A parameter design method for distributed energy storage system

Abstract

The present invention discloses a parameter design method for a distributed energy storage system, comprising the following steps: S1: establishing a small disturbance stability analysis model of a distributed energy storage system considering the influence of lines, and obtaining a state matrix of the system; S2: considering the position change of the system eigenvalue under the change of line parameters, designing the objective function of the ivy optimization algorithm, and initializing the iteration parameters of the algorithm; S3: performing algorithm iteration, designing the optimal line parameters of the system, so as to optimize the stability of the distributed energy storage system. The present invention utilizes the advanced global search and positioning capabilities of the ivy optimization algorithm to quickly and accurately design the optimal line parameters of the distributed energy storage system, and improve the stability of the distributed energy storage system.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage systems, and in particular to a parameter design method for a distributed energy storage system. Background Art

[0002] Distributed energy has output characteristics such as volatility and randomness. When connected to the power system, it is easy to cause voltage fluctuations or even system oscillations. It is often necessary to configure a distributed energy storage system to achieve peak shaving and valley filling of the power system to ensure the stability of the system. However, in practice, the mismatch of line impedance and the coupling of various control parameters in the distributed energy storage system can easily lead to system instability. Therefore, the key to improving the stability of the distributed energy storage system lies in how to design the system's line parameters and control parameters. Most current parameter design methods rely on observing the movement path and parameter change laws of the system's characteristic roots under different working conditions to obtain the parameter range that makes the system stable, but such methods cannot obtain specific quantitative results. Summary of the invention

[0003] In order to solve the above technical problems, the present invention provides a parameter design method for a distributed energy storage system. Through the advanced global search and positioning capabilities of the ivy optimization algorithm, the optimal line parameters of the distributed energy storage system are quickly and accurately designed to improve the stability of the distributed energy storage system.

[0004] To achieve the above purpose, the technical solution provided by the present invention is:

[0005] A parameter design method for a distributed energy storage system comprises the following steps:

[0006] S1: Establish a small disturbance stability analysis model of the distributed energy storage system considering the influence of the line and obtain the state matrix of the system;

[0007] S2: Considering the position change of system eigenvalues ​​under line parameter changes, the objective function of the Ivy optimization algorithm is designed and the iteration parameters of the algorithm are initialized;

[0008] S3: Perform algorithm iteration to design the optimal line parameters of the system to optimize the stability of the distributed energy storage system;

[0009] Furthermore, the specific steps of step S1 are as follows:

[0010] S1-1: Establish a small disturbance stability analysis model of the distributed energy storage system considering the influence of the line, and obtain the state matrix of the system:

[0011] The distributed energy storage system consists of four distributed energy storage modules and one power load module, which are connected to the DC bus through a transmission line. In the distributed energy storage module, the energy storage side converter adopts droop control and additional capacitor control to achieve power sharing and voltage stability.oN is the rated output voltage of the energy storage side, u s and i s are the output voltage and output current of the energy storage side before the converter, K is the droop coefficient, C v is the additional capacitance coefficient, T is the time constant, and s is the Laplace operator; in the power load module, the load side converter adopts double closed-loop control to adjust the load voltage, u LN and u L is the rated value and actual value of the load-side output voltage before the converter, i L is the load-side output current before the converter. The parameter subscripts 1, 2, 3, and 4 are used to distinguish the parameters of different modules. The small disturbance stability analysis model of the system is:

[0012]

[0013] In formula (1), Δx S and Δx P are the state vectors of the control parameters in the four distributed energy storage modules and the power load module, Δu o , Δi o , Δu f , Δi f and Δu dc They are the output voltage u of the energy storage side o , energy storage side output current i o , load side output voltage u f , load side output current i f and DC bus voltage u dc A is the state variable of the distributed energy storage system, which is composed of the distributed energy storage module parameters, load module parameters and corresponding transmission line parameters;

[0014] Furthermore, the specific steps of step S2 are as follows:

[0015] S2-1: Design the objective function of the ivy optimization algorithm:

