WO2024045582A1 - 光储系统储能容量优化配置方法、装置、设备及存储介质 - Google Patents

光储系统储能容量优化配置方法、装置、设备及存储介质 Download PDF

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WO2024045582A1
WO2024045582A1 PCT/CN2023/083588 CN2023083588W WO2024045582A1 WO 2024045582 A1 WO2024045582 A1 WO 2024045582A1 CN 2023083588 W CN2023083588 W CN 2023083588W WO 2024045582 A1 WO2024045582 A1 WO 2024045582A1
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energy storage
storage capacity
population
individuals
cost
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PCT/CN2023/083588
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English (en)
French (fr)
Inventor
徐若晨
刘明义
曹传钊
曹曦
孙周婷
刘大为
朱勇
裴杰
张江涛
王佳运
Original Assignee
中国华能集团清洁能源技术研究院有限公司
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Publication of WO2024045582A1 publication Critical patent/WO2024045582A1/zh

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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/12—Computing arrangements based on biological models using genetic models
    • G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E70/00—Other energy conversion or management systems reducing GHG emissions
    • Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin

Definitions

  • This application relates to the field of new energy, and in particular to methods, devices, equipment and storage media for optimizing the allocation of energy storage capacity in optical storage systems.
  • hydroelectric power generation and thermal power generating units serve as the main peak-shaving and frequency-modulating power sources.
  • the unit output is continuously changed according to changes in system frequency.
  • traditional frequency regulation methods which may affect the quality of grid frequency regulation and even the safe and stable operation. Therefore, in order to adapt to the trend of the increasing proportion of photovoltaic energy in the installed capacity of the power system and reduce the impact of its large-scale grid connection on the power system, configuring energy storage devices on the grid side is an effective means to improve the absorption capacity of wind and solar energy. .
  • performance and economy need to be balanced. If the balance is not good, the energy of the photovoltaic power station will be wasted, resulting in a reduction in the operating efficiency of the energy storage hybrid system.
  • the purpose of this application is to provide a method, device, equipment and storage medium for optimizing the configuration of energy storage capacity of a photovoltaic storage system, so as to at least solve the problem of improper configuration of energy storage capacity in related technologies that leads to reduced operating efficiency of photovoltaic power stations.
  • the technical solution of this application is as follows:
  • a method for optimizing the allocation of energy storage capacity of a photovoltaic storage system targeting a stepped power generation plan including:
  • An optimization operation is performed based on the genetic algorithm, the original output data and the fitness function to obtain the recommended energy storage capacity, and the energy storage device is constructed according to the recommended energy storage capacity.
  • algorithm parameters specifically include:
  • the fitness function is formulated as:
  • LCC is the total operating cost of the energy storage device
  • ICC is the initial cost of the energy storage device
  • N is the 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 replacement component
  • R is the total number of replacements during the operation cycle of the energy storage device
  • ICC C is the cost of the component to be replaced
  • lc is the life of the c-th component to be replaced
  • s is the residual value (yuan)
  • penalty is the electricity that does not reach the ideal grid-connected value in one year
  • M is the penalty electricity price
  • the total number of replacements R is a function of the life of the component to be replaced.
  • the initial cost ICC of the energy storage device is formulated as:
  • ICC Cost battery ⁇ Capacity battery +Cost inverter ⁇ Capacity battery ;
  • Cost battery is the cost of the energy storage device per unit energy storage capacity
  • Capacity battery is the energy storage capacity value
  • Cost inverter is the cost of the inverter per unit energy storage capacity.
  • performing an optimization operation based on the genetic algorithm, the original output data and the fitness function to obtain the recommended energy storage capacity includes:
  • the population X(t) is subjected to evolution processing until the fitness function value of the individuals in the population meets the termination optimization condition, where t is the number of evolutions of the population.
  • the evolutionary processing of the population X(t) includes:
  • the obtained Y individuals are mutated according to the mutation probability Pm to generate Y mutated individuals;
  • the evolution process is performed on the population X(t) until the fitness function value of the individuals in the population meets the termination optimization condition, which includes:
  • an electronic device including:
  • memory for storing instructions executable by the processor
  • the processor is configured to execute the instructions to implement the optical storage system energy storage capacity optimization configuration method targeting the stepped power generation plan as described in any one of the above first aspects.
  • a computer-readable storage medium which when instructions in the storage medium are executed by a processor of an electronic device, enables the electronic device to execute any of the above first aspects.
  • an optical storage system energy storage capacity optimization configuration device targeting a stepped power generation plan including:
  • the acquisition module is used to obtain the original output data of the photovoltaic power station for step output;
  • a building module 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 optimization calculations based on the genetic algorithm and the original output data to obtain recommended energy storage capacity, and construct energy storage devices based on the recommended energy storage capacity.
  • the recommended energy storage capacity is obtained, thereby optimizing the energy storage capacity of the photovoltaic power station, avoiding wasting the energy of the photovoltaic power station, and improving the operating efficiency of the energy storage hybrid system.
  • FIG. 1 is a flow chart illustrating a method for optimizing the allocation of energy storage capacity of a photovoltaic storage system targeting a stepped power generation plan according to an exemplary embodiment.
  • Figure 2 is a schematic diagram of the ladder output of a photovoltaic power station.
  • FIG. 3 is a flow chart illustrating a method for optimizing the allocation of energy storage capacity of a photovoltaic storage system targeting a stepped power generation plan according to an exemplary embodiment.
  • FIG. 4 is a flow chart illustrating a method for optimizing the configuration of energy storage capacity of a photovoltaic storage system targeting a stepped power generation plan according to an exemplary embodiment.
  • Figure 5 is an arrangement diagram optimized by a genetic algorithm according to an exemplary embodiment.
  • Figure 6 is a schematic diagram illustrating the relationship between energy storage capacity, total operating cost and light abandonment rate according to an exemplary embodiment.
  • Figure 7 shows the output data of the energy storage device equipped with an energy storage capacity of 1000MWh.
  • FIG. 8 is a block diagram of a device according to an exemplary embodiment.
  • Figure 9 is a block diagram of a device according to an exemplary embodiment.
  • FIG. 10 is a block diagram of an optical storage system energy storage capacity optimization configuration device 1000 targeting a stepped power generation plan according to an exemplary embodiment.
  • hydroelectric power generation and thermal power generating units serve as the main peak-shaving and frequency-modulating power sources.
  • the unit output is continuously changed according to changes in system frequency.
  • traditional frequency regulation methods which may affect the quality of grid frequency regulation and even the safe and stable operation. Therefore, in order to adapt to the trend of the increasing proportion of photovoltaic energy in the installed capacity of the power system and reduce the impact of its large-scale grid connection on the power system, configuring energy storage devices on the grid side is an effective means to improve the absorption capacity of photovoltaic energy. .
  • performance and economy need to be balanced. If the balance is not good, the energy of the photovoltaic power station will be wasted, resulting in a reduction in the operating efficiency of the energy storage hybrid system.
  • FIG. 1 is a flow chart illustrating a method for optimizing the allocation of energy storage capacity of a photovoltaic storage system targeting a stepped power generation plan according to an exemplary embodiment. As shown in Figure 1, the method includes:
  • Step 101 Obtain the original output data of the photovoltaic power station for step output.
  • the meteorological data since the output data of the photovoltaic power station is affected by the meteorological data around the power station, the meteorological data has the characteristics of randomness, intermittent and fluctuation.
