Wind-solar-hydrogen storage integrated energy system capacity optimization configuration method and device

Abstract

The embodiment of the invention discloses a capacity optimization configuration method and device for a wind-solar-hydrogen storage integrated energy system. The method comprises the following steps: constructing a wind-solar hydrogen storage comprehensive energy system model; setting an objective function and constraint conditions for optimal configuration; and obtaining historical data of the target installation area, performing multi-target optimization configuration on the target function based on a multi-target optimization algorithm of rapid non-dominated sorting and the historical data, obtaining an optimal configuration result of the wind-light hydrogen storage comprehensive energy system model, and displaying the optimal configuration result. The optimal configuration result obtained by the embodiment of the specification keeps the population diversity and prevents premature convergence, the energy use efficiency is higher, and the capacity optimal configuration effect is better.

Description

Technical Field

[0001] One or more embodiments of this specification relate to integrated energy system technology, and in particular, to a method and device for optimizing the capacity configuration of a wind, solar, and hydrogen storage integrated energy system. Background Art

[0002] Wind energy and solar energy are the two most mature renewable energy sources currently. Due to their wide distribution and easy access, the global wind and solar installed capacity is increasing rapidly. Although both wind and solar renewable energy sources have the defects of unstable power generation and susceptibility to environmental factors, the combined use of wind and solar can form a good complementary advantage and is the mainstream development trend today. The integrated energy system is a new integrated energy system currently proposed to promote sustainable energy development. It can not only meet the diversified energy needs of users, but also improve energy utilization efficiency.

[0003] The integrated energy system is a comprehensive management and utilization of multiple energy sources including wind energy, solar energy, hydrogen energy, chemical energy, etc., to achieve complementary advantages among multiple energy sources, thereby breaking through the technical barriers between energy subsystems, achieving coordinated optimization of multiple energy sources, and promoting the sustainable and healthy development of renewable energy. The main purpose of the research on the system optimization configuration of the integrated energy system is to optimize the capacity configuration of each component in the system based on the wind and solar energy distribution characteristics of the construction area and the energy demand characteristics of users on the basis of achieving safe and stable power supply, so as to improve the economy and practicality of the system. However, the complex topological structure of the integrated energy system itself and the randomness of wind and solar resources themselves have brought great challenges to the optimal configuration of the system.

[0004] At present, the optimization configuration algorithm for the capacity of integrated energy systems generally uses particle swarm algorithms to optimize the object. On the one hand, the algorithm itself only selects economic indicators for configuration when optimizing the system capacity, and does not consider other indicators, making it difficult to perform multi-objective optimization configuration. On the other hand, due to the number of iterations and other reasons, the algorithm will have local optimal situations. In summary, the current capacity optimization configuration effect of the integrated energy system is not ideal, and the energy efficiency after configuration based on the existing optimization results is not high enough. Summary of the invention

[0005] In order to solve the above problems, one or more embodiments of this specification describe a method and device for optimizing the capacity configuration of a wind, solar, hydrogen and storage integrated energy system.

[0006] According to a first aspect, a method for optimizing the capacity configuration of a wind-solar-hydrogen-storage integrated energy system is provided, the method comprising:

[0007] Constructing a wind-solar-hydrogen storage integrated energy system model, wherein the wind-solar-hydrogen storage integrated energy system model includes a system power supply model and a system power consumption model, wherein the system power supply model includes a wind power generation model, a photovoltaic power generation model and a battery model, and the system power consumption model includes an electrolyzer model and a park power consumption model;

[0008] Setting an objective function and constraint conditions for optimizing the configuration for the wind-solar-energy-storage integrated energy system model, wherein the objective function includes a first objective function characterizing the system cost and a second objective function characterizing the load power failure rate, and the constraint conditions include wind-solar installed capacity constraint, wind-solar power constraint, energy storage charge state constraint, power supply and consumption balance constraint, and hydrogen consumption constraint;

[0009] Acquire historical data of the target installation area, perform multi-objective optimization configuration on the objective function based on a multi-objective optimization algorithm of fast non-dominated sorting and the historical data, obtain the optimal configuration result of the wind, solar and hydrogen storage integrated energy system model, and display the optimal configuration result, wherein the historical data includes wind power generation data, photovoltaic power generation data and power supply load data.

[0010] Preferably, the acquiring of historical data of the target installation area and performing multi-objective optimization configuration on the objective function based on a multi-objective optimization algorithm of fast non-dominated sorting and the historical data include:

[0011] The historical data of the target installation area is obtained, the historical data is preprocessed to obtain configuration data, and the objective function is multi-objective optimized based on a multi-objective optimization algorithm of fast non-dominated sorting and the configuration data. The preprocessing includes data cleaning, data normalization and time series processing.

