An adaptive frequency coordination support method and system for energy storage and new energy grid-connected devices under weak power grid conditions

Abstract

This invention discloses an adaptive frequency-coordinated support method and system for energy storage and renewable energy grid-connected devices in weak power grids. First, the available active power is determined, and the grid connection point frequency is locked and input into a neural network model. Then, a fuzzy rule inference table is constructed, and the fuzziness is resolved to obtain the adaptive proportional coefficients of the neural network. The adaptive correction amount of the frequency reference value is determined, and the reference value of the total active power is calculated. Simultaneously, considering the safe operation of the system and equipment protection, the power adjustment range and power change rate of the units are limited. An economic-frequency coordinated optimization objective function is established, the power reference value is calculated, and a power command is issued. Finally, the actual output power of the power station is monitored, and an early warning is issued when the deviation exceeds a threshold. This invention achieves synergy, economy, and reliability of energy storage devices and renewable energy power station outputs by determining the correction amount of the frequency reference value from the perspective of eliminating the steady-state error of the weak power grid and allocating active power from the perspective of coordinated support.

Description

Technical Field

[0001] This invention belongs to the field of new energy grid-connected power generation, specifically involving an adaptive frequency coordinated support method for energy storage and new energy grid-connected devices under weak power grid conditions. Background Technology

[0002] In recent years, my country's photovoltaic and wind power generation technologies have developed rapidly. However, due to constraints such as natural resources and environmental protection, new energy power plants are usually built in remote areas and connected to the main power grid via ultra-high voltage technology and long-distance transmission lines. This makes the line impedance non-negligible, resulting in a weak connection between the new energy power plants and the power grid. As the penetration rate of new energy increases, the power grid gradually exhibits characteristics of a weak grid, characterized by "high impedance, low inertia, and low disturbance rejection." Its frequency is susceptible to fluctuations, and the "fluctuation" and "randomness" of new energy output further exacerbate frequency instability. Therefore, research on frequency support technologies for weak power grids is of great significance.

[0003] Currently, there are two main methods for frequency support at renewable energy power plants: centralized and decentralized. Decentralized methods involve each generating unit independently adjusting and supporting the weak grid frequency. However, decentralized control of units is prone to conflicts, resulting in local optima but poor overall performance, failing to leverage the synergistic effect of the power plants. Centralized methods involve the power plant issuing commands to allocate power, often using a fixed-coefficient averaging method. This allocation method does not consider the actual operating conditions of the generating units, leading to resource waste. Existing strategies for the coordinated support of energy storage and renewable energy power plants have the following problems:

[0004] 1. The overall control strategy design is complex, the frequency regulation speed is slow, there are many restrictions on energy storage, the dynamic performance is poor, and the flexibility of plug-and-play energy storage and the synergy of peak shaving and valley filling are not fully utilized.

[0005] 2. Existing energy storage and new energy power stations mostly use primary frequency regulation, which has steady-state error. They cannot adaptively adjust the controller parameters according to the actual frequency situation, thereby adjusting the frequency reference value and eliminating steady-state error.

[0006] 3. Current frequency support strategies for weak power grids only consider frequency regulation, without taking into account the economy and robustness of the strategy at the same time as frequency regulation. Summary of the Invention

[0007] This invention addresses the problems existing in the prior art by providing an adaptive frequency coordination support method for energy storage and new energy grid-connected devices under weak grid conditions. This method can take into account the coordinated frequency support of new energy power plants with additional energy storage, thereby achieving optimal overall frequency regulation effect and rational resource utilization.

[0008] To address the above technical problems, this invention provides the following technical solution: an adaptive frequency coordination support method for energy storage and new energy grid-connected devices under weak power grid conditions, comprising the following steps:

[0009] S1. Measure the wind speed and solar intensity of the new energy power station to assess the short-term output active power of the energy storage and new energy power station.

[0010] S2. Based on the active power that the energy storage and new energy station can output in the short term, the positive sequence complex vector of the fundamental voltage at the grid connection point is obtained by using a pre-filter, and then input into the phase-locked loop device to lock the frequency of the weak grid connection point.

[0011] S3. Input the frequency of the weak grid connection point in step S2 into the neuron model, then construct a fuzzy rule inference table for various operating conditions, defuzzify to obtain the adaptive neuron proportional coefficient, determine the adaptive correction amount of the frequency reference value, and then calculate the reference value of the total active power of energy storage and new energy power station through active-frequency control.

[0012] S4. Based on the short-term output active power of the energy storage and new energy power stations in step S1 and the reference value of the total active power of the energy storage and new energy power stations in step S3, considering the safe operation of the system and equipment protection, limiting the power adjustment range and power change rate of the units, establishing an economic-frequency collaborative optimization objective function, calculating the power reference value of the energy storage and new energy power stations through intelligent optimization algorithms, and finally issuing power commands by the microgrid central controller.

[0013] S5. Monitor the actual active power output of the unit in step S4 according to the preset time. When the error between the actual active power output of the unit and the power reference value exceeds the threshold, an alarm is issued and the power command issued by the microgrid central controller is adjusted.

[0014] Furthermore, the aforementioned includes the following sub-steps:

[0015] S101, Based on the wind speed v measured by the wind speed sensor i Real-time assessment of the active power P output of the current wind turbine in the short term. m As shown in the following formula:

[0016]

[0017] In the formula, P m P represents the total power output of the wind turbines, m represents the number of wind turbines, and P represents the total power generated by the wind turbines. i Let ρ be the power generated by the i-th wind turbine, ρ be the air density, and C be the power generated by the i-th wind turbine. p V is the wind energy utilization coefficient, S is the swept area of ​​the wind turbine rotor, and v i The wind speed measured by the sensor of the i-th fan;

[0018] S102. Based on the light intensity G measured by the light sensor, evaluate the active power P output by the photovoltaic array in the short term in real time. pV As shown in the following formula:

[0019]

[0020] In the formula, P pV P represents the total power output of the photovoltaic array, where n is the number of photovoltaic arrays, s is the number of photovoltaic cells in the array, and P is the total power output of the array. pVi U represents the power generated by the i-th photovoltaic array. oc Let A be the open-circuit voltage of the photovoltaic cell, A be a constant, G be the light intensity, and I be the light source. D I is the diode current of the photovoltaic cell. sh The current is the series resistor current of the photovoltaic cell;

[0021] S103, Calculate the short-term output active power P of energy storage and new energy power stations. G As shown in the following formula:

[0022]

[0023] In the formula, P ES The output power of the energy storage device depends on the state of charge of the stored energy.