  • the output data of the photovoltaic power station also has these characteristics and cannot be manually determined. Control the processing data of the photovoltaic power station, so when analyzing the recommended energy storage capacity suitable for the energy storage hybrid system, it is necessary to analyze the past original output data of the photovoltaic power station.
  • the original output data is the active power output by the photovoltaic power station.
  • the step size of the original output data is 1h (hour).
  • FIG. 2 is a schematic diagram of the stepped output of a photovoltaic power station. As shown in Figure 2, the output corresponding to each sampling point represents the output of the photovoltaic power station within one hour where the sampling point is located.
  • Step 102 Construct a genetic algorithm model and determine the algorithm parameters and fitness function of the genetic algorithm.
  • a genetic algorithm is used to perform optimization operations to obtain appropriate energy storage capacity.
  • the genetic algorithm (Genetic Algorithm, GA) originated from computer simulation research on biological systems and is a stochastic global search optimization method. It simulates phenomena such as replication, crossover, and mutation that occur in natural selection and inheritance. Starting from any population (Population), through random selection, crossover, and mutation operations, a group of individuals more suitable for the environment is generated. Let the group evolve to better and better areas in the search space, so that it continues to reproduce and evolve from generation to generation, and finally converges to a group of individuals (Individuals) that are most adapted to the environment, thereby obtaining the quality of the problem untie.
  • the algorithm parameters are used to control the process of population evolution of the genetic algorithm.
  • the fitness function is used to determine whether the individuals in the population meet the optimization goal.
  • Step 103 Perform an optimization operation based on the genetic algorithm, the original output data and the fitness function to obtain recommended energy storage capacity, and build an energy storage device based on the recommended energy storage capacity.
  • a population is generated based on a genetic algorithm.
  • the individuals in the population are the energy storage capacity values.
  • the population is evolved based on the original output data until a condition that meets the conditions for stopping evolution appears in the population.
  • the corresponding adaptation of the individuals in the population is The degree function value satisfies the preset conditions.
  • energy storage devices can be built according to the recommended energy storage capacity, and the built photovoltaic power station can meet the optimization goals during operation, which is economical, has low operating costs, and will not waste the power generation capacity of the photovoltaic power station.
  • algorithm parameters specifically include:
  • the population size is the number of individuals in a population.
  • one individual corresponds to one energy storage capacity value.
  • the crossover operation refers to randomly selecting two individuals from the population as a pair of mothers according to a certain crossover probability Pe. Through the exchange and combination of two chromosomes, the excellent characteristics of the mother are passed on to the substring, thereby generating new individuals. Common crossover Operators include single-point crossover, two-point crossover, multi-point crossover, uniform crossover and arithmetic crossover, etc. The crossover position is also random.
  • the crossover probability is usually very large, usually 0.6 to 0.9.
  • the mutation operation is to change a small part of the genes into alleles according to a certain mutation probability Pm for each individual in the population. 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 ranging from 0.001 to 0.1.
  • the fitness function is formulated as:
  • LCC is the total operating cost of the energy storage device
  • ICC is the initial cost of the energy storage device
  • N is the 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 replacement component
  • R is the total number of replacements during the operation cycle of the energy storage device
  • ICC C is the cost of the component to be replaced
  • lc is the life of the c-th component to be replaced
  • s is the residual value (yuan)
  • penalty is the electricity that does not reach the ideal grid-connected value in one year
  • M is the penalty electricity price
  • the total number of replacements R is a function of the life of the component to be replaced.
  • the fitness function is constructed based on the operating cost of the energy storage device.
  • ICC is the initial cost of the energy storage device, that is, the cost required to purchase and install the energy storage device.
  • ICC and the energy storage device The energy storage capacity is directly proportional. The larger the energy storage capacity, the higher the initial cost required.
  • Energy storage devices are depreciated every year, and the depreciated price can reduce the total operating cost. It reflects the cost of maintaining and operating the energy storage device from the first to the Nth year. During the operation cycle of the energy storage device, some components have a short lifespan and require regular replacement of components. Reflects the residual value of the energy storage device.
  • the residual value refers to the residual value that is expected to be recovered when the useful life of an asset expires, that is, the price that can be charged for disposing of the asset when the fixed asset is scrapped at the end of its useful life, N The larger it is, the smaller the residual value is.
  • penalty*M reflects the penalty during the operating cycle.
  • the formula expression of the total number of substitutions R is: The total number of times each component to be replaced during the operation cycle of the energy storage device is related to the life of the component to be replaced.
  • the specific formula is: floor is a Matlab function used to round a number to the next small integer, taking into account the residual value, subtracting 1 in order to avoid the last replacement of the energy storage device to reduce the operating cost of the energy storage device.
  • Cost battery is the cost of the energy storage device per unit energy storage capacity
  • Capacity battery is the energy storage capacity value
  • Cost inverter is the cost of the inverter per unit energy storage capacity.
  • an inverter is required to convert the DC power into AC power when outputting power to the power grid through the energy storage device.
  • the inverter can convert photovoltaic solar energy into The variable DC voltage generated by the panels is converted into AC power at mains frequency, which can be fed back into the commercial transmission system or used off-grid.
  • the capacity of the inverter is proportional to the capacity of the energy storage device. The greater the energy storage capacity of the energy storage device, the greater the capacity of the inverter, and the higher the initial cost of the inverter.
  • FIG. 3 is a flow chart illustrating a method for optimizing the allocation of energy storage capacity of a photovoltaic storage system targeting a stepped power generation plan according to an exemplary embodiment.
  • step 104 in Figure 1 specifically includes:
  • Step 301 randomly generate Q energy storage capacity values, and use the energy storage capacity values as individuals to construct a population X(0);
  • Q energy storage capacity values that is, individuals
  • the population X(0) is formed based on these individuals.
  • X(0) is the initial population.
  • the 0 in X(0) means that the population has not evolved, that is, it has evolved 0 times.
  • Step 302 Obtain the fitness function value corresponding to the individual in the population according to the original output data
  • the fitness function is a mathematical function used to evaluate the merits and demerits of an individual.
  • the objective function is mapped to the fitness function, or the objective function directly represents the fitness of the individual.
  • Step 303 Evolve the population X(0) to obtain the population X(t) until the fitness function value of the individuals in the population meets the termination optimization condition, where t is the number of evolutions of the population.
  • the genetic algorithm draws on Darwin's theory of biological evolution and Mendel's laws of inheritance, and uses the principle of "survival of the fittest" to successively generate an approximately optimal solution among potential solutions.
  • selection is made based on the fitness value of the individual, and a new generation of individuals is generated according to the laws of genetics.
  • the fitness of individuals in the population continues to increase, and the solution obtained continues to approach the optimal solution.
  • the population X(0) needs to be evolved to obtain the population X(t).
  • the fitness function of the individuals in the population is getting closer and closer to the preset target. .
  • FIG. 4 is a flow chart illustrating a method for optimizing the configuration of energy storage capacity of a photovoltaic storage system targeting a stepped power generation plan according to an exemplary embodiment.
  • the steps for evolving the population specifically include:
  • Step 401 Use the preset selection operator to select Y/2 pairs of matrices from X(t), where Y is greater than or equal to Q.