[0012] Preferably, the obtaining of historical data of the target installation area includes:

[0013] The power consumption period is determined based on the park power consumption model, and the historical data of the target installation area during the power consumption period is obtained.

[0014] Preferably, the battery model is used to store excess power in the battery when the sum of wind power generation and photovoltaic power generation is greater than the total power load demand and the battery is not full; the battery model is also used to supplement power according to the battery when the sum of wind power generation and photovoltaic power generation is less than the total power load demand and the battery is greater than a preset power.

[0015] Preferably, the wind and solar installed capacity constraint is determined based on the geographical data of the target installation area, the wind and solar power constraint is determined based on the wind and solar installed capacity, the energy storage charge state constraint is determined based on the maximum capacity of the battery, the power supply and consumption balance constraint is used to make the power consumption of the system power supply model and the system power consumption model the same, and the hydrogen consumption constraint is used to make the amount of hydrogen produced by the electrolyzer model greater than the industrial production demand for hydrogen.

[0016] Preferably, the system power supply model also includes a power grid power purchase model, and the system power consumption model also includes a power grid power sales model.

[0017] Preferably, the method further comprises:

[0018] Acquire weather data within a preset time period, calculate actual power consumption data of the wind, solar, and hydrogen storage integrated energy system model based on the weather data, and generate prompt information based on the actual power consumption data, wherein the prompt information is used to estimate the cost of purchasing and / or selling electricity within a preset time period.

[0019] According to a second aspect, a capacity optimization configuration device for a wind-solar-hydrogen-storage integrated energy system is provided, the device comprising:

[0020] A construction module is used to construct a wind-solar-hydrogen storage integrated energy system model, wherein the wind-solar-hydrogen storage integrated energy system model includes a system power supply model and a system power consumption model, wherein the system power supply model includes a wind power generation model, a photovoltaic power generation model and a battery model, and the system power consumption model includes an electrolyzer model and a park power consumption model;

[0021] A setting module, used to set an objective function and constraint conditions for optimizing the configuration of the wind-solar-energy-storage integrated energy system model, wherein the objective function includes a first objective function representing the system cost and a second objective function representing the load power failure rate, and the constraint conditions include wind-solar installed capacity constraint, wind-solar power constraint, energy storage charge state constraint, power supply and consumption balance constraint, and hydrogen consumption constraint;

[0022] A processing module is used to obtain historical data of the target installation area, perform multi-objective optimization configuration on the objective function based on a multi-objective optimization algorithm of fast non-dominated sorting and the historical data, obtain the optimal configuration result of the wind-solar-hydrogen storage integrated energy system model, and display the optimal configuration result, wherein the historical data includes wind power generation data, photovoltaic power generation data and power supply load data.

[0023] According to a third aspect, there is provided an electronic device, comprising a processor and a memory;

[0024] The processor is connected to the memory;

[0025] The memory is used to store executable program code;

[0026] The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute the steps of the method provided in the first aspect or any possible implementation manner of the first aspect.

[0027] According to a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, and the computer-readable storage medium stores instructions, which, when the instructions are executed on a computer or a processor, cause the computer or the processor to execute the method provided in the first aspect or any possible implementation of the first aspect.

[0028] The method and device provided in the embodiments of this specification can calculate the optimal configuration result of the wind-solar-energy-storage integrated energy system model in the target installation area based on multiple objective functions and constraints set for the wind-solar-energy-storage integrated energy system model, combined with historical data and a multi-objective optimization algorithm of fast non-dominated sorting. The optimal configuration result maintains group diversity and prevents premature convergence, has higher energy utilization efficiency, and better capacity optimization configuration effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0030] Figure 1 It is a flow chart of a method for optimizing capacity configuration of a wind, solar, hydrogen and storage integrated energy system in one embodiment of this specification.

[0031] Figure 2 It is a structural schematic diagram of a capacity optimization configuration device for a wind, solar, and hydrogen storage integrated energy system in one embodiment of this specification.

[0032] Figure 3 It is a structural diagram of an electronic device in one embodiment of this specification. DETAILED DESCRIPTION

[0033] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0034] In the following introduction, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance. The following introduction provides multiple embodiments of the present application, and different embodiments can be replaced or combined, so the present application can also be considered to include all possible combinations of the same and / or different embodiments recorded. Therefore, if one embodiment includes features A, B, and C, and another embodiment includes features B and D, then the present application should also be considered to include embodiments containing one or more of all other possible combinations of A, B, C, and D, although the embodiment may not be clearly recorded in the following text.