[0016] By solving the state matrix A of the distributed energy storage system, all characteristic roots of the distributed energy storage system can be obtained. The characteristic root closest to the imaginary axis on the left side of the complex plane is the dominant mode of the system. The position of the dominant mode on the left side of the complex plane is the main influencing factor of the system stability margin. Therefore, the position of the dominant mode in the distributed energy storage system is used as the basis for parameter design. The designed parameters keep the real part of the dominant mode of the system negative and away from the imaginary axis to improve the stability of the system. Therefore, the objective function of the ivy optimization algorithm is set to:

[0017] f(x)=Max{[Λ(i,i)] RP} (2)

[0018] In formula (2), Max is the function of taking the maximum value in a set of numbers, x is the system parameter to be designed, Λ is the characteristic root diagonal matrix of the distributed energy storage system, [Λ(i,i)] RP is the real part of the i-th oscillation mode in the characteristic root diagonal matrix Λ, i = 1, 2, 3…n, n is the total number of state variables of the system;

[0019] S2-2: Initialize the iteration parameters of the ivy optimization algorithm:

[0020] The energy storage line parameters that have the greatest impact on the dominant mode change of the system are set as the ivy population individuals in the algorithm, that is, the parameters to be optimized, and the upper and lower limits of the parameters are set. U and I L , maximum number of iterations T max , ivy population size N and ivy population dimension D;

[0021] Furthermore, the specific steps of step S3 are as follows:

[0022] S3-1: Initialize the ivy population and obtain the initial population particles with a population size of N and a dimension of D. The initial position of the population is expressed as:

[0023] I a =I L +rand(1,D).*(I U -I L ) (3)

[0024] In formula (3), I a is the initial position of the ivy population, a=1,2,3…N, I U and I L are the upper and lower limits of the population individuals, respectively. rand(1,D) generates a random array of 1 row and D columns in the range [0,1]. .* is the dot product calculation.

[0025] S3-2: The ivy optimization algorithm uses the two formation stages of simulating ivy "young leaves" and "mature leaves" to realize the update of population position. In the "young leaves" stage, ivy grows upward and explores the optimal position vertically in the target area; in the "mature leaves" stage, ivy begins to expand the width of branches and leaves and explores the optimal position horizontally in the target area. Through these two position update strategies, global search is realized in the target area to avoid falling into local solutions. The update formula of population position is:

[0026]

[0027] In formula (4), is the new position of the population obtained in this iteration, randn(1,D) is a normally distributed random number generated with 1 row and D columns, I a is the population position in the previous iteration, I Best is the population position that makes the objective function best in the previous iteration, α is a random number in the range of [0,1.5], f(·) is the objective function, I aa For I a The reference position, Δν a is the growth rate of the ivy population, I aa and Δν a The expression is:

[0028]

[0029] In formula (5), rand is a random number in the range [0,1]. and I a Arrange the ath and a-1th positions in the array in descending order according to the fitness value of the objective function, t is the number of iterations, and . / is the dot division calculation;

[0030] S3-3: Substitute the obtained ivy population position into the objective function, and selectively update the ivy population position by comparing the objective function fitness values ​​of various populations, that is, retain the ivy population that makes the dominant mode farther away from the imaginary axis on the left side of the complex plane than the previous generation, and continue to iterate. The update formula is:

[0031]

[0032] S3-4: Check whether the number of iterations t reaches the maximum number of iterations T max If it is reached, the position of the ivy population with the best fitness value at this time is output, that is, the optimal line parameters designed as the result, and the iteration ends; otherwise, let t=t+1 and proceed to step S3-2.

[0033] Compared with the prior art, the principles and advantages of this solution are as follows:

[0034] This scheme first establishes a small disturbance stability analysis model of the distributed energy storage system considering the influence of the line, and obtains the state matrix of the system. Then, considering the position change of the system eigenvalue under the change of line parameters, the objective function of the ivy optimization algorithm is designed, and the iteration parameters of the algorithm are initialized. Finally, the algorithm is iterated using the ivy optimization algorithm to design the optimal line parameters of the system to optimize the stability of the distributed energy storage system. This scheme uses the advanced global search and positioning capabilities of the ivy optimization algorithm to quickly and accurately design the optimal line parameters of the distributed energy storage system and improve the stability of the distributed energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 A flow chart of parameter design of a distributed energy storage system in an embodiment of the present invention;

[0036] Figure 2 A topological structure and control block diagram of a distributed energy storage system in an embodiment of the present invention;