  • Step 402 For the found Y/2 mother pairs, determine the target mother pair for the crossover operation according to the crossover probability Pe and perform the crossover operation to obtain Y individuals;
  • the crossover operation refers to the Y/2 pair of mothers found from the population, and the excellent characteristics of the mother are passed on to the offspring through the exchange and combination of the two chromosomes, thereby producing new excellent individuals.
  • some chromosomes between them are exchanged with a certain probability (genetic probability).
  • the crossover probability controls the crossover operation.
  • a larger crossover probability can enhance the genetic algorithm to open up new search fields, but it is more destructive to the solution and is generally 0.25 to 1.
  • the target parent can be selected from the parent according to the crossover probability, and the crossover operation can be performed through the crossover operator.
  • the crossover operator includes:
  • Double-point crossover or multi-point crossover that is, randomly setting two or more crossover points on paired chromosomes, and then performing crossover operations to change the chromosome gene sequence.
  • Arithmetic crossover refers to crossover between paired chromosomes using a linear combination to change the chromosomal gene sequence.
  • Step 403 mutate the obtained Y individuals according to the mutation probability Pm to generate Y mutated individuals;
  • each individual in the population changes a small portion of genes into alleles with 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 ranging from 0.001 to 0.1. Determine which positions of the individual need to be mutated based on the mutation probability, and perform mutation operations through mutation operators.
  • single-point mutation also called bit mutation
  • bit mutation is mainly used, that is, only a certain bit in the individual's genetic sequence needs to be mutated.
  • binary coding that is, 0 becomes 1, and 1 becomes 0.
  • Step 404 From the generated Y mutant individuals, Q individuals are screened according to the corresponding fitness function values to generate the next generation population X(t+1).
  • performing evolutionary processing on the population X(0) to obtain the population X(t) until the fitness function value of individuals in the population meets the termination optimization conditions includes:
  • the fitness function can be set to be less than a certain preset threshold.
  • the fitness function value of the individual in X(t+1) is less than the preset threshold, it means that When the operating costs of energy storage devices reach expectations, they can stop evolving. Otherwise, the population needs to continue evolving.
  • the fitness function also includes a formula related to the light abandonment rate:
  • PV_a is the light abandonment rate
  • PV is the power generation of the photovoltaic power station
  • PV_plan is the electricity absorbed by the power grid.
  • the goal of setting the light abandonment rate in the embodiment of this application is to be less than a certain preset threshold.
  • the original output data uses 1h as the time step, selects one year's photovoltaic output data, a total of 8760 pieces of data, and optimizes the energy storage capacity through the above genetic algorithm.
  • Figure 5 is a genetic algorithm according to an exemplary embodiment. Optimized Pareto Chart. As shown in Figure 5, the light abandonment rate of photovoltaic power stations is about 0% to 20%, the total cost fluctuates in the range of 2.3*109 yuan to 1.80*1010 yuan, and the light abandonment rate can reach a minimum of 0.35%. In the process of reducing the light abandonment rate, the cost increases, which reflects the contradiction.
  • the energy storage capacity range with a light abandonment rate of 5%-20% is 300MWh to 2837MWh.
  • the energy storage capacity range is selected from 500MWh to 2000MWh, that is, compared to a 10 million kilowatt photovoltaic power station, it is equipped with an energy storage device with a capacity of 5% to 20%.
  • the ideal grid connection value based on genetic algorithm is optimized under different energy storage capacities.
  • Figure 6 is a schematic diagram illustrating the relationship between energy storage capacity, total operating cost and light abandonment rate according to an exemplary embodiment.
  • the light abandonment rate decreases with the increase of the energy storage capacity of the energy storage device, and the cost increases with the increase of the energy storage capacity.
  • Figure 7 is a graph showing the output data of an energy storage device equipped with an energy storage capacity of 1000MWh.
  • the part where the dotted line overlaps with the solid line indicates that the actual grid connection value is consistent with the ideal grid connection value, and the part where the line overlaps with the solid line indicates the actual grid connection.
  • the value does not reach or exceed the ideal grid connection value. It can be seen that after taking into account the economy and light abandonment rate, the output stability of the energy storage device does not show large fluctuations, and the actual grid-connected value and the ideal grid-connected value have a small difference, indicating that the photovoltaic The feasibility of a power station equipped with an energy storage device with an energy storage capacity of 10% of its installed capacity.
  • FIG. 8 is a block diagram of a device 800 according to an exemplary embodiment.
  • the device 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, or the like.
  • the device 800 may include one or more of the following components: a processing component 802, a memory 804, a power component 806, a multimedia component 808, an audio component 810, an input/output (I/O) interface 812, a sensor component 814, and communications component 816.
  • Processing component 802 generally controls the overall operations of device 800, such as operations associated with display, phone calls, data communications, camera operations, and recording operations.
  • the processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the above method.
  • processing component 802 may include one or more modules that facilitate interaction between processing component 802 and other components.
  • processing component 802 may include a multimedia module to facilitate interaction between multimedia component 808 and processing component 802.
  • Memory 804 is configured to store various types of data to support operations at device 800 . Examples of such data include instructions for any application or method operating on device 800, contact data, phonebook data, messages, pictures, videos, etc.
  • Memory 804 may be implemented by any type of volatile or non-volatile storage device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EEPROM), Programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk.
  • SRAM static random access memory
  • EEPROM electrically erasable programmable read-only memory
  • EEPROM erasable programmable read-only memory
  • EPROM Programmable read-only memory
  • PROM programmable read-only memory
  • ROM read-only memory
  • magnetic memory flash memory, magnetic or optical disk.
  • Power supply component 806 provides power to the various components of device 800 .
  • Power supply components 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to device 800 .
  • Multimedia component 808 includes a screen that provides an output interface between the device 800 and the user.
  • the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user.
  • the touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide action.
  • multimedia component 808 includes a front-facing camera and/or a rear-facing camera.
  • the front camera and/or the rear camera may receive external multimedia data.
  • Each front-facing camera and rear-facing camera can be a fixed optical lens system or have a focal length and optical zoom capabilities.
  • Audio component 810 is configured to output and/or input audio signals.
  • audio component 810 includes a microphone (MIC) configured to receive external audio signals when device 800 is in operating modes, such as call mode, recording mode, and voice recognition mode. The received audio signal may be further stored in memory 804 or sent via communication component 816 .
  • audio component 810 also includes a speaker for outputting audio signals.
  • the I/O interface 812 provides an interface between the processing component 802 and a peripheral interface module, which may be a keyboard, a click wheel, a button, etc. These buttons may include, but are not limited to: Home button, Volume buttons, Start button, and Lock button.
  • Sensor component 814 includes one or more sensors that provide various aspects of status assessment for device 800 .
  • the sensor component 814 may detect the open/closed state of the device 800, the relative positioning of components, such as the display and keypad of the device 800, and the sensor component 814 may also detect a change in position of the device 800 or a component of the device 800. , the presence or absence of user contact with the device 800 , device 800 orientation or acceleration/deceleration and temperature changes of the device 800 .
  • Sensor component 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact.
  • Sensor component 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications.
  • the sensor component 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
  • Communication component 816 is configured to facilitate wired or wireless communication between apparatus 800 and other devices.
  • Device 800 can access a wireless network based on a communication standard, such as WiFi, a carrier network (such as 2G, 3G, 4G or 5G), or a combination thereof.
  • the communication component 816 receives broadcast signals or broadcast related information from an external broadcast management system via a broadcast channel.
