Method, device, equipment and storage medium for optimizing the layout of energy storage capacity of a solar energy 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] The present application relates to the field of new energy, and in particular to a method, device, equipment and storage medium for optimizing the arrangement of energy storage capacity in a solar energy storage system. Cross-Citation of Related Applications This application claims priority to a Chinese patent application filed with the China Patent Office on August 31, 2022, bearing application number 202211058430.X and entitled "Method for optimizing the allocation of energy storage capacity of a solar 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 power generation technology, a large amount of new energy is being generated through connection to the power grid. Therefore, rationally improving the energy distribution structure and ensuring the safe and reliable operation of the power grid has become an important research direction in the development of the power system. Among them, the high instability and variability of the output of photovoltaic power plants affects the safe operation and peak adjustment of the power grid, and also greatly restricts the full utilization of renewable energy.

[0003] In conventional power grids, hydroelectric and thermal power generation units, as the main peak-modulating power sources, constantly adjust their output in response to changes in system frequency through methods such as primary frequency modulation and secondary frequency modulation. However, due to the limitations of their own power generation characteristics, the use of traditional frequency modulation methods in solar power plants has certain limitations, which may affect the quality of frequency regulation and the safe and stable operation of the power grid. Therefore, to adapt to the trend of increasing the proportion of solar energy installed capacity in the power system and to mitigate the impact of large-scale connection to the power grid, the deployment of energy storage devices on the power grid side is an effective means of improving the full utilization of wind and solar energy. Meanwhile, the deployment of energy storage capacity in a combined energy storage system must consider a balance between performance and economy; an imbalance will result in wasted energy from the solar power plant and reduced operating efficiency of the combined energy storage system. Summary of the Invention [Problem to be solved by the invention]

[0004] The purpose of this application is to provide a method, device, equipment and storage medium for optimizing the allocation of energy storage capacity in a solar energy storage system, so as to at least solve the problem of the reduction in the operating efficiency of a solar power plant caused by the inappropriate allocation of energy storage capacity in the related art. [Means for solving the problem]

[0005] In a first aspect of an embodiment of the present application, there is provided a method for optimizing allocation of energy storage capacity of a solar energy storage system targeting a staged power generation plan, including: Obtain the raw output data of the staged output of the solar power plant; Constructing a genetic algorithm model and determining algorithm parameters and a fitness function of the genetic algorithm; An optimization calculation is performed based on the genetic algorithm, the original 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 specifically include: Population size Q, crossover probability Pe and mutation probability Pm.

[0007] Optionally, the fitness function is expressed as: It becomes TIFF2025530619000002.tif21151, where LCC is the total operating cost of the energy storage device, ICC is the initial cost of the energy storage device, N is the useful life of the energy storage device, n is the nth year of operation of the energy storage device, and 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 operation 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 operation 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 (yuan), penalty is the amount of electricity that does not reach the ideal grid connection value in one year, M is the penalty electricity fee, and the total number of replacements R is a function of the lifespan of the part to be replaced.

[0008] Optionally, an initial cost ICC of the energy storage device is expressed as: It becomes TIFF2025530619000003.tif13157, Here, Cost battery is the cost of the energy storage device corresponding to the energy storage capacity per unit, and Capacity battery is the energy storage capacity value, Cost inverter is the cost of the inverter corresponding to the energy storage capacity per unit.

[0009] Optionally, the total number of exchanges R is expressed as: The result is TIFF2025530619000004.tif16145.

[0010] Optionally, performing an optimization operation based on the genetic algorithm, the original output data and the fitness function to obtain a recommended energy storage capacity includes: Q energy storage capacity values ​​are randomly generated, and a population X(t) is constructed using the energy storage capacity values ​​as individuals; obtaining a fitness function value corresponding to an individual in the population based on the original output data; The population X(t) is subjected to evolutionary processing until the fitness function values ​​of the individuals in the population satisfy an optimization termination condition, where t is the number of evolutions of the population.

