Energy storage station planning stage optimization method, device and equipment and storage medium

Abstract

The invention discloses an energy storage station planning stage optimization method and device, equipment and a storage medium, and the method comprises the steps: obtaining the operation data of an energy storage system in real time, and obtaining the health state of an energy storage station according to the operation data; obtaining a first target function by taking the reliability maximization of the energy storage station as a target, obtaining a second target function by taking the renewable energy consumption maximization as a target, and obtaining a third target function by taking the frequency deviation minimization of the power system as a target; obtaining an energy storage station planning stage optimization model according to the first objective function, the second objective function, the third objective function and corresponding constraint conditions; and according to the operation data and the health state, solving the energy storage station planning stage optimization model by using a genetic algorithm to obtain the reserve capacity of the energy storage station and the optimal power configuration of the energy storage station, thereby improving the accuracy of the energy storage station planning stage optimization result.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid dispatching, and in particular to an energy storage resource planning method, device, equipment and storage medium. Background Art

[0002] With the advent of the intelligent era and the advent of smart grids, the multi-station integration of substation + energy storage station + data center model is the main direction of smart grid development. After the integration of multiple stations, it will not only help intensive development and save land costs, but also make it easier to carry out comprehensive management and control of the integrated multiple stations.

[0003] In the two-stage configuration of energy storage stations, there are multiple optimization objectives, such as minimizing costs, maximizing reliability, maximizing environmental benefits, etc. These objectives often conflict with each other. For example, in order to improve the reliability of the system, more energy storage capacity may need to be configured, which will increase costs; while excessive pursuit of cost minimization may reduce the energy storage station's ability to support the system and affect the reliability of the system. How to find a balance between these conflicting objectives is an important issue facing the two-stage optimization configuration of energy storage stations. Summary of the invention

[0004] In order to solve the above technical problems, the embodiments of the present invention provide a method, device, equipment and storage medium for energy storage resource planning, which solves the problem that the prior art cannot consider multiple optimization objectives to configure energy storage stations, resulting in low accuracy of optimization results in the energy storage station planning stage.

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

[0006] Obtain 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;

[0007] The first objective function is obtained by maximizing the reliability of the energy storage station, the second objective function is obtained by maximizing the consumption of renewable energy, and the third objective function is obtained by minimizing the frequency deviation of the power system. The optimization model of the energy storage station planning stage is obtained according to the first objective function, the second objective function, the third objective function and the corresponding constraints;

[0008] According to the operation data and health status, the genetic algorithm is used to solve the optimization model of the energy storage station planning stage to obtain the backup capacity of the energy storage station and the optimal power configuration of the energy storage station.

[0009] In a possible implementation of the first aspect, obtaining a first objective function with the goal of maximizing the reliability of the energy storage station includes:

[0010] The first objective function is constructed according to the backup capacity coefficient of the energy storage station and the preset value of the health status of the energy storage station, wherein the first objective function is:

[0011] max R ESS =μP×SOC m

[0012] Where μ is the number of simulation hours, P is the rated power of the energy storage station, and SOC t is the health status of the energy storage station at time t.

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

[0014] According to the output data of each power generation system, the fluctuation rate of each power generation system is obtained, where the expression of the fluctuation rate is:

[0015]

[0016] Where P RE (t) is the output data of the power generation system at time t, P RE-smooth (t) The 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 fluctuation rate of each power generation system, the second objective function is constructed, where the second objective function is:

[0018] minF2=σ1+σ2

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

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

[0021] According to the rated frequency and the actual frequency of the power system, a frequency deviation is obtained, and the frequency deviation is integrated to obtain an integrated frequency deviation;

[0022] According to the integrated frequency deviation, the third objective function is constructed, where the third objective function is:

[0023]

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

[0025] In a possible implementation of the first aspect, an energy storage station planning phase optimization model is obtained according to the first objective function, the second objective function, the third objective function, and corresponding constraints, including:

[0026] The objective function of the optimization model in the energy storage station planning stage is obtained according to the first objective function, the second objective function, and the third objective function, wherein the objective function of the optimization model in 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 respectively;

[0029] According to the constraints corresponding to the first objective function, the second objective function, and the third objective function, the constraints of the optimization model in the energy storage station planning stage are obtained, wherein the constraints of the optimization model in the energy storage station planning stage are:

[0030]

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

[0032] In a possible implementation of the first aspect, a genetic algorithm is used to solve an optimization model of an energy storage station planning stage according to operation data and health status to obtain configurable capacity of the energy storage station, spare capacity of the energy storage station, 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 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;

[0035] Select individuals with probability values ​​greater than a preset threshold to perform crossover and mutation operations to obtain a new population;

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

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

[0038] The acquisition module is used to obtain 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;

[0039] A construction module is used to obtain a first objective function with the goal of maximizing the reliability of the energy storage station, obtain a second objective function with the goal of maximizing the consumption of renewable energy, and obtain a third objective function with the goal of minimizing the frequency deviation of the power system, and obtain an optimization model for the energy storage station planning stage according to the first objective function, the second objective function, the third objective function and the corresponding constraints;

[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, and obtain the spare capacity of the energy storage station and the optimal power configuration of the energy storage station.

