An energy storage station planning stage optimization method, device, equipment and storage medium

Abstract

The application discloses an energy storage station planning stage optimization method and device, equipment and a storage medium, real-time operation data of an energy storage system is acquired, and a health state of the energy storage station is obtained according to the operation data; a first target function is obtained with the maximum reliability of the energy storage station as a target, a second target function is obtained with the maximum renewable energy consumption as a target, a third target function is obtained with the minimum frequency deviation of the power system as a target, and an energy storage station planning stage optimization model is obtained according to the first target function, the second target function, the third target function and corresponding constraint conditions; the energy storage station planning stage optimization model is solved by using a genetic algorithm according to the operation data and the health state, and the standby capacity of the energy storage station and the optimal power configuration of the energy storage station are obtained, thereby improving the accuracy of the optimization result of the energy storage station planning stage.

Description

Technical Field

[0001] This invention relates to the field of power grid dispatching technology, and in particular to an energy storage resource planning method, apparatus, equipment and storage medium. Background Technology

[0002] With the advent of the intelligent era and the arrival of smart grids, the integration of multiple substations (substations, energy storage stations, and data centers) is the main direction of smart grid development. This integration not only facilitates intensive development and saves land costs, but also makes comprehensive management and control of the integrated substations easier.

[0003] In the two-stage configuration of energy storage stations, there are multiple optimization objectives, such as cost minimization, reliability maximization, and environmental benefit maximization. These objectives often conflict with each other. For example, to improve system reliability, it may be necessary to configure more energy storage capacity, which increases costs; however, excessively pursuing cost minimization may reduce the energy storage station's support capacity for the system and affect system reliability. Finding a balance between these conflicting objectives is a crucial issue facing the two-stage optimization configuration of energy storage stations. Summary of the Invention

[0004] To address the aforementioned technical problems, embodiments of the present invention provide an energy storage resource planning method, apparatus, equipment, and storage medium, which solves the problem that the prior art cannot consider multiple optimization objectives when configuring energy storage stations, resulting in low accuracy of optimization results during the energy storage station planning stage.

[0005] A first aspect of this invention provides an energy storage resource planning method, the method comprising:

[0006] Real-time acquisition of operational data from the energy storage system; and determination of the health status of the energy storage station based on the operational data.

[0007] The first objective function is obtained with the goal of maximizing the reliability of the energy storage station, the second objective function is obtained with the goal of maximizing the consumption of renewable energy, and the third objective function is obtained with the goal of minimizing the frequency deviation of the power system. Based on the first objective function, the second objective function, the third objective function, and the corresponding constraints, the optimization model for the planning stage of the energy storage station is obtained.

[0008] Based on operational data and health status, a genetic algorithm is used to solve the optimization model for the planning stage of the energy storage station, thereby obtaining the backup capacity and optimal power configuration of the energy storage station.

[0009] In one possible implementation of the first aspect, a first objective function is obtained with the goal of maximizing the reliability of the energy storage station, including:

[0010] The first objective function is constructed based on the backup capacity coefficient and the preset health status of the energy storage station. The first objective function is:

[0011] max R ESS =μP×SOC m

[0012] In the formula, μ is the number of simulated hours, P is the rated power of the energy storage station, and SOC is... t This represents the health status of the energy storage station at time t.

[0013] In one possible implementation of the first aspect, a second objective function is obtained with the goal of maximizing renewable energy consumption, including:

[0014] Based on the output data of each power generation system, the volatility of each power generation system is obtained, where the expression for volatility is:

[0015]

[0016] In the formula, P RE (t) represents the power output data of the power generation system at time t, P RE-smooth (t) Output data of the power generation system after adjustment by the energy storage station at time t, where T represents the preset time period;

[0017] Based on the volatility of each power generation system, a second objective function is constructed, wherein the second objective function is:

[0018] minF2=σ1+σ2

[0019] In the formula, σ1 is the volatility of the solar power generation system, and σ2 is the volatility of the wind power generation system.

[0020] In one possible implementation of the first aspect, a third objective function is obtained with the goal of minimizing the frequency deviation of the power system, including:

[0021] The frequency deviation is obtained by combining the rated frequency and the actual frequency of the power system. The frequency deviation is then integrated to obtain the integrated frequency deviation.