[0011] Optionally, said evolving said population X(t) includes: Select Y / 2 pairs of entities from X(t) using a preset selection operator, where Y is equal to or greater than Q; For the selected Y / 2 pairs of mothers, determine target mother pairs for crossover operation based on the crossover probability Pe, and perform the crossover operation to obtain Y individuals; Mutation is performed on the obtained Y individuals based on the mutation probability Pm to generate Y mutated individuals; From the generated Y mutant individuals, Q individuals are screened based on the corresponding fitness function values ​​to generate the next generation population X(t+1).

[0012] Optionally, evolving the population X(t) until fitness function values ​​of individuals in the population satisfy an optimization termination condition includes: If the fitness function value of an individual in the population X(t+1) satisfies the optimization termination condition, output the individual in X(t+1) with the largest fitness function value as the optimal solution, and stop the evolution process. If not, the population continues to undergo evolutionary processing.

[0013] In a second aspect of an embodiment of the present application, an electronic device is provided, including: processor, a memory for storing instructions executable by said processor; Here, the processor is configured to execute the commands to implement a method for optimizing allocation of energy storage capacity of a solar energy storage system targeting a staged power generation plan as described in any one of the first aspects.

[0014] In a third aspect of an embodiment of the present application, there is provided a computer-readable storage medium, wherein instructions in the storage medium, when executed by a processor of electronic equipment, enable the electronic equipment to perform the method for optimizing allocation of energy storage capacity of a solar energy storage system targeting a staged power generation plan as set forth in any one of the first aspects.

[0015] In a fourth aspect of an embodiment of the present application, there is provided an apparatus for optimizing allocation of energy storage capacity of a solar energy storage system targeting a staged power generation plan, which includes: an acquisition module for acquiring raw power output data of the staged power output of the solar power plant; A construction model is used to construct a genetic algorithm model and determine the algorithm parameters and fitness function of the genetic algorithm; An optimization module is used to 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. [Effects of the Invention]

[0016] The technical solutions provided by the embodiments of the present application have at least the following advantageous effects: By performing optimization calculations based on genetic algorithms, raw output data and fitness functions, and obtaining recommended energy storage capacity, it is possible to realize optimization of the energy storage capacity of the solar power plant, avoid energy waste in the solar power plant, and improve the operating efficiency of the energy storage mixed system.

[0017] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a flow chart of a method for optimizing allocation of energy storage capacity of a solar energy storage system to target a staged power generation plan according to an exemplary embodiment. [Figure 2] FIG. 1 is a schematic diagram of the staged output of a solar power plant. [Figure 3] 1 is a flow chart of a method for optimizing allocation of energy storage capacity of a solar energy storage system to target a staged power generation plan according to an exemplary embodiment. [Figure 4] 1 is a flow chart of a method for optimizing allocation of energy storage capacity of a solar energy storage system to target a staged power generation plan according to an exemplary embodiment. [Figure 5] FIG. 10 is a diagram of a layout after genetic algorithm optimization according to an exemplary embodiment. [Figure 6] FIG. 1 is a schematic diagram of energy storage capacity versus total operating cost and loss rate according to an exemplary embodiment. [Figure 7] This is an output data diagram of the energy storage device after installing a storage capacity of 1000 MWh. [Figure 8] FIG. 1 is a block diagram of an apparatus according to one exemplary embodiment. [Figure 9] FIG. 1 is a block diagram of an apparatus according to one exemplary embodiment. [Figure 10] FIG. 1 is a block diagram of an apparatus 1000 for optimizing placement of energy storage capacity in a solar energy storage system targeting a staged power generation plan according to an exemplary embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0019] In order to make those skilled in the art better understand the technical solutions of the present application, the following clearly and completely describes the technical solutions of the embodiments of the present application in combination with drawings.

[0020] It should be noted that the terms "first" and "second" used in the specification, claims, and above-mentioned drawings of this application are used to distinguish between similar objects and are not necessarily used to describe a particular order or priority. It should be understood that the examples of the application described herein can be practiced in orders other than those shown and described herein, and that the data used in this manner can be interchanged where appropriate. The embodiments described in the following illustrative examples are not all embodiments consistent with this application, but merely examples of apparatus and methods consistent with certain aspects of this application as detailed in the appended claims.

