Method, apparatus, equipment, and storage medium for optimizing the energy storage capacity of a solar power storage system.

Abstract

This application provides a method, device, equipment, and storage medium for optimizing the allocation of energy storage capacity in a solar energy storage system, which is related to the field of solar power generation, and specifically includes the following steps: obtain original data of the staged output of a solar power station, construct a genetic algorithm model, determine algorithm parameters and a fitness function of the genetic algorithm, perform an optimization calculation based on the genetic algorithm and the original output data, obtain a recommended energy storage capacity, and construct an energy storage device based on the recommended energy storage capacity.This application performs an optimization calculation based on the genetic algorithm, the original output data, and the fitness function, obtains the recommended energy capacity, and realizes the optimization of the energy storage capacity of the solar power station, avoids energy waste in the solar power station, and improves the operating efficiency of the energy storage device.

Description

[Technical Field]

[0001] This application relates to the field of new energy, and more particularly to methods, apparatus, equipment, and storage media for optimizing the arrangement of energy storage capacity in solar energy storage systems. Cross-citation of related applications This application claims priority to the Chinese patent application filed with the Chinese National Patent Office on 31 August 2022, application number 202211058430.X, title of invention "Method for optimizing the arrangement of energy storage capacity of a solar power storage system targeting a phased power generation plan," the entire contents of which are incorporated herein by reference. [Background technology]

[0002] In recent years, with the rapid development of renewable energy generation technologies, and with large amounts of new energy being connected to the power grid for generation, rationally improving the energy distribution structure and ensuring the safe and reliable operation of the power grid has become an important research direction for the development of the power system. In this context, the high degree of inaction and variability in the output of solar power plants affects the safe operation of the power grid and peak load balancing, and also significantly limits the full utilization of renewable energy.

[0003] In conventional power grids, hydroelectric and thermal power generation units, as major peak-modulated power sources, constantly change their output in response to changes in system frequency through methods such as primary frequency modulation and secondary frequency modulation. However, solar power plants, due to the limitations of their own power generation characteristics, have certain limitations in using conventional frequency modulation methods, which can affect the quality of frequency adjustment and safe and stable operation of the power grid. Therefore, in order to adapt to the trend of increasing the proportion of installed capacity of solar energy in the power system and to mitigate the impact on the power system caused by large-scale connection to the power grid, placing energy storage devices on the power grid side is an effective means of improving the full utilization of wind and solar energy. On the other hand, when arranging the energy storage capacity of an energy storage mixed system, it is necessary to consider the balance between performance and economy, as a poor balance will lead to energy waste at solar power plants and a decrease in the operational efficiency of the energy storage mixed system. [Overview of the Initiative] [Problems that the invention aims to solve]

[0004] The purpose of this application is to provide a method, apparatus, equipment, and storage medium for optimizing the arrangement of energy storage capacity in a solar power storage system, which at least solves the problem of reduced operational efficiency of solar power plants due to inappropriate arrangement of energy storage capacity in related technologies. The technical solution of this application is as follows. [Means for solving the problem]

[0005] A first embodiment of the present invention provides a method for optimizing the arrangement of energy storage capacity in a solar power storage system targeting a phased power generation plan, and includes the following: We obtain raw output data for the phased output of solar power plants. Genetic algorithms of Construct a model and determine the algorithm parameters and fitness function of the aforementioned genetic algorithm. An optimization calculation is performed based on the aforementioned genetic algorithm, the raw output data, and the fitness function to obtain a recommended energy storage capacity, and an energy storage device is constructed based on the recommended energy storage capacity.

[0006] Optionally, the algorithm parameters include the following: Population size Q, crossover probability Pe, and variation probability Pm.

[0007] If we optionally express the fitness function as an equation, It becomes TIFF0007927097000001.tif21151, Here, LCC is the total operating cost of the energy storage device, ICC is the initial cost of the energy storage device, N is the service life of the energy storage device, n is the nth year of operation of the energy storage device, d n is the depreciation for the nth year, i is the interest rate, tr is the tax rate, a n is the maintenance and operating cost of the energy storage device in its nth year, r is the rth component replacement, R is the total number of replacements within the energy storage device's operating cycle, ICC c The cost of the parts to be replaced, l c R is the lifespan of the c-th replacement component, s is the remaining value (original), penalty is the amount of electricity that has not reached the ideal power grid connection value in one year, M is the penalty electricity charge, and of these, the total number of replacements R is a function of the lifespan of the replacement component.

[0008] If we optionally express the initial cost ICC of the energy storage device as an equation, It becomes TIFF0007927097000002.tif9103, Here, Cost battery This is the cost of an energy storage device corresponding to the energy storage capacity per unit, Capacity battery This is the energy storage capacity value, Cost inverter This represents the cost of the inverter corresponding to the energy storage capacity per unit.

[0009] If we optionally express the total number of replacements R as an equation, This will result in TIFF0007927097000003.tif16145.