[0041] In a possible implementation of the second aspect, obtaining a first objective function with the goal of maximizing the reliability of the energy storage station includes:

[0042] The first objective function is constructed according to the backup capacity coefficient of the energy storage station and the preset value of the health status of the energy storage station, wherein the first objective function is:

[0043] max R ESS =μP×SOC m

[0044] Where μ is the number of simulation hours, P is the rated power of the energy storage station, and SOC t is the health status of the energy storage station at time t.

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

[0046] Memory for storing computer programs;

[0047] A processor is used to implement the energy storage station planning stage optimization method of the first aspect when executing a computer program.

[0048] A fourth aspect of an embodiment of the present invention provides a storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the energy storage station planning phase optimization method of the first aspect are implemented.

[0049] The embodiment of the present invention provides real-time acquisition of operating data of an energy storage system, and obtains the health status of an energy storage station based on the operating data; obtains a first objective function with the goal of maximizing the reliability of the energy storage station, obtains a second objective function with the goal of maximizing the consumption of renewable energy, and obtains a third objective function with the goal of minimizing the frequency deviation of the power system; obtains an optimization model for the energy storage station planning stage based on the first objective function, the second objective function, the third objective function and corresponding constraints; solves the optimization model for the energy storage station planning stage based on the operating data and the health status using a genetic algorithm to obtain the backup capacity of the energy storage station and the optimal power configuration of the energy storage station. The present invention configures the energy storage station by considering multiple optimization objectives, thereby improving the reliability of the optimization results of the energy storage station planning stage. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0051] Figure 1 Flow chart of the energy storage station planning phase optimization method according to an embodiment of the present invention;

[0052] Figure 2 4 is a structural block diagram of an energy storage station planning phase optimization device in an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

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

[0055] S101. 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.

[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. The data is collected by sensors installed on the energy storage device and transmitted to the monitoring system.

[0057] The health status of the energy storage station is evaluated based on the monitoring data. The health status of the energy storage equipment is obtained by using a neural network trained on a large amount of historical data and combining it with real-time operating data for calculation.

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

[0059] In this embodiment, when the energy storage station is connected to the power grid, it is required to provide a specified backup capacity within a certain period of time and have fault ride-through capability, that is, to be able to maintain connection and provide support during power grid failures. Therefore, with the goal of maximizing the reliability of the energy storage station, the reliability of the energy storage station is crucial to smoothing these power fluctuations. A reliable energy storage station can quickly adjust charging and discharging according to the output changes of renewable energy and load requirements in real time to ensure system power balance.

[0060] Building energy storage stations with the goal of maximizing renewable energy consumption can effectively solve the intermittent and volatile problems of renewable energy. Energy storage stations can store energy when there is excess renewable energy generation and release energy when there is insufficient generation. For example, during periods of strong wind power generation, energy storage stations can store excess wind power. When the wind speed decreases and wind power decreases, the energy storage station releases electricity, thereby making the output power of renewable energy smoother and improving the grid's ability to accept renewable energy.

[0061] Energy storage stations have fast power regulation capabilities and can absorb or release power in a short period of time. By building energy storage stations with the goal of minimizing the frequency deviation of the power system, we can make full use of their fast response characteristics to adjust the system frequency. When the system frequency increases, the energy storage station can charge quickly and absorb excess power; when the system frequency decreases, the energy storage station can discharge quickly and provide power support to restore the system frequency to near the rated value as soon as possible. This fast frequency regulation function is crucial to maintaining the dynamic stability of the power system.

[0062] In some embodiments, the 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 according to the backup capacity coefficient of the energy storage station and the preset value of the health status of the energy storage station, wherein the first objective function is:

[0064] max R ESS =μP×SOC m

[0065] Where μ is the number of simulation hours, P is the rated power of the energy storage station, and SOC t is the health status of the energy storage station at time t.

