure 7 is a schematic diagram of the input membership function of the SOH in the present invention;

[0038] Figure 8 is a schematic diagram of the output membership function of the energy storage output depth coefficient ε in the present invention;

[0039] Figure 9It is the flowchart of the primary frequency regulation strategy considering battery consistency in the present invention;

[0040] Figure 10 It is the frequency deviation curve diagram under the step disturbance condition in the embodiment of the present invention;

[0041] Figure 11 It is the continuous load disturbance curve diagram within 30 minutes in the embodiment of the present invention;

[0042] Figure 12 It is the frequency deviation curve diagram under the continuous disturbance condition in the embodiment of the present invention. Detailed implementation manners

[0043] Next, the technical solution of the present invention will be clearly and completely described in conjunction with the accompanying drawings. In the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. is based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be construed as a limitation of the present invention. In addition, the terms "first", "second", and "third" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance.

[0044] Next, the present invention will be further described in conjunction with the accompanying drawings.

[0045] As Figure 1 shown, a two - layer fuzzy primary frequency regulation control method with SOC self - adaptive constraint includes:

[0046] Step S1, analyze the control model of the energy storage system responding to the primary frequency regulation of the system, and use virtual droop control, virtual inertia control, and virtual negative inertia control to cooperate with the output respectively for different output stages.

[0047] In the embodiment, establish a typical energy storage participation system frequency response model, and analyze the energy storage system responding to the primary frequency regulation of the system. As Figure 2 shown, ΔP g and ΔP b are respectively the change amount of the output of the traditional unit responding to the primary frequency regulation and the change amount of the output of the energy storage power station responding to the primary frequency regulation. G BESS is the transfer function of the energy storage power station, H is the grid inertia time constant, and D is the disturbance damping coefficient. Among them, the dynamic equation of the traditional unit participating in frequency regulation is:

[0048]

[0049] In the formula, ΔP g is the change amount of the output of the traditional unit responding to the primary frequency regulation, Kg is the regulation coefficient of the traditional unit, Δf is the real-time system frequency deviation, s is, F HP is the reheater gain, T RH is the reheater time constant, T g is the time constant of the traditional unit governor, T HC is the time constant of the steam turbine;

[0050] The primary frequency regulation methods adopted by the energy storage system are virtual droop control, virtual inertia control and virtual negative inertia control, and the primary frequency regulation output ΔP b is as follows:

[0051]

[0052] K b = ε;

[0053] M b = K S ε;

[0054] In the formula, G BESS_VD and G BESS_VI are the virtual droop control transfer function and the virtual inertia control transfer function respectively, T b is the time constant of the energy storage device, K b is the virtual droop regulation coefficient for the energy storage to participate in primary frequency regulation, M b is the virtual inertia regulation coefficient, 1 (*) is the exponential function, and its output value is 0 or 1. When Δf * d(Δf) / dt > 0, virtual inertia control is adopted. When Δf * d(Δf) / dt < 0, virtual negative inertia control is adopted. ε is the energy storage output depth coefficient, K S is the energy storage output SOC feedback constraint coefficient.

[0055] Step S2, design a two-layer fuzzy control including the system fluctuation state and SOH to constrain the output depth of the energy storage unit with different SOH.

[0056] In the embodiment, an energy storage participation system primary frequency regulation strategy considering frequency fluctuation, SOC and SOH is designed; among them,

[0057] The first layer of fuzzy control:

[0058] When the system frequency fluctuates, the system frequency deviation Δf and the frequency deviation change rate d(Δf) / dt are normalized and used as the first layer input of the fuzzy control, and the frequency fluctuation factor ξ is used as the output to reflect the fluctuation degree of the current system frequency. Among them, the input quantity universe of discourse is [-0.5, 0.5], the output quantity universe of discourse is [0, 1], the input quantity selects the triangular function as the membership function, and the membership function of the output quantity is the Gaussian membership function to improve the smoothness of the output value. The membership function is asFigures 3 - 5 As shown, they respectively represent the membership functions of the frequency deviation Δf of the first-layer fuzzy input quantity, the rate of change of frequency deviation d(Δf) / dt, and the output quantity frequency fluctuation factor ξ. The input and output universes of discourse are equally divided into 5 fuzzy subsets {MIN, NB, ZO, PB, MAX}, which represent negative large, negative small, zero, positive small, and positive large respectively. The fuzzy rules are shown in Table 1.