[0024] Furthermore, the aforementioned step S2 includes the following sub-steps:

[0025] S201. Transform the three-phase voltage at the grid connection point of a weak power grid from the three-phase stationary coordinate system to the αβ coordinate system using Clark transformation:

[0026] Let the voltage at the point of common coupling (PCC) of the weak current network be as follows:

[0027]

[0028] In the formula, U pccm Let θ be the voltage amplitude at point PCC. s This is the actual phase angle at point PCC;

[0029] S202. The voltage in the three-phase stationary coordinate system is transformed to the αβ coordinate system using Clark transformation to obtain the α component U of the three-phase voltage at the grid connection point. pccα and β component U pccβ As shown in the following formula:

[0030]

[0031] S203. Using the α component of the voltage in the αβ coordinate system as the real axis and the β component as the imaginary axis, construct a voltage complex vector input pre-filter to obtain the fundamental positive sequence component of the voltage. Then, perform a Park transform on it to obtain the fundamental positive sequence dq axis component of the voltage. Multiply the β component in equation (5) by the unit imaginary part j and add the α component to obtain the complex electrical quantity, i.e., the voltage complex vector u. pccαβ =u pccα +jupccβ The voltage complex vector is input to the pre-filter G. P (s), to obtain the fundamental positive sequence synchronization component u of the voltage. pccαβP :

[0032]

[0033] In the formula, k s ω is the damping coefficient. n This is the system's rated angular frequency. The fundamental positive-sequence synchronization component u of the voltage... pccαβP The fundamental positive-sequence dq component of the voltage is obtained through Park transform, and a complex vector u is constructed. pccdqP =u pccdP +ju pccqP ;

[0034] S204. By designing a phase transfer function with a fast dynamic response and zero overshoot in phase detection, derive the transfer function of the phase-locked loop (PLL) controller; the closed-loop transfer function G of the PLL is also derived. PLL (s) is as follows:

[0035]

[0036] In the formula, G θ (s) is the transfer function of the controller;

[0037] Pre-filter G P (s) The phase transfer function G is obtained after linearizing the small signal. D (s) is as follows:

[0038]

[0039] S205, Based on transfer function G PLL (s) and transfer function G D The transfer function H(s) between the input phase and the output phase is obtained as follows:

[0040]

[0041] S206. According to the closed-loop transfer function G of the phase-locked device in step S204... PLL (s), the transfer function of the phase-locked device controller is obtained as follows:

[0042]

[0043] S207. The desired phase transfer function is used to deduce the transfer function of the phase-locked loop controller. The expression for the phase transfer function H(s) is:

[0044]

[0045] In the formula, a i b j m and n are constants, satisfying the following constraints:

[0046]

[0047] Using n first-order low-pass filters in series, the expression for the phase transfer function H(s) is:

[0048]

[0049] In the formula, T Hi Let be the time constant of the i-th filter;

[0050] S208. From the transfer function of the phase-locked device controller in step S206, the transfer function of the phase-locked device controller is obtained as follows:

[0051]

[0052] The fundamental positive sequence q-axis component of the three-phase voltage is input into the phase-locked loop device, and the current phase angle is detected to obtain the current frequency f at the weak grid connection point.

[0053] Furthermore, the aforementioned step S3 includes the following sub-steps:

[0054] S301, the rated frequency f n The detected instantaneous frequency f(k) is converted into the state variables x1(k) and x2(k) required by the frequency ratio and integral for the artificial intelligence algorithm:

[0055]

[0056] In the formula, k is the number of iterations, and Δ is the difference;

[0057] S302. Establish a neuron model in the artificial intelligence algorithm, sum the detected frequency ratio and integral state variables according to the corresponding weight coefficients, and continuously adjust the weight coefficients to ensure algorithm convergence.

[0058] The neuron model is as follows:

[0059]

[0060] In the formula, u(k) is the neuron output, i.e., the correction amount of the frequency reference value, K is the neuron's proportional coefficient, w(k) is the weighting coefficient, and η p η i These are the proportional and integral learning rates, respectively;

[0061] The weight coefficient w(k) is normalized as follows:

[0062]

[0063] In the formula, These are the normalized weighting coefficients;

[0064] Considering that the proportional and integral parameters are highly positively correlated with the deviation e(k) and the difference Δe(k), equation (16) is simplified as follows:

[0065]

[0066] S303. Construct a fuzzy controller. Select triangular membership functions for both input and output variables. Construct a fuzzy rule inference table. Input the proportional and integral state variables of the frequency into the fuzzy controller. Use its output as the proportional coefficient of the neuron model.

[0067] The following logic is used to determine the fuzzy rule inference table and the adaptive neuron proportional coefficient.

[0068] When the frequency deviates from the rated value to zero, the scaling factor of the neuron model remains unchanged;

[0069] When the frequency deviates from the rated value to the threshold AS, and the rate of change is the threshold BS, the scaling factor of the neuron model is set to the first preset range.

[0070] When the frequency deviates from the rated value to the threshold CM, and the rate of change is the threshold DM, the scaling factor of the neuron model is set to the second preset range.