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

[0067] R ESS =μP×SOC m

[0068] Where μ is the number of simulation hours, P is the rated power of the energy storage station, and SOC t is the health status of the energy storage station at time t.

[0069] Through the above relationship between 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, the second objective function is obtained with the goal of maximizing renewable energy consumption, including:

[0071] According to the output data of each power generation system, the fluctuation rate of each power generation system is obtained, where the expression of the fluctuation rate is:

[0072]

[0073] Where P RE (t) is the output data of the power generation system at time t, P RE-smooth (t) The 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 fluctuation rate of each power generation system, the second objective function is constructed, where the second objective function is:

[0075] minF2=σ1+σ2

[0076] Where 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 a system connected to renewable energy generation (such as solar energy and wind energy), the energy storage station can smooth the output power of the renewable energy. Assume that the original output of the renewable energy is P RE (t), then the output after adjustment by the energy storage station is P RE-smooth (t), and then construct the objective function by minimizing the volatility of renewable energy output, which can be calculated by the following formula:

[0078]

[0079] Where P RE (t) is the output data of the power generation system at time t, P RE-smooth (t) The 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, according to the fluctuation rate of solar energy and wind power generation systems, the objective function is constructed, where the second objective function is:

[0081] minF2=σ1+σ2

[0082] Where 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, the third objective function is obtained with the goal of minimizing the frequency deviation of the power system, including:

[0084] According to the rated frequency and the actual frequency of the power system, a frequency deviation is obtained, and the frequency deviation is integrated to obtain an integrated frequency deviation;

[0085] According to the integrated frequency deviation, the third objective function is constructed, where the third objective function is:

[0086]

[0087] Where 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 variable system frequency variable is determined, and the actual operating frequency of the power system is assumed to be f0, and its unit is usually Hertz. The rated frequency of a general power system is known, for example, the rated frequency is 50 Hertz. According to the dynamic characteristics of the power system, the change in system frequency is related to the power imbalance between the generator and the load. In the case of small disturbances, the frequency response characteristics can be described by a first-order linear model. Therefore, with the goal of minimizing the frequency deviation, the objective function that can be constructed is:

[0089]

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

[0091] In order to ensure the normal operation of the power system, the frequency deviation also has certain restrictions, such as:

[0092] Δf≤Δf max

[0093] Where Δf max is the maximum frequency deviation allowed.

[0094] In actual systems, this value is usually determined based on the power system operating standards, and the frequency deviation generally does not exceed ±0.2Hz.

[0095] In some embodiments, the energy storage station planning phase optimization model is obtained according to the first objective function, the second objective function, the third objective function and the corresponding constraints, including:

[0096] The objective function of the optimization model in the energy storage station planning stage is obtained according to the first objective function, the second objective function, and the third objective function, wherein the objective function of the optimization model in 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 respectively;

[0099] According to the constraints corresponding to the first objective function, the second objective function, and the third objective function, the constraints of the optimization model in the energy storage station planning stage are obtained, wherein the constraints of the optimization model in the energy storage station planning stage are:

[0100]

[0101] In the formula, SOC min , SOC max are the minimum and maximum health status of the energy storage station, respectively. S is the energy storage station backup capacity required by the power system, P RE_min , P RE_max are the minimum and maximum output of the power generation system, Δf is the frequency deviation of the power system, Δf max is the maximum allowed frequency deviation.

[0102] In this embodiment, a weight coefficient is assigned to each objective function according to the importance of each optimization objective in the overall optimization. A single objective function is constructed according to the weight coefficient. The objective function of the optimization model of the energy storage station planning stage is constructed with the goal of maximizing the reliability of the energy storage station, the goal of maximizing the consumption of renewable energy, and the goal of minimizing the frequency deviation of the power system. The objective function of the optimization model of the energy storage station planning stage is:

[0103] Max F1=λ1R ESS -λ2F2-λ3F3

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

[0105] According to the constraints corresponding to the first objective function, the second objective function, and the third objective function, the constraints of the optimization model in the energy storage station planning stage are obtained, wherein the constraints of the optimization model in the energy storage station planning stage are:

[0106]

[0107] In the formula, SOC min , SOC max are the minimum and maximum health status of the energy storage station, respectively. S is the energy storage station backup capacity required by the power system, P RE_min , P RE_max are the minimum and maximum output of the power generation system, Δf is the frequency deviation of the power system, Δf max is the maximum allowed frequency deviation.

[0108] S103. Based on the operation data and health status, a genetic algorithm is used to solve the optimization model of the energy storage station planning stage to obtain the spare capacity of the energy storage station and the optimal power configuration of the energy storage station.

