Power system control optimization method considering distributed wind power generation random volatility

Abstract

The invention discloses an electric power system control optimization method considering distributed wind power generation random volatility, belongs to the technical field of novel electric power system control, and solves the problem of how to effectively resist the distributed wind power generation random volatility. Fitting a probability density function of distributed wind power generation output power; establishing an impedance relation model of the distributed wind power generation grid-connected current and the control parameters; generating a power sample representing distributed wind power generation randomness by using a probability density function of distributed wind power generation output power, obtaining a corresponding wind speed value through a relational expression of power and wind speed, and establishing a control optimization model; based on a batch simulation function of an electromagnetic transient simulation platform CloudPSS, obtaining an optimal control parameter combination meeting frequency and voltage stability indexes; according to the method, the random volatility of distributed wind power generation can be effectively resisted, so that the stability and robustness of the system are improved.

Description

Technical Field

[0001] The invention belongs to the technical field of novel power system control, and relates to a power system control optimization method taking into account the random volatility of distributed wind power generation. Background Art

[0002] With the rapid development of new power systems, the access ratio of distributed wind power generation has been increasing, and it has gradually become one of the main forms of power generation. However, the random volatility of distributed wind power generation makes the operation of the power system more and more complicated. In this complex operation scenario, the power system may face a series of operation risks, such as voltage fluctuations, frequency deviations, and even cascading failures in extreme cases. The existence of these risks has posed unprecedented challenges to the safety and stability of the power system, making how to achieve stable operation of the power system under changing scenarios an urgent problem to be solved. In order to ensure the safe and reliable operation of the system under changing scenarios, the adoption of effective optimization control strategies has become an important measure to meet these challenges.

[0003] In the context of the current new power system with a high proportion of wind power generation access, the primary task of formulating effective control strategies is to accurately characterize the random characteristics of distributed wind power generation. At present, the methods for characterizing the randomness of wind power generation mainly include time series analysis (such as autoregressive model and moving average model), Monte Carlo simulation method, power spectrum analysis method and probability statistics method. Among them, probability statistics methods usually use parameter estimation technology. However, these methods are generally based on the assumption that wind power generation obeys a certain specific distribution, which may bring certain deviations. In addition, the current control strategies mainly include automatic generation control system (Automatic Generation Control, AGC), automatic voltage regulator (Automatic Voltage Regulator, AVR), optimal control, intelligent control and artificial intelligence methods. However, these control strategies are usually based on the deterministic assumption of the system, and the strong randomness of high-proportion distributed wind power generation is less considered.

[0004] In the existing technology, the uncertainty of the power system is usually characterized by parameter estimation models such as Gaussian mixture model (GMM), but the limited Gaussian distribution is difficult to accurately characterize the randomness of wind power generation, and the number of distributions is difficult to determine. In terms of control optimization, the existing methods are mostly based on fixed topology grid design, without considering real-time changing scenarios, which limits the robustness of the system to cope with the fluctuations of distributed wind power generation. When characterizing the randomness of wind power generation, the existing methods usually simplify its output into high wind or low wind scenarios, which is difficult to adapt to the random volatility caused by a high proportion of wind power generation. Other studies have tried to use Gaussian mixture models for characterization, but the number of distributions is difficult to accurately determine, which affects the applicability of the model. The existing control strategy is only designed for deterministic power system operation scenarios, without considering multiple operation scenarios and the randomness of wind power generation, so the control optimization effect is difficult to cope with the random volatility caused by a high proportion of wind power generation. Summary of the invention

[0005] The technical solution of the present invention is used to solve the problem of how to effectively resist the random volatility of distributed wind power generation, thereby improving the stability and robustness of the system.

[0006] The present invention solves the above technical problems through the following technical solutions: The present invention provides a power system control optimization method taking into account the random volatility of distributed wind power generation, comprising: S1 randomly generates power sample data of distributed wind power generation output; S2 uses kernel function and non-parametric estimation to estimate the probability density of each power sample data, and fits the probability density function of the output power of distributed wind power generation; S3 Establish an impedance relationship model between the grid-connected current and control parameters of distributed wind power generation to determine the value range of the control parameters when the system is connected to the grid and stable under small disturbances, which serves as a constraint condition for the optimization of the control parameters; S4 uses the probability density function of the output power of distributed wind power generation to generate power samples that represent the randomness of distributed wind power generation, and obtains the corresponding wind speed value for batch simulation driving through the relationship between power and wind speed; S5 Establish a control optimization model with frequency and voltage stability indicators as objective functions, control parameters as optimization variables, and control parameter value ranges of system power balance and small disturbance stability as constraints; S6 solves the control parameters of the control optimization model to obtain the optimal control parameter combination that meets the frequency and voltage stability indicators.

