Improved whale optimization algorithm-based network construction type equipment optimization configuration method, system, equipment and medium

Abstract

The invention discloses a network construction type equipment optimal configuration method, system and equipment based on an improved whale optimization algorithm, and a medium, and relates to the technical field of power systems. The method comprises the following steps: selecting and configuring nodes of network-building equipment based on system sensitivity analysis; the net construction type equipment comprises a net construction type energy storage device and a net construction type fan; utilizing an improved whale optimization algorithm to optimize the capacity ratio of the network-forming equipment, and determining a capacity ratio configuration strategy; the improved whale optimization algorithm comprises an objective function and corresponding constraint conditions. According to the method, the improved whale optimization algorithm is used for global optimization, the problem that a traditional whale optimization algorithm is prone to falling into local optimization can be solved, and the configuration effect for the network construction type equipment is improved.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and in particular to a method, system, equipment and medium for optimizing the configuration of network-type equipment based on an improved whale optimization algorithm. Background Technology

[0002] With the vigorous development of new energy power generation technologies, photovoltaic, wind power, and other new energy sources are connected to the same AC power grid through power electronic equipment. These new energy sources and the AC power grid together constitute a multi-new energy feed-in system. New energy and energy storage devices based on grid-type converters exhibit voltage source characteristics to the power grid, which can approximately simulate the characteristics of conventional synchronous generators. They provide active frequency, voltage support, and damping functions to the power grid, effectively alleviating transient overvoltage problems and insufficient frequency support capabilities in high-proportion new energy and multi-DC grid connections. In high-proportion new energy transmission systems, they act as a buffer while providing flexible regulation and effective support, showing broad application prospects in supporting the stable operation of high-proportion new energy systems. However, due to the unclear analytical relationship between the capacity and stability margin of grid-type converters, the selection of node locations and the estimation of reasonable capacity proportions are extremely difficult. Grid-type technology is widely used in microgrid scenarios and has achieved small-scale application in independent power supply systems. However, the scenario of large-scale grid-type equipment being connected to interconnected large power grids is still in the exploratory stage. When large-scale grid-type equipment is connected to the grid, it will bring some new problems and changes. Summary of the Invention

[0003] The purpose of this invention is to provide a method, system, device and medium for optimizing the configuration of network-type devices based on an improved whale optimization algorithm, which aims to solve or improve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, the present invention provides the following solution: A method for optimizing the configuration of network-type devices based on an improved whale optimization algorithm, comprising: The nodes for configuring grid-type equipment are selected based on system sensitivity analysis; the grid-type equipment includes grid-type energy storage and grid-type wind turbines. An improved whale optimization algorithm is used to optimize the capacity allocation of the network-type equipment and determine the capacity allocation configuration strategy. The improved whale optimization algorithm includes an objective function and corresponding constraints. The objective function consists of total investment cost, operating cost, key constraint penalty, total constraint penalty, stability reward, and adaptive penalty coefficient. The constraints include power-capacity ratio constraint, simultaneous charging and discharging constraint, power limit constraint, SOC range constraint, phase margin constraint, critical equivalent short-circuit ratio constraint, QV droop coefficient constraint, capacity ratio constraint, line capacity constraint, voltage support constraint, MRSCR stability constraint, energy storage stability constraint violation, wind turbine capacity ratio penalty, MRSCR stability penalty, and stability reward.

[0005] Optionally, the process of selecting and configuring nodes for network-type devices based on system sensitivity analysis includes: Step 1: Input the number of nodes, voltage values, and voltage phase angles to construct the node admittance matrix; Step 2: Construct the Jacobian admittance matrix; Step 3: Calculate the sensitivity matrix. The calculation formula is as follows: In the formula: Let be the voltage change at node i under the influence of active power; Let be the change in active power at node j; for The element in the i-th row and j-th column of the matrix; Let i be the voltage-active power sensitivity of node i to node j; Let be the voltage change at node i under the influence of reactive power; for The element in the i-th row and j-th column of the matrix; Step 4: Select and configure the network-type equipment based on the sensitivity of the nodes.

