Energy storage optimization configuration method and system for county power grid

Abstract

This invention discloses a method for optimizing energy storage configuration for county-level power grids, comprising: acquiring data information of the target county-level power grid; constructing an objective function for optimizing energy storage configuration of the target county-level power grid; constructing constraints for optimizing energy storage configuration of the target county-level power grid; solving the constraints using a biomimetic swarm intelligence optimization algorithm; and completing the optimized energy storage configuration of the target county-level power grid based on the solution results. This invention also discloses a system for implementing the aforementioned method for optimizing energy storage configuration for county-level power grids. This invention not only achieves optimized energy storage configuration for target county-level power grids but also offers higher reliability and better accuracy.

Description

Technical Field

[0001] This invention belongs to the field of electrical automation, and specifically relates to a method and system for optimizing the configuration of energy storage for county power grids. Background Technology

[0002] With economic and technological development and the improvement of people's living standards, electricity has become an indispensable secondary energy source in people's production and daily life, bringing endless convenience. Therefore, ensuring a stable and reliable supply of electricity has become one of the most important tasks of the power system.

[0003] Currently, an increasing number of new energy power generation systems are being integrated into county-level power grids. The intermittent and fluctuating output of these new energy power generation systems, coupled with the time-varying load demand of county-level power grids, significantly increases the complexity of real-time balancing and operational regulation. Simultaneously, the power exchange capacity between county-level and higher-level power grids is typically constrained by factors such as grid structure, substation capacity, and operational boundaries, further increasing the difficulty of local absorption, power balancing, and power supply assurance. Against this backdrop, battery energy storage systems, with their rapid power response, bidirectional energy regulation, and energy time-shifting capabilities, have become a crucial support for county-level power grids, enhancing their adaptability to fluctuations, operational recovery capabilities, and power supply reliability.

[0004] Current research on the optimal configuration of energy storage systems mainly employs various optimization schemes to solve for energy storage capacity, aiming to achieve comprehensive optimization of indicators such as power generation operating costs and energy storage configuration costs. However, most existing studies focus on the main power grid, while county-level power grids differ significantly from the main power grid. Therefore, existing schemes cannot be directly applied to the optimal configuration of energy storage systems in county-level power grids. Summary of the Invention

[0005] One of the objectives of this invention is to provide a highly reliable and accurate energy storage optimization configuration method for county-level power grids.

[0006] The second objective of this invention is to provide a system for implementing the energy storage optimization configuration method for county-level power grids.

[0007] The energy storage optimization configuration method for county power grids provided by this invention includes the following steps:

[0008] S1. Obtain data information about the power grid in the target county;

[0009] S2. Based on the data obtained in step S1, construct the objective function for the optimal configuration of energy storage in the target county power grid;

[0010] S3. Based on the objective function constructed in step S2, construct the energy storage optimization configuration constraints for the target county power grid;

[0011] S4. The model constructed in steps S2 and S3 is solved using a biomimetic swarm intelligence optimization algorithm;

[0012] S5. Based on the solution obtained in step S4, complete the energy storage optimization configuration of the target county power grid.

[0013] Step S1, which involves obtaining data information about the target county power grid, specifically includes the following steps:

[0014] Obtain data information about the power grid in the target county;

[0015] The data information includes load data, equipment lifespan information, power grid parameter information, energy storage device data, and new energy power generation information.

[0016] Step S2, which involves constructing an objective function for optimizing the energy storage configuration of the target county power grid based on the data obtained in step S1, specifically includes the following steps:

[0017] The following formula is used as the objective function for optimizing the energy storage configuration of the target county power grid:

[0018] In the formula Optimize the energy storage configuration of the target county power grid using the objective function value; The operating cost of the power generation units in the target county power grid, and T represents the total number of time periods in the scheduling cycle, and N represents the total number of power generation units. Let be the fuel cost of the i-th power generation unit at time t. Let be the operation and maintenance cost of the i-th power generation unit at time t. The duration of the time period; The full-cycle equivalent configuration cost of the battery energy storage system, and , The unit capacity investment cost of energy storage batteries, The rated power of the energy storage system, The unit capacity investment cost of the energy storage converter. For the installation of energy storage systems and related costs, This refers to the power occupancy cost of the energy storage system or the equivalent cost of the power occupancy of the energy storage system.