[0071] When the frequency deviates from the nominal value to the threshold EB, and the rate of change is the threshold FB, the scaling factor of the neuron model is set to the third preset interval. The centroid method is used for defuzzification to determine the adaptive neuron scaling factor. The adaptive correction amount for the frequency reference value is:

[0072]

[0073] By using the frequency reference value correction, the frequency can be adjusted without deviation, eliminating the steady-state error in the primary frequency regulation, and ultimately stabilizing the grid frequency to the rated value.

[0074] S304. Input the adaptive correction amount u of the frequency reference value obtained in step S303 into the active power-frequency control to obtain the total active power reference value P of the energy storage and new energy power station. ref As shown in the following formula:

[0075]

[0076] In the formula, m1 is the droop coefficient, and P is the actual power generated by the new energy power station.

[0077] Furthermore, the aforementioned step S4 includes the following sub-steps:

[0078] S401. Establish the economic-frequency joint optimization objective function:

[0079] max J = max(r(r)(p) m (t)+p pv (t)+p ES (t))-(R b +R ES ))

[0080] min J = min(R) b +R ES +τ(f ref -f( t )) 2 )

[0081] R b =r p (t)λ p +r m (t)λ m +r n (t)λ n (twenty one)

[0082] In the formula, r(t) is the day-ahead electricity price, and R b For the planned reserve cost, R ES Let τ be the energy storage cost, τ be the robustness weight, and r be the weight. p r m r n The price is the day-ahead standby plan price, λ p For positive rotation reserve capacity, λ m For negative spinning reserve capacity, λ n This is non-rotating reserve capacity;

[0083] S402. Combining step S401, considering the safe operation of the system and equipment protection, the power balance constraints of the new energy power station, the output constraints of the generator unit, and the capacity of the energy storage device are used as constraints to solve for the reference values ​​of the active power of the energy storage device and the new energy generator unit.

[0084] Power balance constraint: p m (t)+p PV (t)+p ES (t)=p ref (t) (22) Wind power output and ramping constraints:

[0085]

[0086] In the formula, P m(t) represents the wind power generation during time period t; H m_p H represents the permissible uphill gradient for wind power generation systems. m_m The ramp rate is the allowable downhill ramp rate for the wind power generation system, and both ramp rate limits are positive.

[0087] Photovoltaic power generation output and ramping constraints:

[0088] 0 < p PV (t)<P PV

[0089] -H PV_m <|p PV (t)-p PV (t-1)<H PV_p (24) In the formula, P PV (t) represents the photovoltaic power generation during time period t; H PV_p H represents the permissible ramp rate for a photovoltaic power generation system. PV_m The ramp-up rate is the allowable ramp-up rate for the photovoltaic power generation system, and both ramp-up rate limits are positive.

[0090] Energy storage device capacity constraints:

[0091] p ES (t)=[SOC(t)-SOC0]P ES

[0092] SOC min <SOC(t)<SOC max (25)

[0093]

[0094] In the formula, SOC(t) represents the state of charge of the energy storage device during time period t, SOC0 represents the initial state of charge of the energy storage device, and S m P represents the total capacity of the energy storage device. ES SOC is the total power of the energy storage device. max The upper limit of the state of charge (SOC) of an energy storage device. min η represents the state-of-charge limit of the energy storage device, δ represents the charge retention capability, and η represents the charge holding capability. C For charging efficiency, less than 1, η F The discharge efficiency is less than 1.

[0095] S403. In MATLAB, the active power reference values ​​of the energy storage device and the new energy power station are obtained by simultaneously solving the intelligent optimization algorithm. Finally, the power command is issued by the microgrid central controller.

[0096] Furthermore, step S5 mentioned above specifically involves: monitoring the execution status of the active power target command at fixed time intervals; if the following equation is satisfied, issuing an early warning to the microgrid central controller and checking the execution status of the command.

[0097] E1=|p m,t -p m，t-τ |≥δ1

[0098] E2=|p pV，t -p pV,t-τ ≥δ2 (27)

[0099] In the formula, p m,t Let p be the active power output of the wind turbine at time t. m,t-τ p is the active power output of the wind turbine at time t-τ. pV,t Let p be the active power output of the photovoltaic array at time t. pV,t-τ δ1 and δ2 are the active power output of the photovoltaic array at time t-τ, and the power deviation thresholds are δ1 and δ2. If the deviation exceeds the threshold, an early warning will be issued immediately.

[0100] Another aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any of the methods described in the present invention.

[0101] In another aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods described in the present invention.

[0102] Compared with the prior art, the beneficial technical effects of the present invention using the above technical solution are as follows:

[0103] 1. Fully utilize the rapid charging and discharging capabilities of energy storage devices, with fast response speed, and can quickly increase power to support the frequency in the early stages of power shortage;

[0104] 2. By adopting an adaptive frequency reference value correction, it can adapt to various operating conditions of weak power grids, while eliminating the steady-state error of primary frequency regulation, so that the frequency can eventually stabilize to the rated value.

[0105] 3. While adjusting the frequency, economy and robustness were considered, achieving multi-objective optimization of frequency modulation. Attached Figure Description

[0106] Figure 1 This is a schematic diagram of the overall process of the present invention.

[0107] Figure 2 This is a membership function graph of the frequency deviation e of the present invention.

[0108] Figure 3 This is a membership function graph of the frequency deviation change rate Δe of the present invention.

[0109] Figure 4 The neuron proportionality coefficient of this invention The membership function graph.

[0110] Figure 5 This is the frequency response curve of the present invention.

[0111] Figure 6 This is a schematic diagram showing the increased power output of the power station and energy storage in this invention.

[0112] Figure 7 The neuron proportionality coefficient of this invention Line graph. Detailed Implementation

[0113] To better understand the technical content of the present invention, specific embodiments are described below in conjunction with the accompanying drawings.