[0035] The following description provides examples and does not limit the scope, applicability or examples set forth in the claims. Changes may be made to the functions and arrangements of the elements described without departing from the scope of the present application. Various processes or components may be appropriately omitted, substituted or added to each example. For example, the described method may be performed in an order different from the order described, and various steps may be added, omitted or combined. In addition, features described in some examples may be combined in other examples.

[0036] See also Figure 1 , Figure 1 : is a flow chart of a method for optimizing the capacity configuration of a wind, solar, hydrogen and storage integrated energy system provided in an embodiment of the present application. In an embodiment of the present application, the method includes:

[0037] S101. Construct a wind, solar, and hydrogen storage integrated energy system model.

[0038] Among them, the wind, solar and hydrogen storage integrated energy system model includes a system power supply model and a system power consumption model. The system power supply model includes a wind power generation model, a photovoltaic power generation model and a battery model. The system power consumption model includes an electrolyzer model and a park power consumption model.

[0039] In the embodiments of this specification, the wind, solar, and hydrogen storage integrated energy system model can be understood as a comprehensive model that integrates multiple models to simulate the actual wind, solar, and hydrogen storage integrated energy system. The model is mainly composed of a wind power generation model, a photovoltaic power generation model, a battery model, an electrolyzer model, and a park power consumption model. The wind power generation model, the photovoltaic power generation model, and the battery model are used for system power supply, and the electrolyzer model and the park power consumption model are used for system power consumption. Among them, the battery model, due to its characteristics of being able to store or release electricity, although it belongs to the system power supply model, when it stores electricity, the corresponding power consumption can be expressed as a negative value.

[0040] The power generated by wind power generation is mainly affected by wind speed. The wind power generation model can be expressed by the following formula:

[0041]

[0042] Among them, P wt is the wind power, ρ is the air density, A is the contact area of ​​the wind wheel, V is the wind speed, C p (λ,β) is the wind energy utilization coefficient, where λ is the tip speed ratio and β is the pitch angle.

[0043] The expression of wind energy utilization coefficient is as follows:

[0044]

[0045] Among them, c1, c2, c3, c4, c5, and c6 are all constant coefficients calculated and determined based on actual wind energy data in the target installation area.

[0046] If the time lag effect and wake effect of the wind farm are ignored, the wind power generation can be written as:

[0047]

[0048] Among them, P N is the wind power rated power, V i is the cut-in wind speed, V r is the rated wind speed, V o is the cut-out wind speed.

[0049] Photovoltaic power generation is mainly affected by photovoltaic irradiance. The photovoltaic power generation model can be expressed by the following formula:

[0050]

[0051] Among them, P pv Photovoltaic power generation, P pvN is the rated power of photovoltaic power generation under standard conditions, G is the solar radiation intensity, G ref is the solar radiation intensity under standard conditions, ε is the power temperature coefficient under standard conditions, T is the module temperature, T ref is the reference temperature under standard conditions, T a is the ambient temperature, T N is the rated temperature.

[0052] In actual situations, many factors will affect the photovoltaic power, causing a certain deviation from the ideal value. The photovoltaic power generation efficiency is generally considered, usually 90%.

[0053] The battery model can be expressed by the following formula:

[0054]

[0055] Among them, SOC is the state of charge of the battery, Δ is the self-discharge efficiency, P is the charge and discharge power, Δ cf is the charge and discharge efficiency, E h Is the maximum capacity of the battery.

[0056] The electrolyzer model is used to produce hydrogen by electrolysis of water. According to Faraday's law, the rate of hydrogen production is proportional to the electron transfer rate in the electrolyzer. The relationship between the rated power of the electrolyzer and the rated hydrogen production is:

[0057]

[0058] Among them, P rAE Rated power of electrolyzer, Q r is the rated hydrogen production of the electrolyzer, N A is Avogadro's constant, V M is the molar volume of the gas at room temperature, C 0 is the number of electrons per coulomb, V N is the rated voltage of the electrolyzer, η EL is the hydrogen production efficiency of the electrolyzer.

[0059] The park power consumption model is a mathematical model constructed based on the historical power consumption data of each enterprise in the park to represent the overall power consumption of the park. For example, the average daily power consumption of each enterprise can be determined based on the historical power consumption data, and the sum of all daily average power consumption can be used as the overall power consumption of the park.