[0109] In this embodiment, for parameters such as the power and capacity of the energy storage station, real number coding can be used to convert the power of the energy storage station P RE and capacity R ESS Directly use real numbers to represent the genes of chromosomes. A chromosome can be represented as [P RE , R ESS ], where P RE and R ESS The value range of is determined according to the actual equipment and system requirements. Then the genetic algorithm is used to solve the optimization model of the energy storage station planning stage to obtain the energy storage station backup capacity and the optimal power configuration of the energy storage station.

[0110] In some embodiments, based on the operation data and health status, a genetic algorithm is used to solve the optimization model of the energy storage station planning stage to obtain the configurable capacity of the energy storage station, the spare capacity of the energy storage station, and the 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 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;

[0113] Select individuals with probability values ​​greater than a preset threshold to perform crossover and mutation operations to obtain a new population;

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

[0115] In this embodiment, the initial population is randomly generated, and each individual (chromosome) represents a combination of power and capacity of the energy storage station. The gene value of the individual is randomly generated within a pre-set value range. The fitness function is designed according to the objective function and constraints of the optimization model in the energy storage station planning stage. At the same time, for individuals that do not meet the constraints, a penalty function is used to reduce their fitness. For example, if the power configuration of an individual leads to SOC m If the value exceeds the range, a larger penalty value is subtracted from the fitness function.

[0116] The roulette selection method is used to calculate the proportion of each individual's fitness to the total fitness of the population as the probability of its selection. According to this probability, some individuals are selected as the parents of the next generation population by simulating roulette. Then the uniform crossover method is selected to perform a crossover operation on the selected population. Specifically, if the two parent individuals are and, a crossover operation is performed with a certain crossover probability to generate offspring individuals and obtain the population after the crossover.

[0117] Then, the population after crossover is mutated. Specifically, one or more individuals are randomly selected from the population for mutation, and one or more mutation positions are randomly selected within the length of the individual's gene sequence, and the gene value at the selected position is changed. If the gene is binary coded, 0 becomes 1 or 1 becomes 0; if it is other coding methods, it is changed according to the pre-defined mutation rules to obtain a new population.

[0118] Based on the new population, the optimization model of the energy storage station planning stage is continued to be used for iterative calculations. When the fitness convergence of the population is reached, that is, 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 backup capacity of the energy storage station 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 in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps may include multiple steps or multiple stages. These steps or stages are not necessarily performed at the same time, but can be performed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed in turn or alternately with other steps or at least part of the steps or stages in other steps.

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

[0121] An acquisition module 201 is used to acquire the operation data of the energy storage system in real time and obtain the health status of the energy storage station according to the operation data;

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

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

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

[0125] In one embodiment of the present application, a computer device is provided, which includes a memory and a processor, wherein a computer program is stored in the memory, and the above steps are implemented when the processor executes the computer program; the computer device provided in this embodiment has an implementation principle and technical effects similar to those of the above method embodiments, and will not be repeated here.

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

[0127] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing related hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may 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 many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0128] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, 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 solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. An optimization method for energy storage station planning stage, characterized in that: include: Acquire the operating data of the energy storage system in real time, and obtain the health status of the energy storage station according to the operating data; The first objective function is obtained by taking the maximization of the reliability of the energy storage station as the goal, the second objective function is obtained by taking the maximization of the consumption of renewable energy as the goal, and the third objective function is obtained by taking the minimization of the frequency deviation of the power system as the goal. The optimization model of the energy storage station planning stage is obtained according to the first objective function, the second objective function, the third objective function and the corresponding constraints; According to the operation data and the health status, a genetic algorithm is used to solve the optimization model of the energy storage station planning stage to obtain the energy storage station backup capacity and the energy storage station optimal power configuration.

2. The energy storage station planning phase optimization method according to claim 1, characterized in that: The first objective function is obtained with the goal of maximizing the reliability of the energy storage station, including: A first objective function is constructed according to the backup capacity coefficient of the energy storage station and a preset value of the health status of the energy storage station, wherein the first objective function is: max R ESS =μP×SOC m Where μ is the number of simulation hours, P is the rated power of the energy storage station, and SOC t is the health status of the energy storage station at time t.