[0114] In this invention, various aspects of the invention are described with reference to the accompanying drawings, in which numerous illustrative embodiments are shown. Embodiments of the invention are not limited to those depicted in the drawings. It should be understood that the invention is implemented through any of the various concepts and embodiments described above, as well as the concepts and embodiments described in detail below, because the concepts and embodiments disclosed herein are not limited to any particular implementation. Furthermore, some aspects of the invention disclosed may be used alone or in any suitable combination with other aspects of the invention disclosed.

[0115] This example is based on a new energy power station consisting of wind turbines, photovoltaic arrays, and energy storage batteries. It uses a pre-filter and phase-locked loop device to quickly detect the frequency of the weak grid connection point. It uses a neuron model and fuzzy rule inference table to optimize the controller parameters, obtain the total frequency reference value and the required power adjustment. Considering the power constraints of the units and energy storage, an economic-frequency co-optimization objective function is established to rationally allocate active power. The microgrid central controller issues power commands. Finally, it monitors the actual active power output of the units at fixed time intervals and issues an early warning when the deviation from the reference frequency exceeds a threshold.

[0116] refer to Figure 1 This invention discloses an adaptive frequency coordination support method for energy storage and new energy grid-connected devices under weak power grid conditions, comprising the following steps:

[0117] S1. Calculate the total available active power capacity of the wind turbine by measuring real-time wind speed using a high-precision wind speed sensor, and calculate the total available active power capacity of the photovoltaic power station by measuring solar irradiance using photovoltaic modules connected in series with an optimizer. This leads to the total available active power capacity of the energy storage and new energy power station. Step S1 includes the following sub-steps:

[0118] S101. Measure the wind speed v sensed by each wind turbine blade using a high-precision wind speed sensor. i Real-time assessment of the active power P output of the current wind turbine in the short term. m As shown in the following formula:

[0119]

[0120] In the formula, P m P represents the total power output of the wind turbines, m represents the number of wind turbines, and P represents the total power generated by the wind turbines. i Let ρ be the power generated by the i-th wind turbine, ρ be the air density, and C be the power generated by the i-th wind turbine. p V is the wind energy utilization coefficient, S is the swept area of ​​the wind turbine rotor, and v i The wind speed is measured by the sensor of the i-th fan. Due to the wake effect, although the fans are in the same environment, the wind speed felt by the fan blades at different locations is different.

[0121] S102. Based on the illuminance G measured by the photovoltaic modules of the series optimizer, evaluate in real time the active power P output by the current photovoltaic array in the short term. pV As shown in the following formula:

[0122]

[0123] In the formula, P pV P represents the total power output of the photovoltaic array, where n is the number of photovoltaic arrays, s is the number of photovoltaic cells in the array, and P is the total power output of the array. pVi U represents the power generated by the i-th photovoltaic array. oc Let A be the open-circuit voltage of the photovoltaic cell, A be a constant, G be the light intensity, and I be the light source. D I is the diode current of the photovoltaic cell. sh The current is the series resistor current of the photovoltaic cell;

[0124] S103. Since photovoltaic arrays receive the same amount of sunlight under the same conditions, the power generated by the entire photovoltaic array group is the sum of the power generated by each individual photovoltaic array. Therefore, the short-term output active power P of energy storage and new energy power stations can be calculated. G As shown in the following formula:

[0125]

[0126] In the formula, P ES The output power of the energy storage device depends on the state of charge of the stored energy.

[0127] S2. Based on the short-term output active power of energy storage and new energy power stations, the fundamental positive sequence complex vector of the grid connection point voltage is obtained using a pre-filter, and then input into the phase-locked loop device to lock the frequency of the weak grid connection point; Step S2 includes the following sub-steps:

[0128] S201. Transform the three-phase voltage at the grid connection point of a weak power grid from the three-phase stationary coordinate system to the αβ coordinate system using Clark transformation:

[0129] Let the voltage at the point of common coupling (PCC) of the weak current network be as follows:

[0130]

[0131] In the formula, U pccm Let θ be the voltage amplitude at point PCC. s This is the actual phase angle at point PCC;

[0132] S202. The voltage in the three-phase stationary coordinate system is transformed to the αβ coordinate system using Clark transformation to obtain the α component U of the three-phase voltage at the grid connection point. pccα and β component U pccβ As shown in the following formula:

[0133]

[0134] S203. Using the α component of the voltage in the αβ coordinate system as the real axis and the β component as the imaginary axis, construct a voltage complex vector input pre-filter to obtain the fundamental positive sequence component of the voltage. Then, perform a Park transform on it to obtain the fundamental positive sequence dq axis component of the voltage. Multiply the β component in equation (5) by the unit imaginary part j and add the α component to obtain the complex electrical quantity, i.e., the voltage complex vector u. pccαβ =u pccα +ju pccβ To improve the accuracy and dynamic performance of phase and frequency detection, and to suppress harmonic interference, a voltage complex vector input pre-filter G is used. P (s), to obtain the fundamental positive sequence synchronization component u of the voltage. pccαβP :

[0135]

[0136] In the formula, k s ω is the damping coefficient. n This is the system's rated angular frequency. The fundamental positive-sequence synchronization component u of the voltage... pccαβP The fundamental positive-sequence dq component of the voltage is obtained through Park transform, and a complex vector u is constructed. pccdqP =u pccdP +ju pccqP ;

[0137] S204. By designing a phase transfer function with a fast dynamic response and zero overshoot in phase detection, derive the transfer function of the phase-locked loop (PLL) controller; the closed-loop transfer function G of the PLL is also derived. PLL (s) is as follows:

[0138]

[0139] In the formula, G θ (s) is the transfer function of the controller;

[0140] Pre-filter G P (s) The phase transfer function G is obtained after linearizing the small signal. D (s) is as follows:

[0141]

[0142] S205, Based on transfer function G PLL (s) and transfer function G D The transfer function H(s) between the input phase and the output phase is obtained as follows:

[0143]

[0144] S206. According to the closed-loop transfer function G of the phase-locked device in step S204... PLL (s), the transfer function of the phase-locked device controller is obtained as follows:

[0145]

[0146] S207. The desired phase transfer function is used to deduce the transfer function of the phase-locked loop controller. The expression for the phase transfer function H(s) is:

[0147]

[0148] In the formula, a i b j m and n are constants, satisfying the following constraints:

[0149]

[0150] Using n first-order low-pass filters in series, the expression for the phase transfer function H(s) is:

[0151]

[0152] In the formula, T Hi Let be the time constant of the i-th filter;

[0153] S208. From the transfer function of the phase-locked device controller in step S206, the transfer function of the phase-locked device controller is obtained as follows:

[0154] The fundamental positive sequence q-axis component of the three-phase voltage is input into the phase-locked loop device, and the current phase angle is detected to obtain the current frequency f at the weak grid connection point.