[0019] Step S3, which involves constructing energy storage optimization configuration constraints for the target county power grid based on the objective function built in step S2, specifically includes the following steps:

[0020] Energy storage side constraints:

[0021] The following formula is used to calculate the energy storage capacity of the energy storage system:

[0022] In the formula Let T represent the energy stored in the energy storage system at time T. Power for charging energy storage systems; Improve the charging efficiency of energy storage systems; This refers to the discharge power of the energy storage system. For the discharge efficiency of the energy storage system; The duration of the time period;

[0023] The following formula is used as the state of charge constraint for the energy storage system:

[0024] In the formula The minimum state of charge value is set. Let t represent the state of charge of the energy storage system at time t. This is the set maximum state of charge value; Let be the discharge amount of the energy storage system at time t; The amount of charge applied to the energy storage system at time t; Let t be the output power of the energy storage system at time t;

[0025] The following formula is used as the power constraint for the energy storage system:

[0026] In the formula This refers to the maximum permissible charge and discharge power of the energy storage system. Let t be the output power of the energy storage system at time t;

[0027] The following formula is used as the energy constraint for the energy storage system:

[0028] In the formula This refers to the rated capacity of the energy storage system.

[0029] Source-side constraints:

[0030] In the formula This refers to the permissible instantaneous frequency variation on the power generation side; This represents the upper limit of the maximum frequency variation. This is the lower limit of the voltage. This refers to the voltage value on the generator side. This is the upper limit of the voltage. This is the lower limit of power; This refers to the output power of the energy storage system on the power generation side. This is the upper limit of power;

[0031] Network-side constraints:

[0032] In the formula The lower limit of the power injected into the grid for energy storage systems; Power injected into the grid to power the energy storage system; The upper limit of the power that can be injected into the grid for energy storage systems; This is the lower limit of the node voltage. This refers to the voltage value at the grid-side node. This represents the upper limit of the node voltage. This refers to the critical circuit current value. This is the upper limit of the line current.

[0033] Load-side constraints:

[0034] In the formula This is the lower limit of the discharge power of the energy storage system; Let be the discharge power of the energy storage system at time t; This represents the upper limit of the discharge power of the energy storage system. This represents the minimum state of charge. State of charge of the energy storage system; This represents the maximum value of the state of charge.

[0035] Step S4 describes solving the model constructed in steps S2 and S3 using a biomimetic swarm intelligence optimization algorithm, specifically including the following steps:

[0036] The constraints set in step S3 constitute the solution space; an initial population is randomly generated in the solution space and serves as the current population; the population includes several individuals, and each individual corresponds to a solution.

[0037] The objective function for optimizing the energy storage configuration of the target county power grid constructed in step S2 is used as the fitness function value; for each individual in the current population, the fitness function value of each individual is calculated.

[0038] The current group is optimized through iterative optimization.

[0039] The optimization process includes the following updates:

[0040] Attraction:

[0041] The following formula is used to calculate the attraction between individuals:

[0042] In the formula The attraction value between individuals; The set baseline attractiveness value; The attenuation coefficient; The distance between individuals;

[0043] Location update:

[0044] The following formula is used to update the position of an individual:

[0045] In the formula Let i be the position of the i-th individual at time t+1; The distance between individual i and individual j; The set step size parameter; It is a random vector calculated based on a Gaussian distribution;

[0046] Based on the location update results, the fitness value of each individual is recalculated, and the location is updated again;

[0047] Repeat the above steps until the set iteration termination condition is met;

[0048] Ultimately, the optimal location and corresponding energy storage optimization configuration scheme for each individual are obtained. The energy storage system charging and discharging power, state of charge, and power injected into the grid corresponding to the optimal scheme all meet the constraints of the source side, grid side, and load side, and the overall objective function reaches the minimum value, thereby completing the energy storage optimization configuration of the target county power grid.