[0060] In one possible implementation, the battery model is used to store excess power in the battery when the sum of wind power generation and photovoltaic power generation is greater than the total power load demand and the battery is not full; the battery model is also used to supplement power according to the battery when the sum of wind power generation and photovoltaic power generation is less than the total power load demand and the battery is greater than a preset power.

[0061] In the embodiment of this specification, the battery model will store excess power in the battery only when the sum of wind power generation and photovoltaic power generation is greater than the total power load demand of the system, that is, the power load demand of the park power model and the electrolyzer model, and the battery power is not full. Similarly, the battery model will control the battery to release power for charging only when the sum of wind power generation and photovoltaic power generation is less than the total power load demand and the battery power is greater than the preset power (for example, greater than 60% of the total power).

[0062] S102: Setting objective functions and constraints for optimizing configuration for the wind-solar-energy-storage integrated energy system model.

[0063] Among them, the objective function includes a first objective function characterizing the system cost and a second objective function characterizing the load power failure rate, and the constraint conditions include wind and solar installed capacity constraints, wind and solar power constraints, energy storage charge state constraints, power supply and consumption balance constraints and hydrogen usage constraints.

[0064] In the embodiments of this specification, taking into account various economic, power supply reliability, safety and other considerations, multiple objective functions are set instead of a single objective function for optimization configuration. Among them, the first objective function is used to characterize the system cost, and its corresponding expression can be as follows:

[0065] C total =N pv ·C pv +N wt ·C wt +N el ·C el +N h ·C h +C o +C p

[0066] Among them, N pv 、N wt 、N el 、N h They are photovoltaic installed capacity, wind power installed capacity, electrolyzer installed capacity, and energy storage installed capacity. pv , C wt , C el , C h They are the average cost of photovoltaic installation, the average cost of wind power installation, the average cost of electrolyzer installation, and the average cost of energy storage installation. o is the maintenance cost, C p It is the cost of purchasing electricity from the grid.

[0067] The second objective function is used to characterize the load power failure rate, and its corresponding expression can be as follows:

[0068]

[0069] Among them, the out is the unmet user demand power, P t is the total power demanded by the users.

[0070] In addition, for the objective function of the model, it is also necessary to set a number of constraints in the model based on the geographical environment, installation costs, system requirements, etc. of the target installation area. Specifically, these may include wind and solar installed capacity constraints, wind and solar power constraints, energy storage charge state constraints, power supply and consumption balance constraints, and hydrogen usage constraints.

[0071] In one possible implementation, the wind and solar installed capacity constraint is determined based on the geographic data of the target installation area, the wind and solar power constraint is determined based on the wind and solar installed capacity, the energy storage charge state constraint is determined based on the maximum capacity of the battery, the power supply and consumption balance constraint is used to make the power consumption of the system power supply model and the system power consumption model the same, and the hydrogen consumption constraint is used to make the amount of hydrogen produced by the electrolyzer model greater than the industrial production demand of hydrogen.

[0072] In the embodiment of this specification, the expression of the wind and solar installed capacity constraint can be as follows:

[0073] 0≤N pv ≤N pvmax

[0074] 0≤N wt ≤N wtmax

[0075] 0≤N el ≤N elmax

[0076] 0≤N h ≤N hmax

[0077] The expression of wind and solar power constraint can be as follows:

[0078] 0≤P pv ≤P pvN

[0079] 0≤P wt ≤P N

[0080] The expression of energy storage state of charge constraint can be as follows:

[0081] 0≤SOC(t)≤E h

[0082] The expression of power balance constraint can be as follows:

[0083] P pv +P wt +P h =P el +P t

[0084] Among them, P h P is the energy storage power supply power; el P is the power consumption of the electrolytic cell, which can be determined based on the installed capacity of the electrolytic cell and the rated power of the electrolytic cell; t The power consumption of the park.

[0085] The expression for hydrogen consumption constraint can be as follows:

[0086] ∫Q(t)≥∫N C (t)

[0087] Where Q(t) is the amount of hydrogen produced by the electrolyzer, N C (t) is the hydrogen consumption in industrial production.

[0088] S103. Acquire historical data of the target installation area, perform multi-objective optimization configuration on the objective function based on a multi-objective optimization algorithm of fast non-dominated sorting and the historical data, obtain the optimal configuration result of the wind-solar-hydrogen storage integrated energy system model, and display the optimal configuration result.

[0089] The historical data include wind power generation data, photovoltaic power generation data and power supply load data.