[0021] In recent years, with the rapid development of renewable energy power generation technology, a large amount of new energy is being generated through connection to the power grid. Therefore, rationally improving the energy distribution structure and ensuring the safe and reliable operation of the power grid has become an important research direction in the development of the power system. Among them, the high instability and variability of the output of photovoltaic power plants affects the safe operation and peak adjustment of the power grid, and also greatly restricts the full utilization of renewable energy.

[0022] In conventional power grids, hydroelectric and thermal power generation units, as the main peak-modulating power sources, constantly adjust their output in response to changes in system frequency through methods such as primary frequency modulation and secondary frequency modulation. However, due to the limitations of their own power generation characteristics, the use of traditional frequency modulation methods in solar power plants has certain limitations, which may affect the quality of frequency regulation and the safe and stable operation of the power grid. Therefore, to adapt to the trend of increasing the proportion of solar energy installed capacity in the power system and to mitigate the impact of large-scale connection to the power grid, the deployment of energy storage devices on the power grid side is an effective means of improving the full utilization of wind and solar energy. Meanwhile, the deployment of energy storage capacity in a combined energy storage system must consider a balance between performance and economy; an imbalance will result in wasted energy from the solar power plant and reduced operating efficiency of the combined energy storage system.

[0023] FIG. 1 is a flow chart of a method for optimizing the allocation of energy storage capacity of a solar energy storage system targeting a staged power generation plan, shown according to an exemplary embodiment. As shown in FIG. 1, the method includes: Step 101: Obtain the original output data of the staged output of the solar power plant.

[0024] In the embodiment of the present application, the output data of a solar power plant is affected by meteorological data around the power receiving station, and meteorological data is characterized by randomness, intermittency, and fluctuation, and the output data of a solar power plant also has such characteristics. Therefore, when analyzing the recommended energy storage capacity suitable for the energy storage mixed system, it is necessary to refer to and analyze the past original output data of the solar power plant, the original output data is the active power output from the solar power plant, and the step length of the original output data is 1h (hour).

[0025] Here, since the light irradiation conditions are different in different time periods, and the corresponding photovoltaic power output data, i.e., output power, is also significantly different, the photovoltaic power plant adopts a stepped output method to provide different outputs in different time periods. Figure 2 is a schematic diagram of the stepped output of a photovoltaic power plant, and as shown in Figure 2, the output corresponding to each sampling point is the output within one hour of the photovoltaic power plant where the sampling point is located.

[0026] Step 102: construct a genetic algorithm model, and determine algorithm parameters and a fitness function of the genetic algorithm.

[0027] In the embodiments of the present application, optimization calculations are performed based on a genetic algorithm to obtain an appropriate energy storage capacity. Genetic algorithms (GAs) originate from computer simulation research on biological systems and are a random global search optimization method. They simulate phenomena such as replication, crossover, and mutation that occur during natural selection and inheritance. They generate populations that are more suited to the environment through random selection, crossover, and mutation from a population, and evolve the population into increasingly better regions in the search space. This process of continuous evolution, generation after generation, ultimately converges on the population that is most suited to the environment, thereby obtaining an optimal solution to the problem. First, the algorithm parameters and fitness function of the genetic algorithm must be set. The algorithm parameters are used to control the population evolution process of the genetic algorithm, and the fitness function is used to determine whether individuals in the population meet the optimization goal.

[0028] Step 103: Perform an optimization calculation based on the genetic algorithm, the original output data and the fitness function to obtain a recommended energy storage capacity, and construct an energy storage device based on the recommended energy storage capacity.

[0029] In an embodiment of the present application, a population is generated based on a genetic algorithm, and each individual in the population is an energy storage capacity value. The population evolves based on the original output data until the population meets an evolution stopping condition, at which point the fitness function value corresponding to the individual in the population meets a predetermined condition. The energy storage capacity corresponding to the optimal individual is then obtained as the recommended energy storage capacity. An energy storage device can then be constructed based on the recommended energy storage capacity, and the constructed solar power plant can meet the optimization goal, i.e., it not only meets the economical efficiency 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 specifically include: Population size Q, crossover probability Pe and mutation probability Pm.