[0155] S3. Input the frequency of the weak grid connection point in step S2 into the neuron model, then construct a fuzzy rule inference table for various operating conditions, defuzzify to obtain the adaptive neuron proportional coefficient, determine the adaptive correction amount of the frequency reference value, and then calculate the reference value of the total active power of energy storage and new energy power station through active-frequency control.

[0156] Step S3 includes the following sub-steps:

[0157] S301, the rated frequency f n The detected instantaneous frequency f(k) is converted into the state variables x1(k) and x2(k) of the frequency ratio and integral of the artificial intelligence algorithm:

[0158]

[0159] In the formula, k is the number of iterations, and Δ is the difference;

[0160] S302. Establish a neuron model in the artificial intelligence algorithm, sum the proportion of detected frequencies and integral state variables according to the corresponding weight coefficients, and continuously adjust the weight coefficients to ensure algorithm convergence.

[0161] To overcome the problems of divergence and unclear objectives in traditional Hebb learning, a supervised Hebb learning rule is adopted, and a neuron model is established accordingly. The neuron model is as follows:

[0162]

[0163] In the formula, u(k) is the neuron output, i.e., the correction amount of the frequency reference value, K is the neuron's proportional coefficient, w(k) is the weighting coefficient, and η p η i These are the proportional and integral learning rates, respectively;

[0164] To ensure the convergence of the algorithm, the weight coefficients w(k) are normalized as follows:

[0165]

[0166] In the formula, These are the normalized weighting coefficients;

[0167] Considering that the proportional and integral parameters are highly positively correlated with the deviation e(k) and the difference Δe(k), equation (16) is simplified as follows:

[0168]

[0169] S303. Construct a fuzzy controller. Select triangular membership functions for both input and output variables. Construct a fuzzy rule inference table. Input the proportional and integral state variables of the frequency into the fuzzy controller. Use its output as the proportional coefficient of the neuron model.

[0170] Because weak power grids are susceptible to frequency disturbances and have complex operating conditions, fuzzy inference is used to obtain the proportional coefficients of neurons in order to achieve adaptive parameter adjustment under different operating conditions. Specifically as follows:

[0171] The proportional gain K of a neuron directly affects the values ​​of the proportional and integral parameters, thus indirectly affecting the controller's performance. Furthermore, the proportional gain K determines the strength of the controller's response to error signals; a larger K can enhance the system's response speed but may cause overshoot and instability, while a smaller K can reduce system fluctuations but may lead to a slow response. The proportional gain K affects the magnitude of weight updates and the sensitivity of the network output; adjusting K can improve the network's convergence speed and learning accuracy.

[0172] Therefore, we consider using fuzzy control to adaptively adjust the proportional coefficient of the neuron based on the relationship between frequency deviation and frequency change rate. Choose the membership function for the triangle, such as Figure 2 , Figure 3 , Figure 4 As shown.

[0173] The fuzzy rule inference table is determined according to the following logic:

[0174] When the frequency deviates from the rated value to zero, the scaling factor of the neuron model... constant;

[0175] When the frequency deviates very little from the nominal value, reaching the threshold AS, and the rate of change is also very small, reaching the threshold BS, the scaling factor of the neuron model... Very small is set as the first preset range;

[0176] When the frequency deviates moderately from the rated value, reaching the threshold CM, and the rate of change is also moderate, reaching the threshold DM, the scaling factor of the neuron model... Moderate, set as the second preset range;

[0177] When the frequency deviates significantly from the rated value (threshold EB) and the rate of change is also very large (threshold FB), the scaling factor of the neuron model... The third preset interval is set, and the membership relationships of more specific variables are shown in Table 1, the fuzzy rule inference table. The centroid method is used for defuzzification to determine the adaptive neuron proportional coefficient.

[0178] Table 1

[0179]

[0180] Note: NB, NM, NS, Z, PS, PM, and PB represent negative large, negative medium, negative small, zero, positive small, positive medium, and positive large, respectively.

[0181] The adaptive correction amount for the frequency reference value is:

[0182]

[0183] By using the new frequency reference value correction, frequency adjustment without deviation can be achieved, eliminating the steady-state error problem in primary frequency regulation, and ultimately stabilizing the grid frequency to the rated value.

[0184] S304. Input the adaptive correction amount u of the frequency reference value obtained in step S303 into the active power-frequency control to obtain the total active power reference value P of the energy storage and new energy power station. ref As shown in the following formula:

[0185]

[0186] In the formula, m1 is the droop coefficient, and P is the actual power generated by the new energy power station.