[0007] Furthermore, the kernel function adopts the Epanechnikov function.

[0008] Furthermore, the probability density function of the output power of distributed wind power generation is expressed as: (2) in, represents the total number of samples, Indicates Output power samples, The width of the x-axis region representing the probability density function estimate, Indicates that the independent variable is The Epanechnikov function.

[0009] Furthermore, the impedance relationship model between the distributed wind power grid-connected current and the control parameters is as follows: (10) in, represents the transfer function of the proportional-integral controller, , and are the proportional coefficient and the integral coefficient respectively, represents the Laplace transform factor; represents the transfer function from the control signal to the inverter output signal, , represents the pulse width modulation ratio, is the time delay constant; is the grid-connected current, is the grid-connected reference current, is the voltage at the common connection point, The matrix form of grid-connected current is: Represents the matrix form of grid-connected reference current, Matrix form representing the voltage at the common connection point; and All are intermediate variables.

[0010] Furthermore, the method of generating power samples representing the randomness of distributed wind power generation by using the probability density function of the output power of distributed wind power generation is as follows: 1) Select the probability density function covering the output power of distributed wind power generation Auxiliary distribution of high probability areas , and define the constant ,satisfy For all Established; 2) From auxiliary distribution Generate a candidate sample ; 3) Calculate the value of kernel density estimate and the values ​​in the auxiliary distribution ; 4) Generate a uniform random number in the interval [0, 1] ; 5) If the following inequality If true, then accept the candidate sample ; 6) Repeat steps 1) to 5) until the set sample size is completed.

[0011] Furthermore, the formula of the control optimization model is as follows: (12) in, For stability indicators, is the weight parameter, i=1,2,…n, is the real part of the characteristic root of the closed-loop transfer function of the system, is the inequality constraint of the power grid, is the independent variable, is the deterministic parameter vector, and are the susceptance and conductance parameter matrices of the system respectively; , Represents a node set The voltage phase angle and active power, , Represents a node collection The voltage amplitude and reactive power, Node Collection The voltage phase angle in Represents a node collection The voltage amplitude in .

[0012] Furthermore, the stability indicators include: frequency deviation rate, frequency drift rate, voltage amplitude, and phase angle deviation rate.

[0013] Furthermore, the method for solving the control parameters of the control optimization model is: based on the batch simulation function of the electromagnetic transient simulation platform CloudPSS, the control parameters of the control optimization model are solved using a simulation-driven method.

[0014] The present invention also provides an electronic device, comprising a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the above-mentioned power system control optimization method taking into account the random volatility of distributed wind power generation, and the processor is configured to execute the program stored in the memory.

[0015] The present invention also provides a storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned power system control optimization method taking into account the random volatility of distributed wind power generation are executed.

[0016] The advantages of the present invention are: The present invention uses non-parametric estimation to perform probability density estimation on each power sample data, and fits the probability density function of the output power of distributed wind power generation; establishes an impedance relationship model between the grid-connected current and the control parameters of distributed wind power generation; uses the probability density function of the output power of distributed wind power generation to generate power samples that characterize the randomness of distributed wind power generation, and obtains the corresponding wind speed value through the relationship between power and wind speed; establishes a control optimization model with frequency and voltage stability indicators as objective functions, control parameters as optimization variables, and control parameter value ranges for system power balance and small disturbance stability as constraints; based on the batch simulation function of the electromagnetic transient simulation platform CloudPSS, obtains the optimal control parameter combination that meets the frequency and voltage stability indicators; by obtaining suitable control parameters, the present invention enables the system to effectively resist the random volatility of distributed wind power generation, thereby improving the stability and robustness of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flow chart of a power system control optimization method taking into account the random volatility of distributed wind power generation; Figure 2 It is the structure diagram of electromagnetic transient model of direct drive wind turbine; Figure 3 It is the model diagram of the inverter main circuit and control system; Figure 4 (a) is a block diagram of the phase-locked loop transfer function matrix; Figure 4 (b) is a transfer matrix block diagram of the inverter grid-connected system; Figure 5 It is the structural diagram of the standard example system; Figure 6 It is the power curve of the direct-drive wind turbine for 24 hours a day and every minute generated by Matlab calculation software; Figure 7 It is the system voltage simulation curve. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in combination with the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and specific embodiments: Embodiment 1 like Figure 1 As shown, the power system control optimization method taking into account the random volatility of distributed wind power generation according to the embodiment of the present invention includes the following steps: Step 1: Randomly generate power sample data of distributed wind power generation output.