[0031] In the present application, the population size refers to the number of individuals in a population, and in this application, one individual corresponds to one energy storage capacity value. The crossover operation randomly selects two individuals from a population as a pair of mothers based on a predetermined crossover probability Pe, and then inherits the superior characteristics of the mothers to the offspring through the exchange and combination of two chromosomes, thereby generating new individuals. Common 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 between 0.6 and 0.9. The mutation operation involves changing a small portion of genes to alleles for each individual in the population based on a certain mutation probability Pm. Mutation can maintain population diversity and prevent the loss of important genes, but the mutation probability generally takes a low value between 0.001 and 0.1.

[0032] Optionally, the fitness function is expressed as: It becomes TIFF2025530619000005.tif21151, where LCC is the total operating cost of the energy storage device, ICC is the initial cost of the energy storage device, N is the useful life of the energy storage device, n is the nth year of operation of the energy storage device, and 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 operation 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 operation 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 (yuan), penalty is the amount of electricity that does not reach the ideal grid connection value in one year, M is the penalty electricity fee, and the total number of replacements R is a function of the lifespan of the part 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, where ICC is the initial cost of the energy storage device, i.e., the cost required to purchase and install the energy storage device, and ICC is proportional to the energy storage capacity of the energy storage device, and the larger the energy storage capacity, the higher the initial cost required.

[0034] Energy storage devices are depreciated annually, and the depreciated price can reduce the operating costs. TIFF2025530619000006.tif1244 reflects the costs incurred in maintaining and operating the energy storage device from year 1 to year N. During the operation of the energy storage device, some of its components have a short lifespan and need to be replaced periodically. TIFF2025530619000007.tif18121 reflects the residual value of the energy storage device, where residual value is the expected residual value that can be recovered when the asset's usage period expires, that is, the payment that can be received when disposing of a fixed asset when it is discarded after its usage period expires, and the larger N, the lower the residual value. In a power grid, if an energy storage device does not reach the ideal grid connection value of power within one year, a penalty will be imposed, and penalty*M reflects the penalty within the operating cycle.

[0035] Optionally, the total number of exchanges R can be expressed as: TIFF2025530619000008.tif16145. The total number of replacements for each replacement part within the operating cycle of the energy storage device is related to the lifespan of the replacement part, and can be expressed as follows: TIFF2025530619000009.tif16145, where floor is a Matlab function used to round a number to the next lower integer. Taking into account the residual value, we subtract 1 to avoid the final replacement of the energy storage unit and reduce the operating costs of the energy storage unit.

[0036] Optionally, an initial cost of the energy storage device is expressed as: It becomes TIFF2025530619000010.tif13157, Here, Cost battery is the cost of the energy storage device corresponding to the energy storage capacity per unit, and Capacity battery is the energy storage capacity value, Cost inverter is the cost of the inverter corresponding to the energy storage capacity per unit.

[0037] In the embodiment of the present application, the solar power plant outputs DC power, but the power grid transmits AC power. Therefore, when outputting power to the power grid through the energy storage device, an inverter must be used to convert DC power to AC power. The inverter converts the variable DC voltage generated by the solar power generation panel into commercial frequency AC power, which can be transmitted to the commercial power grid or used in 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 capacity of the inverter and the higher the initial cost of the inverter.

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

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

[0040] Step 302: Obtain a fitness function value corresponding to an individual in the population based on the original output data.

[0041] In the embodiment of the present application, the fitness function is a mathematical function for evaluating the merits or demerits of an individual, which is mapped to the fitness function by the objective function, or the fitness of the individual is directly represented by the objective function.

[0042] Step 303: Evolve the population X(0) until the fitness function value of the individuals in the population meets the optimization termination condition to obtain a population X(t), where t is the number of evolutions of the population.