[0037] Figure 3 It is a diagram of an iterative process of designing the energy storage side line length of a distributed energy storage system in an embodiment of the present invention;

[0038] Figure 4 It is a characteristic root distribution diagram before parameter design of the distributed energy storage system in an embodiment of the present invention;

[0039] Figure 5 This is a characteristic root distribution diagram after the parameters of the distributed energy storage system are designed in an embodiment of the present invention. DETAILED DESCRIPTION

[0040] The present invention will be further described below in conjunction with specific embodiments:

[0041] Figure 1 The figure shows the parameter design flow chart of distributed energy storage system. Figure 2 The figure shows a topological structure and control block diagram of a distributed energy storage system. A parameter design method of a distributed energy storage system described in this embodiment includes the following steps:

[0042] S1: Establish a small disturbance stability analysis model of the distributed energy storage system considering the influence of the line, and obtain the state matrix of the system; the specific process is as follows:

[0043] S1-1: Establish a small disturbance stability analysis model of the distributed energy storage system considering the influence of the line, and obtain the state matrix of the system:

[0044] The distributed energy storage system consists of four distributed energy storage modules and a power load module, which are connected to the DC bus through transmission lines. Figure 2 As shown in the figure, in the distributed energy storage module, the energy storage side converter adopts droop control and additional capacitor control to achieve power sharing and voltage stability, u oN is the rated output voltage of the energy storage side, u s and i s are the output voltage and output current of the energy storage side before the converter, K is the droop coefficient, C v is the additional capacitance coefficient, T is the time constant, and s is the Laplace operator; in the power load module, the load side converter adopts double closed-loop control to adjust the load voltage, u LN and u L is the rated value and actual value of the load-side output voltage before the converter, iL is the load-side output current before the converter. The parameter subscripts 1, 2, 3, and 4 are used to distinguish the parameters of different modules. The small disturbance stability analysis model of the system is:

[0045]

[0046] In formula (7), Δx S and Δx P are the state vectors of the control parameters in the four distributed energy storage modules and the power load module, Δu o , Δi o , Δu f , Δi f and Δu dc They are the output voltage u of the energy storage side o , energy storage side output current i o , load side output voltage u f , load side output current i f and DC bus voltage u dc A is the state variable of the distributed energy storage system, which is composed of the distributed energy storage module parameters, load module parameters and corresponding transmission line parameters;

[0047] The following step S2 considers the position change of the system eigenvalue under the change of line parameters, designs the objective function of the ivy optimization algorithm, and initializes the iteration parameters of the algorithm. The specific process is as follows:

[0048] S2-1: Design the objective function of the ivy optimization algorithm:

[0049] By solving the state matrix A of the distributed energy storage system, all characteristic roots of the distributed energy storage system can be obtained. The characteristic root closest to the imaginary axis on the left side of the complex plane is the dominant mode of the system. The position of the dominant mode on the left side of the complex plane is the main influencing factor of the system stability margin. Therefore, the position of the dominant mode in the distributed energy storage system is used as the basis for parameter design. The designed parameters keep the real part of the dominant mode of the system negative and away from the imaginary axis to improve the stability of the system. Therefore, the objective function of the ivy optimization algorithm is set to:

[0050] f(x)=Max{[Λ(i,i)] RP} (8)

[0051] In formula (8), Max is the function of taking the maximum value in a set of numbers, x is the system parameter to be designed, Λ is the characteristic root diagonal matrix of the distributed energy storage system, [Λ(i,i)] RP is the real part of the i-th oscillation mode in the characteristic root diagonal matrix Λ, i = 1, 2, 3…n, n is the total number of state variables of the system;

[0052] S2-2: Initialize the iteration parameters of the ivy optimization algorithm:

[0053] The energy storage line parameters that have the greatest impact on the dominant mode change of the system are set as the ivy population individuals in the algorithm, that is, the parameters to be optimized, and the upper and lower limits of the parameters are set. U and I L , maximum number of iterations T max , ivy population size N and ivy population dimension D;

[0054] The following step S3 is to iterate the algorithm and design the optimal line parameters of the system to optimize the stability of the distributed energy storage system. The specific process is as follows:

[0055] S3-1: Initialize the ivy population and obtain the initial population particles with a population size of N and a dimension of D. The initial position of the population is expressed as:

[0056] I a =I L +rand(1,D).*(I U -I L ) (9)

[0057] In formula (9), I a is the initial position of the ivy population, a=1,2,3…N, I U and I L are the upper and lower limits of the population individuals, respectively. rand(1,D) generates a random array of 1 row and D columns in the range [0,1]. .* is the dot product calculation.