[0006] Optionally, the expression for the objective function is: In the formula: The total investment cost, For adaptive penalty coefficient, For energy storage power, This is the energy storage power cost coefficient. For energy storage capacity, This is the energy storage capacity cost coefficient. For the fan capacity, This is the cost coefficient for wind turbines; For operating costs, This represents the annual cycle cost coefficient per unit power of energy storage. This represents the annual operation and maintenance cost coefficient per unit capacity of the wind turbine. Where is the fuel price, and T is the daily operating time. Additional service revenue coefficient for network-type equipment. The proportion of grid-type wind turbines; For overall constraint and punishment, As a penalty for energy storage constraints, Due to power-to-capacity ratio constraints, Simultaneous charge and discharge constraints. For power limiting constraints, For SOC range constraints, Phase margin constraint; As a constraint penalty for wind turbines, This is a critical equivalent short-circuit ratio constraint. For QV droop coefficient constraint, Capacity ratio constraint; As a system constraint and penalty, Due to line capacity constraints, For voltage support constraints, For MRSCR stability constraints; As a key constraint and punishment, For the violation of energy storage stability constraints, Penalty based on wind turbine capacity ratio. MRSCR stability penalty; As a stability bonus.

[0007] Optionally, the expression for the constraint condition is: In the formula: For charging power timing (MW), For discharge power timing, For SOC state timing, For charging efficiency, For discharge efficiency, This is the actual equivalent short-circuit ratio. For the ideal equivalent short-circuit ratio, As the system's baseline capacity, For the voltage at the new energy collection point, Total load power, For time step, k q The sag coefficient of the grid-type wind turbine. U N U is the rated voltage, and U is the actual voltage.

[0008] Optionally, the computation process of the improved whale optimization algorithm specifically includes: (a) Initialization: Let the whale population size be N and the maximum number of iterations be T. max The location of each individual in a whale population ; (b) Calculate the fitness value for each whale, select the n vectors with the smallest fitness values ​​as the initial population positions, and record the optimal fitness value and corresponding position. ; (c) when When needed, update the individual's position using the following formula: In the formula, This represents the current iteration number. This is the position vector of the current best solution. Let be the position vector of an individual whale. The distance between an individual whale and its prey. For coefficient vectors; (d) when When needed, update the individual's position using the following formula: In the formula, This represents the position vector of a randomly selected individual whale from the group. This represents the distance from a randomly selected individual whale to its prey. (e) When When needed, update the individual's position using the following formula: In the formula, Let b be the distance from the individual whale to its prey, and b be a constant used to define the shape of the logarithmic spiral. l for Random numbers; (f) Calculate the fitness and update the current optimal solution; (g) If the termination condition is met, output the optimal individual; otherwise, return to step (c).

[0009] Optionally, in the optimization process, the multi-infeed short-circuit ratio of new energy power plants is used as the core evaluation index of voltage stability. If the stability index does not meet the set requirements, the algorithm is guided to continue iterating through a constraint penalty mechanism. After multiple optimizations, the optimal installed capacity that meets the stability requirements is selected.

[0010] Optionally, the process of determining whether the voltage stability is met, using the multi-infeed short-circuit ratio of new energy power plants as the core evaluation indicator, is as follows: Step 1: Obtain the capacity ratio of historical network-type equipment as known data; Step 2: Calculate the multi-infeed short-circuit ratio of the new energy power station according to the following formula. MRSCR i : In the formula: U N for, Voltage at the new energy collection point; Step 3: Based on the calculated multi-infeed short-circuit ratio of the new energy power station MRSCR i To determine whether the configured new energy power plants meet the voltage stability requirement: if MRSCR i If the value is ≥3.5, it is determined that the strong system threshold has been exceeded, and the voltage is stable at this point. Step 4: Based on the calculation MRSCR i Substitute the values ​​into the objective function to solve the problem, and output the solution when the set iteration termination requirements are met.

[0011] This invention also provides a network-based device optimization configuration system based on an improved whale optimization algorithm, comprising: A node determination unit is used to select nodes for configuring grid-type equipment based on system sensitivity analysis; the grid-type equipment includes grid-type energy storage and grid-type wind turbines; The configuration optimization unit is used to optimize the capacity allocation of the network-type equipment using an improved whale optimization algorithm to determine the capacity allocation configuration strategy. The improved whale optimization algorithm includes an objective function and corresponding constraints. The objective function consists of total investment cost, operating cost, key constraint penalty, total constraint penalty, stability reward, and adaptive penalty coefficient. The constraints include power-capacity ratio constraint, simultaneous charging and discharging constraint, power limit constraint, SOC range constraint, phase margin constraint, critical equivalent short-circuit ratio constraint, QV droop coefficient constraint, capacity ratio constraint, line capacity constraint, voltage support constraint, MRSCR stability constraint, energy storage stability constraint violation, wind turbine capacity ratio penalty, MRSCR stability penalty, and stability reward.