[0010] Selectively performing optimization calculations based on the genetic algorithm, the raw output data, and the fitness function to obtain a recommended energy storage capacity includes the following: Q of the aforementioned energy storage capacity values ​​are randomly generated, and a population X(t) is constructed with each of the aforementioned energy storage capacity values ​​as an individual. Based on the raw output data, obtain the fitness function value corresponding to the individual in the population. The population X(t) is subjected to evolutionary processing until the fitness function values ​​of the individuals within the population satisfy the optimization termination condition, where t is the number of evolutionary cycles of the population.

[0011] Selectively performing evolutionary treatment on the aforementioned population X(t) includes the following: Select a pair of Y / 2 pairs of parentheses from X(t) using a predefined selection operator, where Y is equal to or greater than Q. For the selected Y / 2 pairs of parent organisms, the target parent organism pair for the cross-operation is determined based on the cross-probability Pe, and the cross-operation is performed to obtain Y individuals. Mutations are performed on the obtained Y individuals based on the mutation probability Pm to generate Y mutant individuals. From the Y mutant individuals generated, Q individuals are screened based on their corresponding fitness function values ​​to generate the next generation population X(t+1).

[0012] Selectively evolving the population X(t) until the fitness function values ​​of the individuals within the population satisfy the optimization termination condition includes the following: If the fitness function value of an individual within population X(t+1) satisfies the optimization termination condition, the individual with the largest fitness function value within X(t+1) is output as the optimal solution, and the evolution process is stopped. Otherwise, the evolutionary process continues for the aforementioned population.

[0013] In a second aspect of the embodiments of the present application, an electronic device is provided, comprising: a processor, a memory configured to store instructions executable by the processor. wherein the processor is configured to execute the instructions to implement the optimal configuration method for energy storage capacity of a photovoltaic-storage system targeting stepwise power generation planning according to any one of the first aspect.

[0014] In a third aspect of the embodiments of the present application, a computer-readable storage medium is provided, wherein when the instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the optimal configuration method for energy storage capacity of a photovoltaic-storage system targeting stepwise power generation planning according to any one of the first aspect.

[0015] In a fourth aspect of the embodiments of the present application, an optimal configuration device for energy storage capacity of a photovoltaic-storage system targeting stepwise power generation planning is provided, comprising: an obtaining module, configured to obtain raw output data of stepwise output of a photovoltaic power station, a model construction module, configured to construct a model based on a genetic algorithm of and determine algorithm parameters and a fitness function of the genetic algorithm, an optimization module, configured to perform an optimization operation based on the genetic algorithm and the raw output data, obtain a recommended energy storage capacity, and construct an energy storage device based on the recommended energy storage capacity. Effects of the Invention

[0016] The technical solution provided by the embodiments of the present application has at least the following beneficial effects. By performing optimization calculations based on a genetic algorithm, raw output data, and fitness function, and obtaining a recommended energy storage capacity, it is possible to optimize the energy storage capacity of a solar power plant, avoid energy waste, and improve the operational efficiency of the mixed energy storage system.

[0017] Herein, the general explanation above and the detailed explanation below are for illustrative and interpretive purposes only and should not be understood as limiting this application. [Brief explanation of the drawing]

[0018] [Figure 1] This is a flowchart illustrating a method for optimizing the energy storage capacity of a solar energy storage system targeting a phased power generation plan, based on an exemplary embodiment. [Figure 2] This is a schematic diagram illustrating the phased output of a solar power plant. [Figure 3] This is a flowchart illustrating a method for optimizing the energy storage capacity of a solar energy storage system targeting a phased power generation plan, based on an exemplary embodiment. [Figure 4] This is a flowchart illustrating a method for optimizing the energy storage capacity of a solar energy storage system targeting a phased power generation plan, based on an exemplary embodiment. [Figure 5] This is a diagram showing the configuration after optimization of the genetic algorithm, based on an exemplary embodiment. [Figure 6] This is a schematic diagram illustrating the relationship between energy storage capacity, total operating costs, and loss rate, based on an exemplary embodiment. [Figure 7] This is a diagram showing the output data of an energy storage device after it has been equipped with a storage capacity of 1000 MWh. [Figure 8] This is a block diagram of the apparatus shown based on an exemplary embodiment. [Figure 9] This is a block diagram of the apparatus shown based on an exemplary embodiment. [Figure 10]This is a block diagram of an energy storage capacity optimization device 1000 for a solar energy storage system, which aims at a phased power generation plan based on an exemplary embodiment. [Modes for carrying out the invention]

[0019] To enable those skilled in the art to better understand the technical solutions of this application, the technical solutions of the embodiments of this application will be described clearly and completely below, accompanied by the drawings.

[0020] Herein, the terms “First,” “Second,” and so forth used in the specification, claims, and drawings of this application are used to distinguish similar subjects and are not intended to describe any particular order or priority. It should be understood that the embodiments of this application described herein may be carried out in an order other than those illustrated or described herein, and that the data used in this manner may be interchangeable where appropriate. The embodiments described in the following exemplary embodiments are not all embodiments consistent with this application, but merely examples of apparatuses and methods consistent with some aspects of this application described in detail in the appended claims.

