A method for reconfiguring energy storage arrays with inconsistent capacity based on vulture search algorithm

Abstract

This invention relates to a method for reconfiguring energy storage arrays with inconsistent capacities based on a vulture search algorithm. This method simulates the behavior of a vulture searching for food, with each individual vulture representing an energy storage unit. The individual's position represents the electrical arrangement of the energy storage array, and the objective function is to maximize the array's output power. The algorithm first collects the array's electrical parameters, including unit capacity and SOH, and then dynamically adjusts the array's electrical connections according to its own mechanism until the algorithm converges, outputting the optimal electrical arrangement of the energy storage array. This method exhibits strong global search capabilities.

Description

Technical field:

[0001] The present invention relates to the field of battery control technology, and in particular to a method for reconfiguring an energy storage array with inconsistent capacity based on a vulture search algorithm. Background technology:

[0002] The installed capacity of renewable energy sources such as photovoltaics and wind power is expanding year by year, and their volatility will have an adverse impact on the safe and stable operation of the power grid. Energy storage is a key technology to solve the problem of new energy consumption and improve the stability of new power systems.

[0003] Energy storage units can be connected in series or parallel to form an energy storage array, meeting power demands while improving system efficiency and lifespan. During array operation, factors such as frequent fluctuations in renewable energy and random load changes can cause some units in the array to experience severe operating conditions such as frequent starts and stops. This can reduce the state of health (SOH) and actual available capacity of the energy storage units, thereby deteriorating the output characteristics of the energy storage system.

[0004] Energy storage array reconfiguration technology is an effective method for mitigating output power disparities between rows. It can be categorized into dynamic and static reconfiguration methods. Dynamic reconfiguration involves using a switch matrix to dynamically change the electrical connections of the energy storage array, thereby evenly distributing energy storage units with lower SOH and lower actual usable capacity across each row. This ultimately balances the capacity of each row, improving the overall operating power and lifespan of the energy storage array. It offers flexible adjustment and strong real-time performance. Summary of the invention:

[0005] The present invention proposes a method for reconstructing energy storage arrays with inconsistent capacity based on a vulture search algorithm, which simulates the behavior of vultures searching for food and has strong global search capabilities. A vulture individual represents an energy storage unit, and the position of the individual is the electrical arrangement of the energy storage array. The objective function is to maximize the output power of the array. The algorithm first collects the electrical parameters of the energy storage array (such as energy storage unit capacity, SOH, etc.), and then dynamically changes the electrical connection of the array according to its own mechanism until the algorithm converges, and outputs the optimal configuration of the electrical arrangement of the energy storage array. To achieve the above purpose, the present invention adopts the following technical solutions:

[0006] A method for reconfiguring an energy storage array with inconsistent capacity based on a vulture search algorithm comprises the following steps:

[0007] Step S1: First, establish an N×N energy storage array, where the components in the same row are connected in parallel, and the components in the same column are connected in series.

[0008]

[0009]

[0010] Where V out is the total output voltage; V maxh is the maximum output voltage of the hth row; I out is the output current of each node; I hc is the output current of the component in row h and column c;

[0011] Step S2: Based on the vulture search algorithm, establish the objective function and constraints. To reduce the impact of reduced capacity of some energy storage units, a reconstruction method is used to balance the total capacity of each row. The variance of each row is used to describe the degree of capacity balance, which is the objective function of this method.

[0012]

[0013] Where: S 2 is the variance between the capacity of each row and the average capacity; C i is the capacity of each column of energy storage units; is the average capacity of the energy storage array; n is the number of columns of the energy storage array;

[0014] Constraints: During the reconfiguration of the energy storage unit, each component only exchanges its row with another component in the same column. Therefore, the reconfiguration variables consisting of the electrical switch states should satisfy the following constraints:

[0015]

[0016] Where x hc Indicates the electrical switch status of row h and column c;

[0017] Step S3: Collect the electrical parameters of the energy storage array. Each vulture represents an energy storage unit. The position of the individual represents the electrical arrangement of the energy storage array. The amount of food consumed by the vulture represents the capacity of each row of the energy storage array. The fitness of the vulture is the variance between the capacity of each row and the average capacity of the array. Initialize the vulture population and fitness, i.e., the energy storage array and the capacity variance of the array.