[0022] Based on the frequency deviation after integration, a third objective function is constructed, which is:

[0023]

[0024] In the formula, f(t) is the rated frequency of the power system, and f0 is the actual frequency of the power system.

[0025] In one possible implementation of the first aspect, an optimization model for the energy storage station planning stage is obtained based on the first objective function, the second objective function, the third objective function, and the corresponding constraints, including:

[0026] Based on the first objective function, the second objective function, and the third objective function, the objective function of the optimization model for the energy storage station planning stage is obtained. The objective function of the optimization model for the energy storage station planning stage is:

[0027] Max F1=λ1R ESS -λ2F2-λ3F3

[0028] In the formula, λ1, λ2 and λ3 are non-negative weight parameters;

[0029] Based on the constraints corresponding to the first, second, and third objective functions, the constraints of the optimization model for the energy storage station planning stage are obtained. The constraints of the optimization model for the energy storage station planning stage are as follows:

[0030]

[0031] In the formula, SOC min SOC max These are the minimum and maximum health values ​​of the energy storage station, respectively, R. S P represents the backup capacity of energy storage stations required by the power system. RE_min P RE_max These represent the minimum and maximum power output of the power generation system, respectively, and Δf is the frequency deviation of the power system.

[0032] In one possible implementation of the first aspect, based on operational data and health status, a genetic algorithm is used to solve the optimization model for the energy storage station planning phase, yielding the configurable capacity, reserve capacity, and optimal power configuration of the energy storage station, including:

[0033] Initialize the population size and calculate the fitness of individuals in the population based on the objective function and constraints of the optimization model in the energy storage station planning stage;

[0034] The overall fitness of the population is calculated based on the fitness of each individual, and the probability value of each individual is then calculated based on the overall fitness.

[0035] Individuals with a probability value greater than a preset threshold are selected for crossover and mutation operations to obtain a new population;

[0036] The fitness of each individual in the new population is calculated based on the optimization model in the planning stage of the energy storage station, and the new fitness of each new individual is obtained. The new total fitness of the new population is calculated based on the new fitness, and the new probability value of each new individual is obtained based on the new total fitness. Individuals with new probability values ​​greater than a preset threshold are selected for crossover and mutation operations to obtain the updated population. This step is repeated until the convergence condition is met to obtain the optimal solution. The optimal solution includes the backup capacity of the energy storage station and the optimal power configuration of the energy storage station.

[0037] A second aspect of the present invention provides an optimization device for the planning stage of an energy storage station, the device comprising:

[0038] The acquisition module is used to acquire real-time operating data of the energy storage system and determine the health status of the energy storage station based on the operating data.

[0039] The module is used to obtain the first objective function with the goal of maximizing the reliability of the energy storage station, the second objective function with the goal of maximizing the consumption of renewable energy, and the third objective function with the goal of minimizing the frequency deviation of the power system. Based on the first objective function, the second objective function, the third objective function, and the corresponding constraints, the optimization model for the planning stage of the energy storage station is obtained.

[0040] The calculation module is used to solve the optimization model of the energy storage station planning stage using a genetic algorithm based on the operating data and health status, so as to obtain the backup capacity and the optimal power configuration of the energy storage station.

[0041] In one possible implementation of the second aspect, the first objective function is obtained with the goal of maximizing the reliability of the energy storage station, including:

[0042] The first objective function is constructed based on the backup capacity coefficient and the preset health status of the energy storage station. The first objective function is:

[0043] max R ESS =μP×SOC m

[0044] In the formula, μ is the number of simulated hours, P is the rated power of the energy storage station, and SOC is... t This represents the health status of the energy storage station at time t.

[0045] A third aspect of the present invention provides a computer device, comprising:

[0046] Memory, used to store computer programs;

[0047] A processor is used to execute computer programs to implement optimization methods for the energy storage station planning phase, as described in the first aspect.

[0048] A fourth aspect of the present invention provides a storage medium storing a computer program, which, when executed by a processor, implements the steps of the energy storage station planning phase optimization method of the first aspect.