[0020] Randomly generate distributed wind power output operation sample data to characterize its output within a year. Considering the active power and reactive power data of a year in strong wind, weak wind and random fluctuation scenarios, according to the large differences in wind power generation in the four seasons, it is divided into four sections with each season as a sample set to characterize the output of each season. The power data of each season is sampled, with a sampling time interval of one minute and one hour a day as a sample, so as to construct a power sample set for each season.

[0021] Step 2: Select the Epanechnikov function as the kernel function, use non-parametric estimation to estimate the probability density of each power sample data, and fit the probability density function of the distributed wind power output power.

[0022] The Epanechnikov function is expressed as: (1) in, represents bandwidth, Represents the independent variable of the Epanechnikov function.

[0023] The probability density function of the distributed wind power generation output power is expressed as: (2) in, represents the total number of samples, Indicates Output power samples, The width of the x-axis region representing the probability density function estimate, Indicates that the independent variable is The Epanechnikov function.

[0024] Step 3: Establish an impedance relationship model between the grid-connected current and the control parameters of distributed wind power generation to determine the value range of the control parameters when the system is connected to the grid and stable under small disturbances, which serves as a constraint condition for the optimization of the control parameters.

[0025] The electromagnetic transient model structure of the direct-drive wind turbine is as follows: Figure 2 As shown in Figure 1, the adjustable control parameters of the model include wind rotor speed, pitch angle, generator torque control, grid-side converter control, and cut-in and cut-out wind speed. Figure 2It can be seen that the direct-drive wind turbine generator needs to be connected to the grid through an inverter device, and when a large capacitor is connected in parallel to the DC side of the inverter, the direct-drive wind turbine generator can be simplified as an inverter for analysis. Therefore, in the embodiment of the present invention, the direct-drive wind turbine generator is simplified as an inverter for analysis when calculating the small disturbance stability. The model of the inverter main circuit and control system is as follows: Figure 3 As shown in the figure, the system consists of a main circuit and a control system. The main circuit includes an inverter, a filter, and a transmission line. Represents the DC voltage on the stabilizing capacitor. , , Indicates the inverter side inductance, grid side inductance, filter capacitor, represents the passive damping resistance, Represents the grid impedance. and They represent the output voltage of the inverter and the node voltage vector of the capacitor branch respectively, , and Represent the current vectors of the inverter side, the grid side and the capacitor. The three phases are represented by subscripts a, b and c respectively. The phase-locked loop (PLL) is used to track the phase of the grid voltage. Indicates the output phase of the phase-locked loop. It represents the modulation signal obtained by the control system, and the pulse width modulation ratio is expressed as .

[0026] The proportional-integral controller is used for the current regulator in the dq coordinate system. Represents a proportional-integral controller, the transfer function matrix of the PLL phase-locked loop is shown in Figure 4(a), and the transfer function matrix of the inverter grid-connected system is shown in Figure 4(b).

[0027] In summary, the impedance relationship model between the grid-connected current and control parameters of distributed wind power generation is established as follows:

[0028] (5) (6) (7)

[0029] (10) in, represents the transfer function of the proportional-integral controller, , and are the proportional coefficient and the integral coefficient respectively, represents the Laplace transform factor; represents the transfer function from the control signal to the inverter output signal, , represents the pulse width modulation ratio, is the time delay constant; represents the passive damping resistance, represents the filter capacitance, represents the inductance on the inverter side, represents the grid side inductance, represents the pulse width modulation ratio; Represents the identity matrix. , , , , , , , , , , , and The grid-connected current obtained by simplifying Figure 4 (a) and Figure 4 (b) is shown in Figure 4 (a) and Figure 4 (b) The intermediate variable is the grid-connected reference current, is the voltage at the point of common coupling (PCC); The matrix form of grid-connected current is: Represents the matrix form of grid-connected reference current, A matrix representation of the voltage at the point of common connection.