[0049] The energy storage optimization configuration method for county power grids further includes the following steps:

[0050] S6. Conduct a reliability assessment on the energy storage optimization configuration obtained in step S5.

[0051] Step S6, which involves performing a reliability assessment on the optimized energy storage configuration obtained in step S5, specifically includes the following steps:

[0052] The availability and unavailability of energy storage system components are calculated using the following formula:

[0053] In the formula For the availability of component i; Let i be the repair rate of component i; Let i be the failure rate of component i; The unavailability of component i;

[0054] The availability and unavailability of the energy storage system are calculated using the following formula:

[0055] In the formula This indicates the unavailability of the energy storage system. To ensure the availability of energy storage systems;

[0056] The load loss probability of the target county power grid is calculated using the following formula:

[0057] In the formula Let i be the load loss probability of the i-th load point; The number of system fault states where the total generating capacity is less than the load demand; Let be the probability of the i-th state, and , The number of power generation units in the system. Let be the failure probability of component j;

[0058] The expected load loss and unsupplied expected energy of the target county power grid are calculated using the following formula:

[0059] In the formula Let T be the expected load loss of the target county power grid; T is the total number of dispatching time periods. The unsupplied expected energy of the target county's power grid; This represents the load amount or load demand corresponding to the i-th load node when it experiences a load loss state.

[0060] Complete the reliability assessment of the energy storage optimization configuration of the target county power grid.

[0061] This invention also provides a system for implementing the energy storage optimization configuration method for county-level power grids, comprising a data acquisition module, a target construction module, a constraint construction module, a model solving module, an optimization configuration module, and a reliability evaluation module; the data acquisition module, target construction module, constraint construction module, model solving module, optimization configuration module, and reliability evaluation module are connected in series; the data acquisition module is used to acquire data information of the target county-level power grid and upload the data information to the target construction module; the target construction module is used to construct an energy storage optimization configuration objective function for the target county-level power grid based on the received data information and the acquired data information, and upload the data information to the constraint construction module. The system comprises three modules: a constraint construction module, a model solving module, and an optimization configuration module. The constraint construction module constructs energy storage optimization configuration constraints for the target county power grid based on the received data and the constructed objective function, and uploads the data to the model solving module. The model solving module solves the constructed model using a biomimetic swarm intelligence algorithm based on the received data, and uploads the data to the optimization configuration module. The optimization configuration module completes the energy storage optimization configuration for the target county power grid based on the received data and the solution results, and uploads the data to the reliability assessment module. The reliability assessment module performs a reliability assessment on the obtained energy storage optimization configuration based on the received data.

[0062] The energy storage optimization configuration method and system for county power grids provided by this invention not only achieves the optimization configuration of energy storage in the target county power grid by acquiring data from the target county power grid, constructing and solving the corresponding energy storage optimization configuration model, but also has higher reliability and better accuracy. Attached Figure Description

[0063] Figure 1 This is a schematic diagram of the method flow of the present invention.

[0064] Figure 2 This is a schematic diagram of the functional modules of the system of the present invention. Detailed Implementation

[0065] like Figure 1 The diagram shown is a flowchart of the method of the present invention: The energy storage optimization configuration method for county power grids disclosed in this invention includes the following steps:

[0066] S1. Obtain data information about the power grid in the target county; specifically including the following steps:

[0067] Obtain data information about the power grid in the target county;

[0068] The data information includes load data, equipment lifespan information, power grid parameter information, energy storage device data, and new energy power generation information;

[0069] S2. Based on the data obtained in step S1, construct the objective function for the optimal energy storage configuration of the target county power grid; specifically including the following steps:

[0070] Since optimizing the type and capacity of energy storage systems is not simply about pursuing the best of a single technical indicator, but rather requires a unified assessment of the configuration scale, operational impact, and overall cost of different energy storage solutions while meeting the operational constraints and power supply reliability requirements of the county power grid, this invention adopts the following formula as the objective function for optimizing the configuration of energy storage in the target county power grid:

[0071] In the formula Optimize the energy storage configuration of the target county power grid using the objective function value; The operating cost of the power generation units in the target county power grid, and T represents the total number of time periods in the scheduling cycle, and N represents the total number of power generation units. Let be the fuel cost of the i-th power generation unit at time t. Let be the operation and maintenance cost of the i-th power generation unit at time t. The duration of the time period; The full-cycle equivalent configuration cost of the battery energy storage system, and , The unit capacity investment cost of energy storage batteries, The rated power of the energy storage system, The unit capacity investment cost of the energy storage converter. For the installation of energy storage systems and related costs, The power occupancy cost of the energy storage system or the equivalent cost of the power occupancy of the energy storage system;

[0072] In practical implementation, the type and capacity of the energy storage system are core decision variables involved in optimization. The energy storage type variable is used to determine the energy storage technology route. Different energy storage types correspond to different charging and discharging efficiencies, life parameters, operation and maintenance parameters, failure rates, repair rates, and cost coefficients. The energy storage capacity variable (including rated power and rated energy) is used to characterize the rated power and rated energy levels of the energy storage system, directly determining the power regulation range and energy support capacity of the energy storage system. The above variables, by influencing the time-series charging and discharging behavior of the energy storage system, the output distribution of the power generation unit, and the calculation results of reliability indicators, jointly affect the objective function value, thus forming the basic relationship of joint optimization of energy storage system type and capacity.

[0073] The main decision variables are energy storage type selection, rated energy storage power, and rated energy storage; the system operation status variables are energy storage charging power, discharging power, state of charge, and power exchanged with the main grid.

[0074] S3. Based on the objective function constructed in step S2, construct the energy storage optimization configuration constraints for the target county power grid; specifically including the following steps:

[0075] Energy storage side constraints:

[0076] The following formula is used to calculate the energy storage capacity of the energy storage system:

[0077] In the formula Let T represent the energy stored in the energy storage system at time T. Power for charging energy storage systems; Improve the charging efficiency of energy storage systems; This refers to the discharge power of the energy storage system. For the discharge efficiency of the energy storage system; The duration of the time period;

[0078] The following formula is used as the state of charge constraint for the energy storage system:

[0079] In the formula The minimum state of charge value is set. Let t represent the state of charge of the energy storage system at time t. This is the set maximum state of charge value; Let be the discharge amount of the energy storage system at time t; The amount of charge applied to the energy storage system at time t; Let t be the output power of the energy storage system at time t; a higher state-of-charge capacity allows more energy to be stored, resulting in increased energy availability; conversely, a lower state-of-charge capacity may lead to reduced energy reserves, potentially affecting the reliability of the county power grid during critical situations.

[0080] The following formula is used as the power constraint for the energy storage system:

[0081] In the formula This refers to the maximum permissible charge and discharge power of the energy storage system. Let t be the output power of the energy storage system at time t;

[0082] The following formula is used as the energy constraint for the energy storage system:

[0083] In the formula This refers to the rated capacity of the energy storage system.

[0084] Source-side constraints:

[0085] In the formula This refers to the permissible instantaneous frequency variation on the power generation side; This represents the upper limit of the maximum frequency variation. This is the lower limit of the voltage. This refers to the voltage value on the generator side. This is the upper limit of the voltage. This is the lower limit of power; This refers to the output power of the energy storage system on the power generation side. This is the upper limit of power;

[0086] Network-side constraints:

[0087] In the formula The lower limit of the power injected into the grid for energy storage systems; Power injected into the grid to power the energy storage system; The upper limit of the power that can be injected into the grid for energy storage systems; This is the lower limit of the node voltage. This refers to the voltage value at the grid-side node. This represents the upper limit of the node voltage. This refers to the critical circuit current value. This is the upper limit of the line current.

[0088] Load-side constraints:

[0089] In the formula This is the lower limit of the discharge power of the energy storage system; Let be the discharge power of the energy storage system at time t; This represents the upper limit of the discharge power of the energy storage system. This represents the minimum state of charge. State of charge of the energy storage system; This represents the maximum value of the state of charge.

[0090] S4. The model constructed in steps S2 and S3 is solved using a biomimetic swarm intelligence optimization algorithm; specifically, the following steps are included:

[0091] Bionic swarm intelligence optimization algorithms are heuristic algorithms characterized by their simplicity and ease of application. These algorithms solve optimization problems by simulating the collective intelligent behavior of natural groups such as flocks of birds, schools of fish, and fireflies.