[0021] In recent years, with the rapid development of renewable energy generation technologies, and with large amounts of new energy being connected to the power grid for generation, rationally improving the energy distribution structure and ensuring the safe and reliable operation of the power grid has become an important research direction for the development of the power system. In this context, the high degree of inaction and variability in the output of solar power plants affects the safe operation of the power grid and peak load balancing, and also significantly limits the full utilization of renewable energy.

[0022] In conventional power grids, hydroelectric and thermal power generation units, as major peak-modulated power sources, constantly change their output in response to changes in system frequency through methods such as primary frequency modulation and secondary frequency modulation. However, solar power plants, due to the limitations of their own power generation characteristics, have certain limitations in using conventional frequency modulation methods, which can affect the quality of frequency adjustment and safe and stable operation of the power grid. Therefore, in order to adapt to the trend of increasing the proportion of installed capacity of solar energy in the power system and to mitigate the impact on the power system caused by large-scale connection to the power grid, placing energy storage devices on the power grid side is an effective means of improving the full utilization of wind and solar energy. On the other hand, when arranging the energy storage capacity of an energy storage mixed system, it is necessary to consider the balance between performance and economy, as a poor balance will lead to energy waste at solar power plants and a decrease in the operational efficiency of the energy storage mixed system.

[0023] Figure 1 is a flowchart of a method for optimizing the energy storage capacity of a solar energy storage system targeting a phased power generation plan, based on an exemplary embodiment. As shown in Figure 1, the method includes the following: Step 101: Obtain raw output data for the tiered output of the solar power plant.

[0024] In the embodiments of this application, the output data of the solar power plant is affected by weather data around the substation, and weather data has characteristics such as inaction, intermittency, and variability. The output data of the solar power plant also has these characteristics, and humans cannot control the processing data of the solar power plant. Therefore, when analyzing the recommended energy storage capacity suitable for an energy storage mixed system, it is necessary to refer to the past raw output data of the solar power plant for analysis. The raw output data is the active power output from the solar power plant, and the step length of the raw output data is 1h (hour).

[0025] Here, it should be explained that because the light irradiation conditions differ at different time periods, and the corresponding output data of the solar power plant, i.e., the output power, also differs significantly, the aforementioned solar power plant employs a stepped output method, outputting different values ​​at different time periods. Figure 2 is a schematic diagram of the stepped output of the solar power plant, and as shown in Figure 2, the output corresponding to each sampling point is the output of the solar power plant located at the sampling point within one hour.

[0026] Step 102, Genetic Algorithm of A model is constructed, and the algorithm parameters and fitness function of the genetic algorithm are determined.

[0027] In the embodiments of this application, optimization calculations are performed based on a genetic algorithm to obtain an appropriate energy storage capacity. A genetic algorithm (GA) originates from computer simulation studies of biological systems and is a method for optimizing random global searches. It simulates phenomena such as replication, crossover, and mutation that occur during natural selection and inheritance, and generates a population that is better suited to the environment through random selection, crossover, and mutation operations from any population. The population evolves to an increasingly better region in the search space, thus constantly evolving generation by generation, and finally converging to the population (Individual) that is best suited to the environment, thereby finding the optimal solution to the problem. First, it is necessary to set the algorithm parameters and fitness function of the genetic algorithm. The algorithm parameters are used to control the process of population evolution of the genetic algorithm, and the fitness function is used to determine whether the individuals in the population meet the optimization goal.

[0028] Step 103: An optimization operation is performed based on the genetic algorithm, the raw output data, and the fitness function to obtain a recommended energy storage capacity, and an energy storage device is constructed based on the recommended energy storage capacity.

[0029] In the embodiments of this application, a population is generated based on a genetic algorithm, and each individual in the population is equivalent to an energy storage capacity value. Population evolution is performed based on primitive output data until the population satisfies an evolutionary cessation condition, at which point the fitness function value corresponding to each individual in the population satisfies a preset condition. The recommended energy storage capacity is obtained as the energy storage capacity corresponding to the optimal individual. Subsequently, an energy storage device can be constructed based on the recommended energy storage capacity, and the solar power plant constructed thereby can satisfy the optimization goal, that is, it not only satisfies economic requirements but also has relatively low operating costs and does not waste the power generation capacity of the solar power plant.

[0030] Preferably, the algorithm parameters include the following: Population size Q, crossover probability Pe, and variation probability Pm.

[0031] In the embodiments of this application, the population size is the number of individuals in a population, and in this application, one individual corresponds to one energy storage capacity value. Crossover operations randomly select two individuals from a population as a pair of parents based on a predetermined crossover probability Pe, and generate new individuals by exchanging and combining two chromosomes to pass on superior traits of the parents to the offspring. Commonly used crossover operators include single-point crossover, two-point crossover, multi-point crossover, uniform crossover, and arithmetic crossover, and the crossover position is also random. The crossover probability generally takes a large value of 0.6 to 0.9. Mutation operations are, in other words, changing a small portion of genes into alleles for each individual in the population based on a constant mutation probability Pm. Mutation operations can maintain population diversity and prevent the loss of important genes, but the mutation probability generally takes a low value of 0.001 to 0.1.