[0090] In the embodiments of this specification, the geographical conditions, system installation conditions and system installation costs of different installation areas are different, so it is necessary to first determine the target installation area for the system to be installed this time, so as to obtain the historical data of the target installation area, and then realize multi-objective optimization configuration based on the multi-objective optimization algorithm based on fast non-dominated sorting to process the historical data and the objective function, and obtain the optimal configuration result of the wind, light and hydrogen storage integrated energy system model, which is used to characterize the specific capacity of photovoltaic installed capacity, wind power installed capacity, electrolyzer installed capacity and energy storage installed capacity. After the results are generated, the optimal configuration results will also be displayed so that users can arrange the actual system installation according to the optimal configuration results.

[0091] Among them, the multi-objective optimization algorithm for fast non-dominated sorting can be NSGA-II (Non-dominated Sorting Genetic Algorithm II) algorithm, SPEA2 (Strength Pareto Evolutionary Algorithm 2) algorithm, PAES (Pareto Archived Evolution Strategy) algorithm, etc.

[0092] Taking the NSGA-II algorithm as an example, it introduces a fast non-dominated sorting method, which reduces the time required to calculate the non-dominated level from O(mN^3) of NSGA to O(mN^2), where m is the number of targets and N is the number of individuals. At the same time, NSGA-II uses an elite strategy, which means that the algorithm saves the best individuals when updating the population, ensuring that excellent solutions are not lost even if these individuals may no longer be optimal temporarily during the genetic process. In addition, in order to maintain the diversity of the population, NSGA-II uses crowding instead of the fitness sharing strategy in NSGA. Crowding is a metric used to measure the density of space around an individual. When selecting individuals, in addition to considering the non-dominated level, the degree of crowding between individuals is also considered, thereby encouraging the population to disperse throughout the solution space.

[0093] The steps of the NSGA-II algorithm may specifically include:

[0094] 1. Generate an initial population and perform a fast non-dominated sort.

[0095] 2. Generate the first generation of offspring population through selection, inheritance and mutation.

[0096] 3. Merge the initial population and the first-generation child population, perform fast non-dominated sorting and crowding calculation, and select the new generation parent population based on the crowding.

[0097] 4. The new generation parent population is selected, inherited, and mutated to generate a new generation child population, which is then sorted and selected after merging to generate the next generation parent population until the maximum number of iterations is reached.

[0098] In one possible implementation, the acquiring of historical data of the target installation area and performing multi-objective optimization configuration on the objective function based on a multi-objective optimization algorithm of fast non-dominated sorting and the historical data include:

[0099] The historical data of the target installation area is obtained, the historical data is preprocessed to obtain configuration data, and the objective function is multi-objective optimized based on a multi-objective optimization algorithm of fast non-dominated sorting and the configuration data. The preprocessing includes data cleaning, data normalization and time series processing.

[0100] In the embodiments of this specification, in order to improve the quality and accuracy of subsequent analysis, the historical data will first be preprocessed, and the preprocessing may include data cleaning, data format unification, data normalization and time series processing. Among them, data cleaning refers to checking and processing missing values, outliers or erroneous data records. Data format unification refers to converting data from different sources into a consistent format for easy merging and analysis. Data normalization refers to converting data of different dimensions to the same scale, including min-max scaling (Min-Max Scaling), Z-score standardization, etc. Time series processing refers to the need to organize time series to ensure that the data is arranged at the same time interval (such as every hour, every day) because wind power generation, photovoltaic power generation and load data are usually recorded in chronological order.

[0101] In one possible implementation, the acquiring of historical data of the target installation area includes:

[0102] The power consumption period is determined based on the park power consumption model, and the historical data of the target installation area during the power consumption period is obtained.

[0103] In the embodiments of this specification, considering that the wind volume, the intensity of solar radiation on photovoltaic power, etc. will change significantly with different time periods, in order to make the results obtained in the subsequent processing process more accurate, the power consumption period of the park will be determined. When obtaining historical data, the historical data corresponding to the power consumption period will be used as the main data used for subsequent processing and judgment.

[0104] In one possible implementation, the system power supply model further includes a power grid power purchase model, and the system power consumption model further includes a power grid power sales model.