[0043] In the embodiment of the present application, the genetic algorithm refers to Darwin's theory of biological evolution and Mendel's law of inheritance, and uses the principle of "survival of the fittest" to sequentially generate an approximate optimal solution among potential solutions. In each generation of the genetic algorithm, individuals are selected based on their fitness value, and the next generation of individuals is generated based on genetic laws. In this process, the fitness of individuals in the population is constantly increased, and the obtained solution also constantly approaches the optimal solution. After generating X(0), the population X(0) must be subjected to an evolutionary process to obtain population X(t), and through continuous iterative evolution, the fitness function of individuals in the population is made to approach a predetermined target.

[0044] FIG. 4 is a flowchart of a method for optimizing the allocation of energy storage capacity of a solar energy storage system targeting a staged power generation plan, shown based on an exemplary embodiment. As shown in FIG. 4, the procedure for evolving the population specifically includes: Step 401, select Y / 2 pairs of members from X(t) using a preset selection operator, where Y is equal to or greater than Q.

[0045] In the embodiment of this application, in the evolution process of a population, for example, the evolution process of X(t), the process simulated is the process by which the population leaves offspring, and in this process, the genetic algorithm uses three genetic operators: selection operator, crossover operator, and mutation operator. First, two mothers are selected to breed (generate) offspring (new individuals), and then a preset selection operator is used to select Y / 2 pairs of mothers from X(t), and new individuals are generated by the mothers. Each pair of mothers generates two new individuals, and since the new individuals need to be screened, the number of new individuals must be greater than Q, i.e., 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 selection probability. Taking the Roulette method as an example, let the number of populations be Q and the fitness of individual i be fi. i Assuming that, the probability that individual i is selected is TIFF2025530619000011.tif21124, and after determining the probability of individual selection, A uniform random number is generated between TIFF2025530619000012.tif12151 to determine which individual will participate in mating as the mother. If the selection probability of an individual is high, it will have multiple opportunities to be selected, and its genes will be spread within the population. Conversely, if the selection probability of an individual is low, it is likely to be eliminated.

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

[0047] In the embodiment of this application, the crossover operation involves finding Y / 2 pairs of mothers from a population, and then inheriting the superior characteristics of the mothers to offspring through the exchange and combination of two chromosomes, thereby generating new superior individuals. After obtaining superior individuals from the population, partial chromosomes between the individuals are exchanged with a certain probability (genetic 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 is highly destructive to the solution and generally ranges from 0.25 to 1. A target mother can be selected from the mothers based on the crossover probability, and the crossover operation can be performed using the crossover operator.

[0048] where the intersection operators include: a) Two-point crossover or multi-point crossover, that is, a crossover operation is performed by randomly setting two or more crossover points for a chromosome pair to change the gene sequence of the chromosome. b) Uniform crossover, i.e., crossover occurs with equal probability for each position on the gene sequence of a chromosome pair to form a new gene sequence. c) Arithmetic crossover: chromosome pairs are crossed over in a linear combinational manner to change the gene arrangement of the chromosomes. d) Single-point crossover operator: This operator randomly selects a crossover position within a chromosome pair and performs a gene position transformation for the chromosome pair at this crossover position.

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

[0050] In the embodiment of the present application, each individual in the population changes some of its genes to alleles with a mutation probability. Mutation can maintain the diversity of the population and prevent the loss of important genes, but the mutation probability should not be too large, generally between 0.001 and 0.1. Based on the mutation probability, the position of the individual that needs to be mutated is determined, and the mutation operation is performed using a mutation operator.

[0051] In practical applications, single-point mutation, also known as positional mutation, is mainly used, that is, it simply mutates any position in an individual's gene sequence. For example, in binary code, this would change 0 to 1 or 1 to 0.

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

[0053] After selection, crossover, and mutation of the individuals in X(t), one complete evolution of the population is completed. Q individuals are screened based on their fitness function values, and these individuals form the next generation population X(t+1). Each time an evolution occurs, the number in X() is incremented by 1.