[0032] If we optionally express the fitness function as an equation, It becomes TIFF0007927097000004.tif21151, Here, LCC is the total operating cost of the energy storage device, ICC is the initial cost of the energy storage device, N is the service life of the energy storage device, n is the n-th year of operation of the energy storage device, d n is depreciation in the n-th year, i is the interest rate, tr is the tax rate, a n is the maintenance and operating cost of the energy storage device in the n-th year, r is the r-th component replacement, R is the total number of replacements within the operation cycle of the energy storage device, ICC c is the cost of the component to be replaced, l c is the service life of the c-th component to be replaced, s is the residual value (original), penalty is the electric energy that does not reach the ideal power grid connection value in one year, M is the penalty electricity price, wherein the total number of replacements R is a function of the service life of said component to be replaced.

[0033] In the embodiment of the present application, the fitness function is established based on the operating cost of the energy storage device, and ICC is the initial cost of the energy storage device, that is, the cost required for purchasing and installing the energy storage device. ICC is proportional to the energy storage capacity of the energy storage device, and the larger the energy storage capacity, the higher the required initial cost.

[0034] The energy storage device is depreciated every year, and the price after depreciation can reduce said operating cost. TIFF0007927097000005.tif1244 reflects the cost of maintenance and operation of the energy storage device from the first year to the N-th year. During the operation period of the energy storage device, some components have short service life and need to be replaced regularly. TIFF0007927097000006.tif18121 reflects the residual value of said energy storage device. The residual value refers to the residual value that is expected to be recovered when the service life of the asset expires, that is, the money that can be received when disposing of the fixed asset after its service life expires and it is discarded. The larger N is, the lower the residual value is. In a power grid, if the energy storage device does not reach the electric energy of the ideal power grid connection value in one year, a penalty will be imposed, and penalty*M reflects the penalty within the operation cycle.

[0035] If we optionally express the total number of replacements R as an equation, The result is TIFF0007927097000007.tif16145. The total number of replacements for each component within the operating cycle of the energy storage device is related to the lifespan of the component, and can be expressed by the formula: The result is TIFF0007927097000008.tif16145, where floor is a Matlab function used to round a number to the next smallest integer. Taking residual value into account, we subtract 1 to avoid the last replacement of the energy storage device and reduce the operating costs of the energy storage device.

[0036] If we optionally express the initial cost of the energy storage device as an equation, It becomes TIFF0007927097000009.tif9103, Here, Cost battery This is the cost of an energy storage device corresponding to the energy storage capacity per unit, Capacity battery This is the energy storage capacity value, Cost inverter This represents the cost of the inverter corresponding to the energy storage capacity per unit.

[0037] In the embodiments of this application, the output from the solar power plant is direct current (DC), but the power grid transmits alternating current (AC). Therefore, when outputting power to the power grid through the energy storage device, it is necessary to use an inverter to convert the DC to AC. The inverter converts the variable DC voltage generated by the solar power generation panels into commercial frequency AC and can be transmitted to the commercial power transmission system or supplied to an independent power system. The capacity of the inverter is proportional to the capacity of the energy storage device; the larger the energy storage capacity of the energy storage device, the larger the inverter capacity, and the higher the initial cost of the inverter.

[0038] Figure 3 is a flowchart of a method for optimizing the energy storage capacity of a solar energy storage system targeting a phased power generation plan, based on an exemplary embodiment. As shown in Figure 3, step 104 in Figure 1 specifically includes the following: Step 301: Q energy storage capacity values ​​are randomly generated, and a population X(0) is constructed using these energy storage capacity values ​​as individuals.

[0039] In the embodiments of this application, optimizing the genetic algorithm requires first generating a population and individuals within that population. Based on a set population size Q, Q energy storage capacity values, i.e., individuals, are randomly generated, and a population X(0) is constructed based on these individuals. X(0) is the initial population, and a 0 in X(0) indicates that the population has not evolved, i.e., it has evolved 0 times.

[0040] Step 302: Based on the raw output data, obtain fitness function values ​​corresponding to individuals within the population.

[0041] In the embodiments of this application, the fitness function is a mathematical function for evaluating the superiority or inferiority of individuals, and is mapped to the fitness function by the target function, or the fitness of individuals is directly represented by the target function.

[0042] Step 303: The population X(0) is subjected to evolutionary processing until the fitness function values ​​of the individuals within the population satisfy the optimization termination condition, thereby obtaining population X(t), where t is the number of evolutionary steps for the population.

[0043] In the embodiments of this application, the genetic algorithm draws inspiration from Darwin's theory of biological evolution and Mendel's laws of inheritance, and uses the principle of "survival of the fittest" to sequentially generate approximate optimal solutions among potential solutions. In each generation of the genetic algorithm, individuals are selected based on their fitness values, and the next generation of individuals is generated based on genetic laws. In this process, the fitness of individuals within the population is constantly increased, and the obtained solutions constantly approach the optimal solution. After generating X(0), it is necessary to evolve the population X(0) to obtain population X(t), and by constantly iteratively evolving, the fitness function of individuals within the population is brought closer to a predetermined target.