[0105] In the embodiments of this specification, in special circumstances or special installation areas, relying solely on wind power generation cannot meet the power demand of the system. In these cases, a power grid purchase model and a power grid sales model will also be set in the wind, solar and energy storage integrated energy system model to simulate the situation where electricity needs to be purchased from other regions through the power grid when electricity is insufficient or the surplus electricity is sold to other regions through the power grid when electricity is sufficient. The data processing priority of the power grid purchase model and the power grid sales model is the lowest, that is, the power grid purchase model will only be used when wind power generation, photovoltaic power generation and battery power replenishment cannot meet the electricity demand, and the power grid sales model will only be used when the battery is fully charged and wind power generation and photovoltaic power generation are in surplus. The power grid purchase model and the power grid sales model are specifically used to represent the lack or excess electricity, and the price corresponding to this part of the electricity.

[0106] In one embodiment, the method further comprises:

[0107] Acquire weather data within a preset time period, calculate actual power consumption data of the wind, solar, and hydrogen storage integrated energy system model based on the weather data, and generate prompt information based on the actual power consumption data, wherein the prompt information is used to estimate the cost of purchasing and / or selling electricity within a preset time period.

[0108] In the embodiments of this specification, the optimal capacity configuration obtained by the above process is determined from an overall perspective based on historical data. After the actual configuration is performed in this way, the actual power consumption will inevitably have a certain deviation, which may result in that although the overall power supply is balanced for a long time, the weather may change, causing the power to overflow or shortfall in a specific time period. In these cases, it is still necessary to balance by purchasing or selling electricity. Therefore, the application will also determine the weather data within a preset time period, and simulate and calculate the actual power consumption data within a preset time period in the wind, solar and hydrogen storage integrated energy system model based on these weather data, so as to determine whether there is a situation of power overflow or shortfall, and estimate the estimated cost that may be paid for purchasing and / or selling electricity during this period based on the current power price, so as to generate corresponding prompt information and inform the user so that the user can prepare in advance.

[0109] The following will be combined with the attached Figure 2 , the wind, solar, hydrogen and storage integrated energy system capacity optimization configuration device provided in the embodiment of the present application is introduced in detail. Figure 2 The wind-solar-hydrogen-storage integrated energy system capacity optimization configuration device shown is used to implement this application Figure 1 For the convenience of explanation, only the part related to the embodiment of the present application is shown. For the specific technical details not disclosed, please refer to the present application. Figure 1 The embodiment shown.

[0110] See also Figure 2 , Figure 2 Schematic diagram of a wind-solar-hydrogen-storage integrated energy system capacity optimization configuration device provided in the embodiment of the present application. Figure 2 As shown, the device comprises:

[0111] Construction module 201 is used to construct a wind-solar-hydrogen storage integrated energy system model, wherein the wind-solar-hydrogen storage integrated energy system model includes a system power supply model and a system power consumption model, wherein the system power supply model includes a wind power generation model, a photovoltaic power generation model and a battery model, and the system power consumption model includes an electrolyzer model and a park power consumption model;

[0112] A setting module 202 is used to set an objective function and constraint conditions for optimizing the configuration of the wind-solar-energy-storage integrated energy system model, wherein the objective function includes a first objective function representing the system cost and a second objective function representing the load power failure rate, and the constraint conditions include wind-solar installed capacity constraint, wind-solar power constraint, energy storage charge state constraint, power supply and consumption balance constraint, and hydrogen consumption constraint;

[0113] Processing module 203 is used to obtain historical data of the target installation area, perform multi-objective optimization configuration on the objective function based on a multi-objective optimization algorithm of fast non-dominated sorting and the historical data, obtain the optimal configuration result of the wind-solar-hydrogen storage integrated energy system model, and display the optimal configuration result, wherein the historical data includes wind power generation data, photovoltaic power generation data and power supply load data.

[0114] In one possible implementation, the processing module 203 is specifically used for:

[0115] The historical data of the target installation area is obtained, the historical data is preprocessed to obtain configuration data, and the objective function is multi-objective optimized based on a multi-objective optimization algorithm of fast non-dominated sorting and the configuration data. The preprocessing includes data cleaning, data normalization and time series processing.

[0116] In one possible implementation, the processing module 203 is further configured to:

[0117] The power consumption period is determined based on the park power consumption model, and the historical data of the target installation area during the power consumption period is obtained.

[0118] In one possible implementation, the battery model is used to store excess power in the battery when the sum of wind power generation and photovoltaic power generation is greater than the total power load demand and the battery is not full; the battery model is also used to supplement power according to the battery when the sum of wind power generation and photovoltaic power generation is less than the total power load demand and the battery is greater than a preset power.

[0119] In one possible implementation, the wind and solar installed capacity constraint is determined based on the geographic data of the target installation area, the wind and solar power constraint is determined based on the wind and solar installed capacity, the energy storage charge state constraint is determined based on the maximum capacity of the battery, the power supply and consumption balance constraint is used to make the power consumption of the system power supply model and the system power consumption model the same, and the hydrogen consumption constraint is used to make the amount of hydrogen produced by the electrolyzer model greater than the industrial production demand of hydrogen.