[0018] Step S4: Select the search space. In the selection phase, the vulture (energy storage unit) determines and selects the best area within the selected search space (energy storage array) based on the amount of food (capacity of each row of the energy storage array) and preys there. The formula is as follows:

[0019] P newi =P best +αr(P mean -P i ),

[0020] Where: P newiis the updated position of the i-th vulture; according to the distribution of different capacity units in the energy storage array, the changed position is adjusted by parameters, where α is a parameter used to control the change of the vulture's position, and its value is between 1.5 and 2. r is a random number between 0 and 1. By coordinating r and α, a search of any distance between 1.5 and 2 is achieved. The above process represents the vulture (energy storage unit) moving in the area to search for the best area; P best represents the search space currently selected by the vulture based on the best position determined in the previous search; P mean is the average position of the vultures after the previous search; P i Update the previous position of the i-th vulture; after this stage, the vulture successfully selects the best area;

[0021] Step S5: In the search space search phase, the vulture searches for prey in the selected search space, that is, the optimal position of energy storage units of different capacities in the selected area, and moves in different directions in the spiral space to accelerate the search; the optimal position of the dive is expressed by the following formula,

[0022] P newi =P i +y(i)·(P i -P i+1 )+x(i)·(P i -P mean ),

[0023]

[0024]

[0025] θ(i)=a·π·rand,

[0026] r(i)=θ(i)+R rand,

[0027] In the formula, a and R represent the parameters of the spiral shape change; a is a parameter between 5 and 10, which is used to determine the inter-point search angle of the center point. The angle change simulates the position change of the energy storage unit near the center point of the selected search area to achieve the optimization reconstruction process; R takes a value between 0.5 and 2 and is used to determine the number of search cycles and the search range; rand is a random number between (0, 1); x(i) and y(i) represent the position of the vulture in polar coordinates, and both have values ​​of (-1, 1); according to the distribution of different capacity units in the selected area, the position of the energy storage unit change is adjusted by parameters. The above process represents the search for the optimal arrangement position of the energy storage unit in the selected area, with the purpose of minimizing the fitness (capacity variance);

[0028] Step S6: Dive phase. During the dive phase, the vulture swings from the optimal position in the search space to the target prey, and all points also move toward the optimal point.

[0029] P newi =rand·P best +x1(i)·(P i -c1·P mean )+y1(i)·(P i -c2·P best ),

[0030]

[0031]

[0032] θ(i)=a·π·rand,

[0033] r(i)=θ(i),

[0034] Where c1 and c2 are parameters between 1 and 2. After the dive phase, the vulture algorithm has determined the energy storage array arrangement formed after this search. The optimization of the energy storage array is evaluated by the fitness of the following formula:

[0035]

[0036] Step S7: t=t+1;

[0037] Step S8: Determine whether t <t max If yes, return to step S4; otherwise, go to step S9;

[0038] Step S9: Output the optimal reconstruction result and the maximum output power.

[0039] The present invention has strong global search capability. Description of the drawings:

[0040] Figure 1 It is a schematic diagram of the N×N energy storage array model.

[0041] Figure 2 This is a schematic diagram of the switch matrix. Specific implementation method:

[0042] A method for reconfiguring an energy storage array with inconsistent capacity based on a vulture search algorithm comprises the following steps:

[0043] Step S1: First, establish an N×N energy storage array, where the components in the same row are connected in parallel, and the components in the same column are connected in series.

[0044]

[0045]

[0046] Where V out is the total output voltage; V maxh is the maximum output voltage of the hth row; I out is the output current of each node; I hc is the output current of the component in row h and column c;

[0047] Step S2: Based on the vulture search algorithm, establish the objective function and constraints. To reduce the impact of reduced capacity of some energy storage units, a reconstruction method is used to balance the total capacity of each row. The variance of each row is used to describe the degree of capacity balance, which is the objective function of this method.