[0012] The present invention also provides an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to cause the electronic device to perform the above-described method for optimizing the configuration of networked devices based on the improved whale optimization algorithm.

[0013] The present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the network-based device optimization configuration method based on the improved whale optimization algorithm as described above.

[0014] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects: This invention discloses a method, system, equipment, and medium for optimizing the configuration of network-type equipment based on an improved whale optimization algorithm. The method includes selecting nodes for configuring network-type equipment based on system sensitivity analysis; the network-type equipment includes network-type energy storage and network-type wind turbines; optimizing the capacity ratio of the network-type equipment using the improved whale optimization algorithm to determine the capacity ratio configuration strategy; the improved whale optimization algorithm includes an objective function and corresponding constraints; the objective function consists of total investment cost, operating cost, key constraint penalty, total constraint penalty, stability reward, and adaptive penalty coefficient; the constraints include power-to-capacity ratio constraint, simultaneous charge / discharge constraint, power limit constraint, SOC range constraint, phase margin constraint, critical equivalent short-circuit ratio constraint, QV droop coefficient constraint, capacity ratio constraint, line capacity constraint, voltage support constraint, MRSCR stability constraint, energy storage stability constraint violation, wind turbine capacity ratio penalty, MRSCR stability penalty, and stability reward. This invention utilizes an improved whale optimization algorithm for global optimization, which can overcome the local optima problem that traditional whale optimization algorithms are prone to get stuck in, and improve the configuration effect for network-type devices. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart of the network-type device optimization configuration method of the present invention; Figure 2 This is a diagram of the NEW ENGLAND 10-machine 39-node system after the method is applied in this embodiment; Figure 3 This is a schematic diagram of the MRSCR results after the optimized configuration of each new energy power station in this embodiment; Figure 4 This is a schematic diagram showing the improvement in node voltage after the optimized configuration of each new energy power station in this embodiment. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] The purpose of this invention is to provide a method, system, device and medium for optimizing the configuration of network-type devices based on an improved whale optimization algorithm, which aims to solve or improve at least one of the above-mentioned technical problems.

[0019] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0020] As a first aspect, such as Figures 1-4 As shown, this invention provides a method for optimizing the configuration of network-type devices based on an improved whale optimization algorithm, comprising: 1) Construct the input and output sets of the neural network; Selecting and configuring nodes for network-type devices based on system sensitivity analysis; Step 1: Input the number of nodes, voltage values, and voltage phase angles to construct the node admittance matrix; Step 2: Construct the Jacobian admittance matrix; Step 3: Calculate the sensitivity matrix. The calculation formula is as follows: In the formula: Let be the voltage change at node i under the influence of active power; Let be the change in active power at node j; for The element in the i-th row and j-th column of the matrix; Let be the voltage-active power sensitivity of node i to node j. Let be the voltage change at node i under the influence of reactive power; for The element in the i-th row and j-th column of the matrix; Step 4: Select and configure the network-type equipment based on the sensitivity of the nodes.

[0021] 2) Select the capacity ratio using an improved whale optimization algorithm; Step 1: Construct the objective function of the optimization algorithm: Compared to the traditional whale optimization algorithm, this invention adds an MRSCR reward item, which rewards the system when it reaches a strong system threshold and gives a secondary reward when it reaches basic stability. It also adds a penalty item, which first classifies the degree of penalty: ordinary penalty is violation of ordinary constraints, and severe penalty is violation of critical constraints and MRSCR stability. This satisfies the constraints and encourages the algorithm to find a more stable voltage configuration.

[0022] In the formula: The total investment cost, For adaptive penalty coefficient, For energy storage power, This is the energy storage power cost coefficient. For energy storage capacity, This is the energy storage capacity cost coefficient. For the fan capacity, This is the cost coefficient for wind turbines; For operating costs, This represents the annual cycle cost coefficient per unit power of energy storage. This represents the annual operation and maintenance cost coefficient per unit capacity of the wind turbine. Where is the fuel price, and T is the daily operating time. Additional service revenue coefficient for network-type equipment. The proportion of grid-type wind turbines; For overall constraint and punishment, As a penalty for energy storage constraints, Due to power-to-capacity ratio constraints, Simultaneous charge and discharge constraints. For power limiting constraints, For SOC range constraints, Phase margin constraint; As a constraint penalty for wind turbines, This is a critical equivalent short-circuit ratio constraint. For QV droop coefficient constraint, Capacity ratio constraint; As a system constraint and penalty, Due to line capacity constraints, For voltage support constraints, For MRSCR stability constraints; As a key constraint and punishment, For the violation of energy storage stability constraints, Penalty based on wind turbine capacity ratio. MRSCR stability penalty; As a stability bonus.