[0187] S4. Based on the short-term output active power of the energy storage and renewable energy power stations obtained in step S1, and the reference value of the total active power of the energy storage and renewable energy power stations obtained in step S3, considering the safe operation of the system and equipment protection, the power adjustment range and power change rate of the units are limited. An economic-frequency collaborative optimization objective function is established, and the power reference value of the energy storage and renewable energy power stations is calculated through an intelligent optimization algorithm. Finally, the power command is issued by the microgrid central controller. Specifically, this includes the following sub-steps:

[0188] S401. The allocation of active power reference values ​​for power stations should consider both frequency stability and high overall system economic efficiency in conjunction with day-ahead electricity prices. An economic-frequency co-optimization objective function is established as follows:

[0189] max J = max(r(t)(p) m (t)+p pv (t)+p ES (t))-(R b +R ES ))

[0190] min J = min(R) b +R ES +τ(f ref -f(t)) 2 )

[0191] R b =rp (t)λ p +r m (t)λ m +r n (t)λ n (twenty one)

[0192] In the formula, r(t) is the day-ahead electricity price, and R b For the planned reserve cost, R ES Let τ be the energy storage cost, τ be the robustness weight, and r be the weight. p r m r n The price is the day-ahead standby plan price, λ p For positive rotation reserve capacity, λ m For negative spinning reserve capacity, λ n This is non-rotating reserve capacity;

[0193] S402. Combining step S401, considering the safe operation of the system and equipment protection, the power balance constraints of the new energy power station, the output constraints of the generator unit, and the capacity of the energy storage device are used as constraints to solve for the reference values ​​of the active power of the energy storage device and the new energy generator unit.

[0194] Power balance constraint: p m (t)+p PV (t)+p ES (t)=p ref (t) (22)

[0195] Wind power output and ramping constraints:

[0196]

[0197] In the formula, P m (t) represents the wind power generation during time period t; H m_p H represents the permissible uphill gradient for wind power generation systems. m_m The ramp rate is the allowable downhill ramp rate for the wind power generation system, and both ramp rate limits are positive.

[0198] Photovoltaic power generation output and ramping constraints:

[0199] 0 < p PV (t) < PV

[0200] -H PV_m <|p PV (t)-p PV (t-1)<H PV_p (twenty four)

[0201] In the formula, P PV (t) represents the photovoltaic power generation during time period t; HPV_p H represents the permissible ramp rate for a photovoltaic power generation system. PV_m The ramp-up rate is the allowable ramp-up rate for the photovoltaic power generation system, and both ramp-up rate limits are positive.

[0202] To fully utilize the ability of energy storage devices to rapidly absorb and generate power, achieving plug-and-play functionality and peak shaving / valley filling, the following modeling approach is considered: When the power generated by wind and solar power exceeds the total power required for frequency support, the energy storage absorbs the excess power until the device is fully charged and reaches its upper limit. When the power generated by wind and solar power is less than the total power required for frequency support, the energy storage bears the power shortfall, discharging to support the power until it drops to the lower limit of the device's capacity. When the power generated by wind and solar power equals the total power required for frequency support, the energy storage neither absorbs nor generates power.

[0203] Energy storage device capacity constraints:

[0204] p ES (t)=[SOC(t)-SOC0]R ES

[0205] SOC min <SOC(t)<SOC max (25)

[0206]

[0207] In the formula, SOC(t) represents the state of charge of the energy storage device during time period t, SOC0 represents the initial state of charge of the energy storage device, and S m P represents the total capacity of the energy storage device. ES SOC is the total power of the energy storage device. max The upper limit of the state of charge (SOC) of an energy storage device. min η represents the state-of-charge limit of the energy storage device, δ represents the charge retention capability, and η represents the charge holding capability. C For charging efficiency, less than 1, η F The discharge efficiency is less than 1.

[0208] S403. In MATLAB, the active power reference values ​​of the energy storage device and the new energy power station are obtained by simultaneously solving the intelligent optimization algorithm. Finally, the power command is issued by the microgrid central controller.

[0209] S5. Monitor the actual active power output of the generator set in step S4 according to the preset time. When the error between the actual active power output and the power reference value exceeds the threshold, an alarm is issued, and the power command issued by the microgrid central controller is adjusted. Specifically, the execution status of the active power target command is monitored at fixed time intervals. If the following equation is satisfied, an early warning is issued to the microgrid central controller to check the execution status of the command.

[0210] E1=|p m，t -p m,t -τ≥δ1

[0211] E2=|p pV,t -p pV,t-τ ≥δ2 (27)

[0212] In the formula, p m,t Let p be the active power output of the wind turbine at time t. m,t-τ p is the active power output of the wind turbine at time t-τ. pV,t Let p be the active power output of the photovoltaic array at time t. pV,t-τ δ1 and δ2 are the active power output of the photovoltaic array at time t-τ, and the power deviation thresholds are δ1 and δ2. If the deviation exceeds the threshold, an early warning will be issued immediately.

[0213] The method will be further explained below with reference to the results of specific embodiments. Table 2: Key Simulation Parameters of New Energy Power Stations with Energy Storage provides the key simulation parameters of new energy power stations with energy storage.

[0214] Table 2

[0215]

[0216] Figure 5 The presentation shows the frequency response curves of this method and the traditional primary frequency regulation method when the power grid experiences a 7.8MW power deficit. It can be seen that the traditional method has a deeper frequency drop, a slower response rate, and cannot eventually stabilize to the rated value, exhibiting steady-state errors, which are detrimental to the normal operation of the system. In contrast, this method has a faster response rate, a smaller maximum frequency difference, and can stabilize the frequency to the rated value. Figure 6 The data shows the increased power output of power plants and energy storage. It can be seen that in the early stages of a power shortage, energy storage quickly generates power to support the frequency. As the frequency gradually recovers, the power output gradually decreases, while the power output of wind turbines and photovoltaic power plants gradually increases. Figure 7 Displaying neuron proportionality coefficients The curve shows that as the frequency changes, the neuron proportional coefficient adaptively adjusts, effectively supporting the frequency.

[0217] Another aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any of the methods described in the present invention.

[0218] In another aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods described in the present invention.

[0219] While the present invention has been described above with reference to preferred embodiments, it is not intended to limit the invention. Those skilled in the art can make various modifications and refinements without departing from the spirit and scope of the invention. Therefore, the scope of protection of the present invention shall be determined by the claims.