[0054] Optionally, obtaining a population X(t) by evolving the population X(0) until the fitness function values ​​of the individuals in the population satisfy an optimization termination condition includes: If the fitness function value of an individual in the population X(t) satisfies the optimization termination condition, the individual in X(t+1) with the largest fitness function value is output as the optimal solution, and the evolution process is stopped. If not, the population continues to undergo evolutionary processing.

[0055] In an embodiment of the present application, an optimization termination condition can be set, for example, the fitness function is set to be smaller than a preset threshold, and if the fitness function value of an individual in X(t+1) is smaller than the preset threshold, it indicates that the operating cost of the energy storage device has reached the expected value, and the evolution can be stopped. Otherwise, the population needs to continue the evolution process.

[0056] Optionally, the fitness function also includes an expression for the loss rate in addition to the total operating cost of the energy storage device as described above. TIFF2025530619000013.tif15155Here, PV_a is the loss rate, PV is the amount of power generated by the solar power plant, and PV_plan is the amount of power 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 solar power generation electrical energy. In the embodiment of the present application, the target value set for the loss rate is smaller than a preset threshold.

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

[0058] The time step length of the original output data was 1 hour, and a total of 8,760 sets of PV power generation output data for one year were selected. Energy storage capacity was optimized based on the genetic algorithm described above. Figure 5 shows the layout after genetic algorithm optimization based on an exemplary embodiment. As shown in Figure 5, the loss rate of the PV power plant is 0% to 20%, the total cost ranges from 2.3*109 to 1.80*1010, and the loss rate reaches a minimum of 0.35%. A contradictory situation exists where costs increase as the loss rate decreases. To further determine the energy storage capacity, while also considering economic efficiency, the grid connection value was re-optimized after optimization for capacities with a loss rate between 5% and 20%. Through genetic algorithm optimization, the energy storage capacity range for a loss rate between 5% and 20% is 300MWh to 2,837MWh. Considering economic efficiency, the energy storage capacity range is selected to be 500MWh to 2000MWh, i.e., for a 10 million kilowatt solar power plant, an energy storage device with a capacity of 5% to 20% of that will be equipped, 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 of 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 as the energy storage capacity of the energy storage device increases, but its cost increases as the energy storage capacity increases. When the energy capacity is 10% and 15% of the installed capacity of the solar power plant, the mixed performance is best. When the energy storage capacity increases from 10% to 15%, the loss rate increases by about 1%, but the cost increases by 3*109 yuan. Considering everything, in the case of Figure 5, the best performance can be achieved by equipping a 10 million kilowatt solar power plant with a 1,000 MWh energy storage device.

[0060] Figure 7 shows the output data of an energy storage device equipped with a 1000MWh energy storage capacity, where the overlapping areas between the dotted line and the solid line indicate that the actual grid-connected value matches the ideal grid-connected value, and the overlapping areas between the dotted line and the solid line indicate that the actual grid-connected value does not reach or exceed the ideal grid-connected value. As can be seen, when economic efficiency and loss rate are taken into consideration, the fluctuation in the output stability of the energy storage device is not large, and the difference between the actual grid-connected value and the ideal grid-connected value is relatively small, so a solar power plant can be equipped with an energy storage device with an energy storage capacity of 10% of its installed capacity.

[0061] 8 is a block diagram of a device according to one exemplary embodiment. For example, device 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, medical equipment, fitness equipment, a personal digital assistant, etc.

[0062] As shown in FIG. 8 , device 800 may include one or more of 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 operations related to display, phone calls, data communications, camera operation, and recording 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 methods described above. 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 a multimedia component 808.

[0064] Memory 804 is configured to store various types of data to support operation of equipment 800. These data may include commands for any application programs or methods operating on device 800, contact data, phone book data, messages, images, videos, etc. Memory 804 may be implemented by various types of volatile or non-volatile storage devices, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, optical disk, or any combination thereof.

[0065] The power component 806 provides power to the components of the device 800. The power component 806 may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power for the device 800.