[0059]

[0060] Table 1

[0061] The first-layer fuzzy logic control fully considers the representation of frequency deviation and the rate of change of frequency deviation on system stability. When both the frequency deviation and the rate of change are large, the system frequency is in the worst condition; when the frequency deviation is small but the rate of change is large, the system is in the stage of rapid frequency deterioration, and the energy storage must output power quickly to prevent the frequency from deteriorating further.

[0062] Second-layer fuzzy control:

[0063] The state of health (SOH) of the energy storage battery and the output quantity frequency fluctuation factor ξ of the first layer are used as the inputs of the second-layer fuzzy control, and the energy storage output depth coefficient ε is used as the output quantity. Among them, the membership function of the second-layer fuzzy control input quantity is a triangular membership function, and the membership function of the output quantity is a Gaussian membership function. The input and output universes of discourse are both [0, 1], and the fuzzy subsets are all {MIN (negative large), NB (negative small), ZO (zero), PB (positive small), MAX (positive large)}. The fuzzy rules are shown in Table 2, and the membership functions are as Figures 6 - 8 shown, which respectively represent the membership functions of the second-layer fuzzy input quantity frequency fluctuation factor ξ, SOH, and the output quantity energy storage output depth coefficient ε.

[0064]

[0065] Table 2

[0066] The second-layer fuzzy logic control takes the current system fluctuation condition and the battery SOH of the system as constraints. The higher the SOH value, the more the energy storage outputs. The energy storage units with low SOH values appropriately reduce their outputs to extend the service life of the energy storage.

[0067] Step S3, design different state of charge (SOC) constrained outputs using piecewise functions.

[0068] In the embodiment, when the SOC of the energy storage is lower than 0.15 or higher than 0.85, the battery is prone to over-discharge / over-charge. To balance the frequency modulation performance and the energy storage cycle life, the present invention selects 0.15 - 0.85 as the normal working interval of the energy storage. When it exceeds the interval, the energy storage stops responding to the frequency modulation command and stops discharging and charging.

[0069] Furthermore, a piecewise function is selected to design the SOC-constrained energy storage output as follows:

[0070]

[0071] In the formula, K S is the SOC feedback constraint coefficient of the energy storage output, S C and S D are the charging and discharging power adjustment coefficients respectively. When Δf>0, the energy storage charges to absorb the excess power of the system. When Δf<0, the energy storage discharges to fill the power shortage of the system. and are the maximum charging coefficient and the maximum discharging coefficient of the energy storage respectively, and S OC is.

[0072] Step S4: Perform frequency modulation control through the double-layer fuzzy control of SOH and the SOC-constrained output.

[0073] As Figure 9 shown, when the system frequency deviation Δf exceeds the dead zone of energy storage frequency modulation, that is, Δf>0.02, through step S2, the depth of energy storage output ε is confirmed based on double-layer fuzzy logic control; through step S3, considering the real-time state value of the energy storage SOC, the SOC feedback constraint coefficient K S is determined; the virtual droop coefficient K b and the virtual inertia coefficient M b are comprehensively calculated, and then the energy storage system is frequency-modulated. After frequency modulation, it is judged again whether the system frequency deviation Δf is restored, that is, Δf≤0.02. If so, the frequency modulation control ends; if not, the process of steps S2 - S4 is repeated.

[0074] To quantitatively analyze the effect of the system using the strategy of the present invention on primary frequency modulation, the evaluation criteria are defined as follows:

[0075] For step load disturbances, the maximum grid frequency deviation f max and the system frequency stabilization time t sf are proposed to evaluate the proposed strategy. The smaller f max , the better the anti-disturbance ability. The smaller t sf , the stronger the grid recovery ability.

[0076] For continuous load disturbances, the root mean square value f RMS of the system frequency deviation and the peak-to-valley value of the system frequency are proposed as evaluation indicators:

[0077]

[0078] In the formula, f i is the frequency at sampling point i, f0 is the rated frequency of 50 Hz, and n is the total number of sampling points; f RMSThe smaller the value, the stronger the anti-interference ability of the system.