[0048]

[0049] Where: S 2 is the variance between the capacity of each row and the average capacity; C i is the capacity of each column of energy storage units; is the average capacity of the energy storage array; n is the number of columns of the energy storage array;

[0050] Constraints: During the reconfiguration of the energy storage unit, each component only exchanges its row with another component in the same column. Therefore, the reconfiguration variables consisting of the electrical switch states should satisfy the following constraints:

[0051]

[0052] Where x hc Indicates the electrical switch status of row h and column c;

[0053] Step S3: Collect the electrical parameters of the energy storage array. Each vulture represents an energy storage unit. The position of the individual represents the electrical arrangement of the energy storage array. The amount of food consumed by the vulture represents the capacity of each row of the energy storage array. The fitness of the vulture is the variance between the capacity of each row and the average capacity of the array. Initialize the vulture population and fitness, i.e., the energy storage array and the capacity variance of the array.

[0054] Step S4: Select the search space. In the selection phase, the vulture (energy storage unit) determines and selects the best area within the selected search space (energy storage array) based on the amount of food (capacity of each row of the energy storage array) and preys there. The formula is as follows:

[0055] P newi =P best +αr(P mean -P i ),

[0056] Where: P newiis the updated position of the i-th vulture; according to the distribution of different capacity units in the energy storage array, the changed position is adjusted by parameters, where α is a parameter used to control the change of the vulture's position, and its value is between 1.5 and 2. r is a random number between 0 and 1. By coordinating r and α, a search of any distance between 1.5 and 2 is achieved. The above process represents the vulture (energy storage unit) moving in the area to search for the best area; P best represents the search space currently selected by the vulture based on the best position determined in the previous search; P mean is the average position of the vultures after the previous search; P i Update the previous position of the i-th vulture; after this stage, the vulture successfully selects the best area;

[0057] Step S5: In the search space search phase, the vulture searches for prey in the selected search space, that is, the optimal position of energy storage units of different capacities in the selected area, and moves in different directions in the spiral space to accelerate the search; the optimal position of the dive is expressed by the following formula,

[0058] P newi =P i +y(i)·(P i -P i+1 )+x(i)·(P i -P mean ),

[0059]

[0060]

[0061] θ(i)=a·π·rand,

[0062] r(i)=θ(i)+R rand,

[0063] In the formula, a and R represent the parameters of the spiral shape change; a is a parameter between 5 and 10, which is used to determine the inter-point search angle of the center point. The angle change simulates the position change of the energy storage unit near the center point of the selected search area to achieve the optimization reconstruction process; R takes a value between 0.5 and 2 and is used to determine the number of search cycles and the search range; rand is a random number between (0, 1); x(i) and y(i) represent the position of the vulture in polar coordinates, and both have values ​​of (-1, 1); according to the distribution of different capacity units in the selected area, the position of the energy storage unit change is adjusted by parameters. The above process represents the search for the optimal arrangement position of the energy storage unit in the selected area, with the purpose of minimizing the fitness (capacity variance);

[0064] Step S6: Dive phase. During the dive phase, the vulture swings from the optimal position in the search space to the target prey, and all points also move toward the optimal point.

[0065] P newi =rand·P best +x1(i)·(P i -c1·P mean )+y1(i)·(P i -c2·P best ),

[0066]

[0067]

[0068] θ(i)=a·π·rand,

[0069] r(i)=θ(i),

[0070] Where c1 and c2 are parameters between 1 and 2. After the dive phase, the vulture algorithm has determined the energy storage array arrangement formed after this search. The optimization of the energy storage array is evaluated by the fitness of the following formula:

[0071]

[0072] Step S7: t=t+1;

[0073] Step S8: Determine whether t <t max If yes, return to step S4; otherwise, go to step S9;

[0074] Step S9: Output the optimal reconstruction result and the maximum output power.