[0030] The control parameter range of the inverter grid-connected stability can be determined by formula (3) to formula (10) through the Nyquist formula.

[0031] Step 4: Use the probability density function of the distributed wind power output power to generate power samples that characterize the randomness of distributed wind power generation, and obtain the corresponding wind speed value for batch simulation driving through the relationship between power and wind speed.

[0032] The method for generating power samples representing the randomness of distributed wind power generation by using the probability density function of the output power of distributed wind power generation is as follows: 1) Choose a distribution that is easy to sample (such as uniform distribution or Gaussian distribution) , also called auxiliary distribution, and guarantees that the auxiliary distribution Probability density function that can cover the output power of distributed wind power generation The main areas, namely The area of ​​high probability, while defining a constant , which satisfies For all Established; 2) From auxiliary distribution Generate a candidate sample ; 3) Calculate the value of kernel density estimate and the values ​​in the auxiliary distribution ; 4) Generate a uniform random number in the interval [0, 1] ; 5) If the following inequality If true, then accept the candidate sample ; 6) Repeat steps 1) to 5) until the set sample size is completed.

[0033] The relationship between power and wind speed is expressed as follows: (11) in, is the output power of the wind turbine, is the density of air (usually 1.225 kg / m³), is the wind turbine swept area, equal to , is the radius of the wind turbine blade, is the power factor, which indicates the efficiency of the wind turbine in converting wind energy into electrical energy. , It's the wind speed.

[0034] Step 5: Establish a control optimization model with frequency and voltage stability indicators as objective functions, control parameters as optimization variables, and control parameter value ranges of system power balance and small disturbance stability as constraints.

[0035] The formula of the control optimization model is as follows: (12) in, For stability indicators, is the weight parameter, i=1,2,…n, is the real part of the characteristic root of the closed-loop transfer function of the system, is the inequality constraint of the power grid, is the independent variable, is the deterministic parameter vector, and are the susceptance and conductance parameter matrices of the system respectively; , Represents a node set The voltage phase angle and active power, , Represents a node collection The voltage amplitude and reactive power, Node Collection The voltage phase angle in Represents a node collection The voltage amplitude in .

[0036] Step 6: Based on the batch simulation function of the electromagnetic transient simulation platform CloudPSS, the simulation-driven method is used to solve the control parameters of the control optimization model, so as to obtain the optimal control parameter combination that meets the frequency and voltage stability indicators.

[0037] Case Analysis like Figure 5 The structure diagram of the standard example system is shown in FIG. The control parameters are optimized by the method of the present invention on the standard example, and the voltage stability effect before and after the control parameter optimization is compared. The power sample data is generated by Matlab calculation software, and data sampling is performed according to its steps. The power curve is shown in FIG. Figure 6 As shown in the figure, it is used to replace the measured data of the direct-drive fan. Figure 6 As shown in the figure, the direct-drive wind turbine is divided into several large change stages, which respectively represent strong wind and weak wind, and a Gaussian signal is added to each stage to represent the random fluctuation of the output. Figure 6 The power curve data shown in the figure is used to calculate the wind speed change using formula (12). The CloudPSS simulation driver is then used to perform batch simulations to solve the control optimization model to obtain the optimal control parameters, that is, the specific values ​​of the optimized control parameters.

[0038] like Figure 7 As shown in the figure, when the system is running with the parameters before the control parameter optimization, the voltage will fluctuate to a certain extent in the face of the random fluctuation of wind power generation, which is indicated by the dotted line in the figure; and after the optimized control parameters are adopted, the voltage does not fluctuate, which is indicated by the solid line in the figure. Therefore, it can be seen that the method of the present invention is robust in dealing with the uncertainty fluctuation of distributed wind power generation.

[0039] Embodiment 2 An electronic device includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the power system control optimization method taking into account the random volatility of distributed wind power generation in Example 1, and the processor is configured to execute the program stored in the memory.

[0040] Embodiment 3 A storage medium stores a computer program, which, when executed by a processor, executes the steps of the power system control optimization method taking into account the random volatility of distributed wind power generation in embodiment 1.