[0049] This invention provides real-time acquisition of operating data of an energy storage system, and obtains the health status of the energy storage station based on the operating data. A first objective function is obtained with the goal of maximizing the reliability of the energy storage station; a second objective function is obtained with the goal of maximizing renewable energy consumption; and a third objective function is obtained with the goal of minimizing the frequency deviation of the power system. An optimization model for the planning stage of the energy storage station is obtained based on the first, second, and third objective functions and the corresponding constraints. Based on the operating data and health status, a genetic algorithm is used to solve the optimization model for the planning stage of the energy storage station, obtaining the reserve capacity and optimal power configuration of the energy storage station. This invention improves the reliability of the optimization results in the planning stage of the energy storage station by considering multiple optimization objectives in configuring the energy storage station. Attached Figure Description

[0050] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0051] Figure 1 This is a flowchart of the optimization method for the planning stage of an energy storage station in an embodiment of the present invention;

[0052] Figure 2 This is a structural block diagram of the energy storage station planning stage optimization device in an embodiment of the present invention. Detailed Implementation

[0053] 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.

[0054] Please refer to Figure 1 This is a flowchart illustrating an embodiment of the energy storage station planning phase optimization method provided by the present invention, including steps S101 to S103, each step of which is as follows:

[0055] S101. Obtain real-time operating data of the energy storage system and determine the health status of the energy storage station based on the operating data.

[0056] In this embodiment, the operating data of the energy storage system is acquired in real time. The operating data includes the voltage, current, temperature, and SOC of the energy storage unit. This data is collected by sensors installed on the energy storage device and transmitted to the monitoring system.

[0057] The health status of energy storage stations is assessed based on monitoring data. This is achieved by using a neural network trained on a large amount of historical data and combining it with real-time operational data to calculate the health status of the energy storage equipment.

[0058] S102. The first objective function is obtained with the goal of maximizing the reliability of the energy storage station, the second objective function is obtained with the goal of maximizing the consumption of renewable energy, and the third objective function is obtained with the goal of minimizing the frequency deviation of the power system. Based on the first objective function, the second objective function, the third objective function, and the corresponding constraints, the optimization model for the planning stage of the energy storage station is obtained.

[0059] In this embodiment, when an energy storage station is connected to the grid, it is required to provide a specified backup capacity for a certain period of time and possess fault ride-through capability, meaning it can maintain connectivity and provide support during grid failures. Therefore, with the goal of maximizing the reliability of the energy storage station, its reliability is crucial for smoothing these power fluctuations. A reliable energy storage station can perform rapid charging and discharging adjustments in real time based on changes in renewable energy output and load demand, ensuring system power balance.

[0060] Building energy storage stations with the goal of maximizing renewable energy absorption can effectively address the intermittency and volatility issues associated with renewable energy. Energy storage stations can store energy when there is a surplus in renewable energy generation and release it when generation is insufficient. For example, during periods of strong wind power generation, energy storage stations can store excess wind power. When wind speeds decrease and wind power output declines, the energy storage stations release the electricity, thus smoothing out the output of renewable energy and improving the grid's capacity to absorb renewable energy.

[0061] Energy storage stations possess rapid power regulation capabilities, enabling them to absorb or release power in a short period. Constructing energy storage stations with the goal of minimizing power system frequency deviation allows for full utilization of their rapid response characteristics to regulate system frequency. When the system frequency rises, the energy storage station can quickly charge to absorb excess power; when the system frequency falls, the energy storage station can rapidly discharge to provide power support, enabling the system frequency to recover to near its rated value as quickly as possible. This rapid frequency regulation function is crucial for maintaining the dynamic stability of the power system.

[0062] In some embodiments, a first objective function is obtained with the goal of maximizing the reliability of the energy storage station, including:

[0063] The first objective function is constructed based on the backup capacity coefficient and the preset health status of the energy storage station. The first objective function is:

[0064] max R ESS =μP×SOC m

[0065] In the formula, μ is the number of simulated hours, P is the rated power of the energy storage station, and SOC is... t This represents the health status of the energy storage station at time t.