[0092] The constraints set in step S3 constitute the solution space; an initial population is randomly generated in the solution space and serves as the current population; the population includes several individuals, and each individual corresponds to a solution.

[0093] The objective function for optimizing the energy storage configuration of the target county power grid constructed in step S2 is used as the fitness function value; for each individual in the current population, the fitness function value of each individual is calculated.

[0094] The current group is optimized through iterative optimization.

[0095] The optimization process includes the following updates:

[0096] Attraction:

[0097] The attraction between individuals is determined by their fitness values; individuals with higher fitness are more attractive and can guide individuals with lower fitness to move closer to individuals with higher fitness. The attraction between individuals is calculated using the following formula:

[0098] In the formula The attraction value between individuals; The preferred value for the set baseline attraction value is between 0.1 and 1.0. The higher the value of this parameter, the stronger the attraction between individuals. The attenuation coefficient is preferably in the range of 0.1 to 10.0. The higher the value of this parameter, the greater the reduction in attractive force. The distance between individuals;

[0099] Location update:

[0100] Each individual adjusts its position based on the attractiveness of other individuals; the following formula is used to update the individual's position:

[0101] In the formula Let i be the position of the i-th individual at time t+1; The distance between individual i and individual j; The step size parameter is preferably set to a value in the range of 0.01 to 0.1. The smaller the value of this parameter, the finer the granularity of movement. It is a random vector calculated based on a Gaussian distribution, used to introduce randomness into the movement and exploration process;

[0102] Based on the location update results, the fitness value of each individual is recalculated, and the location is updated again;

[0103] Repeat the above steps until the set iteration termination condition is met;

[0104] Ultimately, the optimal location and corresponding energy storage optimization configuration scheme for each individual are obtained; the energy storage system charging and discharging power, state of charge and power injected into the grid corresponding to the optimal scheme all meet the constraints of the source side, grid side and load side, and the overall objective function reaches the minimum value, thus completing the energy storage optimization configuration of the target county power grid;

[0105] S5. Based on the solution results obtained in step S4, complete the energy storage optimization configuration of the target county power grid;

[0106] S6. Conduct a reliability assessment on the energy storage optimization configuration obtained in step S5; specifically including the following steps:

[0107] The availability and unavailability of energy storage system components are calculated using the following formula:

[0108] In the formula For the availability of component i; Let i be the repair rate of component i; Let i be the failure rate of component i; The unavailability of component i;

[0109] The availability and unavailability of the energy storage system are calculated using the following formula:

[0110] In the formula This indicates the unavailability of the energy storage system. To ensure the availability of energy storage systems;

[0111] The load loss probability of the target county power grid is calculated using the following formula:

[0112] In the formula Let i be the load loss probability of the i-th load point; The number of system fault states where the total generating capacity is less than the load demand; Let be the probability of the i-th state, and , The number of power generation units in the system. Let be the failure probability of component j;

[0113] The expected load loss and unsupplied expected energy of the target county power grid are calculated using the following formula:

[0114] In the formula Let T be the expected load loss of the target county power grid; T is the total number of dispatching time periods. The unsupplied expected energy of the target county's power grid; This represents the load amount or load demand corresponding to the i-th load node when it experiences a load loss state.

[0115] Complete the reliability assessment of the energy storage optimization configuration of the target county power grid.

[0116] like Figure 2 The diagram shows the functional modules of the system of this invention: The system disclosed in this invention, which implements the energy storage optimization configuration method for county-level power grids, includes a data acquisition module, a target construction module, a constraint construction module, a model solving module, an optimization configuration module, and a reliability evaluation module; these modules are connected in series. The data acquisition module acquires data information of the target county-level power grid and uploads the data information to the target construction module. The target construction module constructs an energy storage optimization configuration objective function for the target county-level power grid based on the received and acquired data information, and uploads the data information to the target construction module. The system comprises the following modules: a constraint construction module (for data upload), a model solving module (for model solving), and an optimization configuration module (for optimization configuration). The model solving module uses a biomimetic swarm intelligence optimization algorithm to solve the constructed model based on the received data and uploads the data to the optimization configuration module. The optimization configuration module completes the optimization configuration of the energy storage for the target county power grid based on the received data and the solution results, and uploads the data to the reliability assessment module. The reliability assessment module performs a reliability assessment on the obtained optimization configuration of the energy storage based on the received data.