[0044] Figure 4 is a flowchart of a method for optimizing the energy storage capacity of a solar energy storage system targeting a phased power generation plan, based on an exemplary embodiment. As shown in Figure 4, the procedure for evolutionarily processing the population specifically includes the following. Step 401: Select two pairs of matrix Y from X(t) using a predefined selection operator, where Y is equal to or greater than Q.

[0045] In the embodiments of this application, the evolutionary process of a single population, for example, the evolutionary process of X(t), simulates the process of the population reproducing offspring. In this process, the genetic algorithm uses three genetic operators: a selection operator, a crossover operator, and a mutation operator. First, two parent organisms are selected to breed (generate) offspring (new individuals). Using a pre-set selection operator, Y / 2 pairs of parent organisms are selected from X(t), and these organisms generate new individuals. Each pair of parent organisms generates two new individuals, and since it is necessary to screen the new individuals, the number of new individuals must be greater than Q, that is, Y must be equal to or greater than Q. The probability of an individual being selected is related to the fitness function value; the larger the individual fitness function value, the higher the probability of selection. Using the roulette method as an example, if the population size is Q and the fitness of individual i is fi i Assuming this, the probability that individual i is selected is: The result is TIFF0007927097000010.tif21124, and after determining the probability of individual selection, A uniform random number is generated within the range TIFF0007927097000011.tif12151 to determine which individuals will participate in mating as the parent organism. If an individual has a high selection probability, it will have many opportunities to be selected, and its genes will be expanded within the population. Conversely, if an individual has a low selection probability, it is more likely to be eliminated.

[0046] Step 402: For the selected Y / 2 pairs of parent organisms, the pair of organisms to be subjected to the crossover operation is determined based on the crossover probability Pe, and the crossover operation is performed to obtain Y individuals.

[0047] In the embodiments of this application, the crossover operation involves finding Y / 2 pair parent individuals from a population and inheriting superior traits from the parent individuals to their offspring through the exchange and combination of two chromosomes, thereby generating new individuals with superior traits. After obtaining superior individuals from the population, partial chromosomes between individuals are exchanged with a certain probability (inheritance probability). The crossover probability controls the crossover operation; a large crossover probability can enhance the genetic algorithm and open up new search areas, but it has a high destructive potential to the solution and generally takes the value of 0.25 to 1. Based on the crossover probability, target parent individuals can be selected from the parent individuals, and the crossover operation can be performed using the crossover operator.

[0048] Here, the intersection operator includes the following: a) Two-point or multi-point crossover, i.e., two or more crossover points are randomly placed on a chromosome pair and crossover operations are performed to alter the gene sequence of the chromosome. b) Uniform crossover, that is, crossover occurs with equal probability at each position on the gene sequence of a chromosome pair, forming a new gene sequence. c) Arithmetic crossover: chromosome pairs cross over in a linear combinational manner, altering the gene sequence of the chromosomes. d) Single-point crossover operator: This operator randomly selects a crossover site within a chromosome pair and performs a gene position transformation on the chromosome pair at this crossover site.

[0049] Step 403: Mutations are performed on the obtained Y individuals based on the mutation probability Pm to generate Y mutant individuals.

[0050] In the embodiments of this application, each individual in a population changes a portion of its genes into alleles with a mutation probability. Mutation can maintain population diversity and prevent the loss of important genes, but the mutation probability should not be too high, and is generally between 0.001 and 0.1. Based on the mutation probability, the location in the individual where the mutation is needed is determined, and the mutation operation is performed through a mutation operator.

[0051] In practical applications, single-point mutations are primarily employed, also known as positional mutations, which simply involve changing a specific location within the gene sequence of an individual. Using binary code as an example, this would mean changing 0 to 1 and 1 to 0.

[0052] Step 404: From the Y mutant individuals generated, Q individuals are screened based on their corresponding fitness function values ​​to generate the next generation population X(t+1).

[0053] Performing selection, crossover, and mutation on individuals within X(t) completes one full evolutionary cycle of the population. Q individuals are screened based on their fitness function values, and these individuals constitute the next generation population X(t+1). With each evolution, the number in X() is incremented by 1.

[0054] Selectively evolving population X(0) until the fitness function values ​​of individuals within the population satisfy the optimization termination condition to obtain population X(t) includes the following: If the fitness function values ​​of individuals within population X(t) satisfy the optimization termination condition, the individual with the largest fitness function value within X(t+1) is output as the optimal solution, and the evolution process is stopped. Otherwise, the evolutionary process continues for the aforementioned population.

[0055] In the embodiments of the present invention, optimization termination conditions can be set. For example, the fitness function can be set to be smaller than a preset threshold. If the fitness function values ​​of individuals within X(t+1) are smaller than the preset threshold, it indicates that the operating cost of the energy storage device has reached the expected level, and evolution can be stopped. Otherwise, evolutionary processing must continue for the population.