Claims

1. An adaptive frequency coordination support method for energy storage and new energy grid-connected devices under weak power grid conditions, characterized in that, Includes the following steps: S1. Measure the wind speed and solar intensity of the new energy power station to assess the short-term output active power of the energy storage and new energy power station. S2. Based on the active power that the energy storage and new energy power station can output in the short term, the positive sequence complex vector of the fundamental voltage at the grid connection point is obtained by using a pre-filter, and then input into the phase-locked loop device to lock the frequency of the weak grid connection point. S3. Input the frequency of the weak grid connection point in step S2 into the neuron model, then construct a fuzzy rule inference table for various operating conditions, defuzzify to obtain the adaptive neuron proportional coefficient, determine the adaptive correction amount of the frequency reference value, and then calculate the reference value of the total active power of energy storage and new energy power station through active-frequency control. S4. Based on the short-term output active power of the energy storage and new energy power stations in step S1 and the reference value of the total active power of the energy storage and new energy power stations in step S3, considering the safe operation of the system and equipment protection, limiting the power adjustment range and power change rate of the units, establishing an economic-frequency collaborative optimization objective function, calculating the power reference value of the energy storage and new energy power stations through intelligent optimization algorithms, and finally issuing power commands by the microgrid central controller. S5. Monitor the actual active power output of the unit in step S4 according to the preset time. When the error between the actual active power output of the unit and the power reference value exceeds the threshold, an alarm is issued and the power command issued by the microgrid central controller is adjusted.

2. The adaptive frequency coordinated support method for energy storage and new energy grid connection devices under weak power grid conditions according to claim 1, characterized in that, Step S1 includes the following sub-steps: S101, Based on the wind speed v measured by the wind speed sensor i Real-time assessment of the active power P output of the current wind turbine in the short term. m As shown in the following formula: In the formula, P m P represents the total power output of the wind turbines, m represents the number of wind turbines, and P represents the total power generated by the wind turbines. i Let ρ be the power generated by the i-th wind turbine, ρ be the air density, and C be the power generated by the i-th wind turbine. p V is the wind energy utilization coefficient, S is the swept area of ​​the wind turbine rotor, and v i The wind speed measured by the sensor of the i-th fan; S102. Based on the light intensity G measured by the light sensor, evaluate the active power P output by the photovoltaic array in the short term in real time. pV As shown in the following formula: In the formula, P pV P represents the total power output of the photovoltaic array, where n is the number of photovoltaic arrays, s is the number of photovoltaic cells in the array, and P is the total power output of the array. pVi U represents the power generated by the i-th photovoltaic array. oc Let A be the open-circuit voltage of the photovoltaic cell, A be a constant, G be the light intensity, and I be the light source. D I is the diode current of the photovoltaic cell. sh The current is the series resistor current of the photovoltaic cell; S103, Calculate the short-term output active power P of energy storage and new energy power stations. G As shown in the following formula: In the formula, P ES The output power of the energy storage device depends on the state of charge of the stored energy.

3. The adaptive frequency coordinated support method for energy storage and new energy grid connection devices under weak power grid conditions according to claim 1, characterized in that, Step S2 includes the following sub-steps: S201. Transform the three-phase voltage at the grid connection point of the weak grid in the three-phase stationary coordinate system to the αβ coordinate system using Clark transformation: Let the voltage at the point of common coupling (PCC) of the weak grid be as follows: In the formula, U pccm Let θ be the voltage amplitude at point PCC. s This is the actual phase angle at point PCC; S202. The voltage in the three-phase stationary coordinate system is transformed to the αβ coordinate system using Clark transformation to obtain the α component U of the three-phase voltage at the grid connection point. pccα and β component U pccβ As shown in the following formula: S203. Using the α component of the voltage in the αβ coordinate system as the real axis and the β component as the imaginary axis, construct a voltage complex vector input pre-filter to obtain the fundamental positive sequence component of the voltage. Then, perform a Park transform on it to obtain the fundamental positive sequence dq axis component of the voltage. Multiply the β component in equation (5) by the unit imaginary part j and add the α component to obtain the complex electrical quantity, i.e., the voltage complex vector u. pccαβ =u pccα +ju pccβ The voltage complex vector is input to the pre-filter G. P (s), to obtain the fundamental positive sequence synchronization component u of the voltage. pccαβP : In the formula, k s ω is the damping coefficient. n The system's rated angular frequency is the fundamental positive-sequence synchronization component u of the voltage. pccαβP The fundamental positive-sequence dq component of the voltage is obtained through Park transform, and a complex vector u is constructed. pccdqP =u pccdP +ju pccqP ; S204. By designing a phase transfer function with a fast dynamic response and zero overshoot in phase detection, derive the transfer function of the phase-locked loop (PLL) controller; the closed-loop transfer function G of the PLL is also derived. PLL (s) is as follows: In the formula, G θ (s) is the transfer function of the controller; Pre-filter G P (s) The phase transfer function G is obtained after linearizing the small signal. D (s) is as follows: S205, Based on transfer function G PLL (s) and transfer function G D The transfer function H(s) between the input phase and the output phase is obtained as follows: S206. According to the closed-loop transfer function G of the phase-locked device in step S204... PLL (s), the transfer function of the phase-locked device controller is obtained as follows: S207. The desired phase transfer function is used to deduce the transfer function of the phase-locked loop controller. The expression for the phase transfer function H(s) is: In the formula, a i b j m and n are constants, satisfying the following constraints: Using n first-order low-pass filters in series, the expression for the phase transfer function H(s) is: In the formula, T Hi Let be the time constant of the i-th filter; S208. From the transfer function of the phase-locked device controller in step S206, the transfer function of the phase-locked device controller is obtained as follows: The fundamental positive sequence q-axis component of the three-phase voltage is input into the phase-locked loop device, and the current phase angle is detected to obtain the current frequency f at the weak grid connection point.