Claims

1. A method for optimizing energy storage configuration for county-level power grids, comprising the following steps: S1. Obtain data information about the power grid in the target county; S2. Based on the data obtained in step S1, construct the objective function for the optimal configuration of energy storage in the target county power grid; S3. Based on the objective function constructed in step S2, construct the energy storage optimization configuration constraints for the target county power grid; S4. The model constructed in steps S2 and S3 is solved using a biomimetic swarm intelligence optimization algorithm; S5. Based on the solution obtained in step S4, complete the energy storage optimization configuration of the target county power grid.

2. The energy storage optimization configuration method for county power grids according to claim 1, characterized in that... Step S1, which involves obtaining data information about the target county power grid, specifically includes the following steps: Obtain data information about the power grid in the target county; The data information includes load data, equipment lifespan information, power grid parameter information, energy storage device data, and new energy power generation information.

3. The energy storage optimization configuration method for county power grids according to claim 2, characterized in that... Step S2, which involves constructing an energy storage optimization configuration objective function for the target county power grid based on the data information obtained in step S1, specifically includes the following steps: The following formula is used as the objective function for optimizing the energy storage configuration of the target county power grid: In the formula Optimize the energy storage configuration of the target county power grid using the objective function value; The operating cost of the power generation units in the target county power grid, and T represents the total number of time periods in the scheduling cycle, and N represents the total number of power generation units. Let be the fuel cost of the i-th power generation unit at time t. Let be the operation and maintenance cost of the i-th power generation unit at time t. The duration of the time period; The full-cycle equivalent configuration cost of the battery energy storage system, and , The unit capacity investment cost of energy storage batteries, The rated power of the energy storage system, The unit capacity investment cost of the energy storage converter. For the installation of energy storage systems and related costs, This refers to the power occupancy cost of the energy storage system or the equivalent cost of the power occupancy of the energy storage system.

4. The energy storage optimization configuration method for county power grids according to claim 3, characterized in that... Step S3, which involves constructing energy storage optimization configuration constraints for the target county power grid based on the objective function built in step S2, specifically includes the following steps: Energy storage side constraints: The following formula is used to calculate the energy storage capacity of the energy storage system: In the formula Let T represent the energy stored in the energy storage system at time T. Power for charging energy storage systems; Improve the charging efficiency of energy storage systems; This refers to the discharge power of the energy storage system. For the discharge efficiency of the energy storage system; The duration of the time period; The following formula is used as the state of charge constraint for the energy storage system: In the formula The minimum state of charge value is set. Let t represent the state of charge of the energy storage system at time t. This is the set maximum state of charge value; Let be the discharge amount of the energy storage system at time t; The amount of charge applied to the energy storage system at time t; Let t be the output power of the energy storage system at time t; The following formula is used as the power constraint for the energy storage system: In the formula This refers to the maximum permissible charge and discharge power of the energy storage system. Let t be the output power of the energy storage system at time t; The following formula is used as the energy constraint for the energy storage system: In the formula This refers to the rated capacity of the energy storage system. Source-side constraints: In the formula This refers to the permissible instantaneous frequency variation on the power generation side; This represents the upper limit of the maximum frequency variation. This is the lower limit of the voltage. This refers to the voltage value on the generator side. This is the upper limit of the voltage. This is the lower limit of power; This refers to the output power of the energy storage system on the power generation side. This is the upper limit of power; Network-side constraints: In the formula The lower limit of the power injected into the grid for energy storage systems; Power injected into the grid to power the energy storage system; The upper limit of the power that can be injected into the grid for energy storage systems; This is the lower limit of the node voltage. This refers to the voltage value at the grid-side node. This represents the upper limit of the node voltage. This refers to the critical circuit current value. This is the upper limit of the line current. Load-side constraints: In the formula This is the lower limit of the discharge power of the energy storage system; Let be the discharge power of the energy storage system at time t; This represents the upper limit of the discharge power of the energy storage system. This represents the minimum state of charge. State of charge of the energy storage system; This represents the maximum value of the state of charge.