[0058] S3-2: The ivy optimization algorithm uses the two formation stages of simulating ivy "young leaves" and "mature leaves" to realize the update of population position. In the "young leaves" stage, ivy grows upward and explores the optimal position vertically in the target area; in the "mature leaves" stage, ivy begins to expand the width of branches and leaves and explores the optimal position horizontally in the target area. Through these two position update strategies, global search is realized in the target area to avoid falling into local solutions. The update formula of population position is:

[0059]

[0060] In formula (10), is the new position of the population obtained in this iteration, randn(1,D) is a normally distributed random number generated with 1 row and D columns, I a is the population position in the previous iteration, I Best is the population position that makes the objective function best in the previous iteration, α is a random number in the range of [0,1.5], f(·) is the objective function, I aa For I aThe reference position, Δν a is the growth rate of the ivy population, I aa and Δν a The expression is:

[0061]

[0062] In formula (11), rand is a random number in the range [0,1]. and I a Arrange the ath and a-1th positions in the array in descending order according to the fitness value of the objective function, t is the number of iterations, and . / is the dot division calculation;

[0063] S3-3: Substitute the obtained ivy population position into the objective function, and selectively update the ivy population position by comparing the objective function fitness values ​​of various populations, that is, retain the ivy population that makes the dominant mode farther away from the imaginary axis on the left side of the complex plane than the previous generation, and continue to iterate. The update formula is:

[0064]

[0065] S3-4: Check whether the number of iterations t reaches the maximum number of iterations T max If it is reached, the position of the ivy population with the best fitness value at this time is output, that is, the optimal line parameters designed as the result, and the iteration ends; otherwise, let t=t+1 and proceed to step S3-2.

[0066] In order to verify the effectiveness of the parameter design method of the distributed energy storage system, a Figure 2 The distributed energy storage system model shown in the figure sets the parameters to be designed as the energy storage side line parameters of the distributed energy storage system, that is, the energy storage side line length. The initial values ​​of the energy storage side line length before design are 0.5, 0.3, 0.1, and 0.1, respectively. The upper and lower limits of the parameters are set to [0,1], and the maximum number of iterations is T max is 100, the ivy population size N is 45, and the ivy population dimension D is 4. Through the design iteration of the ivy optimization algorithm, the optimal solutions for the energy storage side line length are 0.0891, 0.0352, 0.6184, and 0.2573 respectively. The design iteration process of the energy storage side line length is as follows: Figure 3 shown.

[0067] Substitute the optimal line parameters obtained by design into Figure 2 The distributed energy storage system model shown in Figure 1 is Figure 4 , Figure 5The distribution diagram of the system characteristic roots before and after the distributed energy storage system parameter design is shown. It can be seen that before the distributed energy storage system parameter design, the dominant mode of the system is located on the right side of the complex plane, and the real part is 1.26. At this time, the system is unstable; after the parameter design, the dominant mode of the system moves to the left side of the complex plane, and the real part is -3.82. At this time, the stability margin of the system is improved, indicating that the ivy optimization algorithm can accurately design the system parameters of the distributed energy storage system, so that the real part of the system dominant mode remains negative and away from the imaginary axis, thereby improving the stability of the system.