[0023] Step 2: Construct constraints: In the formula: For charging power timing (MW), For discharge power timing, For SOC state timing, For charging efficiency, For discharge efficiency, This is the actual equivalent short-circuit ratio. For the ideal equivalent short-circuit ratio, As the system's baseline capacity, For the voltage at the new energy collection point, Total load power, For time step, k q The sag coefficient of the grid-type wind turbine. U N This is the rated voltage.

[0024] Step 3: Apply the improved whale optimization function: (a) Initialization: whale population size N, maximum number of iterations T max The location of each individual in a whale population Compared to the traditional whale algorithm, this algorithm is improved to an 80 / 20 hybrid initialization strategy, that is, 80% of the individuals adopt a conservative configuration: a smaller renewable energy capacity (energy storage 8-13%, wind turbines 12-20%) and a very high proportion of grid-type configurations (85-95%), while 20% of the individuals remain random to maintain population diversity.

[0025] (b) Calculate the fitness value for each whale, select the n vectors with the smallest fitness values ​​as the initial population positions, and record the optimal fitness value and corresponding position. ; (c) when When needed, update the individual's position using the following formula: In the formula, This represents the current iteration number. This is the position vector of the current best solution. Let be the position vector of an individual whale. The distance between an individual whale and its prey. For coefficient vectors; (d) when When needed, update the individual's position using the following formula: In the formula, This represents the position vector of a randomly selected individual whale from the group. This represents the distance from a randomly selected individual whale to its prey. (e) When When needed, update the individual's position using the following formula: In the formula, Let b be the distance from the individual whale to its prey, and b be a constant used to define the shape of the logarithmic spiral. l for Random numbers; (f) Calculate the fitness and update the current optimal solution; (g) If the termination condition is met, output the optimal individual, which is the optimal solution found by the algorithm; otherwise, return to step (c).

[0026] 3) Use the multi-infeed short-circuit ratio of new energy power plants as the core evaluation indicator for voltage stability to determine whether the standard is met. Step 1: Extract the capacity ratio of grid-type energy storage and grid-type wind turbines confirmed in step 2) as known data.

[0027] Step 2: Calculate according to the following formula MRSCR i .

[0028] In the formula: U N Rated voltage, Voltage at the new energy collection point; Step 3: Based on the calculated multi-infeed short-circuit ratio of the new energy power station MRSCR i To determine whether the configured new energy power plants meet the voltage stability requirement: if MRSCR i If the value is ≥3.5, it is determined that the system exceeds the strong system threshold, and the voltage is stable at this point.

[0029] Step 4: Substitute the calculated MRSCR into the objective function in step 2) to minimize it. If the objective function is not satisfied, repeat the above steps. If it is satisfied, this is the output evaluation stage. Generate a network-based capacity configuration strategy based on the improved whale optimization algorithm.

[0030] Implementation Case: The specific process of the distribution network fault location method based on branch response data proposed in this invention is as follows: Figure 1As shown. To verify the effectiveness of the proposed method, a simulation model of the improved NEW ENGLAND 10-unit 39-node grid-connected equipment was built based on the MATLAB / Simulink simulation platform. The grid-connected energy storage was connected to nodes 4, 8, and 12, and the grid-connected wind turbines were connected to nodes 10, 25, and 29. The rated frequency was 50Hz, and the base power was 100MVA. The topology is as follows. Figure 2 As shown. The result after configuration optimization is as follows. Figure 3 and Figure 4 As shown. By Figure 3 and Figure 4 As can be seen, the method proposed in this embodiment is applied to the NEW ENGLAND 10-unit 39-bus system. Regarding system stability, the MRSCR values ​​of each renewable energy power station show good performance. Specifically, the MRSCR values ​​of the six renewable energy power stations are distributed between 2.0 and 4.5, all exceeding the critical lower limit (2.0). Among them, three power stations reach the strong system threshold (3.5) or higher, indicating that the system possesses strong stability. This provides stable support for the entire power grid.