[0066] In this embodiment, in a power system, energy storage stations can provide backup capacity to cope with load fluctuations and power generation equipment failures. Let the system's backup capacity requirement be R. S The backup capacity that an energy storage station can provide is related to the power of the energy storage devices and the state of health (SOC) of the energy storage station. Therefore, the energy storage station can provide the maximum backup capacity when its SOC is at its maximum, and its relationship with power can be expressed as:

[0067] R ESS =μP×SOC m

[0068] In the formula, μ is the number of simulated hours, P is the rated power of the energy storage station, and SOC is... t This represents the health status of the energy storage station at time t.

[0069] Based on the relationship between the above-mentioned health status and power, the objective function is obtained with the goal of maximizing the reliability of the energy storage station.

[0070] In some embodiments, a second objective function is obtained with the goal of maximizing renewable energy consumption, including:

[0071] Based on the output data of each power generation system, the volatility of each power generation system is obtained, where the expression for volatility is:

[0072]

[0073] In the formula, P RE (t) represents the power output data of the power generation system at time t, P RE-smooth (t) Output data of the power generation system after adjustment by the energy storage station at time t, where T represents the preset time period;

[0074] Based on the volatility of each power generation system, a second objective function is constructed, wherein the second objective function is:

[0075] minF2=σ1+σ2

[0076] In the formula, σ1 is the volatility of the solar power generation system, and σ2 is the volatility of the wind power generation system.

[0077] In this embodiment, for systems connected to renewable energy generation (such as solar and wind power), the energy storage station can smooth the output power of renewable energy. Let the original output power of the renewable energy be P. RE (t), then the output power after adjustment by the energy storage station is P. RE-smooth Then, the objective function is constructed by minimizing the volatility of renewable energy output, which can be calculated using the following formula:

[0078]

[0079] In the formula, P RE (t) represents the power output data of the power generation system at time t, P RE-smooth (t) Output data of the power generation system after adjustment by the energy storage station at time t, where T represents the preset time period;

[0080] Then, based on the volatility of solar and wind power generation systems, an objective function is constructed, wherein the second objective function is:

[0081] minF2=σ1+σ2

[0082] In the formula, σ1 is the volatility of the solar power generation system, and σ2 is the volatility of the wind power generation system.

[0083] In one embodiment, a third objective function is obtained with the goal of minimizing the frequency deviation of the power system, including:

[0084] The frequency deviation is obtained by combining the rated frequency and the actual frequency of the power system. The frequency deviation is then integrated to obtain the integrated frequency deviation.

[0085] Based on the frequency deviation after integration, a third objective function is constructed, which is:

[0086]

[0087] In the formula, f(t) is the rated frequency of the power system, and f0 is the actual frequency of the power system.

[0088] In this embodiment, the system frequency variable is determined, and the actual operating frequency of the power system is set to f0, typically in Hertz. The rated frequency of a typical power system is known, for example, 50 Hertz. Based on the dynamic characteristics of the power system, the change in system frequency is related to the power imbalance between generators and loads. Under small disturbance conditions, a first-order linear model can be used to describe the frequency response characteristics. Therefore, with the goal of minimizing the frequency deviation, the objective function can be constructed as follows:

[0089]

[0090] In the formula, f(t) is the rated frequency of the power system, and f0 is the actual frequency of the power system.

[0091] To ensure the normal operation of the power system, frequency deviation is also subject to certain limitations, such as:

[0092] Δf≤Δf max

[0093] Where, Δf max This is the maximum permissible frequency deviation.

[0094] In practical systems, this value is usually determined according to the power system operation standards, and the frequency deviation is generally no more than ±0.2Hz.