[0120] In one possible implementation, the system power supply model further includes a power grid power purchase model, and the system power consumption model further includes a power grid power sales model.

[0121] In one embodiment, the device further comprises:

[0122] A prompt module is used to obtain weather data within a preset time period, calculate the actual power consumption data of the wind, solar and hydrogen storage integrated energy system model based on the weather data, and generate prompt information according to the actual power consumption data, wherein the prompt information is used to estimate the cost of purchasing and / or selling electricity within the preset time period.

[0123] Those skilled in the art can clearly understand that the technical solutions of the embodiments of the present application can be implemented with the help of software and / or hardware. The "unit" and "module" in this specification refer to software and / or hardware that can independently complete or cooperate with other components to complete specific functions, where the hardware can be, for example, a field programmable gate array (Field-Programmable Gate Array, FPGA), an integrated circuit (Integrated Circuit, IC), etc.

[0124] Each processing unit and / or module of the embodiments of the present application may be implemented by an analog circuit that implements the functions described in the embodiments of the present application, or may be implemented by software that executes the functions described in the embodiments of the present application.

[0125] See also Figure 3 , which shows a schematic diagram of the structure of an electronic device involved in an embodiment of the present application, the electronic device can be used to implement Figure 1 The method in the embodiment shown. Figure 3 As shown, the electronic device 300 may include: at least one processor 301 , at least one network interface 304 , a user interface 303 , a memory 305 , and at least one communication bus 302 .

[0126] The communication bus 302 is used to realize the connection and communication between these components.

[0127] The user interface 303 may include a display screen (Display) and a camera (Camera). The optional user interface 303 may also include a standard wired interface and a wireless interface.

[0128] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0129] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts within the entire electronic device 300, and executes various functions and processes data of the electronic device 300 by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics central processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0130] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area may store data involved in the above-mentioned method embodiments, etc. The memory 305 may also be optionally at least one storage device located away from the aforementioned processor 301. As Figure 3 As shown, the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module, and program instructions.

[0131] exist Figure 3In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call the wind-solar-hydrogen-storage integrated energy system capacity optimization configuration application stored in the memory 305, and specifically perform the following operations:

[0132] Constructing a wind-solar-hydrogen storage integrated energy system model, wherein the wind-solar-hydrogen storage integrated energy system model includes a system power supply model and a system power consumption model, wherein the system power supply model includes a wind power generation model, a photovoltaic power generation model and a battery model, and the system power consumption model includes an electrolyzer model and a park power consumption model;

[0133] Setting an objective function and constraint conditions for optimizing the configuration for the wind-solar-energy-storage integrated energy system model, wherein the objective function includes a first objective function characterizing the system cost and a second objective function characterizing the load power failure rate, and the constraint conditions include wind-solar installed capacity constraint, wind-solar power constraint, energy storage charge state constraint, power supply and consumption balance constraint, and hydrogen consumption constraint;

[0134] Acquire historical data of the target installation area, perform multi-objective optimization configuration on the objective function based on a multi-objective optimization algorithm of fast non-dominated sorting and the historical data, obtain the optimal configuration result of the wind, solar and hydrogen storage integrated energy system model, and display the optimal configuration result, wherein the historical data includes wind power generation data, photovoltaic power generation data and power supply load data.

[0135] The present application also provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the steps of the above method are implemented. The computer-readable storage medium may include, but is not limited to, any type of disk, including a floppy disk, an optical disk, a DVD, a CD-ROM, a micro drive, and a magneto-optical disk, a ROM, a RAM, an EPROM, an EEPROM, a DRAM, a VRAM, a flash memory device, a magnetic card or an optical card, a nanosystem (including a molecular memory IC), or any type of medium or device suitable for storing instructions and / or data.

[0136] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0137] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0138] In the several embodiments provided in the present application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0139] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0140] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0141] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM), random access memory (RAM), mobile hard disk, disk or optical disk and other media that can store program code.