[0041] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A power system control optimization method taking into account the random volatility of distributed wind power generation, characterized in that: include: S1 randomly generates power sample data of distributed wind power generation output; S2 uses kernel function and non-parametric estimation to estimate the probability density of each power sample data, and fits the probability density function of the output power of distributed wind power generation; S3 Establish an impedance relationship model between the grid-connected current and control parameters of distributed wind power generation to determine the value range of the control parameters when the system is connected to the grid and stable under small disturbances, which serves as a constraint condition for the optimization of the control parameters; S4 uses the probability density function of the output power of distributed wind power generation to generate power samples that represent the randomness of distributed wind power generation, and obtains the corresponding wind speed value for batch simulation driving through the relationship between power and wind speed; S5 Establish a control optimization model with frequency and voltage stability indicators as objective functions, control parameters as optimization variables, and control parameter value ranges of system power balance and small disturbance stability as constraints; S6 solves the control parameters of the control optimization model to obtain the optimal control parameter combination that meets the frequency and voltage stability indicators.

2. The power system control optimization method taking into account the random volatility of distributed wind power generation according to claim 1 is characterized in that: The kernel function uses the Epanechnikov function.

3. The power system control optimization method taking into account the random volatility of distributed wind power generation according to claim 2 is characterized in that: The probability density function of the output power of distributed wind power generation is expressed as: （2） in, represents the total number of samples, Indicates Output power samples, The width of the x-axis region representing the probability density function estimate, Indicates that the independent variable is The Epanechnikov function.

4. The power system control optimization method taking into account the random volatility of distributed wind power generation according to claim 1 is characterized in that: The impedance relationship model between the grid-connected current and control parameters of distributed wind power generation is as follows: （10） in, represents the transfer function of the proportional-integral controller, , and are the proportional coefficient and the integral coefficient respectively, represents the Laplace transform factor; represents the transfer function from the control signal to the inverter output signal, , represents the pulse width modulation ratio, is the time delay constant; is the grid-connected current, is the grid-connected reference current, is the voltage at the common connection point, The matrix form of grid-connected current is: Represents the matrix form of grid-connected reference current, Matrix form representing the voltage at the common connection point; and All are intermediate variables.

5. The power system control optimization method taking into account the random volatility of distributed wind power generation according to claim 1 is characterized in that: The method of generating power samples representing the randomness of distributed wind power generation using the probability density function of distributed wind power generation output power is as follows: 1) Select the probability density function covering the output power of distributed wind power generation Auxiliary distribution of high probability areas , and define the constant ,satisfy For all Established; 2) From auxiliary distribution Generate a candidate sample ; 3) Calculate the value of kernel density estimate and the values ​​in the auxiliary distribution ; 4) Generate a uniform random number in the interval [0, 1] ; 5) If the following inequality If true, then accept the candidate sample ; 6) Repeat steps 1) to 5) until the set sample size is completed.

6. The power system control optimization method according to claim 1 taking into account the random volatility of distributed wind power generation is characterized in that: The formula of the control optimization model is as follows: （12） in, For stability indicators, is the weight parameter, i=1,2,…n, is the real part of the characteristic root of the closed-loop transfer function of the system, is the inequality constraint of the power grid, is the independent variable, is the deterministic parameter vector, and are the susceptance and conductance parameter matrices of the system respectively; , Represents a node set The voltage phase angle and active power, , Represents a node collection The voltage amplitude and reactive power, Node Collection The voltage phase angle in Represents a node collection The voltage amplitude in .

7. The power system control optimization method according to claim 6 taking into account the random volatility of distributed wind power generation is characterized in that: Stability indicators include: frequency deviation rate, frequency drift rate, voltage amplitude, and phase angle deviation rate.

8. The power system control optimization method according to claim 1 taking into account the random volatility of distributed wind power generation is characterized in that: The method for solving the control parameters of the control optimization model is: based on the batch simulation function of the electromagnetic transient simulation platform CloudPSS, the control parameters of the control optimization model are solved using the simulation-driven method.

9. An electronic device, comprising a memory and a processor, characterized in that: The memory is used to store a program that supports the processor to execute the power system control optimization method taking into account the random volatility of distributed wind power generation as described in any one of claims 1 to 8, and the processor is configured to execute the program stored in the memory.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the power system control optimization method taking into account the random volatility of distributed wind power generation as claimed in any one of claims 1 to 8 are executed.

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