Claims

1. A method for reconfiguring an energy storage array with inconsistent capacity based on a vulture search algorithm, characterized in that: The steps include: Step S1: First, establish an N×N energy storage array, where the components in the same row are connected in parallel, and the components in the same column are connected in series. Where V out is the total output voltage; V maxh is the maximum output voltage of the hth row; I out is the output current of each node; I hc is the output current of the component in row h and column c; Step S2: Based on the vulture search algorithm, establish the objective function and constraints. To reduce the impact of reduced capacity of some energy storage units, a reconstruction method is used to balance the total capacity of each row. The variance of each row is used to describe the degree of capacity balance, which is the objective function of this method. Where: S 2 is the variance between the capacity of each row and the average capacity; C i is the capacity of each column of energy storage units; is the average capacity of the energy storage array; n is the number of columns of the energy storage array; Constraints: During the energy storage unit reconfiguration, each component only exchanges its row with another component in the same column. Therefore, the reconfiguration variables consisting of the electrical switch states should satisfy the following constraints: Where x hc Indicates the electrical switch status of row h and column c; Step S3: Collect the electrical parameters of the energy storage array. Each vulture represents an energy storage unit. The position of the individual represents the electrical arrangement of the energy storage array. The amount of food consumed by the vulture represents the capacity of each row of the energy storage array. The fitness of the vulture is the variance between the capacity of each row and the average capacity of the array. Initialize the vulture population and fitness, i.e., the energy storage array and the capacity variance of the array. Step S4: Selecting a search space. In the selection phase, the vulture (i.e., the energy storage unit) determines and selects the best area within the selected search space (i.e., the energy storage array) based on the amount of food (i.e., the capacity of each row of the energy storage array) and preys there. The formula is as follows: P newi =P best +αr(P mean -P i ), Where: P newi is the updated position of the i-th vulture; according to the distribution of different capacity units in the energy storage array, the changed position is adjusted by parameters, where α is a parameter used to control the change of the vulture's position, and its value is between 1.5 and 2. r is a random number between 0 and 1. Through the coordination of r and α, a search of any distance between 1.5 and 2 is achieved. The above process indicates that the vulture, i.e., the energy storage unit, moves within the area to search for the best area; P best represents the search space currently selected by the vulture based on the best position determined in the previous search; P mean is the average position of the vultures after the previous search; P i Update the previous position of the i-th vulture; after this stage, the vulture successfully selects the best area; Step S5: In the search space search phase, the vulture searches for prey in the selected search space, that is, the optimal position of energy storage units of different capacities in the selected area, and moves in different directions in the spiral space to accelerate the search; the optimal position of the dive is expressed by the following formula, P newi =P i +y(i)·(P i -P i+1 )+x(i)·(P i -P mean ), θ(i)=a·π·rand, r(i)=θ(i)+R rand, In the formula, a and R represent the parameters of the spiral shape change; a is a parameter between 5 and 10, which is used to determine the inter-point search angle of the center point. The angle change simulates the position change of the energy storage unit near the center point of the selected search area to achieve the optimization reconstruction process; R takes a value between 0.5 and 2 and is used to determine the number of search cycles and the search range; rand is a random number between (0, 1); x(i) and y(i) represent the position of the vulture in polar coordinates, and both have values ​​of (-1, 1); according to the distribution of different capacity units in the selected area, the position of the energy storage unit change is adjusted by parameters. The above process represents the search for the optimal arrangement position of the energy storage unit in the selected area, with the purpose of minimizing the fitness, that is, the capacity variance; Step S6: Dive phase. During the dive phase, the vulture swings from the optimal position in the search space to the target prey, and all points also move toward the optimal point. P newi =rand·P best +x1(i)·(P i -c1·P mean )+y1(i)·(P i -c2·P best ), θ(i)=a·π·rand, r(i)=θ(i), Where c1 and c2 are parameters between 1 and 2. After the dive phase, the vulture algorithm has determined the energy storage array arrangement formed after this search. The optimization of the energy storage array is evaluated by the fitness of the following formula: Step S7: t=t+1; Step S8: Determine whether t <t max If yes, return to step S4; otherwise, go to step S9; Step S9: Output the optimal reconstruction result and the maximum output power.

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