[0095] In some embodiments, an optimization model for the energy storage station planning stage is obtained based on a first objective function, a second objective function, a third objective function, and corresponding constraints, including:

[0096] Based on the first objective function, the second objective function, and the third objective function, the objective function of the optimization model for the energy storage station planning stage is obtained. The objective function of the optimization model for the energy storage station planning stage is:

[0097] Max F1=λ1R ESS -λ2F2-λ3F3

[0098] In the formula, λ1, λ2 and λ3 are non-negative weight parameters;

[0099] Based on the constraints corresponding to the first, second, and third objective functions, the constraints of the optimization model for the energy storage station planning stage are obtained. The constraints of the optimization model for the energy storage station planning stage are as follows:

[0100]

[0101] In the formula, SOC min SOC max These are the minimum and maximum health values ​​of the energy storage station, respectively, R. S P represents the backup capacity of energy storage stations required by the power system. RE_min P RE_max These represent the minimum and maximum power outputs of the power generation system, respectively, and Δf is the frequency deviation of the power system. max This represents the maximum permissible frequency deviation.

[0102] In this embodiment, a weight coefficient is assigned to each objective function based on the importance of each optimization objective in the overall optimization. A single objective function is then constructed based on these weight coefficients. The objective functions for the energy storage station planning stage optimization model are constructed with the objectives of maximizing energy storage station reliability, maximizing renewable energy absorption, and minimizing power system frequency deviation. The objective function for the energy storage station planning stage optimization model is as follows:

[0103] Max F1=λ1R ESS -λ2F2-λ3F3

[0104] In the formula, λ1, λ2 and λ3 are non-negative weight parameters;

[0105] Based on the constraints corresponding to the first, second, and third objective functions, the constraints of the optimization model for the energy storage station planning stage are obtained. The constraints of the optimization model for the energy storage station planning stage are as follows:

[0106]

[0107] In the formula, SOC min SOC max These are the minimum and maximum health values ​​of the energy storage station, respectively, R. S P represents the backup capacity of energy storage stations required by the power system. RE_min P RE_max These represent the minimum and maximum power outputs of the power generation system, respectively, and Δf is the frequency deviation of the power system. max This represents the maximum permissible frequency deviation.

[0108] S103. Based on the operating data and health status, the optimization model of the energy storage station planning stage is solved using a genetic algorithm to obtain the energy storage station's reserve capacity and optimal power configuration.

[0109] In this embodiment, parameters such as the power and capacity of the energy storage station can be encoded using real numbers, such as the power P of the energy storage station. RE and capacity R ESS Genes on chromosomes are represented directly as real numbers. A chromosome can be represented as [P]. RE R ESS ], where P RE and R ESS The value range is determined based on the actual equipment and system requirements. Then, a genetic algorithm is used to solve the optimization model for the energy storage station planning stage to obtain the energy storage station's reserve capacity and optimal power configuration.

[0110] In some embodiments, based on operational data and health status, a genetic algorithm is used to solve the optimization model for the energy storage station planning phase, obtaining the configurable capacity, reserve capacity, and optimal power configuration of the energy storage station, including:

[0111] Initialize the population size and calculate the fitness of individuals in the population based on the objective function and constraints of the optimization model in the energy storage station planning stage;

[0112] The overall fitness of the population is calculated based on the fitness of each individual, and the probability value of each individual is then calculated based on the overall fitness.

[0113] Individuals with a probability value greater than a preset threshold are selected for crossover and mutation operations to obtain a new population;

[0114] The fitness of each individual in the new population is calculated based on the optimization model in the planning stage of the energy storage station, and the new fitness of each new individual is obtained. The new total fitness of the new population is calculated based on the new fitness, and the new probability value of each new individual is obtained based on the new total fitness. Individuals with new probability values ​​greater than a preset threshold are selected for crossover and mutation operations to obtain the updated population. This step is repeated until the convergence condition is met to obtain the optimal solution. The optimal solution includes the backup capacity of the energy storage station and the optimal power configuration of the energy storage station.

[0115] In this embodiment, an initial population is randomly generated, with each individual (chromosome) representing a combination of power and capacity for an energy storage station. The gene values ​​of individuals are randomly generated within a pre-defined range. A fitness function is designed based on the objective function and constraints of the optimization model during the energy storage station planning phase. Simultaneously, for individuals that do not meet the constraints, a penalty function is used to reduce their fitness. For example, if an individual's power configuration leads to a decrease in SOC... m If the value exceeds the limit, a larger penalty value is subtracted from the fitness function.