[0068] The embodiments described above are only preferred embodiments of the present invention and are not intended to limit the scope of implementation of the present invention. Therefore, all changes made according to the shape and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A parameter design method for a distributed energy storage system, characterized in that: The following steps are involved: S1: Establish a small disturbance stability analysis model of the distributed energy storage system considering the influence of the line and obtain the state matrix of the system; S2: Considering the position change of system eigenvalues ​​under line parameter changes, the objective function of the Ivy optimization algorithm is designed and the iteration parameters of the algorithm are initialized; S3: Perform algorithm iteration to design the optimal line parameters of the system to optimize the stability of the distributed energy storage system; The specific steps of step S1 are as follows: S1-1: Establish a small disturbance stability analysis model of the distributed energy storage system considering the influence of the line, and obtain the state matrix of the system: The distributed energy storage system consists of four distributed energy storage modules and one power load module, which are connected to the DC bus through transmission lines. In the distributed energy storage module, droop control and additional capacitor control are used for the energy storage side converter to achieve power sharing and voltage stability; in the power load module, double closed-loop control is used for the load side converter to adjust the load voltage; the small disturbance stability analysis model of the system is: In formula (1), Δx S and Δx P are the state vectors of the control parameters in the four distributed energy storage modules and the power load module, Δu o , Δi o , Δu f , Δi f and Δu dc They are the output voltage u of the energy storage side o , energy storage side output current i o , load side output voltage u f , load side output current i f and DC bus voltage u dc A is the state variable of the distributed energy storage system, which is composed of the distributed energy storage module parameters, load module parameters and corresponding transmission line parameters; The specific steps of step S2 are as follows: S2-1: Design the objective function of the ivy optimization algorithm: By solving the state matrix A of the distributed energy storage system, all characteristic roots of the distributed energy storage system can be obtained. The characteristic root closest to the imaginary axis on the left side of the complex plane is the dominant mode of the system. The position of the dominant mode on the left side of the complex plane is the main influencing factor of the system stability margin. Therefore, the position of the dominant mode in the distributed energy storage system is used as the basis for parameter design. The designed parameters keep the real part of the dominant mode of the system negative and away from the imaginary axis to improve the stability of the system. Therefore, the objective function of the ivy optimization algorithm is set to: f(x)=Max{[Λ(i,i)] RP } (2) In formula (2), Max is the function of taking the maximum value in a set of numbers, x is the system parameter to be designed, Λ is the characteristic root diagonal matrix of the distributed energy storage system, [Λ(i,i)] RP is the real part of the i-th oscillation mode in the characteristic root diagonal matrix Λ, i = 1, 2, 3…n, n is the total number of state variables of the system; S2-2: Initialize the iteration parameters of the ivy optimization algorithm: The energy storage line parameters that have the greatest impact on the dominant mode change of the system are set as the ivy population individuals in the algorithm, that is, the parameters to be optimized, and the upper and lower limits of the parameters are set. U and I L , maximum number of iterations T max , ivy population size N and ivy population dimension D; The specific steps of step S3 are as follows: S3-1: Initialize the ivy population and obtain the initial population particles with a population size of N and a dimension of D. The initial position of the population is expressed as: I a =I L +rand(1,D).*(I U -I L ) (3) In formula (3), I a is the initial position of the ivy population, a=1,2,3…N, I U and I L are the upper and lower limits of the population individuals, respectively. rand(1,D) generates a random array of 1 row and D columns in the range [0,1]. .* is the dot product calculation. S3-2: The ivy optimization algorithm uses the two formation stages of "young leaves" and "mature leaves" to realize the update of population position. In the "young leaves" stage, the ivy grows upward and explores the optimal position vertically in the target area; in the "mature leaves" stage, the ivy begins to expand the width of the branches and leaves and explores the optimal position horizontally in the target area. Through these two position update strategies, a global search is realized in the target area to avoid falling into a local solution. The update formula of the population position is: In formula (4), is the new position of the population obtained in this iteration, randn(1,D) is a normally distributed random number generated with 1 row and D columns, I a is the population position in the previous iteration, I Best is the population position that makes the objective function best in the previous iteration, α is a random number in the range of [0,1.5], f(·) is the objective function, I aa For I a The reference position, Δν a is the growth rate of the ivy population, I aa and Δν a The expression is: In formula (5), rand is a random number in the range [0,1]. and I a Arrange the ath and a-1th positions in the array in descending order according to the fitness value of the objective function, t is the number of iterations, and . / is the dot division calculation; S3-3: Substitute the obtained ivy population position into the objective function, and selectively update the ivy population position by comparing the objective function fitness values ​​of various populations, that is, retain the ivy population that makes the dominant mode farther away from the imaginary axis on the left side of the complex plane than the previous generation, and continue to iterate. The update formula is: S3-4: Check whether the number of iterations t reaches the maximum number of iterations T max If it is reached, the position of the ivy population with the best fitness value at this time is output, that is, the optimal line parameters designed as the result, and the iteration ends; otherwise, let t=t+1 and proceed to step S3-2.

Images