  • the communications component 816 also includes a near field communications (NFC) module to facilitate short-range communications.
  • NFC near field communications
  • the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.
  • RFID radio frequency identification
  • IrDA infrared data association
  • UWB ultra-wideband
  • Bluetooth Bluetooth
  • apparatus 800 may be configured by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable Gate array (FPGA), controller, microcontroller, microprocessor or other electronic components are implemented for executing the above method.
  • ASICs application specific integrated circuits
  • DSPs digital signal processors
  • DSPDs digital signal processing devices
  • PLDs programmable logic devices
  • FPGA field programmable Gate array
  • controller microcontroller, microprocessor or other electronic components are implemented for executing the above method.
  • a storage medium including instructions such as a memory 804 including instructions, which are executable by the processor 820 of the device 800 to complete the above method is also provided.
  • the storage medium may be a non-transitory computer-readable storage medium, for example, the non-transitory computer-readable storage medium may be ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage Equipment etc.
  • FIG. 9 is a block diagram of a device 900 according to an exemplary embodiment.
  • device 900 may be provided as a server.
  • apparatus 900 includes a processing component 922, which further includes one or more processors, and memory resources represented by memory 932 for storing instructions, such as application programs, executable by processing component 922.
  • the application program stored in memory 932 may include one or more modules, each corresponding to a set of instructions.
  • the processing component 922 is configured to execute instructions to perform the above-described method.
  • Device 900 may also include a power supply component 926 configured to perform power management of device 900, a wired or wireless network interface 950 configured to connect device 900 to a network, and an input-output (I/O) interface 958.
  • the device 900 may operate based on an operating system stored in the memory 932, such as Windows ServerTM, MacOSXTM, UnixTM, LinuxTM, FreeBSDTM or the like.
  • Figure 10 shows an optical storage system energy storage capacity optimization configuration device 1000 targeting a stepped power generation plan according to an exemplary embodiment, including:
  • the acquisition module 1001 is used to obtain the original output data of the photovoltaic power station for step output;
  • Building module 1002 used to construct a genetic algorithm model and determine the algorithm parameters and fitness function of the genetic algorithm
  • the optimization module 1003 is used to perform optimization operations based on the genetic algorithm and the original output data to obtain recommended energy storage capacity, and build energy storage devices based on the recommended energy storage capacity.
  • the device can be used to complete all or part of the steps of the above method, which will not be described in detail here.

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Abstract

本申请提供了光储系统储能容量优化配置方法、装置、设备及存储介质，涉及光伏领域。具体步骤为：获取光伏电站进行阶梯出力的原始出力数据；构建遗传算法模型，确定所述遗传算法的算法参数和适应度函数基于所述遗传算法和所述原始出力数据进行优化运算，以获取推荐储能容量，并根据所述推荐储能容量建设储能装置。本申请通过根据遗传算法、原始出力数据和适应度函数进行优化运算，获取推荐储能容量，实现了对光伏电站配套的储能装置对应储能容量的优化，避免浪费光伏电站的能量，提高了储能装置的运行效率。