[0066] The multimedia component 808 includes a screen that provides an output port between the device 800 and a 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 can 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. The touch sensors can detect not only the boundaries of the 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 a photo mode or a video mode, the in-camera and / or out-camera can receive external multimedia data. Each in-camera and out-camera can have a fixed optical lens system or a focal length and optical zoom capability.

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

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

[0069] The sensor component 814 includes one or more sensors and is used to evaluate the status of various aspects of the device 800. For example, the sensor component 814 can detect whether the device 800 is open or closed, the relative positioning of a component, such as the monitor and keypad of the device 800, etc. The sensor component 814 can also detect changes in the position of the device 800 or a component of the device 800, the presence or absence of contact between the user and the device 800, the orientation or acceleration / deceleration of the device 800, and changes in the temperature of the device 800. The sensor component 814 can include a proximity sensor positioned to detect the presence of a nearby object in the absence of any physical contact. The sensor component 814 can also include an optical sensor used for imaging, such as a CMOS or CCD image sensor. In some embodiments, the sensor component 814 can also include an acceleration sensor, a gyro sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0070] The communication component 816 is configured to facilitate wired or wireless communication between the device 800 and other devices. The device 800 can be connected 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 through a broadcast channel. In one exemplary embodiment, the communication component 816 further includes a near-field communication (NFC) module for facilitating short-range communication. For example, the NFC module can be implemented based on radio frequency identification technology (RFID), infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0071] In an exemplary embodiment, 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 methods described above.

[0072] In an exemplary embodiment, the device 800 further includes a storage medium containing instructions, e.g., a memory 804 containing instructions. The commands may be executed by a processor 820 of the device 800 to perform the method. Optionally, the storage medium may be a non-transitory computer-readable storage medium, e.g., the non-transitory computer-readable storage medium may be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0073] FIG. 9 is a block diagram of an apparatus 900 according to an exemplary embodiment. For example, the apparatus 900 may be provided as a server. As shown in FIG. 9, the apparatus 900 includes a processor component 922, which may further include one or more processors, and a memory source, represented by memory 932, for storing commands, such as application programs, executable by the processor component 922. The application programs stored in memory 932 may include one or more modules, each corresponding to a set of commands. The processor component 922 is also configured to execute the commands to perform the methods described above.

[0074] Device 900 may further include a power component 926 arranged to provide power management for device 900, a wired or wireless network port 950 arranged to connect device 900 to a network, and an input / output (I / O) port 958. Device 900 may operate an operating system stored in memory 932, such as Windows Server™, MacOSX™, Unix™, Linux™, FreeBSD™, or the like.

[0075] FIG. 10 is a block diagram of an apparatus 1000 for optimizing allocation of energy storage capacity of a solar energy storage system targeting a staged power generation plan according to an exemplary embodiment, including: an acquisition module 1001 for acquiring raw output data for performing stepwise output of the solar power plant; a construction module 1002 for constructing a genetic algorithm model and determining algorithm parameters and a fitness function for the genetic algorithm; an optimization module 1003 that performs an optimization calculation based on the genetic algorithm and the original output data, obtains a recommended energy storage capacity, and constructs an energy storage device based on the recommended energy storage capacity; The device may perform all or some of the steps of the above method and will not be further described here.

[0076] Those skilled in the art can readily derive other embodiments of the present application from the practice of the specification and the invention disclosed herein. This application is intended to cover any variations, uses, and adaptations of the present application that comply with the general principles of the present application and include known or commonly used technical means in the art not disclosed in the present application. The specification and examples are exemplary, with the scope and spirit of the present application being defined by the claims.

[0077] The present application is not limited to the exact construction described above and shown in the drawings, and various modifications and changes can be made without departing from the scope thereof, which is defined by the appended claims.

Claims

1. 1. A method for optimizing allocation of energy storage capacity of a solar energy storage system targeting a phased power generation plan, comprising: Obtain the raw output data of the staged output of the solar power plant; Construct a genetic algorithm model, determining algorithm parameters and a fitness function for said genetic algorithm; The method for optimizing the allocation of energy storage capacity of the solar energy storage system includes performing an optimization calculation based on the genetic algorithm and the original output data to obtain a recommended energy storage capacity, and constructing an energy storage device based on the recommended energy storage capacity.