3. The energy storage station planning phase optimization method according to claim 1, characterized in that: The second objective function is obtained with the goal of maximizing the consumption of renewable energy, including: According to the output data of each power generation system, the fluctuation rate of each power generation system is obtained, wherein the expression of the fluctuation rate is: Where P RE (t) is the output data of the power generation system at time t, P RE-smooth (t) The output data of the power generation system after adjustment by the energy storage station at time t, where T represents the preset time period; Based on the fluctuation rate of each power generation system, a second objective function is constructed, wherein the second objective function is: minF2=σ1+σ2 Where σ1 is the volatility of the solar power generation system, and σ2 is the volatility of the wind power generation system.

4. The energy storage station planning phase optimization method according to claim 1, characterized in that: The third objective function is obtained by taking minimizing the frequency deviation of the power system as the goal, including: Obtaining a frequency deviation according to the rated frequency and the actual frequency of the power system, and integrating the frequency deviation to obtain an integrated frequency deviation; According to the integrated frequency deviation, a third objective function is constructed, wherein the third objective function is: minF3=∫0 T (f(t)-f0) 2 dt Wherein, f(t) is the rated frequency of the power system, and f0 is the actual frequency of the power system.

5. The energy storage station planning phase optimization method according to claim 1, characterized in that: The step of obtaining the energy storage station planning phase optimization model according to the first objective function, the second objective function, the third objective function and corresponding constraints includes: The objective function of the optimization model in the energy storage station planning stage is obtained according to the first objective function, the second objective function, and the third objective function, wherein the objective function of the optimization model in the energy storage station planning stage is: Max F1=λ1R ESS -λ2F2-λ3F3 In the formula, λ1, λ2 and λ3 are non-negative weight parameters respectively; According to the constraints corresponding to the first objective function, the second objective function, and the third objective function, the constraints of the optimization model in the energy storage station planning stage are obtained, wherein the constraints of the optimization model in the energy storage station planning stage are: In the formula, SOC min , SOC max are the minimum and maximum health status of the energy storage station, respectively. S is the energy storage station backup capacity required by the power system, P RE_min , P RE_max are the minimum and maximum outputs of the power generation system, respectively; Δf is the frequency deviation of the power system; Δf max is the maximum allowed frequency deviation.

6. The energy storage station planning phase optimization method according to claim 1, characterized in that: The method of solving the optimization model of the energy storage station planning stage by using a genetic algorithm according to the operating data and the health status to obtain the configurable capacity of the energy storage station, the spare capacity of the energy storage station and the optimal power configuration of the energy storage station includes: Initialize the population size, and calculate the fitness of individuals in the population according to the objective function and constraint conditions of the optimization model in the energy storage station planning stage; The total fitness of the population is calculated according to the fitness of each individual, and the probability value of each individual is calculated according to the total fitness; Select individuals whose probability values ​​are greater than a preset threshold to perform crossover and mutation operations to obtain a new population; The fitness of each individual in the new population is calculated according to the optimization model of the energy storage station planning stage to obtain the new fitness of each new individual. The new total fitness of the new population is calculated according to the new fitness. The new probability value of each new individual is obtained according to the new total fitness. The individuals whose new probability value is 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 reached to obtain the optimal solution, wherein the optimal solution includes the spare capacity of the energy storage station and the optimal power configuration of the energy storage station.

7. An energy storage station planning stage optimization device, characterized in that: include: An acquisition module, used to acquire the operating data of the energy storage system in real time, and obtain the health status of the energy storage station according to the operating data; A construction module is used to obtain a first objective function with the goal of maximizing the reliability of the energy storage station, obtain a second objective function with the goal of maximizing the consumption of renewable energy, and obtain a third objective function with the goal of minimizing the frequency deviation of the power system, and obtain an optimization model for the energy storage station planning stage according to the first objective function, the second objective function, the third objective function and corresponding constraints; The calculation module is used to solve the optimization model of the energy storage station planning stage by using a genetic algorithm according to the operating data and the health status, so as to obtain the spare capacity of the energy storage station and the optimal power configuration of the energy storage station.

8. The energy storage station planning stage optimization device according to claim 7, characterized in that: The first objective function is obtained with the goal of maximizing the reliability of the energy storage station, including: A first objective function is constructed according to the backup capacity coefficient of the energy storage station and a preset value of the health status of the energy storage station, wherein the first objective function is: max R ESS =μP×SOC m Where μ is the number of simulation hours, P is the rated power of the energy storage station, and SOC t is the health status of the energy storage station at time t.

9. A computer device, characterized in that: include: Memory for storing computer programs; A processor, configured to implement the energy storage station planning phase optimization method as described in any one of claims 1 to 6 when executing the computer program.

10. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the energy storage station planning stage optimization method according to any one of claims 1 to 6 are implemented.

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