Description

光储系统储能容量优化配置方法、装置、设备及存储介质
相关申请的交叉引用
本申请要求在2022年8月31日提交中国专利局、申请号为202211058430.X、发明名称为“以阶梯发电计划为目标的光储系统储能容量优化配置方法”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及新能源领域，尤其涉及光储系统储能容量优化配置方法、装置、设备及存储介质。
背景技术
近年来，伴随着可再生能源发电技术的迅速发展，在大量新能源并网发电的背景下，合理改善能源配置结构，保证电力网络安全可靠运行，成为电力系统发展的重要研究方向。其中，由于光伏电站出力的高随机性和波动性会对电网的安全运行和调峰调度带来挑战，也会较大程度的限制可再生能源的消纳。
传统电网中水力发电和火力发电机组作为主要的调峰调频电源，通过一次调频、二次调频等方法，根据系统频率变化不断改变机组出力。但是，对于光伏电站来说受自身发电特性限制，使用传统调频方法存在一定的局限性，可能会影响电网频率的调节品质甚至安全稳定运行。因此，为适应光伏能源在电力系统中装机容量占比不断扩大的趋势，减轻其大规模并网对电力系统带来的冲击，在电网侧配置储能装置是改善风光能源消纳能力的有效手段。而配备储能混合系统储能容量的配置过程中，性能和经济性需要平衡，如果平衡不好会浪费光伏电站的能量，导致储能混合系统的运行效率降低。
发明内容
本申请的目的在于提供光储系统储能容量优化配置方法、装置、设备及存储介质，以至少解决相关技术中储能容量配置不当导致光伏电站的运行效率降低的问题。本申请的技术方案如下：
根据本申请实施例的第一方面，提供一种以阶梯发电计划为目标的光储系统储能容量优化配置方法，包括：
获取光伏电站进行阶梯出力的原始出力数据；
构建遗传算法模型，确定所述遗传算法的算法参数和适应度函数；
基于所述遗传算法、所述原始出力数据和所述适应度函数进行优化运算，以获取推荐储能容量，并根据所述推荐储能容量建设储能装置。
可选地，所述算法参数具体包括：
种群规模Q、交叉概率Pe和变异概率Pm。
可选地，所述适应度函数的公式化表达为：
其中，LCC为所述储能装置的运营总成本，ICC为所述储能装置的初始成本；N为所述储能装置的寿命；n为所述储能装置运行的第n年；dn为第n年的折旧；i为利率；tr为税率；an为储能装置第n年的维护和运营成本；r为第r次替换部件；R为储能装置运行周期内的总替换次数；ICCC为待替换部件的成本；lc为第c个待替换部件的寿命；s为残值(元)；penalty为一年中未达到理想并网值的电量；M为惩罚电价；其中，替换的总次数R是所述待替换部件寿命的函数。
可选地，所述储能装置的初始成本ICC的公式化表达为：
ICC＝Costbattery×Capacitybattery+Costinverter×Capacitybattery；
其中，Costbattery为单位储能容量对应储能装置的成本，Capacitybattery为储能容量值，Costinverter为单位储能容量对应逆变器的成本。
可选地，所述替换的总次数R的公式化表达为：
可选地，基于所述遗传算法、所述原始出力数据和所述适应度函数进行优化运算，以获取推荐储能容量包括：
随机生成Q个所述储能容量值，将所述储能容量值作为个体构建种群X(t)；
根据所述原始出力数据获取所述种群中个体对应的适应度函数值；
对所述种群X(t)进行进化处理直到种群中个体的适应度函数值满足终止优化条件，其中，t为所述种群的进化次数。
可选地，所述对所述种群X(t)进行进化处理，包括：
利用预设的选择算子从X(t)中选取Y/2对母体，其中Y大于或等于Q；
对找出的Y/2对母体，按照所述交叉概率Pe确定交叉运算的目标母体对并进行交叉运算得到Y个个体；
对得到的Y个个体分别按照所述变异概率Pm进行变异，以生成Y个变异个体；
从生成的Y个变异个体中，根据对应的适应度函数值筛选得到Q个个体以生成下一代种群X(t+1)。
可选地，所述对所述种群X(t)进行进化处理直到种群中个体的适应度函数值满足终止优化条件，其中，包括：
如果种群X(t+1)中个体的适应度函数值满足终止优化条件，则输出X(t+1)里面适应度函数值最大的个体作为最优解，并停止进化处理；
否则，对所述种群继续进行进化处理。
根据本申请实施例的第二方面，提供一种电子设备，包括：
处理器；
用于存储所述处理器可执行指令的存储器；
其中，所述处理器被配置为执行所述指令，以实现如上述第一方面中任一项所述的以阶梯发电计划为目标的光储系统储能容量优化配置方法。
根据本申请实施例的第三方面，提供一种计算机可读存储介质，当所述存储介质中的指令由电子设备的处理器执行时，使得所述电子设备能够执行如上述第一方面中任一项所述的以阶梯发电计划为目标的光储系统储能容量优化配置方法。
根据本申请实施例的第四方面，提供一种以阶梯发电计划为目标的光储系统储能容量优化配置装置，包括：
获取模块，用于获取光伏电站进行阶梯出力的原始出力数据；
构建模块，用于构建遗传算法模型，确定所述遗传算法的算法参数和适应度函数；
优化模块，用于基于所述遗传算法和所述原始出力数据进行优化运算，以获取推荐储能容量，并根据所述推荐储能容量建设储能装置。
本申请的实施例提供的技术方案至少带来以下有益效果：
通过根据遗传算法、原始出力数据和适应度函数进行优化运算，获取推荐储能容量，实现了对光伏电站储能容量的优化，避免浪费光伏电站的能量，提高了储能混合系统的运行效率。
应当理解的是，以上的一般描述和后文的细节描述仅是示例性和解释性的，并不能限制本申请。
附图说明
此处的附图被并入说明书中并构成本说明书的一部分，示出了符合本申请的实施例，并与说明书一起用于解释本申请的原理，并不构成对本申请的不当限定。
图1是根据一示例性实施例示出的一种以阶梯发电计划为目标的光储系统储能容量优化配置方法的流程图。
图2为一种光伏电站的阶梯出力示意图。
图3是根据一示例性实施例示出的一种以阶梯发电计划为目标的光储系统储能容量优化配置方法的流程图。
图4是根据一示例性实施例示出的一种以阶梯发电计划为目标的光储系统储能容量优化配置方法的流程图。
图5是根据一示例性实施例示出的一种遗传算法优化后的排列图。
图6是根据一示例性实施例示出的储能容量与运营总成本和弃光率的关系示意图。
图7为配备1000MWh的储能容量后储能装置的出力数据图。
图8是根据一示例性实施例示出的一种装置的框图。
图9是根据一示例性实施例示出的一种装置的框图。
图10是根据一示例性实施例示出的一种以阶梯发电计划为目标的光储系统储能容量优化配置装置1000的框图。
具体实施方式
为了使本领域普通人员更好地理解本申请的技术方案，下面将结合附图，对本申请实施例中的技术方案进行清楚、完整地描述。
需要说明的是，本申请的说明书和权利要求书及上述附图中的术语“第一”、“第二”等是用于区别类似的对象，而不必用于描述特定的顺序或先后次序。应该理解这样使用的数据在适当情况下可以互换，以便这里描述的本申请的实施例能够以除了在这里图示或描述的那些以外的顺序实施。以下示例性实施例中所描述的实施方式并不代表与本申请相一致的所有实施方式。相反，它们仅是与如所附权利要求书中所详述的本申请的一些方面相一致的装置和方法的例子。
近年来，伴随着可再生能源发电技术的迅速发展，在大量新能源并网发电的背景下，合理改善能源配置结构，保证电力网络安全可靠运行，成为电力系统发展的重要研究方向。其中，由于光伏电站出力的高随机性和波动性会对电网的安全运行和调峰调度带来挑战，也会较大程度的限制可再生能源的消纳。
传统电网中水力发电和火力发电机组作为主要的调峰调频电源，通过一次调频、二次调频等方法，根据系统频率变化不断改变机组出力。但是，对于光伏电站来说受自身发电特性限制，使用传统调频方法存在一定的局限性，可能会影响电网频率的调节品质甚至安全稳定运行。因此，为适应光伏能源在电力系统中装机容量占比不断扩大的趋势，减轻其大规模并网对电力系统带来的冲击，在电网侧配置储能装置是改善光能源消纳能力的有效手段。而配备储能混合系统储能容量的配置过程中，性能和经济性需要平衡，如果平衡不好会浪费光伏电站的能量，导致储能混合系统的运行效率降低。