[0056] Optionally, the fitness function may include not only the total operating cost of the energy storage device described above, but also an equation relating to the loss rate. TIFF0007927097000012.tif15155 Here, PV_a is the loss rate, PV is the amount of electricity generated by the solar power plant, and PV_plan is the amount of electricity used by the power grid. The higher the loss rate, the more solar power is wasted; the lower the loss rate, the more advantageous it is for saving electrical energy from solar power generation. In the embodiment of this application, the target value set for the loss rate is smaller than a pre-set threshold.

[0057] To verify the effectiveness of the method described in this application, a 10 million kilowatt solar power plant located in Lancang was selected for analysis.

[0058] The raw output data was selected using a time step length of 1 hour, with a total of 8760 solar power output data points for one year. Energy storage capacity was optimized based on the genetic algorithm described above. Figure 5 shows the configuration after optimization using the genetic algorithm, based on an exemplary implementation. As shown in Figure 5, the loss rate of the solar power plant is 0% to 20%, the total cost fluctuates within the range of 2.3*109 yuan to 1.80*1010 yuan, and the loss rate reaches a minimum of 0.35%. A contradictory situation exists where the cost increases as the loss rate decreases. To further determine the energy storage capacity and simultaneously consider economics, the grid connection value was optimized again for a capacity with a loss rate in the range of 5% to 20% after optimization. By optimizing based on the genetic algorithm, the energy storage capacity range with a loss rate in the range of 5% to 20% is 300 MWh to 2837 MWh. For economic reasons, the energy storage capacity range selected is 500 MWh to 2000 MWh. That is, for a 10 million kilowatt solar power plant, energy storage devices with a capacity of 5% to 20% of its capacity will be installed, and the ideal power grid connection value will be optimized based on a genetic algorithm for different energy storage capacities.

[0059] Figure 6 is a schematic diagram showing the relationship between energy storage capacity, total operating costs, and solar waste rate, based on an exemplary embodiment. As shown in Figure 6, the loss rate decreases with increasing energy storage capacity, while the cost increases with increasing energy storage capacity. The best performance is achieved when the energy storage capacity is 10% and 15% of the installed capacity of the solar power plant. In this case, increasing the energy storage capacity from 10% to 15% resulted in an increase in the loss rate of approximately 1%, but the cost increased by 3*10⁹ yuan. Considering all factors, in the case of Figure 5, the best performance can be achieved by equipping a 10 million kilowatt solar power plant with a 1000 MWh energy storage device.

[0060] Figure 7 shows the output data of an energy storage device equipped with an energy storage capacity of 1000 MWh. The overlapping portion of the dotted line and solid line indicates that the actual power grid connection value matches the ideal power grid connection value, while the overlapping portion of the solid line indicates that the actual power grid connection value does not reach or exceed the ideal power grid connection value. As can be seen from this, when economics and loss rates are considered together, the fluctuation in the output stability of the energy storage device is not large, and the difference between the actual power grid connection value and the ideal power grid connection value is relatively small, allowing a solar power plant to be equipped with an energy storage device with an energy storage capacity of 10% of its installed capacity.

[0061] Figure 8 is a block diagram of an apparatus based on an exemplary embodiment. For example, the apparatus 800 could be a mobile phone, computer, digital broadcasting terminal, message transmission / reception device, game control unit, tablet device, medical equipment, fitness equipment, personal digital assistant, etc.

[0062] As shown in Figure 8, the device 800 may include one or more of the following: a processor component 802, a memory 804, a power component 806, a multimedia component 808, an audio component 810, an input / output (I / O) port 812, a sensor component 814, and a communication component 816.

[0063] The processor component 802 typically controls the overall operation of the device 800, such as display, telephone calls, data communication, camera operation, and recording operation and related operations. The processor component 802 may include one or more processors 820 to execute commands to perform all or some of the steps of the above method. The processor component 802 may also include one or more modules to facilitate interaction between the processor component 802 and other components. For example, the processor component 802 may include a multimedia module to facilitate interaction between the processor component 802 and the multimedia component 808.

[0064] Memory 804 is arranged to store various types of data to support operations on the device 800. This data includes commands for any application programs or methods operated on the device 800, contact data, phonebook data, messages, images, videos, etc. Memory 804 can be implemented by various types of volatile or non-volatile storage devices, or combinations thereof, such as static random access memory (SRAM), electrically erasable and programmable read-only memory (EEPROM), erasable and programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disks, optical disks, etc.

[0065] The power supply component 806 provides power to each component of the device 800. The power supply component 806 may include a power management system, one or more power supplies, and other components related to the generation, management, and distribution of power for the device 800.

[0066] The multimedia component 808 includes a screen that provides an output port between the device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may receive user input signals as a touch panel. The touch panel includes one or more touch sensors that detect touches, slides and gestures on the touch panel, and the touch sensors may not only detect the boundary of a touch or slide but also the duration and pressure of the touch or slide. In some embodiments, the multimedia component 808 includes an in-camera and / or an out-camera. When the device 800 is in an operating mode, such as shooting mode or video mode, the in-camera and / or out-camera may receive external multimedia data. Each in-camera and out-camera may be a fixed optical lens system or may have a focal length and optical zoom capability.