[0079] Simulation verification and analysis

[0080] Build a primary frequency regulation example system model of a certain regional power grid in Gansu on the MATLAB / Simulink platform to verify the effectiveness of the strategy proposed in the present invention. The rated capacity of the system unit is set to 1000 MW, which consists of thermal power units, photovoltaic power stations, wind power stations and energy storage power stations. Among them, both photovoltaic and wind power are in the maximum power tracking point working state, and only thermal power units and energy storage power stations participate in frequency regulation. The new energy grid connection ratio is 30%. The energy storage power station contains 3 groups of energy storage units with capacities of 5 MW·h, 3 MW·h, and 3 MW·h respectively. In the present invention, the traditional unit is selected to set the frequency regulation dead zone as ±0.033 Hz, and the frequency regulation dead zone of the energy storage power station is selected as ±0.02 Hz. The system parameters are normalized with the rated capacity of the system unit and the power frequency of 50 Hz as the reference. The remaining parameter configurations are shown in Table 3.

[0081]

[0082] Table 3

[0083] In the actual system, the primary frequency regulation coverage time is 30 - 60 s, and the situation of long-term hovering basically does not occur. Therefore, the present invention conducts simulations under a step load disturbance of 0.05 p.u., and the simulation duration is 40 s. The grid frequency fluctuations under three different strategies of the control strategy of the present invention, the K&M adaptive switching method, and the fixed K coefficient control method are compared. The results are as Figure 10 shown, and the evaluation indexes are shown in Table 4.

[0084] Method <![CDATA[f max / Hz]]> <![CDATA[t sf / s]]> Fixed K Method -0.372 9.8 K&M Adaptive Switching -0.284 9.3 Method of This Paper -0.207 6.1

[0085] Table 4

[0086] From Figure 10 and Table 4, it can be seen that since the method of the present invention and the K&M adaptive switching method introduce virtual inertia control, the rate of change of frequency deviation is suppressed while the maximum frequency deviation is reduced. Compared with the K&M adaptive switching method, the maximum frequency deviation of the method of the present invention is reduced by 27.1%. Due to the introduction of virtual negative inertia, the system inertia direction is consistent with the frequency recovery direction, and the system frequency recovery speed is better than other methods. Compared with the fixed K method, the recovery time of the method of the present invention is reduced by about 34.4%.

[0087] In the actual power system, primary frequency regulation is mostly used to handle continuous random small load fluctuations. To verify the effectiveness of the method of the present invention, the actual load disturbance of a certain regional power grid for 30 minutes is added to the simulation model, and the maximum amplitude is 0.06 p.u., as Figure 11 shown.

[0088] The initial SOC of the energy storage unit is set to 0.5 and the SOH is set to 0.9. The frequency modulation effect of the method of the present invention is compared with that of the K&M adaptive switching method and the fixed-K coefficient control method. The corresponding frequency modulation effect curves are as Figure 12 shown.

[0089] As can be seen from Figure 12 and Table 5, the amplitude of the system frequency deviation using the method of the present invention is lower than that of other methods. The reason is that the double-layer fuzzy control of the method of the present invention can accurately reflect the frequency fluctuation condition and control the energy storage output at the initial stage of frequency change to suppress frequency deterioration. The root mean square value of the frequency deviation of the method of the present invention is 0.184 Hz, which is 4.18% lower than that of the K&M adaptive switching method. The use of virtual negative inertia helps to accelerate the frequency recovery, thus obtaining a better frequency modulation effect. The peak-to-valley value of the frequency of the method of the present invention is 0.149 Hz, which is 14.86% lower than that of the fixed-K method. The frequency modulation effect of the method of the present invention is the best.

[0090] Method <![CDATA[f RMS / Hz]]> Frequency Peak - Valley Value / Hz Fixed K Method 0.206 0.175 K&M Adaptive Switching 0.191 0.161 Method of This Paper 0.183 0.149

[0091] Table 5

[0092] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A double-layer fuzzy primary frequency modulation control method with SOC adaptive constraints, characterized in that, Including: Step S1: Analyze the control model of the energy storage system responding to the primary frequency regulation of the system, and use virtual droop control, virtual inertia control, and virtual negative inertia control to cooperate with the output for different output stages. Step S2: Design a two-layer fuzzy control including the system fluctuation state and SOH to constrain the output depth of energy storage units with different SOH. Step S3: Use a piecewise function to design the output constrained by different SOC. Step S4: Perform frequency regulation control through the two-layer fuzzy control of SOH and the output constrained by SOC.