图1是根据一示例性实施例示出的一种以阶梯发电计划为目标的光储系统储能容量优化配置方法的流程图。如图1所示，所述方法包括：
步骤101，获取光伏电站进行阶梯出力的原始出力数据。
本申请实施例中，由于光伏电站的出力数据是受电站周围的气象数据影响的，气象数据具备随机性、间歇性和波动性的特点，光伏电站的出力数据也是具备这些特点，无法通过人工来控制光伏电站的处理数据，所以在分析适合储能混合系统的推荐储能容量时，需要通过光伏电站过去的原始出力数据来进行分析，所述原始出力数据为光伏电站输出的有功功率，所述原始出力数据的步长为1h(小时)。
需要说明的是，由于不同时间段光照条件不同，对应的光伏出力数据，也即输出功率相差很大，所以所述光伏电站采用阶梯出力的方式，在不同时间段内进行不同的出力。图2为一种光伏电站的阶梯出力示意图，如图2所示，每个取样点对应的出力代表取样点所在的一个小时内光伏电站的出力。
步骤102，构建遗传算法模型，确定所述遗传算法的算法参数和适应度函数。
本申请实施例中，通过遗传算法来进行优化运算，以获取合适的储能容量，遗传算法(GeneticAlgorithm，GA)起源于对生物系统所进行的计算机模拟研究，是一种随机全局搜索优化方法，它模拟了自然选择和遗传中发生的复制、交叉(crossover)和变异(mutation)等现象，从任一种群(Population)出发，通过随机选择、交叉和变异操作，产生一群更适合环境的个体，使群体进化到搜索空间中越来越好的区域，这样一代一代不断繁衍进化，最后收敛到一群最适应环境的个体(Individual)，从而求得问题的优质 解。首先需要设置遗传算法的算法参数和适应度函数，所述算法参数用于控制遗传算法种群进化的过程，所述适应度函数用于判断种群中的个体是否符合优化目标。
步骤103，基于所述遗传算法、所述原始出力数据和所述适应度函数进行优化运算，以获取推荐储能容量，并根据所述推荐储能容量建设储能装置。
本申请实施例中，基于遗传算法生成种群，种群中的个体即为储能容量值，基于原始出力数据对种群进行进化，直到种群中出现满足停止进化的条件，该此时种群个体对应的适应度函数值满足预设的条件。获取最优的个体对应的储能容量，作为所述推荐储能容量。之后即可根据所述推荐储能容量建设储能装置，建设出的光伏电站在运行时满足优化目标，既满足经济性，运营成本较低，也不会浪费光伏电站的发电能力。
可选地，所述算法参数具体包括：
种群规模Q、交叉概率Pe和变异概率Pm。
本申请实施例中，所述种群规模为一个种群中个体的数量，本申请中一个个体即对应一个储能容量值。交叉操作是指按照一定的交叉概率Pe从种群中随机选择两个个体作为一对母体，通过两个染色体的交换组合，把母体的优秀特征遗传给子串，从而产生新的个体，常见的交叉算子有单点交叉、两点交叉、多点交叉、均匀交叉及算术交叉等，交叉位置也是随机的。交叉概率一般取得很大，一般为0.6～0.9。变异操作即对群体中的每一个个体按照一定的变异概率Pm把一小部分基因改变为等位基因。变异能保持群体的多样性，防止重要基因丢失，但变异概率不宜太大，一般取0.001～0.1。
可选地，所述适应度函数的公式化表达为：
其中，LCC为所述储能装置的运营总成本，ICC为所述储能装置的初始成本；N为所述储能装置的寿命；n为所述储能装置运行的第n年；dn为第n年的折旧；i为利率；tr为税率；an为储能装置第n年的维护和运营成本；r为第r次替换部件；R为储能装置运行周期内的总替换次数；ICCC为待替换部件的成本；lc为第c个待替换部件的寿命；s为残值(元)；penalty为一年中未达到理想并网值的电量；M为惩罚电价；其中，替换的总次数R是所述待替换部件寿命的函数。
本申请实施例中，根据储能装置的运营成本构建所述适应度函数，ICC为所述储能装置的初始成本，即购买安装所述储能装置所需的成本，ICC与储能装置的储能容量成正比，储能容量越大，所需的初始成本越高。
储能装置每年都会有折旧，折旧后的价格可以降低所述运营总成本。反映了第一年到第N年中，对储能装置进行维护和运营所花费的成本。在储能装置运行周期内，其中有些部件的寿命较短，需要定期进行部件的更换。反映了所述储能装置的残值，残值是指在一项资产使用期满时预计能够回收到的残余价值，也就是在固定资产使用期满报废时处置资产所能收取的价款，N越大，残值越小。电网中，如果储能装置一年中未达到理想并网值的电量，则会受到罚款，penalty*M就反映了运营周期内的罚款。
可选地，所述替换的总次数R的公式化表达为：每个待替换部件在储能装置运营周期内替换的总次数与待替换部件的寿命有关，具体公式为：floor是Matlab函数，用于将一个数字四舍五入到下一个小的整数，考虑到残值，减去1是为了避免了对储能装置进行最后一次更换，以降低储能装置的运营成本。
可选地，所述储能装置的初始成本ICC的公式化表达为：
ICC＝Costbattery×Capacitybattery+Costinverter×Capacitybattery；
其中，Costbattery为单位储能容量对应储能装置的成本，Capacitybattery为储能容量值，Costinverter为单位储能容量对应逆变器的成本。
本申请实施例中，由于光伏电站产生的为直流电，而电网中传输的电能为交流电，所以通过储能装置向电网输出电能时需要逆变器将直流电转换为交流电，逆变器可以将光伏太阳能板产生的可变直流电压转换为市电频率交流电，可以反馈回商用输电系统，或是供离网的电网使用。所述逆变器的容量与储能装置的容量成正比，储能装置的储能容量越大，则逆变器容量越大，初始成本中逆变器的成本也就越高。
图3是根据一示例性实施例示出的一种以阶梯发电计划为目标的光储系统储能容量优化配置方法的流程图。如图3所示，图1中的步骤104具体包括：
步骤301，随机生成Q个储能容量值，将所述储能容量值作为个体构建种群X(0)；
本申请实施例中，在遗传算法的优化过程中，首先需要生成种群和种群中的个体，
根据设置的种群规模Q，随机生成Q个储能容量值，也即个体，根据这些个体组成种群X(0)。X(0)为初始的种群，X(0)中的0代表种群未进行过进化，即进化了0次。
步骤302，根据所述原始出力数据获取所述种群中个体对应的适应度函数值；
本申请实施例中，适应度函数是用来评价个体优劣的数学函数，一般由目标函数映射成适应度函数，或直接由目标函数来表示个体的适应度。
步骤303，对所述种群X(0)进行进化处理以获取种群X(t)直到种群中个体的适应度函数值满足终止优化条件，其中，t为所述种群的进化次数。
本申请实施例中，遗传算法借鉴了达尔文的生物进化理论和孟德尔的遗传定律，使用“适者生存”的原则，在潜在的解决方案中逐次产生一个近似最优解的方案。在遗传算法的每一代中，根据个体的适应度值进行选择，并根据遗传学法则产生新一代的个体。在这个过程中种群中的个体适应度不断增强，得到解也不断接近最优解。在生成X(0)后，需要对所述种群X(0)进行进化处理以获取种群X(t)，通过不断的迭代进化，使种群中个体的适应度函数越来越接近预设的目标。
图4是根据一示例性实施例示出的一种以阶梯发电计划为目标的光储系统储能容量优化配置方法的流程图。如图4所示，对所述种群进行进化处理的步骤具体包括：
步骤401，利用预设的选择算子从X(t)中选取Y/2对母体，其中Y大于或等于Q。
本申请实施例中，在单次种群进化过程中，如X(t)的进化过程中，模拟的是种群繁衍后代的过程，该过程中遗传算法会使用以下三种遗传算子：选择算子，交叉算子，变异算子。首先要选择两个母体来繁衍(生成)后代(新的个体)，通过预设的选择算子算子从X(t)中选取Y/2对母体，通过母体来生成新的个体，每一对母体会生成两个新的个体，考虑到要对新的个体再进行筛选，新的个体的总数量要大于Q，即Y 要大于或等于Q。个体被选中的概率跟适应度函数值有关，个体适应度函数值越高，被选中的概率越大。以轮盘赌法为例，若设种群数为Q，个体i的适应度为fii，则个体i被选取的概率为：