[0067] The audio component 810 is arranged to output and / or input audio signals. For example, the audio component 810 includes a microphone (MIC), and when the device 800 is in an operating mode, such as call mode, recording mode, and voice recognition mode, the microphone is arranged to receive external audio signals. The received audio signals can be stored in memory 804 or transmitted via communication component 816. In some embodiments, the audio component 810 further includes a speaker, which is used to output audio signals.

[0068] I / O port 812 provides a port between processor component 802 and a peripheral port module. The peripheral port module may be a keyboard, click dial, buttons, etc. The buttons include, but are not limited to, a main screen button, volume buttons, a power button, and a lock button.

[0069] The sensor component 814 includes one or more sensors and is used to evaluate the state of various aspects of the device 800. For example, the sensor component 814 can detect the open / closed state of the device 800, the relative positioning of parts, for example, the monitor and keypad of the device 800. The sensor component 814 can further detect changes in the position of the device 800 or one of its parts, whether or not there is contact between the user and the device 800, the orientation or acceleration / deceleration of the device 800, and temperature changes of the device 800. The sensor component 814 may include proximity sensors, which are positioned to detect the presence of nearby objects when there is no physical contact. The sensor component 814 may further include optical sensors used for imaging, such as CMOS or CCD image sensors. In some embodiments, the sensor component 814 may further include accelerometers, gyroscopes, magnetic sensors, pressure sensors, or temperature sensors.

[0070] The communication component 816 is positioned to facilitate wired or wireless communication between the device 800 and other equipment. The device 800 can connect to a wireless network based on a communication standard, such as WiFi, an operator network (2G, 3G, 4G, or 5G), or a combination thereof. In one exemplary embodiment, the communication component 816 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, the communication component 816 further includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID), infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0071] In exemplary embodiments, the apparatus 800 is implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processor devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components used to perform the above method.

[0072] In exemplary embodiments, the system further includes a storage medium containing commands, for example, a memory 804 containing commands. To perform the above method, the commands can be executed by a processor 820 of the device 800. Optionally, the storage medium may be a non-temporary computer-readable storage medium, for example, a ROM, random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0073] Figure 9 is a block diagram of a device 900 based on an exemplary embodiment. For example, the device 900 can be provided as a server. As shown in Figure 9, the device 900 includes a processor component 922, and further includes one or more processors and a memory source represented by memory 932, which is used to store commands that can be executed by the processor component 922, such as application programs. The application programs stored in memory 932 may include modules corresponding to one or more sets of commands. Furthermore, in order to carry out the above method, the processor component 922 is configured to execute commands.

[0074] The device 900 may further include a power supply component 926 positioned to manage the power supply of the device 900, a wired or wireless network port 950 positioned to connect the device 900 to a network, and a single input / output (I / O) port 958. The device 900 can operate an operating system stored in memory 932, such as Windows Server™, MacOSX™, Unix™, Linux™, FreeBSD™, or similar.

[0075] Figure 10 is a block diagram of an energy storage capacity optimization device 1000 for a solar energy storage system targeting a phased power generation plan, based on an exemplary embodiment, and includes the following: Acquisition module 1001 acquires raw output data for the gradual output of a solar power plant. Genetic algorithms of Construction module 1002 constructs a model and determines the algorithm parameters and fitness function of the genetic algorithm. An optimization module 1003 performs optimization calculations based on the genetic algorithm and the raw output data to obtain a recommended energy storage capacity and constructs an energy storage device based on the recommended energy storage capacity. The apparatus can perform all or part of the above-described procedures, which will not be described further here.

[0076] Those skilled in the art will readily obtain other embodiments of this application by practicing the specification and the invention disclosed herein. This application is intended to cover any variations, uses and adaptability changes of this application, which will conform to the general principles of this application and include prior art knowledge or conventional technical means not published herein. The specification and examples are illustrative, and the scope and spirit of this application are defined by the claims.

[0077] This application is not limited to the exact structure shown in the above description and drawings, and various modifications and changes may be made without departing from that scope. The scope of this application is limited by the attached claims.