2. The double-layer fuzzy primary frequency modulation control method with SOC adaptive constraint according to claim 1, characterized in that, In the above step S1, establish a typical energy storage participation system frequency response model to analyze the energy storage system's response to the primary frequency regulation of the system. Among them, the dynamic equation of the traditional unit participating in frequency regulation is: where, ΔP g is the change in primary frequency regulation output of the traditional unit, K g is the regulation coefficient of the traditional unit, Δf is the real-time system frequency deviation, s is, F HP is the reheater gain, T RH is the reheater time constant, T g is the governor time constant of the traditional unit, T HC is the turbine time constant; The primary frequency regulation methods adopted by the energy storage system are virtual droop control, virtual inertia control, and virtual negative inertia control, and the primary frequency regulation output ΔP b is as follows: K b = ε; M b = K S ε; Where, G BESS_VD and G BESS_VI are the virtual droop control transfer function and the virtual inertia control transfer function respectively, T b is the time constant of the energy storage device, K b is the virtual droop regulation coefficient for the energy storage to participate in primary frequency regulation, M b is the virtual inertia regulation coefficient, 1 (*) is the exponential function, and its output value is 0 or 1. When Δf * d(Δf) / dt > 0, virtual inertia control is adopted; when Δf * d(Δf) / dt < 0, virtual negative inertia control is adopted. ε is the depth coefficient of the energy storage output, and K S is the SOC feedback constraint coefficient of the energy storage output.

3. A double-layer fuzzy primary frequency modulation control method with SOC adaptive constraint according to claim 2, characterized in that, In the above step S2, design a primary frequency regulation strategy for the energy storage participating in the system considering frequency fluctuation, SOC, and SOH. Among them, The first layer of fuzzy control: When the system frequency fluctuates, normalize the system frequency deviation Δf and the rate of change of frequency deviation d(Δf) / dt as the input of the first layer of fuzzy control, and the frequency fluctuation factor ξ as the output, which reflects the degree of fluctuation of the current system frequency. Among them, the domain of the input quantity is [-0.5, 0.5], the domain of the output quantity is [0, 1], the membership function of the input quantity is selected as a triangular function, and the membership function of the output quantity is a Gaussian membership function. The second layer of fuzzy control: The SOH of the energy storage battery and the output quantity frequency fluctuation factor ξ of the first layer are used as the input of the second layer of fuzzy control, and the energy storage output depth coefficient ε is used as the output quantity. Among them, the membership function of the input quantity of the second layer of fuzzy control is a triangular membership function, the membership function of the output quantity is a Gaussian membership function, the domain of both input and output is [0, 1], and the fuzzy subsets are all {MIN (negative large), NB (negative small), ZO (zero), PB (positive small), MAX (positive large)}.

4. A dual-layer fuzzy primary frequency modulation control method with SOC adaptive constraint according to claim 3, characterized in that In the above step S3, select a piecewise function to design the SOC-constrained energy storage output, as follows: Where K S is the SOC feedback constraint coefficient of the energy storage output, and S C and S D are the charging and discharging power adjustment coefficients respectively. When Δf > 0, the energy storage charges to absorb the excess power of the system; when Δf < 0, the energy storage discharges to fill the power deficit of the system. and are the maximum charging coefficient and the maximum discharging coefficient of the energy storage respectively, and S OC is.

5. The double-layer fuzzy primary frequency modulation control method with SOC adaptive constraint according to claim 4, characterized in that In the step S4, when the system frequency deviation Δf exceeds the energy storage frequency modulation dead zone, that is, Δf > 0.02, through the step S2, the energy storage output depth ε is confirmed based on the double-layer fuzzy logic control; through the step S3, considering the real-time state value of the energy storage SOC, the SOC feedback constraint coefficient K is determined S ; comprehensively calculate the virtual droop coefficient K b and the virtual inertia coefficient M b , and then perform frequency modulation control on the energy storage system. After frequency modulation, it is determined again whether the system frequency deviation Δf is restored, that is, Δf ≤ 0.

02. If so, the frequency modulation control is ended; if not, the process of the step S2-step S4 is performed again.

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