2. The algorithm parameters specifically include: The population size Q, the crossover probability Pe and the mutation probability Pm, the method for optimizing the allocation of the energy storage capacity of a solar energy storage system as described in claim 1.

3. The fitness function is expressed as follows: And where LCC is the total operating cost of the energy storage device, ICC is the initial cost of the energy storage device, N is the useful life of the energy storage device, n is the nth year of operation of the energy storage device, and 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 operation 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 operation cycle of the energy storage device, ICC ｃ is the cost of the part to be replaced, l ｃ 3. The method for optimizing the allocation of energy storage capacity of a solar energy storage system according to claim 2, wherein R is the lifespan of the c-th part to be replaced, s is the residual value (yuan), penalty is the amount of electricity that does not reach the ideal grid connection value in one year, and M is the penalty electricity fee, wherein R is the total number of replacements that is a function of the lifespan of the parts to be replaced.

4. The initial cost ICC of the energy storage device is expressed by the formula: And where Capacity 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, Cost inverter 4. The method for optimizing the placement of energy storage capacity in a solar storage system according to claim 3, wherein: ≈ ...

5. The total number of replacements R can be expressed by the following formula: The method for optimizing the placement of energy storage capacity of a solar energy storage system according to claim 3, wherein:

6. Performing an optimization operation based on the genetic algorithm, the original output data, and the fitness function to obtain a recommended energy storage capacity includes: Q energy storage capacity values ​​are randomly generated, and a population X(t) is constructed using the energy storage capacity values ​​as individuals; obtaining a fitness function value corresponding to an individual in the population based on the original output data; 6. The method for optimizing allocation of energy storage capacity of a solar energy storage system according to claim 5, wherein the population X(t) is subjected to an evolution process until the fitness function value of an individual in the population satisfies an optimization termination condition, where t is the number of evolutions of the population.

7. The step of evolving the population X(t) includes: Select Y / 2 pairs of members from X(t) using a preset selection operator, where Y is equal to or greater than Q; For the selected Y / 2 pairs of mothers, a target mother pair for crossover operation is determined based on the crossover probability Pe, and a crossover operation is performed to obtain Y individuals; Mutation is performed on the obtained Y individuals based on the mutation probability Pm to generate Y mutated individuals; 7. The method for optimizing allocation of energy storage capacity of a solar energy storage system according to claim 6, further comprising: screening Q individuals from the generated Y mutant individuals based on their corresponding fitness function values ​​to generate a next-generation population X(t+1).

8. Evolving the population X(t) until the fitness function values ​​of the individuals in the population satisfy an optimization termination condition includes: If the fitness function value of an individual in the population X(t+1) satisfies the optimization termination condition, the individual in X(t+1) with the largest fitness function value is output as the optimal solution, and the evolution process is terminated. Otherwise, the population is subjected to a continuous evolution process.

9. An electronic device comprising: processor, a memory for storing instructions executable by said processor; wherein the processor is configured to execute the commands to realize the method for optimizing allocation of energy storage capacity of a solar energy storage system targeting a staged power generation plan according to any one of claims 1 to 8.

10. 9. A computer-readable storage medium, the computer-readable storage medium being configured such that, when commands in the storage medium are executed by a processor of electronic equipment, the electronic equipment is able to execute a method for optimizing allocation of energy storage capacity of a solar energy storage system targeting a staged power generation plan as claimed in any one of claims 1 to 8.

11. 1. An apparatus for optimizing allocation of energy storage capacity of a solar energy storage system for targeting a phased power generation plan, comprising: an acquisition module for acquiring raw power output data for performing staged output of the solar power plant; A construction model is used to construct a genetic algorithm model and determine the algorithm parameters and fitness function of the genetic algorithm; an optimization module, which performs optimization calculations based on the genetic algorithm and the original output data to obtain a recommended energy storage capacity, and constructs an energy storage device based on the recommended energy storage capacity, as the energy storage capacity optimization arrangement device for the solar energy storage system;

Images