Claims

1. A method for optimizing the energy storage capacity of a solar energy storage system, which is performed by a computer and aims at a phased power generation plan, To obtain raw output data for the phased output of solar power plants, Building a genetic algorithm model, The algorithm parameters and fitness function of the aforementioned genetic algorithm are determined, This includes performing an optimization calculation based on the genetic algorithm and the raw output data to obtain a recommended energy storage capacity, and constructing an energy storage device based on the recommended energy storage capacity. The aforementioned algorithm parameters are, It includes population size Q, crossover probability Pe, and variation probability Pm. The fitness function can be expressed as follows: And so, Here, LCC is the total operating cost of the energy storage device, ICC is the initial cost of the energy storage device, N is the service life of the energy storage device, n is the nth year of operation of the energy storage device, dn is the depreciation in the nth year, i is the interest rate, tr is the tax rate, an is the maintenance and operating cost of the energy storage device in the nth year, r is the rth part replacement, R is the total number of replacements within the operating cycle of the energy storage device, ICCc is the cost of the part to be replaced, lc is the lifespan of the cth part to be replaced, s is the residual value (original), penalty is the amount of electricity that has not reached the ideal power grid connection value in one year, M is the penalty electricity charge, and of these, the total number of replacements R is a function of the lifespan of the part to be replaced. The initial cost ICC of the aforementioned energy storage device can be expressed by the following formula: And so, Here, Cost battery is the cost of the energy storage device corresponding to the energy storage capacity per unit, Capacity battery is the energy storage capacity value, and Cost inverter is the cost of the inverter corresponding to the energy storage capacity per unit, a method for optimizing the energy storage capacity of a solar energy storage system, performed by a computer and targeting a phased power generation plan.

2. The total number of replacements R can be expressed by the following formula: A method for optimizing the energy storage capacity of a solar storage system, performed by a computer as described in claim 1, with the goal of a phased power generation plan.

3. The process of performing an optimization calculation based on the aforementioned genetic algorithm, the aforementioned raw output data, and the aforementioned fitness function to obtain the recommended energy storage capacity is as follows: Randomly generate Q of the aforementioned energy storage capacity values, and construct a population X(t) with each of the aforementioned energy storage capacity values ​​as an individual, Based on the aforementioned raw output data, obtain fitness function values ​​corresponding to individuals within the population, A method for optimizing the energy storage capacity of a solar power storage system targeting a phased power generation plan, performed by a computer, according to claim 2, comprising evolving the population X(t) until the fitness function values ​​of the individuals in the population satisfy the optimization termination condition, where t is the number of evolutions of the population.

4. The evolutionary treatment of the aforementioned population X(t) is performed as follows: Using a predefined selection operator, select two pairs of bases Y / 2 from X(t), where Y is equal to or greater than Q. For the selected Y / 2 pairs of parent organisms, the target parent organism pair for the cross-operation is determined based on the cross-probability Pe, and the cross-operation is performed to obtain Y individuals. To produce Y mutant individuals, mutations are performed on the obtained Y individuals based on the mutation probability Pm, and Y mutant individuals are generated. This includes screening Q individuals from the Y mutant individuals generated based on their corresponding fitness function values, and generating the next generation population X(t+1), A method for optimizing the energy storage capacity of a solar storage system, performed by a computer according to claim 3, with the goal of a phased power generation plan.

5. The evolutionary process of the population X(t) until the fitness function values ​​of the individuals within the population satisfy the optimization termination condition is: If the fitness function value of an individual within population X(t+1) satisfies the optimization termination condition, the individual with the largest fitness function value within X(t+1) is output as the optimal solution, and the evolution process is stopped. Otherwise, a method for optimizing the energy storage capacity of a solar storage system targeting a phased power generation plan, performed by a computer according to claim 4, further comprising performing evolutionary processing on the population.

6. Electronic equipment, including the following: Computers and, The computer includes memory for storing executable commands, Hereinafter, the electronic equipment is executed by a computer according to any one of claims 1 to 5, and the computer is configured to execute the commands in order to realize a method for optimizing the arrangement of energy storage capacity of a solar storage system targeting a phased power generation plan.

7. An optimization device for the energy storage capacity of a solar energy storage system targeting a phased power generation plan, comprising an acquisition module, a construction model, and an optimization module, The aforementioned acquisition module is used to acquire raw power data for performing tiered output of a solar power plant. The aforementioned construction model is used to construct a model of the genetic algorithm and to determine the algorithm parameters and fitness function of the genetic algorithm. The optimization module is used to perform optimization calculations based on the genetic algorithm and the raw output data, obtain a recommended energy storage capacity, and construct an energy storage device based on the recommended energy storage capacity. The algorithm parameters include population size Q, cross-probability Pe, and variation probability Pm. The fitness function can be expressed as follows: And so, Here, LCC is the total operating cost of the energy storage device, ICC is the initial cost of the energy storage device, N is the service life of the energy storage device, n is the nth year of operation of the energy storage device, d n is the depreciation in the nth year, i is the interest rate, tr is the tax rate, a n is the maintenance and operating cost of the energy storage device in the nth year, r is the rth part replacement, R is the total number of replacements within the operating cycle of the energy storage device, ICC c is the cost of the part to be replaced, l c is the lifespan of the cth part to be replaced, s is the residual value (original), penalty is the amount of electricity that has not reached the ideal power grid connection value in one year, M is the penalty electricity charge, and of these, the total number of replacements R is a function of the lifespan of the part to be replaced. The initial cost ICC of the aforementioned energy storage device can be expressed by the following formula: And so, Herein, Cost battery is the cost of the energy storage device corresponding to the energy storage capacity per unit, Capacity battery is the energy storage capacity value, and Cost inverter is the cost of the inverter corresponding to the energy storage capacity per unit, and the device for optimizing the energy storage capacity of the solar storage system is performed by a computer and aims for a phased power generation plan.

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