[0031] As a second aspect, the present invention also provides a network-based device optimization configuration system based on an improved whale optimization algorithm, comprising: A node determination unit is used to select nodes for configuring grid-type equipment based on system sensitivity analysis; the grid-type equipment includes grid-type energy storage and grid-type wind turbines; The configuration optimization unit is used to optimize the capacity allocation of the network-type equipment using an improved whale optimization algorithm to determine the capacity allocation configuration strategy. The improved whale optimization algorithm includes an objective function and corresponding constraints. The objective function consists of total investment cost, operating cost, key constraint penalty, total constraint penalty, stability reward, and adaptive penalty coefficient. The constraints include power-capacity ratio constraint, simultaneous charging and discharging constraint, power limit constraint, SOC range constraint, phase margin constraint, critical equivalent short-circuit ratio constraint, QV droop coefficient constraint, capacity ratio constraint, line capacity constraint, voltage support constraint, MRSCR stability constraint, energy storage stability constraint violation, wind turbine capacity ratio penalty, MRSCR stability penalty, and stability reward.

[0032] As a third aspect, the present invention also provides an electronic device, including a memory and a processor, the memory for storing a computer program, the processor for running the computer program to cause the electronic device to perform the above-described method for optimizing the configuration of networked devices based on the improved whale optimization algorithm.

[0033] As a fourth aspect, the present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the network-type device optimization configuration method based on the improved whale optimization algorithm as described above.

[0034] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0035] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for optimizing the configuration of network-type devices based on an improved whale optimization algorithm, characterized in that, include: The nodes for configuring grid-type equipment are selected based on system sensitivity analysis; the grid-type equipment includes grid-type energy storage and grid-type wind turbines. An improved whale optimization algorithm is used to optimize the capacity allocation of the network-type equipment and determine the capacity allocation configuration strategy. The improved whale optimization algorithm includes an objective function and corresponding constraints. The objective function consists of total investment cost, operating cost, key constraint penalty, total constraint penalty, stability reward, and adaptive penalty coefficient. The constraints include power-capacity ratio constraint, simultaneous charging and discharging constraint, power limit constraint, SOC range constraint, phase margin constraint, critical equivalent short-circuit ratio constraint, QV droop coefficient constraint, capacity ratio constraint, line capacity constraint, voltage support constraint, MRSCR stability constraint, energy storage stability constraint violation, wind turbine capacity ratio penalty, MRSCR stability penalty, and stability reward.

2. The method for optimizing the configuration of network-type equipment based on the improved whale optimization algorithm according to claim 1, characterized in that, The specific process of selecting and configuring network-type devices based on system sensitivity analysis includes: Step 1: Input the number of nodes, voltage values, and voltage phase angles to construct the node admittance matrix; Step 2: Construct the Jacobian admittance matrix; Step 3: Calculate the sensitivity matrix. The calculation formula is as follows: In the formula: Let be the voltage change at node i under the influence of active power; Let be the change in active power at node j; for The element in the i-th row and j-th column of the matrix; Let i be the voltage-active power sensitivity of node i to node j; Let be the voltage change at node i under the influence of reactive power; for The element in the i-th row and j-th column of the matrix; Step 4: Select and configure the network-type equipment based on the sensitivity of the nodes.

3. The method for optimizing the configuration of network-type equipment based on the improved whale optimization algorithm according to claim 1, characterized in that, The expression for the objective function is: In the formula: The total investment cost, For adaptive penalty coefficient, For energy storage power, This is the energy storage power cost coefficient. For energy storage capacity, This is the energy storage capacity cost coefficient. For the fan capacity, This is the cost coefficient for wind turbines; For operating costs, This represents the annual cycle cost coefficient per unit power of energy storage. This represents the annual operation and maintenance cost coefficient per unit capacity of the wind turbine. Where is the fuel price, and T is the daily operating time. Additional service revenue coefficient for network-type equipment. The proportion of grid-type wind turbines; For overall constraint and punishment, As a penalty for energy storage constraints, Due to power-to-capacity ratio constraints, Simultaneous charge and discharge constraints. For power limiting constraints, For SOC range constraints, Phase margin constraint; As a constraint penalty for wind turbines, This is a critical equivalent short-circuit ratio constraint. For QV droop coefficient constraint, Capacity ratio constraint; As a system constraint and penalty, Due to line capacity constraints, For voltage support constraints, For MRSCR stability constraints; As a key constraint and punishment, For the violation of energy storage stability constraints, Penalty based on wind turbine capacity ratio. MRSCR stability penalty; As a stability bonus.