5. The energy storage optimization configuration method for county power grids according to claim 4, characterized in that... Step S4 describes solving the model constructed in steps S2 and S3 using a biomimetic swarm intelligence optimization algorithm, specifically including the following steps: The constraints set in step S3 constitute the solution space; an initial population is randomly generated in the solution space and serves as the current population; the population includes several individuals, and each individual corresponds to a solution. The objective function for optimizing the energy storage configuration of the target county power grid constructed in step S2 is used as the fitness function value; for each individual in the current population, the fitness function value of each individual is calculated. The current group is optimized through iterative optimization. The optimization process includes the following updates: Attraction: The following formula is used to calculate the attraction between individuals: In the formula The attraction value between individuals; The set baseline attractiveness value; The attenuation coefficient; The distance between individuals; Location update: The following formula is used to update the position of an individual: In the formula Let i be the position of the i-th individual at time t+1; The distance between individual i and individual j; The set step size parameter; It is a random vector calculated based on a Gaussian distribution; Based on the location update results, the fitness value of each individual is recalculated, and the location is updated again; Repeat the above steps until the set iteration termination condition is met; Ultimately, the optimal location and corresponding energy storage optimization configuration scheme for each individual are obtained.

6. The energy storage optimization configuration method for county power grids according to claim 5, characterized in that... It also includes the following steps: S6. Conduct a reliability assessment on the energy storage optimization configuration obtained in step S5.

7. The energy storage optimization configuration method for county power grids according to claim 6, characterized in that... Step S6, which involves performing a reliability assessment on the optimized energy storage configuration obtained in step S5, specifically includes the following steps: The availability and unavailability of energy storage system components are calculated using the following formula: In the formula For the availability of component i; Let i be the repair rate of component i; Let i be the failure rate of component i; The unavailability of component i; The availability and unavailability of the energy storage system are calculated using the following formula: In the formula This indicates the unavailability of the energy storage system. To ensure the availability of energy storage systems; The load loss probability of the target county power grid is calculated using the following formula: In the formula Let i be the load loss probability of the i-th load point; The number of system fault states where the total generating capacity is less than the load demand; Let be the probability of the i-th state, and , The number of power generation units in the system. Let be the failure probability of component j; The expected load loss and unsupplied expected energy of the target county power grid are calculated using the following formula: In the formula Let T be the expected load loss of the target county power grid; T is the total number of dispatching time periods. The unsupplied expected energy of the target county's power grid; This represents the load amount or load demand corresponding to the i-th load node when it experiences a load loss state. Complete the reliability assessment of the energy storage optimization configuration of the target county power grid.

8. A system for implementing the method for optimizing configuration of energy storage for a county power grid according to any one of claims 1-7, characterized in that It includes a data acquisition module, a target construction module, a constraint construction module, a model solving module, an optimization configuration module, and a reliability assessment module; the data acquisition module, target construction module, constraint construction module, model solving module, optimization configuration module, and reliability assessment module are connected in series; the data acquisition module is used to acquire data information of the target county power grid and upload the data information to the target construction module; The target construction module is used to construct the energy storage optimization configuration objective function of the target county power grid based on the received data information and the acquired data information, and upload the data information to the constraint construction module; the constraint construction module is used to construct the energy storage optimization configuration constraint conditions of the target county power grid based on the received data information and the constructed objective function, and upload the data information to the model solving module. The model solving module is used to solve the constructed model using a biomimetic swarm intelligence optimization algorithm based on the received data information, and then uploads the data information to the optimization configuration module. The optimization configuration module is used to complete the energy storage optimization configuration of the target county power grid based on the received data and the obtained solution results, and upload the data to the reliability assessment module; the reliability assessment module is used to perform a reliability assessment on the obtained energy storage optimization configuration based on the received data.

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