[0116] A roulette wheel selection method is used to calculate the proportion of each individual's fitness to the total fitness of the population, which serves as the probability of its selection. Based on this probability, a subset of individuals are selected as parents for the next generation through a simulated roulette wheel selection process. Then, a uniform crossover method is applied to the selected population. Specifically, if two parent individuals are equal, crossover is performed with a certain probability to generate offspring individuals, resulting in the crossover-adjusted population.

[0117] Then, a mutation operation is performed on the crossover population. Specifically, one or more individuals are randomly selected from the population for mutation. Within the length of the individual's gene sequence, one or more mutation positions are randomly selected, and the gene value at the selected position is changed. If the gene is binary encoded, 0 becomes 1 or 1 becomes 0; if it is other encoding methods, it is changed according to predefined mutation rules to obtain a new population.

[0118] Based on the new population, the optimization model of the energy storage station planning stage is used to continue iterative calculation. When the fitness of the population converges, that is, when the fitness standard deviation is less than a certain threshold, the genetic algorithm is stopped and the optimal solution is output. The optimal solution includes the energy storage station's reserve capacity and the optimal power configuration of the energy storage station.

[0119] It should be understood that, although Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but may be executed at different times. The execution order of these steps or stages is not necessarily sequential, but may be executed in turn or alternately with other steps or at least some of the steps or stages in other steps.

[0120] In some embodiments, such as Figure 2 As shown, it illustrates a block diagram of a two-stage optimized configuration 200 for an energy storage station provided in an embodiment of this application, including: an acquisition module 201, a construction module 202, and a calculation module 203, wherein:

[0121] The acquisition module 201 is used to acquire the operating data of the energy storage system in real time and obtain the health status of the energy storage station based on the operating data.

[0122] Module 202 is used to obtain a first objective function with the goal of maximizing the reliability of the energy storage station, a second objective function with the goal of maximizing the consumption of renewable energy, and a third objective function with the goal of minimizing the frequency deviation of the power system. Based on the first objective function, the second objective function, the third objective function, and the corresponding constraints, an optimization model for the planning stage of the energy storage station is obtained.

[0123] The calculation module 203 is used to solve the optimization model of the energy storage station planning stage using a genetic algorithm based on the operating data and health status, so as to obtain the energy storage station's reserve capacity and optimal power configuration.

[0124] The specific implementation of the optimization device for the planning stage of energy storage stations is basically the same as the specific implementation of the optimization method for the planning stage of energy storage stations described above, and will not be repeated here.

[0125] In one embodiment of this application, a computer device is provided, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above steps. The implementation principle and technical effects of the computer device provided in this embodiment are similar to those of the above method embodiments, and will not be repeated here.

[0126] In one embodiment of this application, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, it performs the above steps; the implementation principle and technical effects of the computer-readable storage medium provided in this embodiment are similar to those of the above method embodiments, and will not be repeated here.