4. The method for optimizing the configuration of network-type equipment based on the improved whale optimization algorithm according to claim 1, characterized in that, The expression for the constraint condition is: In the formula: For charging power timing (MW), For discharge power timing, For SOC state timing, For charging efficiency, For discharge efficiency, This is the actual equivalent short-circuit ratio. For the ideal equivalent short-circuit ratio, As the system's baseline capacity, For the voltage at the new energy collection point, Total load power, For time step, k q The sag coefficient of the grid-type wind turbine. U N U is the rated voltage, and U is the actual voltage.

5. The method for optimizing the configuration of network-type equipment based on the improved whale optimization algorithm according to claim 1, characterized in that, The computational process of the improved whale optimization algorithm specifically includes: (a) Initialization: Let the whale population size be N and the maximum number of iterations be T. max The location of each individual in a whale population ; (b) Calculate the fitness value for each whale, select the n vectors with the smallest fitness values ​​as the initial population positions, and record the optimal fitness value and corresponding position. ; (c) when When needed, update the individual's position using the following formula: In the formula, This represents the current iteration number. This is the position vector of the current best solution. Let be the position vector of an individual whale. The distance between an individual whale and its prey. For coefficient vectors; (d) when When needed, update the individual's position using the following formula: In the formula, This represents the position vector of a randomly selected individual whale from the group. This represents the distance from a randomly selected individual whale to its prey. (e) When When needed, update the individual's position using the following formula: In the formula, Let b be the distance from the individual whale to its prey, and b be a constant used to define the shape of the logarithmic spiral. l for Random numbers; (f) Calculate the fitness and update the current optimal solution; (g) If the termination condition is met, output the optimal individual; otherwise, return to step (c).

6. The method for optimizing the configuration of network-type equipment based on the improved whale optimization algorithm according to claim 1, characterized in that, In the optimization process, the multi-infeed short-circuit ratio of new energy power plants is used as the core evaluation index of voltage stability. If the stability index does not meet the set requirements, the algorithm is guided to continue iterating through a constraint penalty mechanism. After multiple optimizations, the optimal installed capacity that meets the stability requirements is selected.

7. The method for optimizing the configuration of network-type equipment based on the improved whale optimization algorithm according to claim 6, characterized in that, The process of determining whether the voltage stability is met, using the multi-infeed short-circuit ratio of new energy power plants as the core evaluation indicator, is as follows: Step 1: Obtain the capacity ratio of historical network-type equipment as known data; Step 2: Calculate the multi-infeed short-circuit ratio of the new energy power station according to the following formula. MRSCR i : In the formula: U N Rated voltage, Voltage at the new energy collection point; Step 3: Based on the calculated multi-infeed short-circuit ratio of the new energy power station MRSCR i To determine whether the configured new energy power plants meet the voltage stability requirement: if MRSCR i If the value is ≥3.5, it is determined that the strong system threshold has been exceeded, and the voltage is stable at this point. Step 4: Based on the calculation MRSCR i Substitute the values ​​into the objective function to solve the problem, and output the solution when the set iteration termination requirements are met.

8. A network-based equipment optimization configuration system based on an improved whale optimization algorithm, characterized in that, include: A node determination unit is used to select nodes for configuring grid-type equipment based on system sensitivity analysis; the grid-type equipment includes grid-type energy storage and grid-type wind turbines; The configuration optimization unit is used to optimize the capacity allocation of the network-type devices using an improved whale optimization algorithm, and determine the capacity allocation configuration strategy. The improved whale optimization algorithm includes an objective function and corresponding constraints. The objective function consists of total investment cost, operating cost, critical constraint penalty, total constraint penalty, stability reward, and adaptive penalty coefficient. The constraints include power capacity ratio constraint, simultaneous charge and discharge constraint, power limit constraint, SOC range constraint, phase margin constraint, critical equivalent short-circuit ratio constraint, QV droop coefficient constraint, capacity ratio constraint, line capacity constraint, voltage support constraint, MRSCR stability constraint, energy storage stability constraint violation, wind turbine capacity ratio penalty, MRSCR stability penalty, and stability reward.

9. An electronic device, characterized in that, The device includes a memory and a processor, the memory being used to store a computer program, and the processor running the computer program to cause the electronic device to perform the network-type device optimization configuration method based on the improved whale optimization algorithm according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the network-type device optimization configuration method based on the improved whale optimization algorithm as described in any one of claims 1-7.

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