当个体选择的概率给定后，产生[0,1]之间均匀随机数来决定哪个个体作为母体参加交配。若个体的选择概率大，则有机会被多次选中，那么它的遗传基因就会在种群中扩大；若个体的选择概率小，则被淘汰的可能性会大。
步骤402，对找出的Y/2对母体，按照所述交叉概率Pe确定交叉运算的目标母体对并进行交叉运算得到Y个个体；
本申请实施例中，交叉操作是指从种群中找出的Y/2对母体，通过两个染色体的交换组合，把母体的优秀特征遗传给后代，从而产生新的优秀个体。得到种群中的优质个体后，以某一概率(遗传概率)交换他们之间的部分染色体。交叉概率控制着交叉操作，较大的交叉概率可以增强遗传算法开辟新的搜索领域，但对解的破坏性较大，一般取0.25～1。根据交叉概率可以从母体中选取目标母体，并通过交叉算子进行交叉运算。
其中，所述交叉算子包括：
a)双点交叉或多点交叉，即对配对的染色体随机设置两个或者多个交叉点，然后进行交叉运算，改变染色体基因序列。
b)均匀交叉，即配对的染色体基因序列上的每个位置都以等概率进行交叉，以此组成新的基因序列。
c)算术交叉，是指配对染色体之间采用线性组合方式进行交叉，改变染色体基因序列。
d)单点交叉算子，该算子在配对的染色体中随机的选择一个交叉位置，然后在该交叉位置对配对的染色体进行基因位变换。
步骤403，对得到的Y个个体分别按照所述变异概率Pm进行变异，以生成Y个变异个体；
本申请实施例中，群体中的每一个个体以变异概率把一小部分基因改变为等位基因。变异能保持群体的多样性，防止重要基因丢失，但变异概率不宜太大，一般取0.001～0.1。根据所述变异概率确定个体的哪些位置需要变异，并通过变异算子进行变异运算。
在实际应用中，主要采用单点变异，也叫位变异，即只需要对个体的基因序列中某一个位进行变异，以二进制编码为例，即0变为1，而1变为0。
步骤404，从生成的Y个变异个体中，根据对应的适应度函数值筛选得到Q个个体以生成下一代种群X(t+1)。
在将X(t)中个体进行选择、交叉和变异后，即完成了种群的一次完整的进化过程，根据适应度函数值筛选得到Q个个体，这些个体就组成了下一代种群X(t+1)。每进行一次进化，X()中的数字就会加1。
可选地，所述对所述种群X(0)进行进化处理以获取种群X(t)直到种群中个体的适应度函数值满足终止优化条件，包括：
如果种群X(t+1)中个体的适应度函数值满足终止优化条件，则输出X(t+1)里面适应度函数值最大的个体作为最优解，并停止进化处理；
否则，对所述种群继续进行进化处理。
本申请实施例中设置一定的终止优化条件，如可以设置所述适应度函数小于一定的预设阈值，当X(t+1)中个体的适应度函数值小于所述预设阈值时，说明储能装置的运营成本到达预期，即可停止进化。否则，需要对所述种群继续进行进化处理。
可选地，适应度函数除了上述的储能装置的运营总成本外，还包括弃光率有关公式：
其中，PV_a是弃光率，PV是光伏电站发电量，PV_plan是电网吸纳的电量。弃光率越高，说明被浪费的光伏发电功率越多，所以弃光率越小越有利于节省光伏发电的电能，本申请实施例对弃光率设置的目标为小于一定的预设阈值。
为验证本申请方法的有效性，选取澜沧江位置的1000万千瓦光伏电站进行分析。
原始出力数据以1h为时间步长，选择一年的光伏出力数据，共8760条数据，并通过上述遗传算法进行储能容量的优化，图5是根据一示例性实施例示出的一种遗传算法优化后的排列图。如图5所示，光伏电站的弃光率在0％至20％左右，总成本在2.3*109元至1.80*1010元范围内波动，弃光率最小可达0.35％。弃光率减小的过程中，成本随之增加，体现矛盾性。为进一步确定储能容量，同时考虑经济性，本研究对优化后弃光率在5％-20％之间的容量，再次进行并网值的优化。经过遗传算法的优化，弃光率在5％-20％的储能容量范围为300MWh至2837MWh。考虑经济性，选择储能容量范围为500MWh至2000MWh，即相对于1000万千瓦的光伏电站，配备其5％至20％的容量的储能装置。分别进行不同储能容量下基于遗传算法的理想并网值的优化。
图6是根据一示例性实施例示出的储能容量与运营总成本和弃光率的关系示意图。如图6所示，弃光率随着储能装置的储能容量的增加而降低，成本随着储能容量的增加而提升，储能容量为光伏电站装机容量的10％和15％时表现出最好的混合性能，其中储能容量从10％提升到15％，弃光率提升1％左右，而成本却增加3*109元。综合考虑，在图5中的场景下，为1000万千瓦光伏电站配备1000MWh的储能装置具有最好的性能。
图7为配备1000MWh的储能容量后储能装置的出力数据图，其中，虚线与实线重合的部分表示实际并网值和理想并网值一致，线与实线重合的部分表示实际并网值未达到或超出理想并网值。可以看出在综合考虑了经济和弃光率的情况下，储能装置的出力稳定性并没有展现出较大的波动性，实际并网值和理想并网值相差较小，表明了为光伏电站配备其装机容量10％储能容量的储能装置的可行性。
图8是根据一示例性实施例示出的一种装置800的框图。例如，装置800可以是移动电话，计算机，数字广播终端，消息收发设备，游戏控制台，平板设备，医疗设备，健身设备，个人数字助理等。
参照图8，装置800可以包括以下一个或多个部件：处理部件802，存储器804，电力部件806，多媒体部件808，音频部件810，输入/输出(I/O)的接口812，传感器部件814，以及通信部件816。
处理部件802通常控制装置800的整体操作，诸如与显示，电话呼叫，数据通信，相机操作和记录操作相关联的操作。处理部件802可以包括一个或多个处理器820来执行指令，以完成上述的方法的全部或部分步骤。此外，处理部件802可以包括一个或多个模块，便于处理部件802和其他部件之间的交互。例如，处理部件802可以包括多媒体模块，以方便多媒体部件808和处理部件802之间的交互。
存储器804被配置为存储各种类型的数据以支持在设备800的操作。这些数据的示例包括用于在装置800上操作的任何应用程序或方法的指令，联系人数据，电话簿数据，消息，图片，视频等。存储器804可以由任何类型的易失性或非易失性存储设备或者它们的组合实现，如静态随机存取存储器(SRAM)，电可擦除可编程只读存储器(EEPROM)，可擦除可编程只读存储器(EPROM)，可编程只读存储器(PROM)，只读存储器(ROM)，磁存储器，快闪存储器，磁盘或光盘。
电源部件806为装置800的各种部件提供电力。电源部件806可以包括电源管理系统，一个或多个电源，及其他与为装置800生成、管理和分配电力相关联的部件。
多媒体部件808包括在所述装置800和用户之间的提供一个输出接口的屏幕。在一些实施例中，屏幕可以包括液晶显示器(LCD)和触摸面板(TP)。如果屏幕包括触摸面板，屏幕可以被实现为触摸屏，以接收来自用户的输入信号。触摸面板包括一个或多个触摸传感器以感测触摸、滑动和触摸面板上的手势。所述触摸传感器可以不仅感测触摸或滑动动作的边界，而且还检测与所述触摸或滑动操作相关的持续时间和压力。在一些实施例中，多媒体部件808包括一个前置摄像头和/或后置摄像头。当设备800处于操作模式，如拍摄模式或视频模式时，前置摄像头和/或后置摄像头可以接收外部的多媒体数据。每个前置摄像头和后置摄像头可以是一个固定的光学透镜系统或具有焦距和光学变焦能力。
音频部件810被配置为输出和/或输入音频信号。例如，音频部件810包括一个麦克风(MIC)，当装置800处于操作模式，如呼叫模式、记录模式和语音识别模式时，麦克风被配置为接收外部音频信号。所接收的音频信号可以被进一步存储在存储器804或经由通信部件816发送。在一些实施例中，音频部件810还包括一个扬声器，用于输出音频信号。
I/O接口812为处理部件802和外围接口模块之间提供接口，上述外围接口模块可以是键盘，点击轮，按钮等。这些按钮可包括但不限于：主页按钮、音量按钮、启动按钮和锁定按钮。