4. The adaptive frequency coordinated support method for energy storage and new energy grid connection devices under weak power grid conditions according to claim 1, characterized in that, Step S3 includes the following sub-steps: S301, the rated frequency f n The detected instantaneous frequency f(k) is converted into the state variables x1(k) and x2(k) required by the frequency ratio and integral for the artificial intelligence algorithm: In the formula, k is the number of iterations, and Δ is the difference; S302. Establish a neuron model in the artificial intelligence algorithm, sum the detected frequency ratio and integral state variables according to the corresponding weight coefficients, and continuously adjust the weight coefficients to ensure algorithm convergence. The neuron model is as follows: In the formula, u(k) is the neuron output, i.e., the correction amount of the frequency reference value, K is the neuron's proportional coefficient, w(k) is the weighting coefficient, and η p η i These are the proportional and integral learning rates, respectively; The weight coefficient w(k) is normalized as follows: In the formula, These are the normalized weighting coefficients; Considering that the proportional and integral parameters are highly positively correlated with the deviation e(k) and the difference Δe(k), equation (16) is simplified as follows: S303. Construct a fuzzy controller. Select triangular membership functions for both input and output variables. Construct a fuzzy rule inference table. Input the proportional and integral state variables of the frequency into the fuzzy controller. Use its output as the proportional coefficient of the neuron model. The following logic is used to determine the fuzzy rule inference table and the adaptive neuron proportional coefficient. When the frequency deviates from the rated value to zero, the scaling factor of the neuron model remains unchanged; When the frequency deviates from the rated value to the threshold AS, and the rate of change is the threshold BS, the scaling factor of the neuron model is set to the first preset range. When the frequency deviates from the rated value to the threshold CM, and the rate of change is the threshold DM, the scaling factor of the neuron model is set to the second preset range. When the frequency deviates from the nominal value to the threshold EB, and the rate of change is the threshold FB, the scaling factor of the neuron model is set to the third preset interval. The centroid method is used for defuzzification to determine the adaptive neuron scaling factor. The adaptive correction amount for the frequency reference value is: By using the frequency reference value correction, the frequency can be adjusted without deviation, eliminating the steady-state error in the primary frequency regulation, and ultimately stabilizing the grid frequency to the rated value. S304. Input the adaptive correction amount u of the frequency reference value obtained in step S303 into the active power-frequency control to obtain the total active power reference value P of the energy storage and new energy power station. ref As shown in the following formula: In the formula, m1 is the droop coefficient, and P is the actual power generated by the new energy power station.

5. The adaptive frequency coordination support method for energy storage and new energy grid connection devices under weak power grid conditions according to claim 1, characterized in that, Step S4 includes the following sub-steps: S401. Establish the economic-frequency collaborative optimization objective function: max J=max(r(t)(p m (t)+p pv (t)+p ES (t))-(R b +R ES )) min J′=min(R b +R ES +τ(f ref -f(t) 2 ) R b =r p (t)λ p +r m (t)λ m +r n (t)λ n (21) In the formula, r(t) is the day-ahead electricity price, and R b For the planned reserve cost, R ES Let τ be the energy storage cost, τ be the robustness weight, and r be the weight. p r m r n The price is the day-ahead standby plan price, λ p For positive rotation reserve capacity, λ m For negative spinning reserve capacity, λ n For non-spinning reserve capacity; S402, combined with step S401, considering the safe operation of the system and equipment protection, the power balance constraints of the new energy power station, the unit output constraints, and the capacity of the energy storage device are used as constraints to solve for the reference values ​​of active power of the energy storage device and the new energy unit; Power balance constraint: p m (t)+p PV (t)+p ES (t)=p ref (t) (22) Wind power output and ramping constraints: In the formula, P m (t) represents the wind power generation during time period t; H m_p H represents the permissible uphill gradient for wind power generation systems. m_m The ramp rate is the allowable downhill ramp rate for the wind power generation system, and both ramp rate limits are positive. Photovoltaic power generation output and ramping constraints: 0<p PV (t)<P PV -H PV_m <|p PV (t)-p PV (t-1)|<H PV_p (24) In the formula, P PV (t) represents the photovoltaic power generation during time period t; H PV_p H represents the permissible ramp rate for a photovoltaic power generation system. PV_m The ramp-down rate is the allowable ramp-up rate for the photovoltaic power generation system, and both ramp-up rate limits are positive. Energy storage device capacity constraints: p ES (t)=[SOC(t)-SOC0]P ES SOC mim <SOC(t)<SOC max (25) In the formula, SOC(t) represents the state of charge of the energy storage device during time period t, SOC0 represents the initial state of charge of the energy storage device, and S m P represents the total capacity of the energy storage device. ES SOC is the total power of the energy storage device. max The upper limit of the state of charge (SOC) of an energy storage device. min η represents the state-of-charge limit of the energy storage device, δ represents the charge retention capability, and η represents the charge holding capability. C For charging efficiency, less than 1, η F The discharge efficiency is less than 1. S403. Using intelligent optimization algorithms in MATLAB, the active power parameters of energy storage devices and new energy power plants are obtained by simultaneous solution. The power value is then considered, and finally, the power command is issued by the microgrid central controller.

6. The adaptive frequency coordinated support method for energy storage and new energy grid connection devices under weak power grid conditions according to claim 1, characterized in that, Step S5 specifically involves: monitoring the execution status of active power target instructions at fixed time intervals; if the following equation is satisfied, issuing an early warning to the microgrid central controller and checking the instruction execution status. E1=|p m，t -p m，t-τ |≥δ1 E2=|p pV,t -p pV，t-τ |≥δ2 (27) In the formula, p m,t Let p be the active power output of the wind turbine at time t. m,t-τ p is the active power output of the wind turbine at time t-τ. pV,t Let p be the active power output of the photovoltaic array at time t. pV,t-τ δ1 and δ2 are the active power output of the photovoltaic array at time t-τ, and the power deviation thresholds are δ1 and δ2. If the deviation exceeds the threshold, an early warning will be issued immediately.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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