[0127] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAM bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0128] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0129] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. An optimization method for the planning stage of an energy storage station, characterized in that, include: Real-time acquisition of operational data from the energy storage system; and determination of the health status of the energy storage station based on the operational data. The first objective function is obtained with the goal of maximizing the reliability of the energy storage station, the second objective function is obtained with the goal of maximizing the consumption of renewable energy, and the third objective function is obtained with the goal of minimizing the frequency deviation of the power system. Based on the first objective function, the second objective function, the third objective function, and the corresponding constraints, the optimization model for the planning stage of the energy storage station is obtained. Based on the operational data and the health status, a genetic algorithm is used to solve the optimization model for the planning stage of the energy storage station to obtain the backup capacity and the optimal power configuration of the energy storage station. The first objective function, which aims to maximize the reliability of the energy storage station, includes: A first objective function is constructed based on the backup capacity coefficient of the energy storage station and the preset health status value of the energy storage station, wherein the first objective function is: In the formula, It's a simulated number of hours. This refers to the rated power of the energy storage station. For energy storage stations Constant health status; The second objective function, which aims to maximize the absorption of renewable energy, includes: Based on the output data of each power generation system, the volatility of each power generation system is obtained, wherein the expression for the volatility is: In the formula, This represents the power output data of the power generation system at time t. The power output data of the power generation system at time t after adjustment by the energy storage station. Indicates a preset time period; Based on the volatility of each of the aforementioned power generation systems, a second objective function is constructed, wherein the second objective function is: In the formula, For the volatility of solar power generation systems, 2 represents the volatility of the wind power generation system; The third objective function, which aims to minimize the frequency deviation of the power system, includes: Based on the rated frequency and actual frequency of the power system, the frequency deviation is obtained, and the frequency deviation is integrated to obtain the integrated frequency deviation. Based on the frequency deviation after integration, a third objective function is constructed, wherein the third objective function is: In the formula, The rated frequency of the power system. The actual frequency of the power system; Based on the first objective function, the second objective function, the third objective function, and the corresponding constraints, an optimization model for the energy storage station planning stage is obtained, including: The objective function of the energy storage station planning stage optimization model is obtained based on the first objective function, the second objective function, and the third objective function, wherein the objective function of the energy storage station planning stage optimization model is: Max 3 In the formula, , and These are non-negative weight parameters; Based on the constraints corresponding to the first objective function, the second objective function, and the third objective function, the constraints of the optimization model for the energy storage station planning stage are obtained, wherein the constraints of the optimization model for the energy storage station planning stage are: In the formula, , These represent the minimum and maximum health values ​​of the energy storage station, respectively. For the backup capacity of energy storage stations required by the power system, , These represent the minimum and maximum power output of the power generation system, respectively. The frequency deviation of the power system, This represents the maximum permissible frequency deviation.

2. The optimization method for the planning stage of energy storage stations as described in claim 1, characterized in that, The step involves using a genetic algorithm to solve the optimization model for the energy storage station planning phase based on the operational data and the health status, to obtain the configurable capacity, reserve capacity, and optimal power configuration of the energy storage station, including: Initialize the population size, and calculate the fitness of individuals in the population based on the objective function and constraints of the optimization model in the energy storage station planning stage; The total fitness of the population is calculated based on the fitness of each individual, and the probability value of each individual is calculated based on the total fitness. Individuals with a probability value greater than a preset threshold are selected for crossover and mutation operations to obtain a new population. The fitness of each individual in the new population is calculated based on the optimization model of the energy storage station planning stage, and the new fitness of each new individual is obtained. The new total fitness of the new population is calculated based on the new fitness, and the new probability value of each new individual is obtained based on the new total fitness. Individuals with new probability values ​​greater than a preset threshold are selected for crossover and mutation operations to obtain an updated population. This step is repeated until the convergence condition is met to obtain the optimal solution, wherein the optimal solution includes the energy storage station's reserve capacity and the optimal power configuration of the energy storage station.

3. An optimization device for the planning stage of an energy storage station, characterized in that, The method for optimizing the planning phase of an energy storage station as described in any one of claims 1-2 includes: The acquisition module is used to acquire the operating data of the energy storage system in real time and obtain the health status of the energy storage station based on the operating data. The module is used to obtain a first objective function with the goal of maximizing the reliability of the energy storage station, a second objective function with the goal of maximizing the consumption of renewable energy, and a third objective function with the goal of minimizing the frequency deviation of the power system. Based on the first objective function, the second objective function, the third objective function, and the corresponding constraints, an optimization model for the planning stage of the energy storage station is obtained. The calculation module is used to solve the optimization model of the energy storage station planning stage using a genetic algorithm based on the operating data and the health status, so as to obtain the backup capacity of the energy storage station and the optimal power configuration of the energy storage station.

4. The energy storage station planning phase optimization device as described in claim 3, characterized in that, The first objective function, which aims to maximize the reliability of the energy storage station, includes: A first objective function is constructed based on the backup capacity coefficient of the energy storage station and the preset health status value of the energy storage station, wherein the first objective function is: In the formula, It's a simulated number of hours. This refers to the rated power of the energy storage station. For energy storage stations A person's health status at all times.

5. A computer device, characterized in that, include: Memory, used to store computer programs; A processor is configured to implement the energy storage station planning phase optimization method as described in any one of claims 1 to 2 when executing the computer program.

6. A storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the steps of the energy storage station planning phase optimization method as described in any one of claims 1 to 2.

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