传感器部件814包括一个或多个传感器，用于为装置800提供各个方面的状态评估。例如，传感器部件814可以检测到设备800的打开/关闭状态，部件的相对定位，例如所述部件为装置800的显示器和小键盘，传感器部件814还可以检测装置800或装置800一个部件的位置改变，用户与装置800接触的存在或不存在，装置800方位或加速/减速和装置800的温度变化。传感器部件814可以包括接近传感器，被配置用来在没有任何的物理接触时检测附近物体的存在。传感器部件814还可以包括光传感器，如CMOS或CCD图像传感器，用于在成像应用中使用。在一些实施例中，该传感器部件814还可以包括加速度传感器，陀螺仪传感器，磁传感器，压力传感器或温度传感器。
通信部件816被配置为便于装置800和其他设备之间有线或无线方式的通信。装置800可以接入基于通信标准的无线网络，如WiFi，运营商网络(如2G、3G、4G或5G)，或它们的组合。在一个示例性实施例中，通信部件816经由广播信道接收来自外部广播管理系统的广播信号或广播相关信息。在一个示例性实施例中，所述通信部件816还包括近场通信(NFC)模块，以促进短程通信。例如，在NFC模块可基于射频识别(RFID)技术，红外数据协会(IrDA)技术，超宽带(UWB)技术，蓝牙(BT)技术和其他技术来实现。
在示例性实施例中，装置800可以被一个或多个应用专用集成电路(ASIC)、数字信号处理器(DSP)、数字信号处理设备(DSPD)、可编程逻辑器件(PLD)、现场可编程门阵列(FPGA)、控制器、微控制器、微处理器或其他电子元件实现，用于执行上述方法。
在示例性实施例中，还提供了一种包括指令的存储介质，例如包括指令的存储器804，上述指令可由装置800的处理器820执行以完成上述方法。可选地，存储介质可以是非临时性计算机可读存储介质，例如，所述非临时性计算机可读存储介质可以是ROM、随机存取存储器(RAM)、CD-ROM、磁带、软盘和光数据存储设备等。
图9是根据一示例性实施例示出的一种的装置900的框图。例如，装置900可以被提供为一服务器。参照图9，装置900包括处理部件922，其进一步包括一个或多个处理器，以及由存储器932所代表的存储器资源，用于存储可由处理部件922的执行的指令，例如应用程序。存储器932中存储的应用程序可以包括一个或一个以上的每一个对应于一组指令的模块。此外，处理部件922被配置为执行指令，以执行上述方法。
装置900还可以包括一个电源部件926被配置为执行装置900的电源管理，一个有线或无线网络接口950被配置为将装置900连接到网络，和一个输入输出(I/O)接口958。装置900可以操作基于存储在存储器932的操作系统，例如WindowsServerTM、MacOSXTM、UnixTM,LinuxTM、FreeBSDTM或类似。
图10是根据一示例性实施例示出的一种以阶梯发电计划为目标的光储系统储能容量优化配置装置1000，包括：
获取模块1001，用于获取光伏电站进行阶梯出力的原始出力数据；
构建模块1002，用于构建遗传算法模型，确定所述遗传算法的算法参数和适应度函数；
优化模块1003，用于基于所述遗传算法和所述原始出力数据进行优化运算，以获取推荐储能容量，并根据所述推荐储能容量建设储能装置。
该装置可用于完成上述方法的全部或部分步骤，在此不赘述。
本领域技术人员在考虑说明书及实践这里公开的发明后，将容易想到本申请的其它实施方案。本申请旨在涵盖本申请的任何变型、用途或者适应性变化，这些变型、用途或者适应性变化遵循本申请的一般性原理并包括本申请未公开的本技术领域中的公知常识或惯用技术手段。说明书和实施例仅被视为示例性的，本申请的真正范围和精神由下面的权利要求指出。
应当理解的是，本申请并不局限于上面已经描述并在附图中示出的精确结构，并且可以在不脱离其范围进行各种修改和改变。本申请的范围仅由所附的权利要求来限制。

Claims (11)

  1. 一种以阶梯发电计划为目标的光储系统储能容量优化配置方法，其特征在于，包括：
    获取光伏电站进行阶梯出力的原始出力数据；
    构建遗传算法模型，确定所述遗传算法的算法参数和适应度函数；
    基于所述遗传算法和所述原始出力数据进行优化运算，以获取推荐储能容量，并根据所述推荐储能容量建设储能装置。
  2. 根据权利要求1所述的方法，其特征在于，所述算法参数具体包括：
    种群规模Q、交叉概率Pe和变异概率Pm。
  3. 根据权利要求2所述的方法，其特征在于，所述适应度函数的公式化表达为：
    其中，LCC为所述储能装置的运营总成本，ICC为所述储能装置的初始成本；N为所述储能装置的寿命；n为所述储能装置运行的第n年；dn为第n年的折旧；i为利率；tr为税率；an为储能装置第n年的维护和运营成本；r为第r次替换部件；R为储能装置运行周期内的总替换次数；ICCC为待替换部件的成本；lc为第c个待替换部件的寿命；s为残值(元)；penalty为一年中未达到理想并网值的电量；M为惩罚电价；其中，替换的总次数R是所述待替换部件寿命的函数。
  4. 根据权利要求3所述的方法，其特征在于，所述储能装置的初始成本ICC的公式化表达为：
    ICC＝Costbattery×Capacitybattery+Costinverter×Capacitybattery；
    其中，Costbattery为单位储能容量对应储能装置的成本，Capacitybattery为储能容量值，Costinverter为单位储能容量对应逆变器的成本。
  5. 根据权利要求3所述的方法，其特征在于，所述替换的总次数R的公式化表达为：
  6. 根据权利要求5所述的方法，其特征在于，所述基于所述遗传算法和所述原始出力数据进行优化运算，以获取推荐储能容量包括：
    随机生成Q个所述储能容量值，将所述储能容量值作为个体构建种群X(t)；
    根据所述原始出力数据获取所述种群中个体对应的适应度函数值；
    对所述种群X(t)进行进化处理直到种群中个体的适应度函数值满足终止优化条件，其中，t为所述种群的进化次数。
  7. 根据权利要求6所述的方法，其特征在于，所述对所述种群X(t)进行进化处理，包括：
    利用预设的选择算子从X(t)中选取Y/2对母体，其中Y大于或等于Q；
    对找出的Y/2对母体，按照所述交叉概率Pe确定交叉运算的目标母体对并进行交叉运算得到Y个个体；
    对得到的Y个个体分别按照所述变异概率Pm进行变异，以生成Y个变异个体；
    从生成的Y个变异个体中，根据对应的适应度函数值筛选得到Q个个体以生成下一代种群X(t+1)。
  8. 根据权利要求7所述的方法，其特征在于，所述对所述种群X(t)进行进化处理直到种群中个体的适应度函数值满足终止优化条件，其中，包括：
    如果种群X(t+1)中个体的适应度函数值满足终止优化条件，则输出X(t+1)里面适应度函数值最大的个体作为最优解，并停止进化处理；
    否则，对所述种群继续进行进化处理。
  9. 一种电子设备，其特征在于，包括：
    处理器；
    用于存储所述处理器可执行指令的存储器；
    其中，所述处理器被配置为执行所述指令，以实现如权利要求1至8中任一项所述的以阶梯发电计划为目标的光储系统储能容量优化配置方法。
  10. 一种计算机可读存储介质，当所述存储介质中的指令由电子设备的处理器执行时，使得所述电子设备能够执行如权利要求1至8中任一项所述的以阶梯发电计划为目标的光储系统储能容量优化配置方法。
  11. 一种以阶梯发电计划为目标的光储系统储能容量优化配置装置，其特征在于，包括：
    获取模块，用于获取光伏电站进行阶梯出力的原始出力数据；
    构建模块，用于构建遗传算法模型，确定所述遗传算法的算法参数和适应度函数；
    优化模块，用于基于所述遗传算法和所述原始出力数据进行优化运算，以获取推荐储能容量，并根据所述推荐储能容量建设储能装置。
PCT/CN2023/083588 2022-08-31 2023-03-24 光储系统储能容量优化配置方法、装置、设备及存储介质 WO2024045582A1 (zh)

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