[0142] A person skilled in the art may understand that all or part of the steps in the various methods of the above embodiments may be completed by entering a program to instruct the relevant hardware. The program may be stored in a computer-readable memory, and the memory may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0143] The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the specification and practicing the disclosure here, those skilled in the art will easily think of the implementation scheme of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field not recorded in the present disclosure. The description and examples are regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for optimizing the capacity of a wind, solar, hydrogen and storage integrated energy system, characterized in that: The method comprises: Constructing a wind-solar-hydrogen storage integrated energy system model, wherein the wind-solar-hydrogen storage integrated energy system model includes a system power supply model and a system power consumption model, wherein the system power supply model includes a wind power generation model, a photovoltaic power generation model and a battery model, and the system power consumption model includes an electrolyzer model and a park power consumption model; Setting an objective function and constraint conditions for optimizing the configuration for the wind-solar-energy-storage integrated energy system model, wherein the objective function includes a first objective function characterizing the system cost and a second objective function characterizing the load power failure rate, and the constraint conditions include wind-solar installed capacity constraint, wind-solar power constraint, energy storage charge state constraint, power supply and consumption balance constraint, and hydrogen consumption constraint; Acquire historical data of the target installation area, perform multi-objective optimization configuration on the objective function based on a multi-objective optimization algorithm of fast non-dominated sorting and the historical data, obtain the optimal configuration result of the wind, solar and hydrogen storage integrated energy system model, and display the optimal configuration result, wherein the historical data includes wind power generation data, photovoltaic power generation data and power supply load data.

2. The method according to claim 1, characterized in that The acquiring of historical data of the target installation area and performing multi-objective optimization configuration on the objective function based on a multi-objective optimization algorithm of fast non-dominated sorting and the historical data include: The historical data of the target installation area is obtained, the historical data is preprocessed to obtain configuration data, and the objective function is multi-objective optimized based on a multi-objective optimization algorithm of fast non-dominated sorting and the configuration data. The preprocessing includes data cleaning, data normalization and time series processing.

3. The method according to claim 1, characterized in that The obtaining of historical data of the target installation area includes: The power consumption period is determined based on the park power consumption model, and the historical data of the target installation area during the power consumption period is obtained.

4. The method according to claim 1, characterized in that: The battery model is used to store excess power in the battery when the sum of wind power generation and photovoltaic power generation is greater than the total power load demand and the battery is not full; the battery model is also used to supplement power according to the battery when the sum of wind power generation and photovoltaic power generation is less than the total power load demand and the battery is greater than a preset power.

5. The method according to claim 1, characterized in that The wind and solar installed capacity constraint is determined based on the geographical data of the target installation area, the wind and solar power constraint is determined based on the wind and solar installed capacity, the energy storage charge state constraint is determined based on the maximum capacity of the battery, the power supply and consumption balance constraint is used to make the power consumption of the system power supply model and the system power consumption model the same, and the hydrogen consumption constraint is used to make the amount of hydrogen produced by the electrolyzer model greater than the industrial production demand of hydrogen.

6. The method according to claim 1, characterized in that The system power supply model also includes a power grid power purchase model, and the system power consumption model also includes a power grid power sales model.

7. The method according to claim 1, characterized in that The method further comprises: Acquire weather data within a preset time period, calculate actual power consumption data of the wind, solar, and hydrogen storage integrated energy system model based on the weather data, and generate prompt information based on the actual power consumption data, wherein the prompt information is used to estimate the cost of purchasing and / or selling electricity within a preset time period.

8. A capacity optimization configuration device for a wind, solar, hydrogen and storage integrated energy system, characterized in that: The device comprises: A construction module is used to construct a wind-solar-hydrogen storage integrated energy system model, wherein the wind-solar-hydrogen storage integrated energy system model includes a system power supply model and a system power consumption model, wherein the system power supply model includes a wind power generation model, a photovoltaic power generation model and a battery model, and the system power consumption model includes an electrolyzer model and a park power consumption model; A setting module, used to set an objective function and constraint conditions for optimizing the configuration of the wind-solar-energy-storage integrated energy system model, wherein the objective function includes a first objective function representing the system cost and a second objective function representing the load power failure rate, and the constraint conditions include wind-solar installed capacity constraint, wind-solar power constraint, energy storage charge state constraint, power supply and consumption balance constraint, and hydrogen consumption constraint; A processing module is used to obtain historical data of the target installation area, perform multi-objective optimization configuration on the objective function based on a multi-objective optimization algorithm of fast non-dominated sorting and the historical data, obtain the optimal configuration result of the wind-solar-hydrogen storage integrated energy system model, and display the optimal configuration result, wherein the historical data includes wind power generation data, photovoltaic power generation data and power supply load data.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, wherein the computer-readable storage medium has instructions stored therein, and when the instructions are executed on a computer or a processor, the computer or the processor executes the steps of the method according to any one of claims 1 to 7.

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